System

Abstract

An object of a system according to an embodiment is to clarify required amounts of funds and supplies at the time of occurrence of a disaster and to perform appropriate allocation.SOLUTION: A system includes a disaster prediction part, a necessary amount calculation part, an allocation plan formulation part, a fund raising efficiency improvement part, and a resource management part. The disaster prediction unit performs disaster prediction. The necessary amount calculation unit calculates necessary amounts of funds and supplies based on the disaster predicted by the disaster prediction unit. The distribution plan making section makes a distribution plan of the fund and the commodity based on the necessary amount calculated by the necessary amount calculation section. The fund-raising efficiency improvement unit improves the efficiency of the fund-raising activity based on the distribution plan formulated by the distribution plan formulation unit. The resource management section optimizes resource management based on the distribution plan formulated by the distribution plan formulation section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the amount of funds and supplies needed when a disaster occurs is unclear, making it difficult to allocate them appropriately.

[0005] The system according to the embodiment aims to clarify the amount of funds and supplies required in the event of a disaster and to allocate them appropriately. [Means for solving the problem]

[0006] The system according to the embodiment includes a disaster prediction unit, a required amount calculation unit, a distribution plan formulation unit, a donation efficiency improvement unit, and a materials management unit. The disaster prediction unit performs disaster prediction. The required amount calculation unit calculates the required amount of funds and materials based on the disaster predicted by the disaster prediction unit. The distribution plan formulation unit formulates a distribution plan for funds and materials based on the required amount calculated by the required amount calculation unit. The donation efficiency unit improves the efficiency of donation activities based on the distribution plan formulated by the distribution plan formulation unit. The materials management unit optimizes materials management based on the distribution plan formulated by the distribution plan formulation unit. [Effects of the Invention]

[0007] The system according to the embodiment can clarify the amount of funds and supplies needed in the event of a disaster and allocate them appropriately. [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) The disaster relief system according to the embodiment of the present invention is a system that predicts the amount of funds and supplies required in the event of a disaster and allocates them appropriately. This allows the disaster relief system to improve the efficiency of fundraising activities and the management of supplies at evacuation shelters.

[0029] A disaster support system according to an embodiment includes a disaster prediction unit, a required amount calculation unit, a distribution plan formulation unit, a donation efficiency improvement unit, and a supplies management unit. The disaster prediction unit performs disaster prediction. For example, it analyzes past disaster data, weather data, and population data to predict the amount of funds and supplies required in the event of a disaster. The required amount calculation unit calculates the amount of funds and supplies required based on the disaster predicted by the disaster prediction unit. For example, it lists the types and amounts of supplies required for each disaster area and determines how many supplies to send to each evacuation shelter. The distribution plan formulation unit formulates a fund and supply distribution plan based on the required amount calculated by the required amount calculation unit. For example, it formulates a fund and supply distribution plan and determines how many supplies to send to each evacuation shelter. The donation efficiency improvement unit streamlines donation activities based on the distribution plan formulated by the distribution plan formulation unit. For example, it clearly indicates the amount of funds required and sets a donation target amount. The supplies management unit optimizes supply management based on the distribution plan formulated by the distribution plan formulation unit. For example, we will build a system that collects inventory data for supplies at each evacuation shelter and automatically replenishes necessary supplies. This will enable the disaster relief system to improve the efficiency of fundraising activities and the management of supplies at evacuation shelters.

[0030] The disaster prediction unit can analyze past disaster data, weather data, and population data to predict the amount of funds and supplies needed when a disaster occurs. The disaster prediction unit, for example, analyzes past disaster data to predict the amount needed when a disaster occurs. For example, based on data from past typhoons and earthquakes, it calculates how much supplies are needed in which area. The disaster prediction unit also analyzes weather data to predict the amount needed when a disaster occurs. For example, based on data such as temperature, precipitation, and wind speed, it predicts the type and amount of supplies needed. The disaster prediction unit also analyzes population data to predict the amount needed when a disaster occurs. For example, based on data such as population density and age distribution, it predicts the type and amount of supplies needed. In this way, by analyzing past data, it is possible to accurately predict the amount needed when a disaster occurs.

[0031] The required quantity calculation unit can list the types and quantities of supplies needed for each disaster area and determine how much to send to each evacuation shelter. The required quantity calculation unit, for example, lists the types and quantities of supplies needed for each disaster area. For example, it lists the types and quantities of food, water, medical supplies, etc. The required quantity calculation unit also determines how much to send to each evacuation shelter. For example, it creates a supply distribution plan based on the capacity of each evacuation shelter and the supply consumption status. The required quantity calculation unit also dynamically adjusts the supply distribution plan. For example, it monitors the supply consumption status for each evacuation shelter in real time and changes the supply distribution plan as necessary. This allows the necessary supplies to be accurately distributed to each disaster area.

[0032] The fundraising efficiency improvement unit can clearly indicate the amount of funds needed and set a target amount for donations. The fundraising efficiency improvement unit, for example, clearly indicates the amount of funds needed. For example, it calculates the amount of funds needed in the event of a disaster and makes that information public. The fundraising efficiency improvement unit also sets a target amount for donations. For example, it sets a target amount for donations based on the amount of funds needed. The fundraising efficiency improvement unit also makes public the progress of donations in real time. For example, it ensures transparency by showing how much money has been collected. This improves the transparency and efficiency of fundraising activities.

[0033] The supplies management department can collect inventory data for supplies for each evacuation shelter and build a system that automatically replenishes necessary supplies. The supplies management department, for example, collects inventory data for supplies for each evacuation shelter. For example, it monitors the types and quantities of supplies in real time. The supplies management department also builds a system that automatically replenishes necessary supplies. For example, it automatically replenishes supplies when supply inventory falls below a certain level. The supplies management department also monitors supply consumption in real time and creates a replenishment plan as needed. For example, it analyzes supply consumption and replenishes supplies at the appropriate time. This prevents imbalances and shortages of supplies and enables appropriate management.

