System

Abstract

An object of a system according to an embodiment is to grasp the needs of evacuees in real time and provide appropriate support supplies.SOLUTION: A system according to an embodiment includes a demand collection part, a data collection part, an analysis part, and a supply specification part. The request collection section collects requests from evacuees. The data collector collects the demand data collected by the demand collector, the environmental data, and the health data. The analysis unit performs analysis based on the data collected by the data collection unit. The supply specification unit specifies the relief supplies based on the analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology had the problem of making it difficult to grasp the needs of evacuees in shelters in real time and provide appropriate relief supplies.

[0005] The system according to the embodiment aims to grasp the needs of evacuees in real time and provide appropriate relief supplies. [Means for solving the problem]

[0006] The system according to the embodiment includes a request collection unit, a data collection unit, an analysis unit, and a supply identification unit. The request collection unit collects requests from evacuees. The data collection unit collects the request data collected by the request collection unit, as well as environmental data and health data. The analysis unit performs analysis based on the data collected by the data collection unit. The supply identification unit identifies relief supplies based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the needs of evacuees in real time and provide appropriate relief 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 relief supplies optimization system according to an embodiment of the present invention optimizes relief supplies based on evacuees' requests, environmental data, and health data. The relief supplies optimization system collects requests from evacuees, environmental data, and health data, and analyzes this data to identify relief supplies. For example, in the relief supplies optimization system, evacuees report their requests through a chatbot. For example, they input information such as a lack of food, the need for medicine, or specific allergies. This information is collected by an AI chatbot and analyzed in real time. Next, the relief supplies optimization system collects environmental data such as the temperature and humidity in the evacuation shelter and the evacuees' health status data through sensors. This data is analyzed by AI to identify the relief supplies needed based on the evacuees' needs. For example, specific relief supplies such as blankets needed when the temperature is low, dehumidifiers needed when the humidity is high, and specific medicines needed for evacuees with specific health conditions are identified. This enables the relief supplies optimization system to optimize relief supplies based on the evacuees' needs and provide them at the appropriate time. This allows the relief supplies optimization system to improve the living environment in evacuation shelters and ensure the health and safety of evacuees. For example, by quickly and accurately collecting requests written by evacuees and providing appropriate relief supplies, it will be possible to provide support that meets the needs of evacuees.

[0029] The relief supply optimization system according to the embodiment includes a request collection unit, a data collection unit, an analysis unit, and a supply identification unit. The request collection unit collects requests from evacuees. The requests from evacuees include, but are not limited to, requests for supplies, reports on health status, and suggestions for improving evacuation shelters. The request collection unit collects requests and concerns from evacuees, for example, through a chatbot. The chatbot can understand the evacuees' requests and design an appropriate dialogue flow using natural language processing technology. The data collection unit collects environmental data and health data. The environmental data includes, but is not limited to, temperature, humidity, air quality, and the like. The data collection unit collects environmental data, for example, including temperature and humidity within the evacuation shelter. The temperature is measured using, for example, a temperature sensor. The humidity is measured using, for example, a humidity sensor. The data collection unit collects health status data of the evacuees. The health status data includes, but is not limited to, body temperature, blood pressure, heart rate, and the like. The data collection unit measures body temperature using, for example, a body temperature sensor. The blood pressure is measured using, for example, a sphygmomanometer. The heart rate is measured using, for example, a heart rate sensor. The analysis unit performs analysis based on the collected request data, environmental data, and health data. The analysis is performed using, for example, statistical analysis of data or a machine learning algorithm. The analysis unit, for example, statistically analyzes the collected data to identify the needs of evacuees. The analysis unit can also use a machine learning algorithm to analyze data patterns and predict the needs of evacuees. The supply identification unit identifies specific relief supplies based on the analysis results. Relief supplies include, for example, food, water, medicine, clothing, etc., but are not limited to these examples. The supply identification unit, for example, identifies food needed by evacuees based on the analysis results. The supply identification unit can also identify medicine needed by evacuees based on the analysis results. The supply identification unit can also identify clothing needed by evacuees based on the analysis results. As a result, the relief supply optimization system according to the embodiment can optimize relief supplies based on the requests, environmental data, and health data of evacuees. For example, relief supplies according to the needs of evacuees can be provided quickly and accurately.This will improve living conditions in evacuation centers and ensure the health and safety of evacuees.

[0030] The request collection unit can collect requests and problems from evacuees through the chatbot. The chatbot can understand the requests of evacuees and design an appropriate dialogue flow using, for example, natural language processing technology. For example, the chatbot can analyze text entered by evacuees and classify the requests. The chatbot can also generate appropriate questions and collect detailed information in accordance with the requests of evacuees. The chatbot can also collect the requests of evacuees in real time and store them in a database. This allows requests from evacuees to be collected efficiently through the chatbot. Some or all of the above-mentioned processing in the request collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the request collection unit can input the request data collected by the chatbot into a generation AI and have the generation AI classify and analyze the requests.

[0031] The data collection unit can collect environmental data, including temperature and humidity, within the evacuation shelter. Examples of environmental data include, but are not limited to, temperature, humidity, and air quality. The data collection unit can measure the temperature within the evacuation shelter using, for example, a temperature sensor. The temperature sensors can be installed, for example, at multiple locations within the evacuation shelter and can collect temperature data in real time. The data collection unit can also measure humidity within the evacuation shelter using a humidity sensor. The humidity sensors can be installed, for example, at multiple locations within the evacuation shelter and can collect humidity data in real time. The data collection unit can also measure air quality within the evacuation shelter using an air quality sensor. The air quality sensors can be installed, for example, at multiple locations within the evacuation shelter and can collect air quality data in real time. By collecting environmental data within the evacuation shelter, appropriate relief supplies can be identified. Some or all of the above-described processing in the data collection unit can be performed using, for example, AI, or without AI. For example, the data collection unit can input environmental data collected from the sensors into a generation AI and have the generation AI analyze the data.

[0032] The data collection unit can collect health condition data of evacuees. Examples of health condition data include, but are not limited to, body temperature, blood pressure, and heart rate. The data collection unit can measure the body temperature of evacuees using, for example, a body temperature sensor. The body temperature sensor can measure the body temperature of evacuees in real time and collect data, for example. The data collection unit can also measure the blood pressure of evacuees using a blood pressure monitor. The blood pressure monitor can measure the blood pressure of evacuees in real time and collect data, for example. The data collection unit can also measure the heart rate of evacuees using a heart rate sensor. The heart rate sensor can measure the heart rate of evacuees in real time and collect data, for example. By collecting health condition data of evacuees, appropriate relief supplies can be identified. Some or all of the above-described processing in the data collection unit can be performed using, for example, AI, or without AI. For example, the data collection unit can input the health condition data collected from the sensor to a generation AI and have the generation AI analyze the data.

[0033] The analysis unit can perform analysis based on the collected need data, environmental data, and health data. The analysis can be performed using, for example, statistical analysis of data or a machine learning algorithm. The analysis unit can, for example, perform statistical analysis of the collected data to identify the needs of evacuees. Statistical analysis can, for example, analyze the distribution and correlation of data to clarify the needs of evacuees. The analysis unit can also analyze data patterns and predict the needs of evacuees using a machine learning algorithm. The machine learning algorithm can, for example, learn from past data and make predictions based on new data. This allows appropriate relief supplies to be identified by performing analysis based on the collected data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI analyze the data.

[0034] The supply identification unit can identify specific relief supplies based on the analysis results. Relief supplies include, but are not limited to, food, water, medicine, and clothing. For example, the supply identification unit can identify food needed by evacuees based on the analysis results. Food includes, for example, canned goods, dried foods, and nutritional supplements. The supply identification unit can also identify medicine needed by evacuees based on the analysis results. Medicine includes, for example, painkillers, antibiotics, and allergy medications. The supply identification unit can also identify clothing needed by evacuees based on the analysis results. Clothing includes, for example, blankets, jackets, and socks. By identifying specific relief supplies based on the analysis results, it is possible to provide support tailored to the needs of evacuees. Some or all of the above-described processing in the supply identification unit can be performed using, for example, AI, or without AI. For example, the supply identification unit can input the analysis results to a generation AI and cause the generation AI to identify relief supplies.

[0035] The request collection unit can analyze the past request history of evacuees and select the optimal request collection method. For example, the request collection unit retrieves the past request history from a database and analyzes it using text mining technology. For example, the request collection unit creates a template based on requests frequently submitted in the past and collects requests efficiently. The request collection unit can also analyze the past request history to determine whether many requests are submitted during a specific time period and focus on that time period. The request collection unit can also propose a collection method customized for a specific group of evacuees based on the past request history. In this way, requests can be collected efficiently by analyzing the past request history. Some or all of the above-described processing in the request collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the request collection unit can input the past request history into a generation AI and have the generation AI select the optimal request collection method.

[0036] When collecting requests, the request collection unit can filter requests based on the evacuees' current situation and areas of interest. The request collection unit, for example, prioritizes collecting relevant requests based on the evacuees' current health condition. For example, the request collection unit prioritizes collecting requests with high urgency based on health data such as the evacuees' body temperature and blood pressure. The request collection unit can also filter requests based on the evacuees' areas of interest (food, medicine, etc.) to efficiently collect requests. For example, if the evacuees are interested in food, the request collection unit prioritizes collecting requests related to food. The request collection unit can also prioritize collecting specific requests based on the evacuees' current living environment (family composition, whether or not they have pets, etc.). For example, if the evacuees have pets, the request collection unit prioritizes collecting requests related to pets. In this way, requests can be filtered based on the evacuees' current situation and areas of interest, thereby efficiently collecting requests. Some or all of the above-described processing in the request collection unit may be performed using, or without, AI. For example, the request collection unit can input the evacuees' current situation and areas of interest into the generation AI and cause the generation AI to filter the requests.

