system

Abstract

The system according to the embodiment aims to predict disaster risks, promote evacuation, and provide consulting services to municipal governments. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a promotion unit, and a consulting unit. The collection unit collects aerial photographs or maps and actual disaster situations. The analysis unit analyzes the data collected by the collection unit and predicts disaster risks. The promotion unit promotes evacuation based on the disaster risks predicted by the analysis unit. The consulting unit provides consulting to municipal governments based on the disaster risks predicted by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately predict disaster risks, promote evacuation based on the results, or provide consultation to government agencies, so there is room for improvement.

[0005] The system according to the embodiment aims to predict disaster risks, promote evacuation, and provide consulting services to municipal governments. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a promotion unit, and a consulting unit. The collection unit collects aerial photographs or maps and actual disaster situations. The analysis unit analyzes the data collected by the collection unit and predicts disaster risks. The promotion unit promotes evacuation based on the disaster risks predicted by the analysis unit. The consulting unit provides consulting to municipal governments based on the disaster risks predicted by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can predict disaster risks, promote evacuation, and provide consulting services to municipal governments. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A disaster prevention system according to an embodiment of the present invention utilizes a generative AI to prevent disaster damage. This system predicts disaster risks by inputting aerial photographs, maps, and actual disaster situations into the generative AI as training data. For example, it can predict the risk of landslides and levee breaches caused by heavy rain. This enables appropriate countermeasures to be taken before a disaster occurs. Furthermore, the generative AI promotes evacuation based on the prediction results. For example, it can send messages urging early evacuation to residents in areas at high risk of disasters. This ensures the safety of residents. The generative AI also provides consulting services to municipal governments to enhance regional disaster prevention capabilities. For example, it can propose disaster prevention measures to be taken in areas at high risk of disasters. This improves the disaster prevention capabilities of the entire region. This system protects people from disasters and minimizes damage. For example, even in the case of a disaster that caused damage totaling approximately 651.2 billion yen in 2020, taking appropriate countermeasures in advance can mitigate damage. As a result, the disaster prevention system can predict disaster risks, promote evacuation, and provide consulting services to municipal governments to prevent disaster damage.

[0029] A disaster damage prevention system according to an embodiment includes a collection unit, an analysis unit, a promotion unit, and a consulting unit. The collection unit collects aerial photographs, maps, and actual disaster situations. For example, the collection unit can collect satellite photographs, drone photographs, topographical maps, and the like. The collection unit can also collect disaster situations such as floods, earthquakes, and fires. The collection unit can, for example, periodically collect data. The analysis unit analyzes the data collected by the collection unit and predicts disaster risks. For example, the analysis unit can analyze the data using a machine learning algorithm to predict disaster risks. The analysis unit can also refer to past disaster data to improve the accuracy of disaster risk predictions. The promotion unit promotes evacuation based on the disaster risks predicted by the analysis unit. For example, the promotion unit can send messages urging early evacuation to residents in areas with high disaster risk. The promotion unit can also provide evacuation route guidance. The consulting unit provides consulting to municipal governments based on the disaster risks predicted by the analysis unit. For example, the consulting unit can propose what disaster prevention measures should be taken in areas with high disaster risk. The consulting department can also propose the installation of disaster prevention equipment and the implementation of disaster prevention drills. As a result, the disaster damage prevention system according to the embodiment can prevent disaster damage by predicting disaster risks, promoting evacuation, and providing consulting to municipal governments.

[0030] The collection unit can analyze past disaster data and select an appropriate collection method. For example, the collection unit can adjust the frequency of data collection in a specific area based on past disaster data. The collection unit can also analyze past disaster data and optimize the data collection method under specific weather conditions. The collection unit can also refer to past disaster data and determine the priority of data collection for specific seasons or time periods. This enables efficient data collection by selecting the optimal collection method based on past disaster data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past disaster data into a generation AI and have the generation AI select the optimal collection method.

[0031] The collection unit can perform filtering based on regional characteristics and topographical information when collecting data. For example, the collection unit can prioritize collecting data from areas with a high risk of landslides based on regional topographical information. The collection unit can also prioritize collecting data from areas with a high risk of flooding, taking regional characteristics into consideration. The collection unit can also filter and collect wind speed and rainfall data based on regional topographical information. This enables more accurate data collection by filtering data based on regional characteristics and topographical information. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input regional topographical data into a generation AI and have the generation AI perform data filtering.