[0034] The disaster prediction unit collects information in real time from social media and news articles, improving the accuracy of predictions. For example, the disaster prediction unit uses generative AI to collect information in real time from social media. For example, it analyzes posts and comments from disaster victims to predict the type and amount of supplies needed. The disaster prediction unit also collects information in real time from news articles to improve the accuracy of predictions. For example, it calculates the supplies needed by reflecting the latest weather information and the situation in the disaster-stricken area. The disaster prediction unit also integrates data from social media and news articles to make comprehensive disaster predictions. For example, it formulates a supply distribution plan based on the voices of disaster victims and local reports. This allows for real-time information collection, improving the accuracy of predictions.

[0035] The disaster prediction unit can optimize the delivery route of supplies by taking into account the infrastructure status of the affected area. The disaster prediction unit, for example, uses generative AI to analyze the infrastructure status of the affected area. For example, it analyzes the passability of roads and communication status to identify the optimal delivery route. The disaster prediction unit also optimizes the delivery route of supplies based on the infrastructure status. For example, it plans the optimal delivery route by taking into account road closure information and traffic congestion. The disaster prediction unit also collects infrastructure data of the affected area in real time and dynamically adjusts the delivery route. For example, it changes the route depending on the road restoration status. This makes it possible to optimize the delivery route of supplies by taking into account the infrastructure status of the affected area.

[0036] The required quantity calculation unit predicts the required quantities of medical supplies and medical staff when a disaster occurs, thereby improving the efficiency of medical support. The required quantity calculation unit, for example, uses generative AI to analyze past disaster data and predict the required quantities of medical supplies. For example, it calculates the type and quantity of medical supplies based on data from past earthquakes and typhoons. The required quantity calculation unit also analyzes population data and the status of medical facilities in the disaster-stricken area to predict the required quantity of medical staff. For example, it makes a medical staff deployment plan taking into account the population density of the disaster-stricken area and the capacity of medical facilities. The required quantity calculation unit also monitors the inventory status of medical supplies in real time and makes a replenishment plan as needed. For example, it analyzes the consumption status of medical supplies and replenishes them at the appropriate time. This allows for the prediction of the required quantities of medical supplies and medical staff, thereby improving the efficiency of medical support.

[0037] The need calculation unit predicts evacuation destinations and necessary supplies for pets and livestock in the event of a disaster, thereby supporting animal protection activities. The need calculation unit, for example, uses generative AI to predict evacuation destinations for pets and livestock and calculates the necessary supplies. For example, it predicts the required amount of pet food and medical supplies and creates a plan to distribute them to evacuation destinations. The need calculation unit also analyzes the situation of animal shelters in disaster-stricken areas and supports animal protection activities. For example, it plans the necessary support taking into account the capacity of the shelters and the inventory status of supplies. The need calculation unit also monitors the evacuation status of pets and livestock in real time and adjusts the support plan as needed. For example, it dynamically changes evacuation destinations and replenishes supplies. This allows animal protection activities to be supported by predicting evacuation destinations and necessary supplies for pets and livestock.

[0038] The distribution plan formulation unit can analyze geographic information of the disaster-stricken area and geographically optimize the distribution plan for supplies. The distribution plan formulation unit, for example, uses generative AI to analyze the geographic information of the disaster-stricken area. For example, it sets the optimal distribution route taking into account the topography and traffic conditions of the disaster-stricken area. The distribution plan formulation unit also dynamically adjusts the distribution plan for supplies based on the geographic information. For example, it changes the distribution route for supplies taking into account road closure information and traffic congestion. The distribution plan formulation unit also collects geographic information of the disaster-stricken area in real time and optimizes the distribution plan for supplies based on that data. For example, it adjusts the distribution of supplies according to the topography and traffic conditions of the disaster-stricken area. In this way, the distribution plan for supplies can be geographically optimized by analyzing the geographic information of the disaster-stricken area.

[0039] The distribution plan formulation unit can formulate a supply distribution plan that takes into account the demographic trends of the disaster-stricken area and meets specific needs. The distribution plan formulation unit, for example, uses generative AI to analyze the demographic trends of the disaster-stricken area. For example, it can prioritize the distribution of specific supplies to areas with a large number of elderly people and children. The distribution plan formulation unit also dynamically adjusts the supply distribution plan based on demographic data. For example, it can adjust the type and amount of supplies needed depending on family structure and age group. The distribution plan formulation unit also collects demographic data of the disaster-stricken area in real time and optimizes the supply distribution plan based on that data. For example, it can adjust the distribution of supplies in response to changes in demographic trends. This makes it possible to formulate a supply distribution plan that meets specific needs by taking into account the demographic trends of the disaster-stricken area.

[0040] The Allocation Plan Formulation Department can formulate a volunteer deployment plan when a disaster occurs, thereby improving the efficiency of human support. For example, the Allocation Plan Formulation Department uses generative AI to analyze past disaster data and formulate a volunteer deployment plan. For example, it optimizes volunteer deployment according to the needs of the disaster-stricken area. The Allocation Plan Formulation Department also considers the skills and experience of volunteers to formulate an optimal deployment plan. For example, it appropriately deploys volunteers with specific skills, such as medical staff or construction workers. The Allocation Plan Formulation Department also monitors the status of volunteers in real time and adjusts the deployment plan as necessary. For example, it changes deployment according to the arrival and activity status of volunteers. In this way, by formulating a volunteer deployment plan, the efficiency of human support can be improved.

[0041] The distribution plan formulation unit can analyze the situation of educational institutions in the disaster-stricken areas and formulate a distribution plan for educational relief supplies. The distribution plan formulation unit, for example, uses generative AI to analyze the situation of educational institutions in the disaster-stricken areas. For example, it distributes necessary supplies taking into account the damage to schools and the number of students. The distribution plan formulation unit also dynamically adjusts the distribution plan for educational relief supplies based on situation data for educational institutions. For example, it changes the distribution of supplies depending on the school's recovery status and the needs of students. The distribution plan formulation unit also collects information on the situation of educational institutions in the disaster-stricken areas in real time and optimizes the distribution plan for educational relief supplies based on that data. For example, it adjusts the distribution of supplies depending on the recovery status of educational institutions. In this way, it is possible to formulate a distribution plan for educational relief supplies by analyzing the situation of educational institutions in the disaster-stricken areas.