[0037] When collecting requests, the request collection unit can select the optimal collection method depending on the evacuee's input method. For example, for evacuees who use voice input, the request collection unit collects requests using voice recognition technology. For example, the evacuee uses a microphone to input their requests verbally, and voice recognition software automatically analyzes the voice and converts it into text data. The request collection unit can also provide a simple form to evacuees who use text input, allowing for efficient collection of requests. For example, the evacuee inputs their requests in text, and the form automatically saves the text in a database. The request collection unit can also collect requests using image analysis technology for evacuees who use image input. For example, the evacuee uploads an image, and image analysis software automatically analyzes the image and identifies the request. This allows for efficient collection of requests by selecting the optimal collection method depending on the evacuee's input method. Some or all of the above-described processing in the request collection unit may be performed using, or without, AI. For example, the request collection unit can input voice data or image data into a generation AI and have the generation AI analyze the requests.

[0038] When collecting requests, the request collection unit can prioritize collecting highly relevant requests by taking into account the geographical location information of the evacuees. For example, the request collection unit prioritizes collecting requests needed at nearby evacuation shelters based on the evacuees' current locations. For example, the request collection unit collects requests for supplies needed at nearby evacuation shelters based on the evacuees' GPS data. The request collection unit can also prioritize collecting requests for supplies needed in a specific area based on the evacuees' geographical location information. For example, the request collection unit collects requests for medicines needed in a specific area based on the evacuees' geographical location information. The request collection unit can also prioritize collecting requests suitable for the environment of an evacuation shelter based on the evacuees' geographical location information. For example, the request collection unit collects requests for food suitable for the environment of an evacuation shelter based on the evacuees' geographical location information. In this way, by taking into account the geographical location information of the evacuees, highly relevant requests can be prioritized. Some or all of the above-described processing in the request collection unit may be performed using, or without, AI. For example, the request collection unit can input the geographical location information of the evacuees to the generation AI and cause the generation AI to filter the requests.

[0039] The request collection unit can analyze the social media activities of evacuees and collect related requests when collecting requests. The request collection unit, for example, analyzes content posted by evacuees on social media and collects related requests. For example, the request collection unit analyzes text data posted by evacuees on social media to identify requests. The request collection unit can also identify specific needs and collect requests from the evacuees' social media activities. For example, the request collection unit analyzes images and videos shared by evacuees on social media to identify requests. The request collection unit can also collect related requests by referring to the activities of the evacuees' friends on social media. For example, the evacuees' needs are identified and requests are collected based on content posted by the evacuees' friends. In this way, related requests can be efficiently collected by analyzing the evacuees' social media activities. Some or all of the above-described processing in the request collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the request collection unit can input social media data to a generation AI and have the generation AI analyze the requests.

[0040] The request collection unit can customize the collection method by reflecting the evacuees' past feedback when collecting requests. For example, the request collection unit improves the collection method based on the evacuees' past feedback and collects requests efficiently. For example, the request collection unit retrieves feedback previously submitted by the evacuees from a database and optimizes the collection method. The request collection unit can also preferentially use a specific collection method (voice, text, etc.) by reflecting the evacuees' past feedback. For example, if the evacuees prefer voice input, the request collection unit collects requests using voice recognition technology. The request collection unit can also customize the collection method based on the evacuees' past feedback to meet individual needs. For example, if the evacuees prefer detailed explanations, the request collection unit designs a dialogue flow including detailed questions. This allows the collection method to be customized by reflecting the evacuees' past feedback, and requests can be collected efficiently. Some or all of the above-mentioned processing in the request collection unit may be performed using, for example, AI, or may be performed without AI. For example, the request collection unit can input past feedback data into a generation AI and cause the generation AI to optimize the collection method.

[0041] The data collection unit can analyze past environmental data within the evacuation shelter and select an optimal data collection method. For example, the data collection unit retrieves past environmental data from a database and analyzes it using statistical analysis techniques. For example, the data collection unit can collect data focusing on a specific time period based on the past environmental data. The data collection unit can also analyze the past environmental data and optimize specific sensor placement. For example, the data collection unit can change the sensor placement based on the past data to efficiently collect data. The data collection unit can also select a data collection method that corresponds to specific environmental conditions based on the past environmental data. For example, the data collection unit can select a data collection method that corresponds to a specific season or weather conditions based on the past data. This allows for efficient data collection by analyzing the past environmental data. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input past environmental data into a generation AI and cause the generation AI to optimize the data collection method.

[0042] When collecting environmental data, the data collection unit can filter the data based on the current situation and season of the evacuation shelter. For example, the data collection unit prioritizes collection of specific environmental data (temperature, humidity, etc.) based on the current season. For example, temperature data is prioritized in summer, and humidity data is prioritized in winter. The data collection unit can also filter the environmental data based on the current situation of the evacuation shelter (level of congestion, number of evacuees, etc.). For example, if the evacuation shelter is crowded, air quality data is prioritized. The data collection unit can also prioritize collection of data corresponding to specific environmental conditions for each season. For example, pollen data is prioritized in spring. This allows for efficient data collection by filtering data based on the current situation and season of the evacuation shelter. Some or all of the above-mentioned processing in the data collection unit may be performed using, or without, AI. For example, the data collection unit can input current situation and seasonal data into the generation AI and have the generation AI perform data filtering.

[0043] When collecting environmental data, the data collection unit can select the optimal collection means depending on the type and placement of sensors. The data collection unit, for example, uses a temperature sensor to collect temperature data within the evacuation shelter. The temperature sensors, for example, are installed at multiple locations within the evacuation shelter and can collect temperature data in real time. The data collection unit can also use a humidity sensor to collect humidity data within the evacuation shelter. The humidity sensors, for example, are installed at multiple locations within the evacuation shelter and can collect humidity data in real time. The data collection unit can also use an air quality sensor to collect air quality data within the evacuation shelter. The air quality sensors, for example, are installed at multiple locations within the evacuation shelter and can collect air quality data in real time. This allows efficient data collection by selecting the optimal collection means depending on the type and placement of sensors. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input sensor type and placement data into a generation AI and cause the generation AI to select the optimal collection means.

[0044] When collecting environmental data, the data collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the evacuation shelter. For example, the data collection unit prioritizes collecting environmental data needed in a specific area based on the geographical location information of the evacuation shelter. For example, the data collection unit collects temperature data needed in a specific area based on the GPS data of the evacuation shelter. The data collection unit can also prioritize collecting data corresponding to specific environmental conditions based on the geographical location information of the evacuation shelter. For example, the data collection unit collects humidity data needed in a specific area based on the geographical location information of the evacuation shelter. The data collection unit can also prioritize collecting environmental data related to a specific season based on the geographical location information of the evacuation shelter. For example, the data collection unit collects air quality data related to a specific season based on the geographical location information of the evacuation shelter. In this way, highly relevant data can be prioritized by taking into account the geographical location information of the evacuation shelter. Some or all of the above-described processing in the data collection unit may be performed using, or without, AI. For example, the data collection unit can input the geographical location information of the evacuation shelter to the generation AI and cause the generation AI to filter the data.

[0045] The data collection unit may analyze the social media activities of the shelter when collecting environmental data and collect related data. For example, the data collection unit may analyze the content of posts on the shelter's social media to collect related environmental data. For example, the data collection unit may analyze text data posted on the shelter's social media to identify environmental data. The data collection unit may also collect data corresponding to specific environmental conditions from the shelter's social media activities. For example, the data collection unit may analyze images and videos shared on the shelter's social media to identify environmental data. The data collection unit may also collect related environmental data by referring to the activities of friends on the shelter's social media. For example, the data collection unit may identify the environmental conditions of the shelter based on the content posted by friends of the shelter and collect data. This allows for efficient collection of related data by analyzing the shelter's social media activities. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without AI. For example, the data collection unit may input social media data into a generation AI and cause the generation AI to analyze the environmental data.

[0046] When collecting environmental data, the data collection unit can customize the collection method by reflecting past feedback from the shelter. For example, the data collection unit improves the collection method based on the past feedback from the shelter and efficiently collects environmental data. For example, the data collection unit retrieves feedback previously submitted by the shelter from a database and optimizes the collection method. The data collection unit can also prioritize the use of specific collection methods (such as sensor types) by reflecting the past feedback from the shelter. For example, if the shelter prioritizes temperature data, it will prioritize the use of temperature sensors. The data collection unit can also customize the collection method based on the past feedback from the shelter to meet individual needs. For example, if the shelter prefers detailed data, it will use detailed data collection methods. This allows the collection method to be customized and data to be collected efficiently by reflecting the past feedback from the shelter. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input past feedback data into a generation AI and have the generation AI optimize the collection method.