[0032] When collecting data, the collection unit can prioritize collecting highly relevant data based on local weather information. For example, the collection unit prioritizes collecting data from areas where heavy rain is predicted based on the weather information. The collection unit can also prioritize collecting data from areas where strong winds are predicted, taking into account the weather information. The collection unit can also prioritize collecting data from areas where the risk of avalanches is high, based on the weather information. In this way, by collecting data taking into account the weather information for the area, data with a high disaster risk can be collected preferentially. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input local weather data into the generation AI and cause the generation AI to collect highly relevant data.

[0033] The collection unit can analyze social media posts and collect related disaster information when collecting data. For example, the collection unit can analyze social media posts and collect information on areas where disasters have occurred. The collection unit can also identify the scale and scope of the disaster based on the social media posts and collect data. The collection unit can also analyze social media posts and collect data on the damage situation and evacuation status. In this way, real-time disaster information can be collected by analyzing social media posts. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input social media post data into a generation AI and cause the generation AI to collect related disaster information.

[0034] During analysis, the analysis unit can optimize the analysis algorithm based on the type of disaster. For example, when analyzing the risk of landslides, the analysis unit uses an algorithm that emphasizes topographical data. Furthermore, when analyzing the risk of flooding, the analysis unit can use an algorithm that emphasizes rainfall data. Furthermore, when analyzing the risk of strong winds, the analysis unit can use an algorithm that emphasizes wind speed data. By optimizing the analysis algorithm according to the type of disaster, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data based on the type of disaster into the generation AI and cause the generation AI to optimize the analysis algorithm.

[0035] During analysis, the analysis unit can improve the accuracy of the analysis by comparing it with past disaster data. For example, the analysis unit can improve the accuracy of the analysis by comparing current data based on past disaster data. The analysis unit can also improve the accuracy of the analysis by referring to past disaster data and identifying similar disaster patterns. The analysis unit can also analyze past disaster data and improve the accuracy by improving the analysis algorithm. In this way, the accuracy of the analysis is improved by comparing it with past disaster data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past disaster data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0036] During analysis, the analysis unit can improve the accuracy of the analysis based on regional infrastructure information. For example, the analysis unit improves the analysis accuracy of disaster risk based on regional infrastructure information. The analysis unit can also improve the analysis accuracy of evacuation routes by taking regional infrastructure information into account. The analysis unit can also improve the analysis accuracy of damage prediction by referring to regional infrastructure information. In this way, the analysis accuracy is improved by taking regional infrastructure information into account. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input regional infrastructure data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0037] During analysis, the analysis unit can improve the analysis algorithm by referring to related academic papers. The analysis unit, for example, refers to the latest academic papers and improves the analysis algorithm. The analysis unit can also improve the accuracy of the analysis algorithm based on related academic papers. The analysis unit can also incorporate knowledge from academic papers to optimize the analysis algorithm. In this way, the accuracy of the analysis algorithm is improved by referring to related academic papers. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from academic papers into the generation AI and have the generation AI improve the analysis algorithm.

[0038] When sending an evacuation message, the promotion unit can optimize the message delivery method based on the resident's attribute information. For example, the promotion unit may prioritize sending voice messages to elderly people. The promotion unit may also prioritize sending text messages to younger people. The promotion unit may also send an appropriate message depending on family composition. In this way, by optimizing the message delivery method based on the resident's attribute information, a more effective evacuation message can be sent. Some or all of the above-mentioned processing in the promotion unit may be performed using, for example, AI, or may be performed without using AI. For example, the promotion unit may input the resident's attribute data into the generation AI and cause the generation AI to optimize the message delivery method.

[0039] When transmitting an evacuation message, the promotion unit can refer to past evacuation history to improve the effectiveness of the message. The promotion unit, for example, transmits an effective message based on past evacuation history. The promotion unit can also refer to past evacuation history to transmit a message encouraging evacuation behavior. The promotion unit can also analyze past evacuation history and transmit an optimal message. In this way, by referring to past evacuation history, a more effective evacuation message can be transmitted. Some or all of the above-described processing in the promotion unit may be performed, for example, using AI, or may be performed without using AI. For example, the promotion unit can input past evacuation history data into the generation AI and cause the generation AI to improve the effectiveness of the message.