[0042] The distribution plan formulation unit formulates an energy supply plan in the event of a disaster, enabling efficient distribution of electricity and fuel. The distribution plan formulation unit, for example, uses generative AI to analyze past disaster data and formulates an energy supply plan. For example, it formulates an optimal supply plan taking into account the electricity demand and fuel supply situation in the disaster-stricken area. The distribution plan formulation unit also monitors the energy supply situation in real time and dynamically adjusts the supply plan based on that data. For example, it changes the supply plan depending on the power restoration status and fuel inventory status. The distribution plan formulation unit also analyzes the energy supply situation in the disaster-stricken area and formulates an efficient distribution plan. For example, it formulates a plan to supply electricity and fuel preferentially to important facilities and areas. In this way, formulating an energy supply plan enables efficient distribution of electricity and fuel.

[0043] The fundraising efficiency improvement unit analyzes past fundraising data and can predict the most effective timing and method of a fundraising campaign. The fundraising efficiency improvement unit analyzes past fundraising data, for example, using generative AI. For example, it calculates the optimal time to start a campaign based on past success stories. The fundraising efficiency improvement unit also predicts the most effective fundraising method based on the fundraising data. For example, it selects the optimal method, such as online fundraising or event fundraising. The fundraising efficiency improvement unit also analyzes past fundraising data in real time and dynamically adjusts the timing and method of a fundraising campaign. For example, it changes the content of the campaign depending on the progress of fundraising. In this way, by analyzing past fundraising data, it is possible to predict the most effective timing and method of a fundraising campaign.

[0044] The fundraising efficiency improvement unit monitors the progress of fundraising in real time and can dynamically adjust the fundraising goals and approach as needed. The fundraising efficiency improvement unit monitors the progress of fundraising in real time using, for example, generative AI. For example, if the amount raised does not reach the target, the target amount is lowered. The fundraising efficiency improvement unit also dynamically adjusts the fundraising approach based on the progress of fundraising. For example, if the amount raised is low, the approach is changed to intensify fundraising activities. The fundraising efficiency improvement unit also analyzes the progress of fundraising in real time and optimizes the fundraising goals and approach. For example, the campaign content and message are changed depending on the progress of fundraising. This makes it possible to monitor the progress of fundraising in real time and dynamically adjust the fundraising goals and approach as needed.

[0045] The Fundraising Efficiency Improvement Unit can streamline fundraising activities as part of a company's or organization's CSR activities. For example, the Fundraising Efficiency Improvement Unit uses generative AI to analyze a company's or organization's CSR activity data and create an optimal fundraising plan. For example, it proposes a fundraising campaign that matches the company's CSR goals. The Fundraising Efficiency Improvement Unit also proposes the optimal fundraising method to streamline fundraising activities as part of CSR activities. For example, it plans a fundraising campaign targeting a company's employees and customers. The Fundraising Efficiency Improvement Unit also analyzes a company's or organization's CSR activity data in real time and monitors the progress of fundraising activities. For example, if the amount raised does not reach the target, it adjusts the content of the CSR activity. This allows for the efficiency of fundraising activities as part of a company's or organization's CSR activities.

[0046] The fundraising efficiency improvement unit can analyze social media, identify the most influential influencers, and request their cooperation in fundraising activities. For example, the fundraising efficiency improvement unit can use generative AI to analyze social media data and identify the most influential influencers. For example, influencers can be selected based on the number of followers and engagement rate. The fundraising efficiency improvement unit can also utilize the influence of influencers to plan fundraising activities. For example, influencers can be requested to cooperate in a fundraising campaign to promote widespread dissemination. The fundraising efficiency improvement unit can also analyze social media data in real time to dynamically evaluate the influence of influencers. For example, influencer cooperation can be adjusted according to the progress of the campaign. In this way, by analyzing social media, the most influential influencers can be identified and requested to cooperate in fundraising activities.

[0047] The Fundraising Efficiency Department can optimize crowdfunding campaigns in the event of a disaster, allowing funds to be raised quickly. For example, the Fundraising Efficiency Department uses generative AI to analyze past crowdfunding data and create optimal campaign plans. For example, it can set optimal target amounts and rewards based on success stories. The Fundraising Efficiency Department also monitors the progress of crowdfunding campaigns in real time and dynamically adjusts campaigns based on that data. For example, it can change rewards if the target amount is not reached. The Fundraising Efficiency Department also analyzes crowdfunding campaign data and proposes optimal promotional strategies. For example, it can use social media and email marketing to raise funds quickly. By optimizing crowdfunding campaigns, funds can be raised quickly.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The disaster relief system can further include a health management unit that monitors the health status of disaster victims. For example, the health management unit monitors the vital signs of disaster victims in real time and notifies medical staff if any abnormalities are detected. The health management unit also analyzes the health data of disaster victims and optimizes the allocation of necessary medical supplies and medical staff. For example, it prioritizes the distribution of vaccinations and disinfectant supplies to areas with a high risk of infectious diseases. The health management unit also draws up plans to improve the environment of evacuation centers based on the health status of disaster victims. For example, it may suggest the installation of air purifiers and humidifiers. In this way, monitoring the health status of disaster victims can improve the efficiency of medical relief.

[0050] The disaster support system can further include an infrastructure management unit that supports the restoration of infrastructure in disaster-stricken areas. The infrastructure management unit, for example, monitors the infrastructure status in disaster-stricken areas in real time and determines the priorities of restoration work. For example, it analyzes the damage status of roads and bridges and identifies the most important restoration work. The infrastructure management unit also creates restoration plans based on infrastructure data in the disaster-stricken areas. For example, it formulates plans to efficiently proceed with restoration work for electricity and water. The infrastructure management unit also collects information on the infrastructure status in disaster-stricken areas in real time and dynamically adjusts the restoration plan. For example, it changes the plan depending on the progress of restoration work. This makes it possible to efficiently support the restoration of infrastructure in disaster-stricken areas.