[0047] The analysis unit can optimize the analysis algorithm by referring to past analysis results of the collected data. The analysis unit, for example, retrieves past analysis results from a database and optimizes the analysis algorithm. For example, it optimizes a specific algorithm based on the past analysis results and performs efficient analysis. The analysis unit can also improve the analysis method for a specific data set by referring to the past analysis results. For example, it changes the analysis method based on past data and improves accuracy. The analysis unit can also adjust specific analysis parameters based on the past analysis results and improve accuracy. In this way, by referring to the past analysis results, the analysis algorithm can be optimized and analysis can be performed efficiently. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results into the generation AI and have the generation AI optimize the analysis algorithm.

[0048] The analysis unit can apply different analysis methods depending on the data category during analysis. For example, the analysis unit applies a time series analysis method to environmental data to analyze fluctuation patterns. For example, it performs time series analysis of temperature data and humidity data to identify fluctuation patterns. The analysis unit can also apply a machine learning algorithm to health data to detect anomalies. For example, it analyzes body temperature data and heart rate data to detect abnormal values. The analysis unit can also apply natural language processing technology to perform text analysis on request data. For example, it analyzes the request text of evacuees and extracts important keywords. This allows for efficient analysis by applying different analysis methods depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the data category into the generation AI and have the generation AI select an appropriate analysis method.

[0049] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results of evacuees. The analysis unit, for example, retrieves past analysis results from a database and improves the analysis method. For example, based on past analysis results, the analysis method for a specific data set can be changed to improve accuracy. The analysis unit can also refer to past analysis results to adjust specific analysis parameters and improve accuracy. For example, analysis parameters can be changed based on past data to improve accuracy. The analysis unit can also optimize specific algorithms based on past analysis results and perform analysis efficiently. In this way, the accuracy of the analysis can be improved by referring to past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results into the generation AI and have the generation AI improve the analysis method.

[0050] During analysis, the analysis unit can determine the analysis priority based on the time of data submission. The analysis unit, for example, prioritizes analysis of data with high urgency based on the time of data submission. For example, it prioritizes analysis of recently submitted data and responds quickly. The analysis unit can also prioritize analysis of older submitted data. For example, it analyzes older submitted data to understand past situations. The analysis unit can also dynamically adjust the analysis priority based on the time of data submission. For example, it compares recently submitted data with older data to determine the priority. In this way, by determining the analysis priority based on the time of data submission, it is possible to prioritize analysis of data with high urgency. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data submission to the generation AI and have the generation AI determine the analysis priority.

[0051] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data based on the relevance of the data. For example, the analysis unit analyzes correlations and co-occurrence frequencies of data to identify highly relevant data. The analysis unit can also postpone less relevant data. For example, it postpones less relevant data to the latter half of the analysis and prioritizes analysis of important data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. For example, it analyzes the relevance of data in real time and optimizes the order of analysis. This allows for efficient analysis by adjusting the order of analysis based on the relevance of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the relevance of data to a generation AI and have the generation AI adjust the order of analysis.

[0052] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the evacuee's level of expertise. For example, the analysis unit can refer to questionnaire results and past experience to evaluate the evacuee's level of expertise. For example, the analysis unit can evaluate the level of expertise based on feedback and questionnaire results previously submitted by the evacuee. The analysis unit can also adjust the way the analysis results are expressed according to the evacuee's level of expertise. For example, the analysis unit can provide the analysis results using detailed technical terminology to evacuees with high expertise. On the other hand, the analysis unit can provide the analysis results using concise and easy-to-understand terminology to evacuees with low expertise. The analysis unit can also customize the way the analysis results are expressed according to the evacuee's level of expertise. For example, the analysis unit can adjust the level of detail and the way the analysis results are expressed according to the evacuee's level of expertise. In this way, by adjusting the use of technical terminology in the analysis according to the evacuee's level of expertise, the analysis unit can provide the analysis results in an appropriate way. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the evacuee's level of expertise to the generation AI and cause the generation AI to adjust the way the analysis results are expressed.

[0053] When identifying supplies, the supply identification unit can select the optimal supply identification method by referring to past supply delivery history. The supply identification unit, for example, obtains past supply delivery history from a database and selects the optimal supply identification method. For example, specific supplies can be provided preferentially based on the past supply delivery history. The supply identification unit can also provide customized supplies to a specific group of evacuees by referring to the past supply delivery history. For example, supplies needed by a specific group of evacuees can be identified based on past data. The supply identification unit can also optimize the specific supply delivery method based on the past supply delivery history. For example, the supply delivery method can be changed based on past data to provide supplies efficiently. In this way, supplies can be identified efficiently by referring to the past supply delivery history. Some or all of the above-described processing in the supply identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the supply identification unit can input past supply delivery history into the generation AI and cause the generation AI to select the optimal supply identification method.

[0054] When identifying supplies, the supply identification unit can perform filtering based on the evacuees' current situation and health condition. The supply identification unit, for example, prioritizes providing relevant supplies based on the evacuees' current health condition. For example, the supply identification unit identifies necessary medicines based on the evacuees' health data, such as their body temperature and blood pressure. The supply identification unit can also prioritize providing specific supplies based on the evacuees' current living environment (family composition, whether or not they have pets, etc.). For example, if the evacuees have pets, supplies related to pets are prioritized. The supply identification unit can also filter supplies based on the evacuees' current situation (crowding level, number of evacuees, etc.). For example, if the evacuation shelter is crowded, necessary supplies are prioritized. This allows for efficient supply identification by filtering supplies based on the evacuees' current situation and health condition. Some or all of the above-described processing in the supply identification unit may be performed using, or without, AI. For example, the supply identification unit can input the evacuees' current situation and health data into the generation AI and have the generation AI perform supply filtering.

[0055] The supply identification unit can improve the supply identification method by reflecting evacuee feedback when identifying supplies. For example, the supply identification unit improves the supply identification method based on evacuee feedback and provides supplies efficiently. For example, the supply identification unit retrieves feedback previously submitted by evacuees from a database and optimizes the supply identification method. The supply identification unit can also prioritize the use of specific supply delivery methods (such as delivery methods) by reflecting evacuee feedback. For example, if an evacuee prefers a specific delivery method, that method is used preferentially. The supply identification unit can also customize the supply identification method based on evacuee feedback to meet individual needs. For example, if an evacuee prefers detailed explanations, a detailed supply list is provided. This allows the supply identification method to be improved by reflecting evacuee feedback, thereby providing supplies efficiently. Some or all of the above-described processing in the supply identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the supply identification unit can input feedback data into a generation AI and cause the generation AI to improve the supply identification method.

[0056] When identifying supplies, the supply identification unit can prioritize identifying highly relevant supplies by taking into account the geographical location information of the evacuees. The supply identification unit, for example, prioritizes identifying supplies needed at nearby evacuation shelters based on the evacuees' current locations. For example, it identifies supplies needed at nearby evacuation shelters based on the evacuees' GPS data. The supply identification unit can also prioritize identifying supplies needed in a specific area based on the evacuees' geographical location information. For example, it identifies medicines needed in a specific area based on the evacuees' geographical location information. The supply identification unit can also prioritize identifying supplies suitable for the evacuation shelter environment based on the evacuees' geographical location information. For example, it identifies food suitable for the evacuation shelter environment based on the evacuees' geographical location information. In this way, highly relevant supplies can be prioritized by taking into account the evacuees' geographical location information. Some or all of the above-described processing in the supply identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the supply identification unit can input the evacuees' geographical location information to the generation AI and cause the generation AI to identify supplies.

[0057] When identifying supplies, the supply identification unit can analyze the social media activities of evacuees and identify relevant supplies. The supply identification unit, for example, analyzes content posted by evacuees on social media to identify relevant supplies. For example, it analyzes text data posted by evacuees on social media to identify needed supplies. The supply identification unit can also identify specific needs and identify supplies from the evacuees' social media activities. For example, it can analyze images and videos shared by evacuees on social media to identify needed supplies. The supply identification unit can also identify relevant supplies by referring to the activities of the evacuees' friends on social media. For example, it can identify the evacuees' needs and identify supplies based on content posted by the evacuees' friends. In this way, by analyzing the evacuees' social media activities, relevant supplies can be efficiently identified. Some or all of the above-described processing in the supply identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the supply identification unit can input social media data into a generation AI and have the generation AI identify supplies.

[0058] When identifying supplies, the supply identification unit can customize the supply identification method by reflecting the evacuees' past feedback. The supply identification unit, for example, improves the supply identification method based on the evacuees' past feedback and provides supplies efficiently. For example, the supply identification unit retrieves feedback previously submitted by the evacuees from a database and optimizes the supply identification method. The supply identification unit can also preferentially use a specific supply provision method (such as a delivery method) by reflecting the evacuees' past feedback. For example, if the evacuees prefer a specific delivery method, that method is used preferentially. The supply identification unit can also customize the supply identification method based on the evacuees' past feedback to meet individual needs. For example, if the evacuees prefer detailed explanations, a detailed supply list is provided. This allows the supply identification method to be customized by reflecting the evacuees' past feedback, enabling supplies to be provided efficiently. Some or all of the above-described processing in the supply identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the supply identification unit can input feedback data to a generation AI and cause the generation AI to improve the supply identification method.

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

[0060] The data collection unit can analyze past environmental data within the evacuation shelter and select the optimal data collection method. For example, data can be collected with an emphasis on a specific time period based on past environmental data. It can also analyze past environmental data and optimize specific sensor placement. Furthermore, it can select a data collection method that corresponds to specific environmental conditions based on past environmental data. This allows for efficient data collection by analyzing past environmental data.