[0040] When sending an evacuation message, the promotion unit can select an appropriate transmission method based on the state of the local communication infrastructure. For example, in areas with a well-established communication infrastructure, the promotion unit can send a text message. In areas with unstable communication infrastructure, the promotion unit can also send a voice message. In areas where communication infrastructure is unstable, the promotion unit can also send a message via radio or television. This allows for more effective evacuation messages to be sent by taking into account the state of the local communication infrastructure. Some or all of the above-described processing in the promotion unit may be performed using, for example, AI, or may be performed without using AI. For example, the promotion unit can input local communication infrastructure data into the generation AI and have the generation AI select a transmission method.

[0041] When sending an evacuation message, the promotion unit can spread the message widely by utilizing social media. The promotion unit, for example, quickly spreads the evacuation message through social media. The promotion unit can also use social media followers to spread the evacuation message widely. The promotion unit can also use social media hashtags to effectively spread the evacuation message. In this way, by utilizing social media, the evacuation message can be spread quickly and widely. Some or all of the above-mentioned processing in the promotion unit may be performed using AI, for example, or may be performed without using AI. For example, the promotion unit can input social media posting data into the generation AI and have the generation AI spread the message.

[0042] During consulting, the consulting department can propose appropriate disaster prevention measures by referring to the local disaster prevention plan. For example, the consulting department can propose optimal evacuation routes based on the local disaster prevention plan. The consulting department can also propose the installation of necessary disaster prevention equipment by referring to the local disaster prevention plan. The consulting department can also propose an educational program to raise residents' disaster prevention awareness by taking the local disaster prevention plan into consideration. In this way, optimal disaster prevention measures can be proposed by referring to the local disaster prevention plan. Some or all of the above-mentioned processing by the consulting department may be performed using, for example, AI, or may be performed without using AI. For example, the consulting department can input local disaster prevention plan data into the generation AI and have the generation AI execute the disaster prevention measure proposals.

[0043] During consulting, the consulting department can analyze past consulting results to improve the accuracy of proposals. The consulting department, for example, improves the content of proposals based on past consulting results. The consulting department can also analyze past consulting results to propose effective disaster prevention measures. The consulting department can also refer to past consulting results to propose disaster prevention measures tailored to the characteristics of each region. In this way, the accuracy of proposals is improved by analyzing past consulting results. Some or all of the above-mentioned processing in the consulting department may be performed, for example, using AI, or may be performed without using AI. For example, the consulting department can input past consulting result data into a generation AI and have the generation AI improve the accuracy of proposals.

[0044] During consulting, the consulting department can propose appropriate disaster prevention measures based on the economic situation of the region. For example, the consulting department can propose cost-effective disaster prevention measures based on the economic situation of the region. The consulting department can also propose disaster prevention measures that can be implemented within a budget, taking into account the economic situation of the region. The consulting department can also refer to the economic situation of the region and propose disaster prevention measures from a long-term perspective. This makes it possible to propose cost-effective disaster prevention measures by taking into account the economic situation of the region. Some or all of the above-mentioned processing by the consulting department may be performed using, for example, AI, or may be performed without using AI. For example, the consulting department can input regional economic data into the generation AI and have the generation AI execute the disaster prevention measure proposals.

[0045] During consulting, the consulting department can strengthen the proposal content by referring to related policy documents. The consulting department, for example, strengthens the proposal content based on policy documents. The consulting department can also improve the reliability of the proposal content by referring to related policy documents. The consulting department can also incorporate knowledge from policy documents to optimize the proposal content. In this way, the reliability of the proposal content is improved by referring to related policy documents. Some or all of the above-mentioned processing in the consulting department may be performed using AI, for example, or may be performed without using AI. For example, the consulting department can input data from policy documents into a generation AI and have the generation AI strengthen the proposal content.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The collection unit can track the movement patterns of residents in high-risk disaster areas in real time and determine the need for evacuation. For example, the collection unit can use GPS data to monitor residents' movements and determine the number of people staying in a specific area. The collection unit can also analyze traffic data to determine the congestion status of evacuation routes in real time. Furthermore, the collection unit can collect location information from residents' smartphones and check the progress of evacuation. This allows the collection unit to determine the need for evacuation based on residents' movement patterns and promote evacuation at the appropriate time.

[0048] When sending an evacuation message, the promotion unit can adjust the content of the message based on the health status of the resident. For example, a message containing medical support information necessary for evacuation can be sent to a resident in poor health. A regular evacuation message can also be sent to a resident in good health. Furthermore, a message containing points to be careful about when evacuating can also be sent to a resident with a specific health risk. In this way, the promotion unit can adjust the content of the evacuation message according to the health status of the resident and promote more effective evacuation.