[0051] The disaster support system can further include a life reconstruction support unit that supports disaster victims in rebuilding their lives. The life reconstruction support unit, for example, provides support for disaster victims in rebuilding their homes and finding employment. For example, it creates plans for disaster victims to rebuild their homes and provides the necessary funds and supplies. The life reconstruction support unit also provides job information and implements vocational training to support disaster victims in finding employment. For example, it works with companies in the disaster-stricken areas to create employment opportunities for disaster victims. The life reconstruction support unit also monitors the disaster victims' progress in rebuilding their lives in real time and adjusts the support plan as needed. For example, it changes the content of support depending on the progress of housing reconstruction and employment status. This allows for efficient support for disaster victims in rebuilding their lives.

[0052] The disaster relief system may further include an animal protection unit that supports the protection of pets and livestock of disaster victims. The animal protection unit, for example, secures evacuation destinations for pets and livestock in the disaster area and provides necessary supplies. For example, it predicts the required amount of pet food and medical supplies and creates a plan to distribute them to evacuation destinations. The animal protection unit also analyzes the situation at animal shelters in the disaster area and supports animal protection activities. For example, it plans the necessary support taking into account the capacity of the shelters and the inventory status of supplies. The animal protection unit also monitors the evacuation status of pets and livestock in real time and adjusts the support plan as needed. For example, it dynamically changes evacuation destinations and replenishes supplies. This allows animal protection activities to be supported by predicting evacuation destinations and necessary supplies for pets and livestock.

[0053] The disaster support system can further include an energy management unit that supports energy supply to disaster victims. The energy management unit, for example, monitors the power supply situation in the disaster-stricken area in real time and creates an efficient energy distribution plan. For example, it formulates a plan to supply power preferentially to important facilities and areas. The energy management unit also analyzes the fuel supply situation in the disaster-stricken area and creates an optimal fuel distribution plan. For example, it performs efficient distribution taking into account fuel inventory and demand. The energy management unit also collects information on the energy supply situation in the disaster-stricken area in real time and dynamically adjusts the supply plan. For example, it changes the plan depending on the power restoration status and fuel consumption status. This allows for efficient support of energy supply to the disaster-stricken area.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The disaster prediction unit performs disaster prediction. For example, it analyzes past disaster data, meteorological data, and population data to predict the amount of funds and supplies needed in the event of a disaster. Step 2: The requirements calculation unit calculates the amount of funds and supplies needed based on the disaster predicted by the disaster prediction unit. For example, it lists the types and amounts of supplies needed for each disaster area and determines how much to send to each evacuation shelter. Step 3: The distribution plan formulation unit formulates a fund and / or supply distribution plan based on the required amount calculated by the required amount calculation unit. For example, it formulates a fund and / or supply distribution plan and determines how much of each supply should be sent to each evacuation shelter. Step 4: The Fundraising Efficiency Department will streamline fundraising activities based on the allocation plan developed by the Allocation Plan Development Department, for example by clearly indicating the amount of funds needed and setting a fundraising target. Step 5: The Materials Management Department will optimize materials management based on the distribution plan formulated by the Distribution Planning Department. For example, they will collect inventory data for materials at each evacuation shelter and build a system to automatically replenish necessary supplies.

[0056] (Example 2) The disaster relief system according to the embodiment of the present invention is a system that predicts the amount of funds and supplies required in the event of a disaster and allocates them appropriately. This allows the disaster relief system to improve the efficiency of fundraising activities and the management of supplies at evacuation shelters.

[0057] A disaster support system according to an embodiment includes a disaster prediction unit, a required amount calculation unit, a distribution plan formulation unit, a donation efficiency improvement unit, and a supplies management unit. The disaster prediction unit performs disaster prediction. For example, it analyzes past disaster data, weather data, and population data to predict the amount of funds and supplies required in the event of a disaster. The required amount calculation unit calculates the amount of funds and supplies required based on the disaster predicted by the disaster prediction unit. For example, it lists the types and amounts of supplies required for each disaster area and determines how many supplies to send to each evacuation shelter. The distribution plan formulation unit formulates a fund and supply distribution plan based on the required amount calculated by the required amount calculation unit. For example, it formulates a fund and supply distribution plan and determines how many supplies to send to each evacuation shelter. The donation efficiency improvement unit streamlines donation activities based on the distribution plan formulated by the distribution plan formulation unit. For example, it clearly indicates the amount of funds required and sets a donation target amount. The supplies management unit optimizes supply management based on the distribution plan formulated by the distribution plan formulation unit. For example, we will build a system that collects inventory data for supplies at each evacuation shelter and automatically replenishes necessary supplies. This will enable the disaster relief system to improve the efficiency of fundraising activities and the management of supplies at evacuation shelters.

[0058] The disaster prediction unit can analyze past disaster data, weather data, and population data to predict the amount of funds and supplies needed when a disaster occurs. The disaster prediction unit, for example, analyzes past disaster data to predict the amount needed when a disaster occurs. For example, based on data from past typhoons and earthquakes, it calculates how much supplies are needed in which area. The disaster prediction unit also analyzes weather data to predict the amount needed when a disaster occurs. For example, based on data such as temperature, precipitation, and wind speed, it predicts the type and amount of supplies needed. The disaster prediction unit also analyzes population data to predict the amount needed when a disaster occurs. For example, based on data such as population density and age distribution, it predicts the type and amount of supplies needed. In this way, by analyzing past data, it is possible to accurately predict the amount needed when a disaster occurs.

[0059] The required quantity calculation unit can list the types and quantities of supplies needed for each disaster area and determine how much to send to each evacuation shelter. The required quantity calculation unit, for example, lists the types and quantities of supplies needed for each disaster area. For example, it lists the types and quantities of food, water, medical supplies, etc. The required quantity calculation unit also determines how much to send to each evacuation shelter. For example, it creates a supply distribution plan based on the capacity of each evacuation shelter and the supply consumption status. The required quantity calculation unit also dynamically adjusts the supply distribution plan. For example, it monitors the supply consumption status for each evacuation shelter in real time and changes the supply distribution plan as necessary. This allows the necessary supplies to be accurately distributed to each disaster area.