[0061] The analysis unit can optimize the analysis algorithm by referring to past analysis results of the collected data. For example, a specific algorithm can be optimized based on past analysis results to perform efficient analysis. The analysis method for a specific data set can also be improved by referring to past analysis results. Furthermore, specific analysis parameters can be adjusted based on past analysis results to improve accuracy. In this way, by referring to past analysis results, the analysis algorithm can be optimized and analysis can be performed efficiently.

[0062] The request collection unit can analyze the past request history of evacuees and select the optimal request collection method. For example, a template can be created based on the past request history to efficiently collect requests. It can also analyze the tendency for many requests to be submitted during specific time periods from the past request history and focus on those time periods. Furthermore, it can also propose a collection method customized for a specific group of evacuees based on the past request history. In this way, requests can be collected efficiently by analyzing the past request history.

[0063] During analysis, the analysis unit can apply different analysis methods depending on the data category. For example, time series analysis methods can be applied to environmental data to analyze fluctuation patterns. Machine learning algorithms can also be applied to health data to detect anomalies. Furthermore, natural language processing technology can be applied to request data to perform text analysis. This allows for efficient analysis by applying different analysis methods depending on the data category.

[0064] When identifying supplies, the supply identification unit can filter based on the evacuees' current situation and health condition. For example, it can identify necessary medicines based on health data such as the evacuees' body temperature and blood pressure. It can also prioritize the provision of specific supplies based on the evacuees' current living environment (family composition, presence of pets, etc.). Furthermore, if the evacuation shelter is crowded, it can prioritize the provision of necessary supplies. This allows for efficient identification of supplies by filtering supplies based on the evacuees' current situation and health condition.

[0065] When identifying supplies, the supply identification unit can select the optimal supply identification method by referring to past supply delivery history. For example, specific supplies can be provided preferentially based on past supply delivery history. In addition, customized supplies can be provided to a specific group of evacuees by referring to past supply delivery history. Furthermore, a specific supply delivery method can be optimized based on past supply delivery history. In this way, supplies can be identified efficiently by referring to past supply delivery history.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The request collection unit collects requests from evacuees. Requests from evacuees include requests for supplies, reports on their health status, and suggestions for improving evacuation shelters. The request collection unit collects requests and concerns from evacuees, for example, through a chatbot. The chatbot can use natural language processing technology to understand the requests of evacuees and design an appropriate dialogue flow. Step 2: The data collection unit collects environmental data and health data. Environmental data includes temperature, humidity, air quality, etc. The data collection unit collects environmental data including, for example, the temperature and humidity inside the evacuation shelter. Temperature is measured using a temperature sensor, and humidity is measured using a humidity sensor. The data collection unit also collects health condition data of the evacuees. Health condition data includes body temperature, blood pressure, heart rate, etc. Body temperature is measured using a body temperature sensor, blood pressure is measured using a sphygmomanometer, and heart rate is measured using a heart rate sensor. Step 3: The analysis unit performs analysis based on the collected needs data, environmental data, and health data. The analysis is performed using statistical analysis of the data and machine learning algorithms. The analysis unit performs statistical analysis of the collected data to identify the needs of evacuees. It can also use machine learning algorithms to analyze patterns in the data and predict the needs of evacuees. Step 4: The Supplies Identification Department identifies specific relief supplies based on the analysis results. Relief supplies include food, water, medicine, clothing, etc. Based on the analysis results, the Supplies Identification Department identifies the food, medicine, and clothing needed by the evacuees.

[0068] (Example 2) A relief supplies optimization system according to an embodiment of the present invention optimizes relief supplies based on evacuees' requests, environmental data, and health data. The relief supplies optimization system collects requests from evacuees, environmental data, and health data, and analyzes this data to identify relief supplies. For example, in the relief supplies optimization system, evacuees report their requests through a chatbot. For example, they input information such as a lack of food, the need for medicine, or specific allergies. This information is collected by an AI chatbot and analyzed in real time. Next, the relief supplies optimization system collects environmental data such as the temperature and humidity in the evacuation shelter and the evacuees' health status data through sensors. This data is analyzed by AI to identify the relief supplies needed based on the evacuees' needs. For example, specific relief supplies such as blankets needed when the temperature is low, dehumidifiers needed when the humidity is high, and specific medicines needed for evacuees with specific health conditions are identified. This enables the relief supplies optimization system to optimize relief supplies based on the evacuees' needs and provide them at the appropriate time. This allows the relief supplies optimization system to improve the living environment in evacuation shelters and ensure the health and safety of evacuees. For example, by quickly and accurately collecting requests written by evacuees and providing appropriate relief supplies, it will be possible to provide support that meets the needs of evacuees.

[0069] The relief supply optimization system according to the embodiment includes a request collection unit, a data collection unit, an analysis unit, and a supply identification unit. The request collection unit collects requests from evacuees. The requests from evacuees include, but are not limited to, requests for supplies, reports on health status, and suggestions for improving evacuation shelters. The request collection unit collects requests and concerns from evacuees, for example, through a chatbot. The chatbot can understand the evacuees' requests and design an appropriate dialogue flow using natural language processing technology. The data collection unit collects environmental data and health data. The environmental data includes, but is not limited to, temperature, humidity, air quality, and the like. The data collection unit collects environmental data, for example, including temperature and humidity within the evacuation shelter. The temperature is measured using, for example, a temperature sensor. The humidity is measured using, for example, a humidity sensor. The data collection unit collects health status data of the evacuees. The health status data includes, but is not limited to, body temperature, blood pressure, heart rate, and the like. The data collection unit measures body temperature using, for example, a body temperature sensor. The blood pressure is measured using, for example, a sphygmomanometer. The heart rate is measured using, for example, a heart rate sensor. The analysis unit performs analysis based on the collected request data, environmental data, and health data. The analysis is performed using, for example, statistical analysis of data or a machine learning algorithm. The analysis unit, for example, statistically analyzes the collected data to identify the needs of evacuees. The analysis unit can also use a machine learning algorithm to analyze data patterns and predict the needs of evacuees. The supply identification unit identifies specific relief supplies based on the analysis results. Relief supplies include, for example, food, water, medicine, clothing, etc., but are not limited to these examples. The supply identification unit, for example, identifies food needed by evacuees based on the analysis results. The supply identification unit can also identify medicine needed by evacuees based on the analysis results. The supply identification unit can also identify clothing needed by evacuees based on the analysis results. As a result, the relief supply optimization system according to the embodiment can optimize relief supplies based on the requests, environmental data, and health data of evacuees. For example, relief supplies according to the needs of evacuees can be provided quickly and accurately.This will improve living conditions in evacuation centers and ensure the health and safety of evacuees.

[0070] The request collection unit can collect requests and problems from evacuees through the chatbot. The chatbot can understand the requests of evacuees and design an appropriate dialogue flow using, for example, natural language processing technology. For example, the chatbot can analyze text entered by evacuees and classify the requests. The chatbot can also generate appropriate questions and collect detailed information in accordance with the requests of evacuees. The chatbot can also collect the requests of evacuees in real time and store them in a database. This allows requests from evacuees to be collected efficiently through the chatbot. Some or all of the above-mentioned processing in the request collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the request collection unit can input the request data collected by the chatbot into a generation AI and have the generation AI classify and analyze the requests.

[0071] The data collection unit can collect environmental data, including temperature and humidity, within the evacuation shelter. Examples of environmental data include, but are not limited to, temperature, humidity, and air quality. The data collection unit can measure the temperature within the evacuation shelter using, for example, a temperature sensor. The temperature sensors can be installed, for example, at multiple locations within the evacuation shelter and can collect temperature data in real time. The data collection unit can also measure humidity within the evacuation shelter using a humidity sensor. The humidity sensors can be installed, for example, at multiple locations within the evacuation shelter and can collect humidity data in real time. The data collection unit can also measure air quality within the evacuation shelter using an air quality sensor. The air quality sensors can be installed, for example, at multiple locations within the evacuation shelter and can collect air quality data in real time. By collecting environmental data within the evacuation shelter, appropriate relief supplies can be identified. Some or all of the above-described processing in the data collection unit can be performed using, for example, AI, or without AI. For example, the data collection unit can input environmental data collected from the sensors into a generation AI and have the generation AI analyze the data.

[0072] The data collection unit can collect health condition data of evacuees. Examples of health condition data include, but are not limited to, body temperature, blood pressure, and heart rate. The data collection unit can measure the body temperature of evacuees using, for example, a body temperature sensor. The body temperature sensor can measure the body temperature of evacuees in real time and collect data, for example. The data collection unit can also measure the blood pressure of evacuees using a blood pressure monitor. The blood pressure monitor can measure the blood pressure of evacuees in real time and collect data, for example. The data collection unit can also measure the heart rate of evacuees using a heart rate sensor. The heart rate sensor can measure the heart rate of evacuees in real time and collect data, for example. By collecting health condition data of evacuees, appropriate relief supplies can be identified. Some or all of the above-described processing in the data collection unit can be performed using, for example, AI, or without AI. For example, the data collection unit can input the health condition data collected from the sensor to a generation AI and have the generation AI analyze the data.