[0049] The collection unit can collect environmental data in high-disaster-risk areas in real time and provide it to the analysis unit. For example, the collection unit can use sensors to measure soil humidity and temperature. The collection unit can also use drones to collect image data from the air and monitor changes in the terrain. Furthermore, the collection unit can collect meteorological data in real time and use it to predict heavy rain and strong winds. In this way, the collection unit can collect environmental data in high-disaster-risk areas in real time and provide it to the analysis unit, thereby achieving more accurate disaster risk predictions.

[0050] When sending an evacuation message, the promotion unit can provide the content of the message in multiple languages ​​based on the language of the residents. For example, the message can be sent in English to English-speaking residents. The message can also be sent in Chinese to Chinese-speaking residents. Furthermore, the message can be sent in Spanish to Spanish-speaking residents. In this way, the promotion unit can provide the content of the evacuation message in multiple languages ​​according to the language of the residents, and promote more effective evacuation.

[0051] The collection unit can monitor the behavioral patterns of animals in areas with a high risk of disaster and detect signs of disaster. For example, the collection unit can collect movement data of wild animals and detect abnormal behavior. The collection unit can also collect behavioral data of livestock and monitor abnormal behavior. Furthermore, the collection unit can collect behavioral data of pets and detect abnormal behavior. In this way, by monitoring animal behavioral patterns, the collection unit can detect signs of disaster early and take appropriate measures.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The collection unit collects aerial photographs, maps, and actual disaster situations. For example, the collection unit can collect satellite photographs, drone photographs, topographical maps, etc. The collection unit can also collect situations of disasters such as floods, earthquakes, and fires. For example, the collection unit can collect data periodically. Step 2: The analysis unit analyzes the data collected by the collection unit and predicts disaster risks. For example, the analysis unit can analyze the data using a machine learning algorithm and predict disaster risks. The analysis unit can also refer to past disaster data to improve the accuracy of disaster risk predictions. Step 3: The promotion unit promotes evacuation based on the disaster risk predicted by the analysis unit. For example, the promotion unit can send a message to residents in areas with high disaster risk, urging them to evacuate early. The promotion unit can also provide evacuation route guidance. Step 4: The Consulting Department provides consultation to municipal governments based on the disaster risks predicted by the Analysis Department. For example, the Consulting Department can propose what kind of disaster prevention measures should be taken in areas with high disaster risk. The Consulting Department can also propose the installation of disaster prevention equipment and the implementation of disaster prevention drills.

[0054] (Example 2) A disaster prevention system according to an embodiment of the present invention utilizes a generative AI to prevent disaster damage. This system predicts disaster risks by inputting aerial photographs, maps, and actual disaster situations into the generative AI as training data. For example, it can predict the risk of landslides and levee breaches caused by heavy rain. This enables appropriate countermeasures to be taken before a disaster occurs. Furthermore, the generative AI promotes evacuation based on the prediction results. For example, it can send messages urging early evacuation to residents in areas at high risk of disasters. This ensures the safety of residents. The generative AI also provides consulting services to municipal governments to enhance regional disaster prevention capabilities. For example, it can propose disaster prevention measures to be taken in areas at high risk of disasters. This improves the disaster prevention capabilities of the entire region. This system protects people from disasters and minimizes damage. For example, even in the case of a disaster that caused damage totaling approximately 651.2 billion yen in 2020, taking appropriate countermeasures in advance can mitigate damage. As a result, the disaster prevention system can predict disaster risks, promote evacuation, and provide consulting services to municipal governments to prevent disaster damage.

[0055] A disaster damage prevention system according to an embodiment includes a collection unit, an analysis unit, a promotion unit, and a consulting unit. The collection unit collects aerial photographs, maps, and actual disaster situations. For example, the collection unit can collect satellite photographs, drone photographs, topographical maps, and the like. The collection unit can also collect disaster situations such as floods, earthquakes, and fires. The collection unit can, for example, periodically collect data. The analysis unit analyzes the data collected by the collection unit and predicts disaster risks. For example, the analysis unit can analyze the data using a machine learning algorithm to predict disaster risks. The analysis unit can also refer to past disaster data to improve the accuracy of disaster risk predictions. The promotion unit promotes evacuation based on the disaster risks predicted by the analysis unit. For example, the promotion unit can send messages urging early evacuation to residents in areas with high disaster risk. The promotion unit can also provide evacuation route guidance. The consulting unit provides consulting to municipal governments based on the disaster risks predicted by the analysis unit. For example, the consulting unit can propose what disaster prevention measures should be taken in areas with high disaster risk. The consulting department can also propose the installation of disaster prevention equipment and the implementation of disaster prevention drills. As a result, the disaster damage prevention system according to the embodiment can prevent disaster damage by predicting disaster risks, promoting evacuation, and providing consulting to municipal governments.