[0060] The fundraising efficiency improvement unit can clearly indicate the amount of funds needed and set a target amount for donations. The fundraising efficiency improvement unit, for example, clearly indicates the amount of funds needed. For example, it calculates the amount of funds needed in the event of a disaster and makes that information public. The fundraising efficiency improvement unit also sets a target amount for donations. For example, it sets a target amount for donations based on the amount of funds needed. The fundraising efficiency improvement unit also makes public the progress of donations in real time. For example, it ensures transparency by showing how much money has been collected. This improves the transparency and efficiency of fundraising activities.

[0061] The supplies management department can collect inventory data for supplies for each evacuation shelter and build a system that automatically replenishes necessary supplies. The supplies management department, for example, collects inventory data for supplies for each evacuation shelter. For example, it monitors the types and quantities of supplies in real time. The supplies management department also builds a system that automatically replenishes necessary supplies. For example, it automatically replenishes supplies when supply inventory falls below a certain level. The supplies management department also monitors supply consumption in real time and creates a replenishment plan as needed. For example, it analyzes supply consumption and replenishes supplies at the appropriate time. This prevents imbalances and shortages of supplies and enables appropriate management.

[0062] The disaster prediction unit can estimate the emotions of disaster victims and adjust the type and amount of supplies needed based on those emotions. The disaster prediction unit, for example, uses generative AI to estimate the emotions of disaster victims. For example, it analyzes the facial expressions and voice data of disaster victims to calculate an emotion score. The disaster prediction unit also adjusts the type and amount of supplies needed based on the emotion data. For example, if stress or anxiety is high, it will prioritize the distribution of psychological support supplies and relaxation products. The disaster prediction unit also collects emotional data of disaster victims in real time and dynamically adjusts the needed supplies. For example, it optimizes the distribution of food and medical supplies according to changes in emotions. This makes it possible to distribute supplies according to the emotions of disaster victims.

[0063] The disaster prediction unit collects information in real time from social media and news articles, improving the accuracy of predictions. For example, the disaster prediction unit uses generative AI to collect information in real time from social media. For example, it analyzes posts and comments from disaster victims to predict the type and amount of supplies needed. The disaster prediction unit also collects information in real time from news articles to improve the accuracy of predictions. For example, it calculates the supplies needed by reflecting the latest weather information and the situation in the disaster-stricken area. The disaster prediction unit also integrates data from social media and news articles to make comprehensive disaster predictions. For example, it formulates a supply distribution plan based on the voices of disaster victims and local reports. This allows for real-time information collection, improving the accuracy of predictions.

[0064] The disaster prediction unit can optimize the delivery route of supplies by taking into account the infrastructure status of the affected area. The disaster prediction unit, for example, uses generative AI to analyze the infrastructure status of the affected area. For example, it analyzes the passability of roads and communication status to identify the optimal delivery route. The disaster prediction unit also optimizes the delivery route of supplies based on the infrastructure status. For example, it plans the optimal delivery route by taking into account road closure information and traffic congestion. The disaster prediction unit also collects infrastructure data of the affected area in real time and dynamically adjusts the delivery route. For example, it changes the route depending on the road restoration status. This makes it possible to optimize the delivery route of supplies by taking into account the infrastructure status of the affected area.

[0065] The required quantity calculation unit predicts the required quantities of medical supplies and medical staff when a disaster occurs, thereby improving the efficiency of medical support. The required quantity calculation unit, for example, uses generative AI to analyze past disaster data and predict the required quantities of medical supplies. For example, it calculates the type and quantity of medical supplies based on data from past earthquakes and typhoons. The required quantity calculation unit also analyzes population data and the status of medical facilities in the disaster-stricken area to predict the required quantity of medical staff. For example, it makes a medical staff deployment plan taking into account the population density of the disaster-stricken area and the capacity of medical facilities. The required quantity calculation unit also monitors the inventory status of medical supplies in real time and makes a replenishment plan as needed. For example, it analyzes the consumption status of medical supplies and replenishes them at the appropriate time. This allows for the prediction of the required quantities of medical supplies and medical staff, thereby improving the efficiency of medical support.

[0066] The need calculation unit predicts evacuation destinations and necessary supplies for pets and livestock in the event of a disaster, thereby supporting animal protection activities. The need calculation unit, for example, uses generative AI to predict evacuation destinations for pets and livestock and calculates the necessary supplies. For example, it predicts the required amount of pet food and medical supplies and creates a plan to distribute them to evacuation destinations. The need calculation unit also analyzes the situation of animal shelters in disaster-stricken areas and supports animal protection activities. For example, it plans the necessary support taking into account the capacity of the shelters and the inventory status of supplies. The need calculation unit also monitors the evacuation status of pets and livestock in real time and adjusts the support plan as needed. For example, it dynamically changes evacuation destinations and replenishes supplies. This allows animal protection activities to be supported by predicting evacuation destinations and necessary supplies for pets and livestock.

[0067] The required quantity calculation unit predicts the amount of psychological support needed when a disaster occurs and can plan the allocation of counselors and psychological support supplies. The required quantity calculation unit, for example, uses generative AI to predict the amount of psychological support needed by disaster victims. For example, it identifies areas where psychological support is needed based on past disaster data. The required quantity calculation unit also analyzes emotional data of disaster victims to predict the amount of psychological support supplies needed. For example, it prioritizes the allocation of psychological support supplies to areas with high levels of stress and anxiety. The required quantity calculation unit also monitors the status of psychological support in real time and adjusts the support plan as needed. For example, it dynamically allocates counselors and replenishes supplies. This makes it possible to plan the allocation of counselors and psychological support supplies by predicting the amount of psychological support needed.