[0073] The analysis unit can perform analysis based on the collected need data, environmental data, and health data. The analysis can be performed using, for example, statistical analysis of data or a machine learning algorithm. The analysis unit can, for example, perform statistical analysis of the collected data to identify the needs of evacuees. Statistical analysis can, for example, analyze the distribution and correlation of data to clarify the needs of evacuees. The analysis unit can also analyze data patterns and predict the needs of evacuees using a machine learning algorithm. The machine learning algorithm can, for example, learn from past data and make predictions based on new data. This allows appropriate relief supplies to be identified by performing analysis based on the collected data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI analyze the data.

[0074] The supply identification unit can identify specific relief supplies based on the analysis results. Relief supplies include, but are not limited to, food, water, medicine, and clothing. For example, the supply identification unit can identify food needed by evacuees based on the analysis results. Food includes, for example, canned goods, dried foods, and nutritional supplements. The supply identification unit can also identify medicine needed by evacuees based on the analysis results. Medicine includes, for example, painkillers, antibiotics, and allergy medications. The supply identification unit can also identify clothing needed by evacuees based on the analysis results. Clothing includes, for example, blankets, jackets, and socks. By identifying specific relief supplies based on the analysis results, it is possible to provide support tailored to the needs of evacuees. Some or all of the above-described processing in the supply identification unit can be performed using, for example, AI, or without AI. For example, the supply identification unit can input the analysis results to a generation AI and cause the generation AI to identify relief supplies.

[0075] The request collection unit can estimate the emotions of evacuees and prioritize requests based on the estimated emotions of the evacuees. The request collection unit, for example, uses facial expression recognition technology to estimate the emotions of the evacuees. For example, the request collection unit analyzes facial expression data of the evacuees captured by a camera to estimate their emotions. The request collection unit can also estimate the emotions of the evacuees using voice analysis technology. For example, the request collection unit analyzes the tone and speed of the evacuees' voices to estimate their emotions. The request collection unit can also prioritize requests based on the emotions of the evacuees. For example, if the evacuees are stressed, the request collection unit can prioritize urgent requests. If the evacuees are relaxed, the request collection unit can collect detailed requests and flexibly adjust the priorities. If the evacuees are anxious, the request collection unit can prioritize requests that provide a sense of security. In this way, by prioritizing requests based on the emotions of the evacuees, it is possible to prioritize urgent requests. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the request collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the request collection unit may cause a generation AI to execute an emotion estimation algorithm and determine the priority of requests based on estimated emotion data.

[0076] The request collection unit can analyze the past request history of evacuees and select the optimal request collection method. For example, the request collection unit retrieves the past request history from a database and analyzes it using text mining technology. For example, the request collection unit creates a template based on requests frequently submitted in the past and collects requests efficiently. The request collection unit can also analyze the past request history to determine whether many requests are submitted during a specific time period and focus on that time period. The request collection unit can also propose a collection method customized for a specific group of evacuees based on the past request history. In this way, requests can be collected efficiently by analyzing the past request history. Some or all of the above-described processing in the request collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the request collection unit can input the past request history into a generation AI and have the generation AI select the optimal request collection method.

[0077] When collecting requests, the request collection unit can filter requests based on the evacuees' current situation and areas of interest. The request collection unit, for example, prioritizes collecting relevant requests based on the evacuees' current health condition. For example, the request collection unit prioritizes collecting requests with high urgency based on health data such as the evacuees' body temperature and blood pressure. The request collection unit can also filter requests based on the evacuees' areas of interest (food, medicine, etc.) to efficiently collect requests. For example, if the evacuees are interested in food, the request collection unit prioritizes collecting requests related to food. The request collection unit can also prioritize collecting specific requests based on the evacuees' current living environment (family composition, whether or not they have pets, etc.). For example, if the evacuees have pets, the request collection unit prioritizes collecting requests related to pets. In this way, requests can be filtered based on the evacuees' current situation and areas of interest, thereby efficiently collecting requests. Some or all of the above-described processing in the request collection unit may be performed using, or without, AI. For example, the request collection unit can input the evacuees' current situation and areas of interest into the generation AI and cause the generation AI to filter the requests.

[0078] When collecting requests, the request collection unit can select the optimal collection method depending on the evacuee's input method. For example, for evacuees who use voice input, the request collection unit collects requests using voice recognition technology. For example, the evacuee uses a microphone to input their requests verbally, and voice recognition software automatically analyzes the voice and converts it into text data. The request collection unit can also provide a simple form to evacuees who use text input, allowing for efficient collection of requests. For example, the evacuee inputs their requests in text, and the form automatically saves the text in a database. The request collection unit can also collect requests using image analysis technology for evacuees who use image input. For example, the evacuee uploads an image, and image analysis software automatically analyzes the image and identifies the request. This allows for efficient collection of requests by selecting the optimal collection method depending on the evacuee's input method. Some or all of the above-described processing in the request collection unit may be performed using, or without, AI. For example, the request collection unit can input voice data or image data into a generation AI and have the generation AI analyze the requests.

[0079] The request collection unit can estimate the emotions of the evacuee and adjust the way in which the request is expressed based on the estimated emotions of the evacuee. The request collection unit, for example, uses facial expression recognition technology to estimate the emotions of the evacuee. For example, it analyzes facial expression data of the evacuee captured by a camera to estimate the emotions. The request collection unit can also estimate the emotions of the evacuee using voice analysis technology. For example, it analyzes the tone and speed of the evacuee's voice to estimate the emotions. The request collection unit can also adjust the way in which the request is expressed based on the emotions of the evacuee. For example, if the evacuee is stressed, it can use a concise and clear expression. If the evacuee is relaxed, it can use an expression that includes detailed explanations. If the evacuee is anxious, it can use an expression that gives a sense of security. In this way, by adjusting the way in which the request is expressed based on the emotions of the evacuee, it is possible to collect requests using an appropriate expression. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the request collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the request collection unit may cause a generation AI to execute an emotion estimation algorithm and adjust the way in which requests are expressed based on the estimated emotion data.

[0080] When collecting requests, the request collection unit can prioritize collecting highly relevant requests by taking into account the geographical location information of the evacuees. For example, the request collection unit prioritizes collecting requests needed at nearby evacuation shelters based on the evacuees' current locations. For example, the request collection unit collects requests for supplies needed at nearby evacuation shelters based on the evacuees' GPS data. The request collection unit can also prioritize collecting requests for supplies needed in a specific area based on the evacuees' geographical location information. For example, the request collection unit collects requests for medicines needed in a specific area based on the evacuees' geographical location information. The request collection unit can also prioritize collecting requests suitable for the environment of an evacuation shelter based on the evacuees' geographical location information. For example, the request collection unit collects requests for food suitable for the environment of an evacuation shelter based on the evacuees' geographical location information. In this way, by taking into account the geographical location information of the evacuees, highly relevant requests can be prioritized. Some or all of the above-described processing in the request collection unit may be performed using, or without, AI. For example, the request collection unit can input the geographical location information of the evacuees to the generation AI and cause the generation AI to filter the requests.

[0081] The request collection unit can analyze the social media activities of evacuees and collect related requests when collecting requests. The request collection unit, for example, analyzes content posted by evacuees on social media and collects related requests. For example, the request collection unit analyzes text data posted by evacuees on social media to identify requests. The request collection unit can also identify specific needs and collect requests from the evacuees' social media activities. For example, the request collection unit analyzes images and videos shared by evacuees on social media to identify requests. The request collection unit can also collect related requests by referring to the activities of the evacuees' friends on social media. For example, the evacuees' needs are identified and requests are collected based on content posted by the evacuees' friends. In this way, related requests can be efficiently collected by analyzing the evacuees' social media activities. Some or all of the above-described processing in the request collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the request collection unit can input social media data to a generation AI and have the generation AI analyze the requests.

[0082] The request collection unit can customize the collection method by reflecting the evacuees' past feedback when collecting requests. For example, the request collection unit improves the collection method based on the evacuees' past feedback and collects requests efficiently. For example, the request collection unit retrieves feedback previously submitted by the evacuees from a database and optimizes the collection method. The request collection unit can also preferentially use a specific collection method (voice, text, etc.) by reflecting the evacuees' past feedback. For example, if the evacuees prefer voice input, the request collection unit collects requests using voice recognition technology. The request collection unit can also customize the collection method based on the evacuees' past feedback to meet individual needs. For example, if the evacuees prefer detailed explanations, the request collection unit designs a dialogue flow including detailed questions. This allows the collection method to be customized by reflecting the evacuees' past feedback, and requests can be collected efficiently. Some or all of the above-mentioned processing in the request collection unit may be performed using, for example, AI, or may be performed without AI. For example, the request collection unit can input past feedback data into a generation AI and cause the generation AI to optimize the collection method.

[0083] The data collection unit can estimate the emotions of the evacuees and adjust the timing of collecting environmental data based on the estimated emotions of the evacuees. The data collection unit, for example, uses facial expression recognition technology to estimate the emotions of the evacuees. For example, it analyzes facial expression data of the evacuees captured by a camera to estimate their emotions. The data collection unit can also estimate the emotions of the evacuees using voice analysis technology. For example, it analyzes the tone and speed of the evacuees' voices to estimate their emotions. The data collection unit can also adjust the timing of collecting environmental data based on the emotions of the evacuees. For example, if the evacuees are feeling stressed, it can increase the frequency of collecting environmental data to respond quickly. If the evacuees are feeling relaxed, it can reduce the frequency of collecting environmental data to save resources. If the evacuees are feeling anxious, it can adjust the timing of collecting environmental data to provide a sense of security. This allows for a quick response by adjusting the timing of collecting environmental data based on the emotions of the evacuees. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit may cause a generation AI to execute an emotion estimation algorithm and adjust the timing of collecting environmental data based on the estimated emotion data.