[0056] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling anxious, the collection unit can increase the collection frequency and provide the latest data. Furthermore, if the user is relaxed, the collection unit can return the collection frequency to normal to reduce the load on the system. Furthermore, if the user is facing an emergency, the collection unit can collect data in real time and provide it immediately. This enables more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0057] The collection unit can analyze past disaster data and select an appropriate collection method. For example, the collection unit can adjust the frequency of data collection in a specific area based on past disaster data. The collection unit can also analyze past disaster data and optimize the data collection method under specific weather conditions. The collection unit can also refer to past disaster data and determine the priority of data collection for specific seasons or time periods. This enables efficient data collection by selecting the optimal collection method based on past disaster data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past disaster data into a generation AI and have the generation AI select the optimal collection method.

[0058] The collection unit can perform filtering based on regional characteristics and topographical information when collecting data. For example, the collection unit can prioritize collecting data from areas with a high risk of landslides based on regional topographical information. The collection unit can also prioritize collecting data from areas with a high risk of flooding, taking regional characteristics into consideration. The collection unit can also filter and collect wind speed and rainfall data based on regional topographical information. This enables more accurate data collection by filtering data based on regional characteristics and topographical information. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input regional topographical data into a generation AI and have the generation AI perform data filtering.

[0059] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling anxious, the collection unit prioritizes collecting data associated with a high disaster risk. The collection unit can also perform normal data collection when the user is relaxed. The collection unit can also immediately collect the most important data when the user is facing an emergency. This allows important data to be collected preferentially by determining the priority of data according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0060] When collecting data, the collection unit can prioritize collecting highly relevant data based on local weather information. For example, the collection unit prioritizes collecting data from areas where heavy rain is predicted based on the weather information. The collection unit can also prioritize collecting data from areas where strong winds are predicted, taking into account the weather information. The collection unit can also prioritize collecting data from areas where the risk of avalanches is high, based on the weather information. In this way, by collecting data taking into account the weather information for the area, data with a high disaster risk can be collected preferentially. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input local weather data into the generation AI and cause the generation AI to collect highly relevant data.

[0061] The collection unit can analyze social media posts and collect related disaster information when collecting data. For example, the collection unit can analyze social media posts and collect information on areas where disasters have occurred. The collection unit can also identify the scale and scope of the disaster based on the social media posts and collect data. The collection unit can also analyze social media posts and collect data on the damage situation and evacuation status. In this way, real-time disaster information can be collected by analyzing social media posts. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input social media post data into a generation AI and cause the generation AI to collect related disaster information.

[0062] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is feeling anxious, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is facing an emergency, the analysis unit can provide a quickly understandable analysis result. By adjusting the presentation method of the analysis according to the user's emotions, it is possible to provide an analysis result that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0063] During analysis, the analysis unit can optimize the analysis algorithm based on the type of disaster. For example, when analyzing the risk of landslides, the analysis unit uses an algorithm that emphasizes topographical data. Furthermore, when analyzing the risk of flooding, the analysis unit can use an algorithm that emphasizes rainfall data. Furthermore, when analyzing the risk of strong winds, the analysis unit can use an algorithm that emphasizes wind speed data. By optimizing the analysis algorithm according to the type of disaster, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data based on the type of disaster into the generation AI and cause the generation AI to optimize the analysis algorithm.