[0068] The distribution plan formulation unit can estimate the emotions of disaster victims and formulate a plan to prioritize the distribution of supplies that are most emotionally needed. The distribution plan formulation unit, for example, uses generative AI to estimate the emotions of disaster victims. For example, it analyzes the facial expressions and voice data of disaster victims and calculates an emotion score. The distribution plan formulation unit then formulates a supply distribution plan based on the emotion data. For example, it prioritizes the distribution of psychological support supplies to areas with high levels of stress and anxiety. The distribution plan formulation unit also collects emotional data of disaster victims in real time and dynamically adjusts the supply distribution plan. For example, it optimizes the distribution of food and medical supplies in response to changes in emotions. This allows supplies to be distributed preferentially based on the emotions of disaster victims.

[0069] The distribution plan formulation unit can analyze geographic information of the disaster-stricken area and geographically optimize the distribution plan for supplies. The distribution plan formulation unit, for example, uses generative AI to analyze the geographic information of the disaster-stricken area. For example, it sets the optimal distribution route taking into account the topography and traffic conditions of the disaster-stricken area. The distribution plan formulation unit also dynamically adjusts the distribution plan for supplies based on the geographic information. For example, it changes the distribution route for supplies taking into account road closure information and traffic congestion. The distribution plan formulation unit also collects geographic information of the disaster-stricken area in real time and optimizes the distribution plan for supplies based on that data. For example, it adjusts the distribution of supplies according to the topography and traffic conditions of the disaster-stricken area. In this way, the distribution plan for supplies can be geographically optimized by analyzing the geographic information of the disaster-stricken area.

[0070] The distribution plan formulation unit can formulate a supply distribution plan that takes into account the demographic trends of the disaster-stricken area and meets specific needs. The distribution plan formulation unit, for example, uses generative AI to analyze the demographic trends of the disaster-stricken area. For example, it can prioritize the distribution of specific supplies to areas with a large number of elderly people and children. The distribution plan formulation unit also dynamically adjusts the supply distribution plan based on demographic data. For example, it can adjust the type and amount of supplies needed depending on family structure and age group. The distribution plan formulation unit also collects demographic data of the disaster-stricken area in real time and optimizes the supply distribution plan based on that data. For example, it can adjust the distribution of supplies in response to changes in demographic trends. This makes it possible to formulate a supply distribution plan that meets specific needs by taking into account the demographic trends of the disaster-stricken area.

[0071] The Allocation Plan Formulation Department can formulate a volunteer deployment plan when a disaster occurs, thereby improving the efficiency of human support. For example, the Allocation Plan Formulation Department uses generative AI to analyze past disaster data and formulate a volunteer deployment plan. For example, it optimizes volunteer deployment according to the needs of the disaster-stricken area. The Allocation Plan Formulation Department also considers the skills and experience of volunteers to formulate an optimal deployment plan. For example, it appropriately deploys volunteers with specific skills, such as medical staff or construction workers. The Allocation Plan Formulation Department also monitors the status of volunteers in real time and adjusts the deployment plan as necessary. For example, it changes deployment according to the arrival and activity status of volunteers. In this way, by formulating a volunteer deployment plan, the efficiency of human support can be improved.

[0072] The distribution plan formulation unit can analyze the situation of educational institutions in the disaster-stricken areas and formulate a distribution plan for educational relief supplies. The distribution plan formulation unit, for example, uses generative AI to analyze the situation of educational institutions in the disaster-stricken areas. For example, it distributes necessary supplies taking into account the damage to schools and the number of students. The distribution plan formulation unit also dynamically adjusts the distribution plan for educational relief supplies based on situation data for educational institutions. For example, it changes the distribution of supplies depending on the school's recovery status and the needs of students. The distribution plan formulation unit also collects information on the situation of educational institutions in the disaster-stricken areas in real time and optimizes the distribution plan for educational relief supplies based on that data. For example, it adjusts the distribution of supplies depending on the recovery status of educational institutions. In this way, it is possible to formulate a distribution plan for educational relief supplies by analyzing the situation of educational institutions in the disaster-stricken areas.

[0073] The distribution plan formulation unit formulates an energy supply plan in the event of a disaster, enabling efficient distribution of electricity and fuel. The distribution plan formulation unit, for example, uses generative AI to analyze past disaster data and formulates an energy supply plan. For example, it formulates an optimal supply plan taking into account the electricity demand and fuel supply situation in the disaster-stricken area. The distribution plan formulation unit also monitors the energy supply situation in real time and dynamically adjusts the supply plan based on that data. For example, it changes the supply plan depending on the power restoration status and fuel inventory status. The distribution plan formulation unit also analyzes the energy supply situation in the disaster-stricken area and formulates an efficient distribution plan. For example, it formulates a plan to supply electricity and fuel preferentially to important facilities and areas. In this way, formulating an energy supply plan enables efficient distribution of electricity and fuel.

[0074] The fundraising efficiency improvement unit can estimate the emotions of donors and optimize the messages and approaches of fundraising activities based on those emotions. The fundraising efficiency improvement unit, for example, estimates the emotions of donors using generative AI. For example, it analyzes the facial expressions and voice data of donors and calculates an emotion score. The fundraising efficiency improvement unit also optimizes the messages of fundraising activities based on the emotion data. For example, it prioritizes the use of messages with high emotion scores. The fundraising efficiency unit also collects emotion data of donors in real time and dynamically adjusts the approach of fundraising activities. For example, it changes the messages and approaches in response to changes in emotions. This makes it possible to optimize the messages and approaches of fundraising activities based on the emotions of donors.

[0075] The fundraising efficiency improvement unit analyzes past fundraising data and can predict the most effective timing and method of a fundraising campaign. The fundraising efficiency improvement unit analyzes past fundraising data, for example, using generative AI. For example, it calculates the optimal time to start a campaign based on past success stories. The fundraising efficiency improvement unit also predicts the most effective fundraising method based on the fundraising data. For example, it selects the optimal method, such as online fundraising or event fundraising. The fundraising efficiency improvement unit also analyzes past fundraising data in real time and dynamically adjusts the timing and method of a fundraising campaign. For example, it changes the content of the campaign depending on the progress of fundraising. In this way, by analyzing past fundraising data, it is possible to predict the most effective timing and method of a fundraising campaign.