[0084] The data collection unit can analyze past environmental data within the evacuation shelter and select an optimal data collection method. For example, the data collection unit retrieves past environmental data from a database and analyzes it using statistical analysis techniques. For example, the data collection unit can collect data focusing on a specific time period based on the past environmental data. The data collection unit can also analyze the past environmental data and optimize specific sensor placement. For example, the data collection unit can change the sensor placement based on the past data to efficiently collect data. The data collection unit can also select a data collection method that corresponds to specific environmental conditions based on the past environmental data. For example, the data collection unit can select a data collection method that corresponds to a specific season or weather conditions based on the past data. This allows for efficient data collection by analyzing the past environmental data. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input past environmental data into a generation AI and cause the generation AI to optimize the data collection method.

[0085] When collecting environmental data, the data collection unit can filter the data based on the current situation and season of the evacuation shelter. For example, the data collection unit prioritizes collection of specific environmental data (temperature, humidity, etc.) based on the current season. For example, temperature data is prioritized in summer, and humidity data is prioritized in winter. The data collection unit can also filter the environmental data based on the current situation of the evacuation shelter (level of congestion, number of evacuees, etc.). For example, if the evacuation shelter is crowded, air quality data is prioritized. The data collection unit can also prioritize collection of data corresponding to specific environmental conditions for each season. For example, pollen data is prioritized in spring. This allows for efficient data collection by filtering data based on the current situation and season of the evacuation shelter. Some or all of the above-mentioned processing in the data collection unit may be performed using, or without, AI. For example, the data collection unit can input current situation and seasonal data into the generation AI and have the generation AI perform data filtering.

[0086] When collecting environmental data, the data collection unit can select the optimal collection means depending on the type and placement of sensors. The data collection unit, for example, uses a temperature sensor to collect temperature data within the evacuation shelter. The temperature sensors, for example, are installed at multiple locations within the evacuation shelter and can collect temperature data in real time. The data collection unit can also use a humidity sensor to collect humidity data within the evacuation shelter. The humidity sensors, for example, are installed at multiple locations within the evacuation shelter and can collect humidity data in real time. The data collection unit can also use an air quality sensor to collect air quality data within the evacuation shelter. The air quality sensors, for example, are installed at multiple locations within the evacuation shelter and can collect air quality data in real time. This allows efficient data collection by selecting the optimal collection means depending on the type and placement of sensors. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input sensor type and placement data into a generation AI and cause the generation AI to select the optimal collection means.

[0087] The data collection unit can estimate the emotions of the evacuees and prioritize environmental data based on the estimated emotions of the evacuees. The data collection unit, for example, uses facial expression recognition technology to estimate the emotions of the evacuees. For example, it analyzes facial expression data of the evacuees captured by a camera to estimate their emotions. The data collection unit can also estimate the emotions of the evacuees using voice analysis technology. For example, it can analyze the tone and speed of the evacuees' voices to estimate their emotions. The data collection unit can also prioritize environmental data based on the emotions of the evacuees. For example, if the evacuees are stressed, it can prioritize collecting temperature data. If the evacuees are relaxed, it can prioritize collecting humidity data. If the evacuees are anxious, it can prioritize collecting air quality data. In this way, by prioritizing environmental data based on the emotions of the evacuees, important data can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit may cause a generation AI to execute an emotion estimation algorithm and determine the priority of environmental data based on the estimated emotion data.

[0088] When collecting environmental data, the data collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the evacuation shelter. For example, the data collection unit prioritizes collecting environmental data needed in a specific area based on the geographical location information of the evacuation shelter. For example, the data collection unit collects temperature data needed in a specific area based on the GPS data of the evacuation shelter. The data collection unit can also prioritize collecting data corresponding to specific environmental conditions based on the geographical location information of the evacuation shelter. For example, the data collection unit collects humidity data needed in a specific area based on the geographical location information of the evacuation shelter. The data collection unit can also prioritize collecting environmental data related to a specific season based on the geographical location information of the evacuation shelter. For example, the data collection unit collects air quality data related to a specific season based on the geographical location information of the evacuation shelter. In this way, highly relevant data can be prioritized by taking into account the geographical location information of the evacuation shelter. Some or all of the above-described processing in the data collection unit may be performed using, or without, AI. For example, the data collection unit can input the geographical location information of the evacuation shelter to the generation AI and cause the generation AI to filter the data.

[0089] The data collection unit may analyze the social media activities of the shelter when collecting environmental data and collect related data. For example, the data collection unit may analyze the content of posts on the shelter's social media to collect related environmental data. For example, the data collection unit may analyze text data posted on the shelter's social media to identify environmental data. The data collection unit may also collect data corresponding to specific environmental conditions from the shelter's social media activities. For example, the data collection unit may analyze images and videos shared on the shelter's social media to identify environmental data. The data collection unit may also collect related environmental data by referring to the activities of friends on the shelter's social media. For example, the data collection unit may identify the environmental conditions of the shelter based on the content posted by friends of the shelter and collect data. This allows for efficient collection of related data by analyzing the shelter's social media activities. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without AI. For example, the data collection unit may input social media data into a generation AI and cause the generation AI to analyze the environmental data.

[0090] When collecting environmental data, the data collection unit can customize the collection method by reflecting past feedback from the shelter. For example, the data collection unit improves the collection method based on the past feedback from the shelter and efficiently collects environmental data. For example, the data collection unit retrieves feedback previously submitted by the shelter from a database and optimizes the collection method. The data collection unit can also prioritize the use of specific collection methods (such as sensor types) by reflecting the past feedback from the shelter. For example, if the shelter prioritizes temperature data, it will prioritize the use of temperature sensors. The data collection unit can also customize the collection method based on the past feedback from the shelter to meet individual needs. For example, if the shelter prefers detailed data, it will use detailed data collection methods. This allows the collection method to be customized and data to be collected efficiently by reflecting the past feedback from the shelter. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input past feedback data into a generation AI and have the generation AI optimize the collection method.

[0091] The analysis unit can estimate the emotions of the evacuees and determine the analysis priorities based on the estimated emotions of the evacuees. The analysis unit, for example, uses facial expression recognition technology to estimate the emotions of the evacuees. For example, the analysis unit analyzes facial expression data of the evacuees captured by a camera to estimate the emotions. The analysis unit can also estimate the emotions of the evacuees using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the evacuees' voices to estimate the emotions. The analysis unit can also determine the analysis priorities based on the emotions of the evacuees. For example, if the evacuees are stressed, the analysis unit can prioritize analyzing data with high urgency. If the evacuees are relaxed, the analysis unit can prioritize analyzing detailed data. If the evacuees are anxious, the analysis unit can prioritize analyzing data that gives them a sense of security. In this way, by determining the analysis priorities based on the emotions of the evacuees, the analysis unit can prioritize analyzing data with high urgency. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may cause a generation AI to execute an emotion estimation algorithm and determine the priority of analysis based on the estimated emotion data.

[0092] The analysis unit can optimize the analysis algorithm by referring to past analysis results of the collected data. The analysis unit, for example, retrieves past analysis results from a database and optimizes the analysis algorithm. For example, it optimizes a specific algorithm based on the past analysis results and performs efficient analysis. The analysis unit can also improve the analysis method for a specific data set by referring to the past analysis results. For example, it changes the analysis method based on past data and improves accuracy. The analysis unit can also adjust specific analysis parameters based on the past analysis results and improve accuracy. In this way, by referring to the past analysis results, the analysis algorithm can be optimized and analysis can be performed efficiently. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results into the generation AI and have the generation AI optimize the analysis algorithm.

[0093] The analysis unit can apply different analysis methods depending on the data category during analysis. For example, the analysis unit applies a time series analysis method to environmental data to analyze fluctuation patterns. For example, it performs time series analysis of temperature data and humidity data to identify fluctuation patterns. The analysis unit can also apply a machine learning algorithm to health data to detect anomalies. For example, it analyzes body temperature data and heart rate data to detect abnormal values. The analysis unit can also apply natural language processing technology to perform text analysis on request data. For example, it analyzes the request text of evacuees and extracts important keywords. This allows for efficient analysis by applying different analysis methods depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the data category into the generation AI and have the generation AI select an appropriate analysis method.

[0094] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results of evacuees. The analysis unit, for example, retrieves past analysis results from a database and improves the analysis method. For example, based on past analysis results, the analysis method for a specific data set can be changed to improve accuracy. The analysis unit can also refer to past analysis results to adjust specific analysis parameters and improve accuracy. For example, analysis parameters can be changed based on past data to improve accuracy. The analysis unit can also optimize specific algorithms based on past analysis results and perform analysis efficiently. In this way, the accuracy of the analysis can be improved by referring to past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results into the generation AI and have the generation AI improve the analysis method.