[0064] During analysis, the analysis unit can improve the accuracy of the analysis by comparing it with past disaster data. For example, the analysis unit can improve the accuracy of the analysis by comparing current data based on past disaster data. The analysis unit can also improve the accuracy of the analysis by referring to past disaster data and identifying similar disaster patterns. The analysis unit can also analyze past disaster data and improve the accuracy by improving the analysis algorithm. In this way, the accuracy of the analysis is improved by comparing it with past disaster data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past disaster data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0065] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is facing an emergency, the analysis unit can provide a display method that is easy to understand. By adjusting the display method of the analysis results according to the user's emotions, it is possible to display results that are easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0066] During analysis, the analysis unit can improve the accuracy of the analysis based on regional infrastructure information. For example, the analysis unit improves the analysis accuracy of disaster risk based on regional infrastructure information. The analysis unit can also improve the analysis accuracy of evacuation routes by taking regional infrastructure information into account. The analysis unit can also improve the analysis accuracy of damage prediction by referring to regional infrastructure information. In this way, the analysis accuracy is improved by taking regional infrastructure information into account. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input regional infrastructure data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0067] During analysis, the analysis unit can improve the analysis algorithm by referring to related academic papers. The analysis unit, for example, refers to the latest academic papers and improves the analysis algorithm. The analysis unit can also improve the accuracy of the analysis algorithm based on related academic papers. The analysis unit can also incorporate knowledge from academic papers to optimize the analysis algorithm. In this way, the accuracy of the analysis algorithm is improved by referring to related academic papers. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from academic papers into the generation AI and have the generation AI improve the analysis algorithm.

[0068] The promotion unit can estimate the user's emotions and adjust the content of the evacuation message based on the estimated user emotions. For example, if the user feels anxious, the promotion unit can send a message that gives a sense of security. Furthermore, if the user feels relaxed, the promotion unit can also send a normal evacuation message. Furthermore, if the user is facing an emergency, the promotion unit can also send a message urging the user to evacuate quickly. This allows for more effective evacuation by adjusting the content of the evacuation message according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the promotion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the promotion unit can input the user's facial expression data into the generation AI and cause the generation AI to adjust the content of the evacuation message.

[0069] When sending an evacuation message, the promotion unit can optimize the message delivery method based on the resident's attribute information. For example, the promotion unit may prioritize sending voice messages to elderly people. The promotion unit may also prioritize sending text messages to younger people. The promotion unit may also send an appropriate message depending on family composition. In this way, by optimizing the message delivery method based on the resident's attribute information, a more effective evacuation message can be sent. Some or all of the above-mentioned processing in the promotion unit may be performed using, for example, AI, or may be performed without using AI. For example, the promotion unit may input the resident's attribute data into the generation AI and cause the generation AI to optimize the message delivery method.

[0070] When transmitting an evacuation message, the promotion unit can refer to past evacuation history to improve the effectiveness of the message. The promotion unit, for example, transmits an effective message based on past evacuation history. The promotion unit can also refer to past evacuation history to transmit a message encouraging evacuation behavior. The promotion unit can also analyze past evacuation history and transmit an optimal message. In this way, by referring to past evacuation history, a more effective evacuation message can be transmitted. Some or all of the above-described processing in the promotion unit may be performed, for example, using AI, or may be performed without using AI. For example, the promotion unit can input past evacuation history data into the generation AI and cause the generation AI to improve the effectiveness of the message.

[0071] The facilitating unit can estimate the user's emotions and adjust the timing of sending an evacuation message based on the estimated user's emotions. For example, if the user feels anxious, the facilitating unit can send the evacuation message early. Furthermore, if the user feels relaxed, the facilitating unit can also send the evacuation message at a normal timing. Furthermore, if the user is facing an emergency, the facilitating unit can also send the evacuation message immediately. By adjusting the timing of sending the evacuation message according to the user's emotions, evacuation can be promoted at a more appropriate timing. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the facilitating unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the facilitating unit can input the user's facial expression data into the generation AI and cause the generation AI to adjust the timing of sending the evacuation message.

[0072] When sending an evacuation message, the promotion unit can select an appropriate transmission method based on the state of the local communication infrastructure. For example, in areas with a well-established communication infrastructure, the promotion unit can send a text message. In areas with unstable communication infrastructure, the promotion unit can also send a voice message. In areas where communication infrastructure is unstable, the promotion unit can also send a message via radio or television. This allows for more effective evacuation messages to be sent by taking into account the state of the local communication infrastructure. Some or all of the above-described processing in the promotion unit may be performed using, for example, AI, or may be performed without using AI. For example, the promotion unit can input local communication infrastructure data into the generation AI and have the generation AI select a transmission method.

[0073] When sending an evacuation message, the promotion unit can spread the message widely by utilizing social media. The promotion unit, for example, quickly spreads the evacuation message through social media. The promotion unit can also use social media followers to spread the evacuation message widely. The promotion unit can also use social media hashtags to effectively spread the evacuation message. In this way, by utilizing social media, the evacuation message can be spread quickly and widely. Some or all of the above-mentioned processing in the promotion unit may be performed using AI, for example, or may be performed without using AI. For example, the promotion unit can input social media posting data into the generation AI and have the generation AI spread the message.