[0076] The fundraising efficiency improvement unit monitors the progress of fundraising in real time and can dynamically adjust the fundraising goals and approach as needed. The fundraising efficiency improvement unit monitors the progress of fundraising in real time using, for example, generative AI. For example, if the amount raised does not reach the target, the target amount is lowered. The fundraising efficiency improvement unit also dynamically adjusts the fundraising approach based on the progress of fundraising. For example, if the amount raised is low, the approach is changed to intensify fundraising activities. The fundraising efficiency improvement unit also analyzes the progress of fundraising in real time and optimizes the fundraising goals and approach. For example, the campaign content and message are changed depending on the progress of fundraising. This makes it possible to monitor the progress of fundraising in real time and dynamically adjust the fundraising goals and approach as needed.

[0077] The Fundraising Efficiency Improvement Unit can streamline fundraising activities as part of a company's or organization's CSR activities. For example, the Fundraising Efficiency Improvement Unit uses generative AI to analyze a company's or organization's CSR activity data and create an optimal fundraising plan. For example, it proposes a fundraising campaign that matches the company's CSR goals. The Fundraising Efficiency Improvement Unit also proposes the optimal fundraising method to streamline fundraising activities as part of CSR activities. For example, it plans a fundraising campaign targeting a company's employees and customers. The Fundraising Efficiency Improvement Unit also analyzes a company's or organization's CSR activity data in real time and monitors the progress of fundraising activities. For example, if the amount raised does not reach the target, it adjusts the content of the CSR activity. This allows for the efficiency of fundraising activities as part of a company's or organization's CSR activities.

[0078] The fundraising efficiency improvement unit can analyze social media, identify the most influential influencers, and request their cooperation in fundraising activities. For example, the fundraising efficiency improvement unit can use generative AI to analyze social media data and identify the most influential influencers. For example, influencers can be selected based on the number of followers and engagement rate. The fundraising efficiency improvement unit can also utilize the influence of influencers to plan fundraising activities. For example, influencers can be requested to cooperate in a fundraising campaign to promote widespread dissemination. The fundraising efficiency improvement unit can also analyze social media data in real time to dynamically evaluate the influence of influencers. For example, influencer cooperation can be adjusted according to the progress of the campaign. In this way, by analyzing social media, the most influential influencers can be identified and requested to cooperate in fundraising activities.

[0079] The Fundraising Efficiency Department can optimize crowdfunding campaigns in the event of a disaster, allowing funds to be raised quickly. For example, the Fundraising Efficiency Department uses generative AI to analyze past crowdfunding data and create optimal campaign plans. For example, it can set optimal target amounts and rewards based on success stories. The Fundraising Efficiency Department also monitors the progress of crowdfunding campaigns in real time and dynamically adjusts campaigns based on that data. For example, it can change rewards if the target amount is not reached. The Fundraising Efficiency Department also analyzes crowdfunding campaign data and proposes optimal promotional strategies. For example, it can use social media and email marketing to raise funds quickly. By optimizing crowdfunding campaigns, funds can be raised quickly.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The disaster relief system can further include a health management unit that monitors the health status of disaster victims. For example, the health management unit monitors the vital signs of disaster victims in real time and notifies medical staff if any abnormalities are detected. The health management unit also analyzes the health data of disaster victims and optimizes the allocation of necessary medical supplies and medical staff. For example, it prioritizes the distribution of vaccinations and disinfectant supplies to areas with a high risk of infectious diseases. The health management unit also draws up plans to improve the environment of evacuation centers based on the health status of disaster victims. For example, it may suggest the installation of air purifiers and humidifiers. In this way, monitoring the health status of disaster victims can improve the efficiency of medical relief.

[0082] The disaster support system can further include a psychological support unit that provides psychological support to disaster victims. The psychological support unit, for example, estimates the emotions of disaster victims and determines the need for counseling based on those emotions. For example, it dispatches a professional counselor to disaster victims experiencing high levels of stress or anxiety. The psychological support unit also creates a distribution plan for psychological support supplies based on the emotional data of disaster victims. For example, it prioritizes the distribution of relaxation products and stress relief goods. The psychological support unit also collects emotional data of disaster victims in real time and dynamically adjusts the psychological support plan. For example, it changes the frequency and content of counseling in response to changes in emotions. This makes it possible to provide psychological support based on the emotions of disaster victims.

[0083] The disaster support system can further include an infrastructure management unit that supports the restoration of infrastructure in disaster-stricken areas. The infrastructure management unit, for example, monitors the infrastructure status in disaster-stricken areas in real time and determines the priorities of restoration work. For example, it analyzes the damage status of roads and bridges and identifies the most important restoration work. The infrastructure management unit also creates restoration plans based on infrastructure data in the disaster-stricken areas. For example, it formulates plans to efficiently proceed with restoration work for electricity and water. The infrastructure management unit also collects information on the infrastructure status in disaster-stricken areas in real time and dynamically adjusts the restoration plan. For example, it changes the plan depending on the progress of restoration work. This makes it possible to efficiently support the restoration of infrastructure in disaster-stricken areas.

[0084] The disaster support system may further include a communication support unit that supports communication between disaster victims. The communication support unit, for example, provides means of communication between disaster victims and promotes information sharing. For example, disaster victims can communicate with each other through a dedicated app or website. The communication support unit also estimates the emotions of disaster victims and suggests appropriate communication methods based on their emotions. For example, it provides a chat function to provide psychological support to disaster victims experiencing high levels of stress or anxiety. The communication support unit also collects emotional data from disaster victims in real time and dynamically adjusts the communication support plan. For example, it changes the communication method according to changes in emotions. This makes it possible to provide communication support based on the emotions of disaster victims.

[0085] The disaster support system can further include a mobility support unit that supports the movement of disaster victims. The mobility support unit, for example, optimizes the movement routes of disaster victims and provides safe evacuation routes. For example, it analyzes the passability of roads and traffic conditions and proposes the optimal evacuation route. The mobility support unit also estimates the emotions of disaster victims and creates a mobility support plan based on those emotions. For example, for disaster victims with high levels of stress or anxiety, it supports their movement while providing psychological support. The mobility support unit also collects emotional data of disaster victims in real time and dynamically adjusts the mobility support plan. For example, it changes the movement route and support content according to changes in emotions. This makes it possible to provide mobility support based on the emotions of disaster victims.