[0095] The analysis unit can estimate the emotions of the evacuees and adjust the expression method of the analysis based on the estimated emotions of the evacuees. The analysis unit, for example, uses facial expression recognition technology to estimate the emotions of the evacuees. For example, it analyzes facial expression data of the evacuees captured by a camera to estimate the emotions. The analysis unit can also estimate the emotions of the evacuees using voice analysis technology. For example, it analyzes the tone and speed of the evacuees' voices to estimate the emotions. The analysis unit can also adjust the expression method of the analysis based on the emotions of the evacuees. For example, if the evacuees are stressed, it can use a concise and clear expression method. If the evacuees are relaxed, it can use an expression method that includes detailed explanations. If the evacuees are anxious, it can use an expression method that gives a sense of security. By adjusting the expression method of the analysis based on the emotions of the evacuees, it is possible to provide analysis results in an appropriate expression method. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may cause a generation AI to execute an emotion estimation algorithm and adjust the expression method of the analysis based on the estimated emotion data.

[0096] During analysis, the analysis unit can determine the analysis priority based on the time of data submission. The analysis unit, for example, prioritizes analysis of data with high urgency based on the time of data submission. For example, it prioritizes analysis of recently submitted data and responds quickly. The analysis unit can also prioritize analysis of older submitted data. For example, it analyzes older submitted data to understand past situations. The analysis unit can also dynamically adjust the analysis priority based on the time of data submission. For example, it compares recently submitted data with older data to determine the priority. In this way, by determining the analysis priority based on the time of data submission, it is possible to prioritize analysis of data with high urgency. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data submission to the generation AI and have the generation AI determine the analysis priority.

[0097] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data based on the relevance of the data. For example, the analysis unit analyzes correlations and co-occurrence frequencies of data to identify highly relevant data. The analysis unit can also postpone less relevant data. For example, it postpones less relevant data to the latter half of the analysis and prioritizes analysis of important data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. For example, it analyzes the relevance of data in real time and optimizes the order of analysis. This allows for efficient analysis by adjusting the order of analysis based on the relevance of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the relevance of data to a generation AI and have the generation AI adjust the order of analysis.

[0098] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the evacuee's level of expertise. For example, the analysis unit can refer to questionnaire results and past experience to evaluate the evacuee's level of expertise. For example, the analysis unit can evaluate the level of expertise based on feedback and questionnaire results previously submitted by the evacuee. The analysis unit can also adjust the way the analysis results are expressed according to the evacuee's level of expertise. For example, the analysis unit can provide the analysis results using detailed technical terminology to evacuees with high expertise. On the other hand, the analysis unit can provide the analysis results using concise and easy-to-understand terminology to evacuees with low expertise. The analysis unit can also customize the way the analysis results are expressed according to the evacuee's level of expertise. For example, the analysis unit can adjust the level of detail and the way the analysis results are expressed according to the evacuee's level of expertise. In this way, by adjusting the use of technical terminology in the analysis according to the evacuee's level of expertise, the analysis unit can provide the analysis results in an appropriate way. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the evacuee's level of expertise to the generation AI and cause the generation AI to adjust the way the analysis results are expressed.

[0099] The supply identification unit can estimate the emotions of evacuees and prioritize supplies based on the estimated emotions of the evacuees. The supply identification unit can use, for example, facial expression recognition technology to estimate the emotions of evacuees. For example, it can analyze facial expression data of evacuees captured by a camera to estimate emotions. The supply identification unit can also estimate the emotions of evacuees using voice analysis technology. For example, it can analyze the tone and speed of the evacuees' voices to estimate emotions. The supply identification unit can also prioritize supplies based on the emotions of evacuees. For example, if an evacuee is feeling stressed, it can provide more urgent supplies. If an evacuee is feeling relaxed, it can provide a detailed list of supplies. If an evacuee is feeling anxious, it can provide more urgent supplies. In this way, by prioritizing supplies based on the emotions of evacuees, it is possible to provide more urgent supplies. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the supply identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the supply identification unit may cause a generation AI to execute an emotion estimation algorithm and determine the priority of supplies based on the estimated emotion data.

[0100] When identifying supplies, the supply identification unit can select the optimal supply identification method by referring to past supply delivery history. The supply identification unit, for example, obtains past supply delivery history from a database and selects the optimal supply identification method. For example, specific supplies can be provided preferentially based on the past supply delivery history. The supply identification unit can also provide customized supplies to a specific group of evacuees by referring to the past supply delivery history. For example, supplies needed by a specific group of evacuees can be identified based on past data. The supply identification unit can also optimize the specific supply delivery method based on the past supply delivery history. For example, the supply delivery method can be changed based on past data to provide supplies efficiently. In this way, supplies can be identified efficiently by referring to the past supply delivery history. Some or all of the above-described processing in the supply identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the supply identification unit can input past supply delivery history into the generation AI and cause the generation AI to select the optimal supply identification method.

[0101] When identifying supplies, the supply identification unit can perform filtering based on the evacuees' current situation and health condition. The supply identification unit, for example, prioritizes providing relevant supplies based on the evacuees' current health condition. For example, the supply identification unit identifies necessary medicines based on the evacuees' health data, such as their body temperature and blood pressure. The supply identification unit can also prioritize providing specific supplies based on the evacuees' current living environment (family composition, whether or not they have pets, etc.). For example, if the evacuees have pets, supplies related to pets are prioritized. The supply identification unit can also filter supplies based on the evacuees' current situation (crowding level, number of evacuees, etc.). For example, if the evacuation shelter is crowded, necessary supplies are prioritized. This allows for efficient supply identification by filtering supplies based on the evacuees' current situation and health condition. Some or all of the above-described processing in the supply identification unit may be performed using, or without, AI. For example, the supply identification unit can input the evacuees' current situation and health data into the generation AI and have the generation AI perform supply filtering.

[0102] The supply identification unit can improve the supply identification method by reflecting evacuee feedback when identifying supplies. For example, the supply identification unit improves the supply identification method based on evacuee feedback and provides supplies efficiently. For example, the supply identification unit retrieves feedback previously submitted by evacuees from a database and optimizes the supply identification method. The supply identification unit can also prioritize the use of specific supply delivery methods (such as delivery methods) by reflecting evacuee feedback. For example, if an evacuee prefers a specific delivery method, that method is used preferentially. The supply identification unit can also customize the supply identification method based on evacuee feedback to meet individual needs. For example, if an evacuee prefers detailed explanations, a detailed supply list is provided. This allows the supply identification method to be improved by reflecting evacuee feedback, thereby providing supplies efficiently. Some or all of the above-described processing in the supply identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the supply identification unit can input feedback data into a generation AI and cause the generation AI to improve the supply identification method.

[0103] The supply identification unit can estimate the emotions of evacuees and adjust the display method of supplies based on the estimated emotions of the evacuees. The supply identification unit can use, for example, facial expression recognition technology to estimate the emotions of evacuees. For example, it can analyze facial expression data of evacuees captured by a camera to estimate emotions. The supply identification unit can also estimate the emotions of evacuees using voice analysis technology. For example, it can analyze the tone and speed of the evacuees' voices to estimate emotions. The supply identification unit can also adjust the display method of supplies based on the emotions of the evacuees. For example, if the evacuees are stressed, a concise and clear display method can be used. If the evacuees are relaxed, a display method including detailed explanations can be used. If the evacuees are anxious, a display method that gives a sense of security can be used. In this way, by adjusting the display method of supplies based on the emotions of the evacuees, supplies can be provided in an appropriate display method. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the material identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the material identification unit may cause a generation AI to execute an emotion estimation algorithm and adjust the display method of the material based on the estimated emotion data.

[0104] When identifying supplies, the supply identification unit can prioritize identifying highly relevant supplies by taking into account the geographical location information of the evacuees. The supply identification unit, for example, prioritizes identifying supplies needed at nearby evacuation shelters based on the evacuees' current locations. For example, it identifies supplies needed at nearby evacuation shelters based on the evacuees' GPS data. The supply identification unit can also prioritize identifying supplies needed in a specific area based on the evacuees' geographical location information. For example, it identifies medicines needed in a specific area based on the evacuees' geographical location information. The supply identification unit can also prioritize identifying supplies suitable for the evacuation shelter environment based on the evacuees' geographical location information. For example, it identifies food suitable for the evacuation shelter environment based on the evacuees' geographical location information. In this way, highly relevant supplies can be prioritized by taking into account the evacuees' geographical location information. Some or all of the above-described processing in the supply identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the supply identification unit can input the evacuees' geographical location information to the generation AI and cause the generation AI to identify supplies.

[0105] When identifying supplies, the supply identification unit can analyze the social media activities of evacuees and identify relevant supplies. The supply identification unit, for example, analyzes content posted by evacuees on social media to identify relevant supplies. For example, it analyzes text data posted by evacuees on social media to identify needed supplies. The supply identification unit can also identify specific needs and identify supplies from the evacuees' social media activities. For example, it can analyze images and videos shared by evacuees on social media to identify needed supplies. The supply identification unit can also identify relevant supplies by referring to the activities of the evacuees' friends on social media. For example, it can identify the evacuees' needs and identify supplies based on content posted by the evacuees' friends. In this way, by analyzing the evacuees' social media activities, relevant supplies can be efficiently identified. Some or all of the above-described processing in the supply identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the supply identification unit can input social media data into a generation AI and have the generation AI identify supplies.