[0074] The consulting unit can estimate the user's emotions and adjust the consulting content based on the estimated user emotions. For example, if the user is feeling anxious, the consulting unit can provide reassuring consulting. Furthermore, if the user is relaxed, the consulting unit can provide detailed consulting. Furthermore, if the user is facing an emergency, the consulting unit can provide quick response consulting. This enables more effective consulting by adjusting the consulting content according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the consulting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the consulting unit can input the user's facial expression data into the generation AI and have the generation AI adjust the consulting content.

[0075] During consulting, the consulting department can propose appropriate disaster prevention measures by referring to the local disaster prevention plan. For example, the consulting department can propose optimal evacuation routes based on the local disaster prevention plan. The consulting department can also propose the installation of necessary disaster prevention equipment by referring to the local disaster prevention plan. The consulting department can also propose an educational program to raise residents' disaster prevention awareness by taking the local disaster prevention plan into consideration. In this way, optimal disaster prevention measures can be proposed by referring to the local disaster prevention plan. Some or all of the above-mentioned processing by the consulting department may be performed using, for example, AI, or may be performed without using AI. For example, the consulting department can input local disaster prevention plan data into the generation AI and have the generation AI execute the disaster prevention measure proposals.

[0076] During consulting, the consulting department can analyze past consulting results to improve the accuracy of proposals. The consulting department, for example, improves the content of proposals based on past consulting results. The consulting department can also analyze past consulting results to propose effective disaster prevention measures. The consulting department can also refer to past consulting results to propose disaster prevention measures tailored to the characteristics of each region. In this way, the accuracy of proposals is improved by analyzing past consulting results. Some or all of the above-mentioned processing in the consulting department may be performed, for example, using AI, or may be performed without using AI. For example, the consulting department can input past consulting result data into a generation AI and have the generation AI improve the accuracy of proposals.

[0077] The consulting unit can estimate the user's emotions and determine the priority of consulting based on the estimated user emotions. For example, the consulting unit can prioritize consulting when the user is feeling anxious. Furthermore, the consulting unit can also provide consulting in the normal order when the user is relaxed. Furthermore, the consulting unit can also provide consulting immediately when the user is facing an emergency. This enables more effective consulting by determining the priority of consulting according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the consulting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the consulting unit can input the user's facial expression data into the generation AI and have the generation AI determine the priority of consulting.

[0078] During consulting, the consulting department can propose appropriate disaster prevention measures based on the economic situation of the region. For example, the consulting department can propose cost-effective disaster prevention measures based on the economic situation of the region. The consulting department can also propose disaster prevention measures that can be implemented within a budget, taking into account the economic situation of the region. The consulting department can also refer to the economic situation of the region and propose disaster prevention measures from a long-term perspective. This makes it possible to propose cost-effective disaster prevention measures by taking into account the economic situation of the region. Some or all of the above-mentioned processing by the consulting department may be performed using, for example, AI, or may be performed without using AI. For example, the consulting department can input regional economic data into the generation AI and have the generation AI execute the disaster prevention measure proposals.

[0079] During consulting, the consulting department can strengthen the proposal content by referring to related policy documents. The consulting department, for example, strengthens the proposal content based on policy documents. The consulting department can also improve the reliability of the proposal content by referring to related policy documents. The consulting department can also incorporate knowledge from policy documents to optimize the proposal content. In this way, the reliability of the proposal content is improved by referring to related policy documents. Some or all of the above-mentioned processing in the consulting department may be performed using AI, for example, or may be performed without using AI. For example, the consulting department can input data from policy documents into a generation AI and have the generation AI strengthen the proposal content. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, promotion unit, and consulting unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects aerial photographs, maps, and disaster situations using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and predicts disaster risks. The promotion unit, realized, for example, by the control unit 46A of the smart device 14, sends messages encouraging evacuation to residents in areas with high disaster risk. The consulting unit, realized, for example, by the specific processing unit 290 of the data processing device 12, proposes disaster prevention measures to municipal governments. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, promotion unit, and consulting unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects aerial photographs, maps, and disaster situations using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and predicts disaster risks. The promotion unit, realized, for example, by the control unit 46A of the smart glasses 214, sends messages encouraging evacuation to residents in areas with high disaster risk. The consulting unit, realized, for example, by the specific processing unit 290 of the data processing device 12, proposes disaster prevention measures to municipal governments. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, promotion unit, and consulting unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects aerial photographs, maps, and disaster situations using the camera 42 and communication I / F 44 of the headset terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to predict disaster risks. The promotion unit is realized, for example, by the control unit 46A of the headset terminal 314, and sends messages encouraging evacuation to residents in areas with high disaster risk. The consulting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes disaster prevention measures to municipal governments. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, promotion unit, and consulting unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects aerial photographs, maps, and disaster situations using the camera 42 and communication I / F 44 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data and predicts disaster risks. The promotion unit is realized, for example, by the control unit 46A of the robot 414, and sends messages encouraging residents in areas with high disaster risk to evacuate. The consulting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes disaster prevention measures to municipal governments.