[0086] The disaster support system can further include a life reconstruction support unit that supports disaster victims in rebuilding their lives. The life reconstruction support unit, for example, provides support for disaster victims in rebuilding their homes and finding employment. For example, it creates plans for disaster victims to rebuild their homes and provides the necessary funds and supplies. The life reconstruction support unit also provides job information and implements vocational training to support disaster victims in finding employment. For example, it works with companies in the disaster-stricken areas to create employment opportunities for disaster victims. The life reconstruction support unit also monitors the disaster victims' progress in rebuilding their lives in real time and adjusts the support plan as needed. For example, it changes the content of support depending on the progress of housing reconstruction and employment status. This allows for efficient support for disaster victims in rebuilding their lives.

[0087] The disaster support system can further include an education support unit that supports the education of disaster victims. The education support unit, for example, provides recovery support for schools in disaster-stricken areas and online education. For example, it analyzes the damage situation at schools in disaster-stricken areas and provides necessary educational supplies and support. The education support unit also estimates the emotions of disaster victims and creates an education support plan based on those emotions. For example, it provides education while providing psychological support to students with high levels of stress and anxiety. The education support unit also collects emotional data of disaster victims in real time and dynamically adjusts the education support plan. For example, it changes the educational content and support methods according to changes in emotions. This makes it possible to provide educational support based on the emotions of disaster victims.

[0088] The disaster relief system may further include an animal protection unit that supports the protection of pets and livestock of disaster victims. The animal protection unit, for example, secures evacuation destinations for pets and livestock in the disaster area and provides necessary supplies. For example, it predicts the required amount of pet food and medical supplies and creates a plan to distribute them to evacuation destinations. The animal protection unit also analyzes the situation at animal shelters in the disaster area and supports animal protection activities. For example, it plans the necessary support taking into account the capacity of the shelters and the inventory status of supplies. The animal protection unit also monitors the evacuation status of pets and livestock in real time and adjusts the support plan as needed. For example, it dynamically changes evacuation destinations and replenishes supplies. This allows animal protection activities to be supported by predicting evacuation destinations and necessary supplies for pets and livestock.

[0089] The disaster support system can further include an energy management unit that supports energy supply to disaster victims. The energy management unit, for example, monitors the power supply situation in the disaster-stricken area in real time and creates an efficient energy distribution plan. For example, it formulates a plan to supply power preferentially to important facilities and areas. The energy management unit also analyzes the fuel supply situation in the disaster-stricken area and creates an optimal fuel distribution plan. For example, it performs efficient distribution taking into account fuel inventory and demand. The energy management unit also collects information on the energy supply situation in the disaster-stricken area in real time and dynamically adjusts the supply plan. For example, it changes the plan depending on the power restoration status and fuel consumption status. This allows for efficient support of energy supply to the disaster-stricken area.

[0090] The disaster support system can further include an information collection unit that assists disaster victims in collecting information. The information collection unit, for example, collects information on the situation in the disaster-stricken area in real time and uses it to formulate a support plan. For example, it analyzes the damage situation in the disaster-stricken area using drone or satellite images. The information collection unit also estimates the emotions of the disaster victims and optimizes the method of providing information based on those emotions. For example, disaster victims experiencing high levels of stress or anxiety are given priority in receiving information that provides a sense of security. The information collection unit also collects emotional data from disaster victims in real time and dynamically adjusts the information provision plan. For example, the content and method of providing information can be changed according to changes in emotions. This makes it possible to provide information based on the emotions of the disaster victims.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The disaster prediction unit performs disaster prediction. For example, it analyzes past disaster data, meteorological data, and population data to predict the amount of funds and supplies needed in the event of a disaster. Step 2: The requirements calculation unit calculates the amount of funds and supplies needed based on the disaster predicted by the disaster prediction unit. For example, it lists the types and amounts of supplies needed for each disaster area and determines how much to send to each evacuation shelter. Step 3: The distribution plan formulation unit formulates a fund and / or supply distribution plan based on the required amount calculated by the required amount calculation unit. For example, it formulates a fund and / or supply distribution plan and determines how much of each supply should be sent to each evacuation shelter. Step 4: The Fundraising Efficiency Department will streamline fundraising activities based on the allocation plan developed by the Allocation Plan Development Department, for example by clearly indicating the amount of funds needed and setting a fundraising target. Step 5: The Materials Management Department will optimize materials management based on the distribution plan formulated by the Distribution Planning Department. For example, they will collect inventory data for materials at each evacuation shelter and build a system to automatically replenish necessary supplies.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] 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.

[0120] 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.

[0121] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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 processing similar to that of the specific processing unit 290 using these models.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a disaster prediction unit that performs disaster prediction; a need calculation unit that calculates the amount of funds and supplies required based on the disaster predicted by the disaster prediction unit; an allocation plan formulation unit that formulates an allocation plan for funds and materials based on the required amount calculated by the required amount calculation unit; a fundraising efficiency improvement unit that improves the efficiency of fundraising activities based on the allocation plan formulated by the allocation plan formulation unit; a material management unit that optimizes material management based on the distribution plan formulated by the distribution plan formulation unit. A system characterized by:

2. The disaster prediction unit Analyzing past disaster data, meteorological data, and population data to predict the amount of funds and supplies required in the event of a disaster 2. The system of claim 1.

3. The required amount calculation unit Make a list of the types and quantities of supplies needed for each disaster area, and decide how much of each to send to each evacuation center.

2. The system of claim 1.

4. The Fundraising Efficiency Department shall: Define your funding needs and set a fundraising goal 2. The system of claim 1.

5. The material management department Build a system to collect inventory data for the above supplies at each evacuation center and automatically replenish the necessary supplies.

2. The system of claim 1.

Images