[0106] When identifying supplies, the supply identification unit can customize the supply identification method by reflecting the evacuees' past feedback. The supply identification unit, for example, improves the supply identification method based on the evacuees' past feedback and provides supplies efficiently. For example, the supply identification unit retrieves feedback previously submitted by the evacuees from a database and optimizes the supply identification method. The supply identification unit can also preferentially use a specific supply provision method (such as a delivery method) by reflecting the evacuees' past feedback. For example, if the evacuees prefer a specific delivery method, that method is used preferentially. The supply identification unit can also customize the supply identification method based on the evacuees' past feedback to meet individual needs. For example, if the evacuees prefer detailed explanations, a detailed supply list is provided. This allows the supply identification method to be customized by reflecting the evacuees' past feedback, enabling supplies to be provided efficiently. Some or all of the above-described processing in the supply identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the supply identification unit can input feedback data to a generation AI and cause the generation AI to improve the supply identification method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned request collector, data collector, analyzer, and supply identifier is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the request collector is realized by the control unit 46A of the smart device 14 or the identification processing unit 290 of the data processing device 12. For example, the data collector collects environmental data using the camera 42, temperature sensor, and humidity sensor of the smart device 14, and collects health data by the control unit 46A or the identification processing unit 290 of the data processing device 12. For example, the analyzer is realized by the identification processing unit 290 of the data processing device 12 and analyzes the collected data using statistical analysis or machine learning algorithms. For example, the supply identifier is realized by the identification processing unit 290 of the data processing device 12 and identifies specific relief supplies based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described request collector, data collector, analyzer, and supply identifier, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the request collector is realized by the control unit 46A of the smart glasses 214 or the identification processing unit 290 of the data processing device 12. For example, the data collector collects environmental data using the camera 42, temperature sensor, and humidity sensor of the smart glasses 214, and collects health data by the control unit 46A or the identification processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the collected data using statistical analysis or machine learning algorithms. For example, the supply identifier is realized by the identification processing unit 290 of the data processing device 12 and identifies specific relief supplies based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned request collector, data collector, analyzer, and supply identifier is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the request collector is realized by the control unit 46A of the headset terminal 314 or the identification processing unit 290 of the data processing device 12. For example, the data collector collects environmental data using the camera 42, temperature sensor, and humidity sensor of the headset terminal 314, and collects health data by the control unit 46A or the identification processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the collected data using statistical analysis or a machine learning algorithm. For example, the supply identifier is realized by the identification processing unit 290 of the data processing device 12 and identifies specific relief supplies based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned request collector, data collector, analyzer, and supply identifier is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the request collector is realized by the control unit 46A of the robot 414 or the identification processing unit 290 of the data processing device 12. For example, the data collector collects environmental data using the camera 42, temperature sensor, and humidity sensor of the robot 414, and collects health data by the control unit 46A or the identification processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the collected data using statistical analysis or a machine learning algorithm. For example, the supply identifier is realized by the identification processing unit 290 of the data processing device 12 and identifies specific relief supplies based on the analysis results.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The request collection unit can estimate the emotions of evacuees and determine the priority of requests based on the estimated emotions. For example, if an evacuee is feeling stressed, it can prioritize collecting requests with high urgency. Also, if an evacuee is relaxed, it can collect detailed requests and flexibly adjust the priority. Furthermore, if an evacuee is feeling anxious, it can prioritize collecting requests that give a sense of security. In this way, by determining the priority of requests based on the emotions of evacuees, it is possible to prioritize collecting requests with high urgency.

[0109] The data collection unit can analyze past environmental data within the evacuation shelter and select the optimal data collection method. For example, data can be collected with an emphasis on a specific time period based on past environmental data. It can also analyze past environmental data and optimize specific sensor placement. Furthermore, it can select a data collection method that corresponds to specific environmental conditions based on past environmental data. This allows for efficient data collection by analyzing past environmental data.

[0110] The analysis unit can optimize the analysis algorithm by referring to past analysis results of the collected data. For example, a specific algorithm can be optimized based on past analysis results to perform efficient analysis. The analysis method for a specific data set can also be improved by referring to past analysis results. Furthermore, specific analysis parameters can be adjusted based on past analysis results to improve accuracy. In this way, by referring to past analysis results, the analysis algorithm can be optimized and analysis can be performed efficiently.

[0111] The supplies identification unit can estimate the emotions of evacuees and prioritize supplies based on the estimated emotions. For example, if an evacuee is feeling stressed, it can provide urgent supplies first. If an evacuee is feeling relaxed, it can also provide a detailed list of supplies. Furthermore, if an evacuee is feeling anxious, it can also provide supplies that give a sense of security first. In this way, by prioritizing supplies based on the emotions of evacuees, it is possible to provide urgent supplies first.

[0112] The request collection unit can analyze the past request history of evacuees and select the optimal request collection method. For example, a template can be created based on the past request history to efficiently collect requests. It can also analyze the tendency for many requests to be submitted during specific time periods from the past request history and focus on those time periods. Furthermore, it can also propose a collection method customized for a specific group of evacuees based on the past request history. In this way, requests can be collected efficiently by analyzing the past request history.

[0113] The data collection unit can estimate the emotions of evacuees and adjust the timing of environmental data collection based on the estimated emotions. For example, if an evacuee is feeling stressed, the frequency of environmental data collection can be increased to respond quickly. Also, if an evacuee is feeling relaxed, the frequency of environmental data collection can be reduced to save resources. Furthermore, if an evacuee is feeling anxious, the timing of environmental data collection can be adjusted to provide a sense of security. This allows for a quick response by adjusting the timing of environmental data collection based on the emotions of the evacuees.

[0114] During analysis, the analysis unit can apply different analysis methods depending on the data category. For example, time series analysis methods can be applied to environmental data to analyze fluctuation patterns. Machine learning algorithms can also be applied to health data to detect anomalies. Furthermore, natural language processing technology can be applied to request data to perform text analysis. This allows for efficient analysis by applying different analysis methods depending on the data category.

[0115] When identifying supplies, the supply identification unit can filter based on the evacuees' current situation and health condition. For example, it can identify necessary medicines based on health data such as the evacuees' body temperature and blood pressure. It can also prioritize the provision of specific supplies based on the evacuees' current living environment (family composition, presence of pets, etc.). Furthermore, if the evacuation shelter is crowded, it can prioritize the provision of necessary supplies. This allows for efficient identification of supplies by filtering supplies based on the evacuees' current situation and health condition.

[0116] The analysis unit can estimate the emotions of the evacuees and adjust the way the analysis is expressed based on the estimated emotions. For example, if the evacuees are feeling stressed, a concise and clear way of expression can be used. If the evacuees are feeling relaxed, a way of expression including detailed explanations can be used. Furthermore, if the evacuees are feeling anxious, a way of expression that gives a sense of security can be used. In this way, by adjusting the way the analysis is expressed based on the emotions of the evacuees, the analysis results can be provided in an appropriate way.

[0117] When identifying supplies, the supply identification unit can select the optimal supply identification method by referring to past supply delivery history. For example, specific supplies can be provided preferentially based on past supply delivery history. In addition, customized supplies can be provided to a specific group of evacuees by referring to past supply delivery history. Furthermore, a specific supply delivery method can be optimized based on past supply delivery history. In this way, supplies can be identified efficiently by referring to past supply delivery history.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The request collection unit collects requests from evacuees. Requests from evacuees include requests for supplies, reports on their health status, and suggestions for improving evacuation shelters. The request collection unit collects requests and concerns from evacuees, for example, through a chatbot. The chatbot can use natural language processing technology to understand the requests of evacuees and design an appropriate dialogue flow. Step 2: The data collection unit collects environmental data and health data. Environmental data includes temperature, humidity, air quality, etc. The data collection unit collects environmental data including, for example, the temperature and humidity inside the evacuation shelter. Temperature is measured using a temperature sensor, and humidity is measured using a humidity sensor. The data collection unit also collects health condition data of the evacuees. Health condition data includes body temperature, blood pressure, heart rate, etc. Body temperature is measured using a body temperature sensor, blood pressure is measured using a sphygmomanometer, and heart rate is measured using a heart rate sensor. Step 3: The analysis unit performs analysis based on the collected needs data, environmental data, and health data. The analysis is performed using statistical analysis of the data and machine learning algorithms. The analysis unit performs statistical analysis of the collected data to identify the needs of evacuees. It can also use machine learning algorithms to analyze patterns in the data and predict the needs of evacuees. Step 4: The Supplies Identification Department identifies specific relief supplies based on the analysis results. Relief supplies include food, water, medicine, clothing, etc. Based on the analysis results, the Supplies Identification Department identifies the food, medicine, and clothing needed by the evacuees.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0148] 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.

[0149] 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.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] 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.

[0169] 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.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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, in order to avoid confusion and to 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 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 request collection unit that collects requests from evacuees; a data collection unit that collects the request data collected by the request collection unit, environmental data, and health data; an analysis unit that performs analysis based on the data collected by the data collection unit; a supplies identification unit that identifies relief supplies based on the analysis results obtained by the analysis unit; Equipped with A system characterized by:

2. The request collection unit Collecting requests and concerns from evacuees through chatbots 2. The system of claim 1.

3. The data collection unit Collect environmental data, including temperature and humidity, inside the shelter 2. The system of claim 1.

4. The data collection unit Collecting health data of evacuees 2. The system of claim 1.

5. The analysis unit Conduct analysis based on collected demand, environmental and health data 2. The system of claim 1.

6. The material identification unit Identify specific relief supplies based on the analysis results 2. The system of claim 1.

7. The request collection unit Estimate the feelings of evacuees and prioritize their requests based on the estimated feelings.

2. The system of claim 1.

8. The request collection unit Analyze the past requests of evacuees and select the most appropriate method for collecting requests 2. The system of claim 1.

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