[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 collection unit can track the movement patterns of residents in high-risk disaster areas in real time and determine the need for evacuation. For example, the collection unit can use GPS data to monitor residents' movements and determine the number of people staying in a specific area. The collection unit can also analyze traffic data to determine the congestion status of evacuation routes in real time. Furthermore, the collection unit can collect location information from residents' smartphones and check the progress of evacuation. This allows the collection unit to determine the need for evacuation based on residents' movement patterns and promote evacuation at the appropriate time.

[0082] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, a notification summarizing the analysis results can be sent. Alternatively, if the user is relaxed, a notification including detailed analysis results can be sent. Furthermore, if the user is facing an emergency, the analysis results can be notified in a format that can be immediately understood. This allows the analysis unit to adjust the notification method of the analysis results according to the user's emotions and provide the user with the most appropriate information.

[0083] When sending an evacuation message, the promotion unit can adjust the content of the message based on the health status of the resident. For example, a message containing medical support information necessary for evacuation can be sent to a resident in poor health. A regular evacuation message can also be sent to a resident in good health. Furthermore, a message containing points to be careful about when evacuating can also be sent to a resident with a specific health risk. In this way, the promotion unit can adjust the content of the evacuation message according to the health status of the resident and promote more effective evacuation.

[0084] The consulting unit can estimate the user's emotions and adjust the speed at which the consulting proceeds based on the estimated user emotions. For example, if the user feels anxious, the consulting unit can provide explanations slowly and carefully. If the user feels relaxed, the consulting unit can proceed at a normal speed. Furthermore, if the user is facing an emergency, the consulting unit can quickly provide the necessary information. In this way, the consulting unit can adjust the speed at which the consulting proceeds based on the user's emotions and provide the optimal support for the user.

[0085] The collection unit can collect environmental data in high-disaster-risk areas in real time and provide it to the analysis unit. For example, the collection unit can use sensors to measure soil humidity and temperature. The collection unit can also use drones to collect image data from the air and monitor changes in the terrain. Furthermore, the collection unit can collect meteorological data in real time and use it to predict heavy rain and strong winds. In this way, the collection unit can collect environmental data in high-disaster-risk areas in real time and provide it to the analysis unit, thereby achieving more accurate disaster risk predictions.

[0086] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the most important analysis results can be provided first. If the user is relaxed, the analysis results can be provided in the normal order. Furthermore, if the user is facing an emergency, the analysis results that are needed immediately can be provided. In this way, the analysis unit can prioritize the analysis results according to the user's emotions and provide the most appropriate information for the user.

[0087] When sending an evacuation message, the promotion unit can provide the content of the message in multiple languages ​​based on the language of the residents. For example, the message can be sent in English to English-speaking residents. The message can also be sent in Chinese to Chinese-speaking residents. Furthermore, the message can be sent in Spanish to Spanish-speaking residents. In this way, the promotion unit can provide the content of the evacuation message in multiple languages ​​according to the language of the residents, and promote more effective evacuation.

[0088] The consulting unit can estimate the user's emotions and adjust the consulting format based on the estimated user's emotions. For example, if the user is feeling anxious, the consulting can be provided in a format that uses visually easy-to-understand graphs and diagrams. If the user is relaxed, the consulting can be provided in a detailed text-based format. Furthermore, if the user is facing an emergency, the consulting can be provided in a concise, to-the-point format. This allows the consulting unit to adjust the consulting format according to the user's emotions and provide the user with the most appropriate information.

[0089] The collection unit can monitor the behavioral patterns of animals in areas with a high risk of disaster and detect signs of disaster. For example, the collection unit can collect movement data of wild animals and detect abnormal behavior. The collection unit can also collect behavioral data of livestock and monitor abnormal behavior. Furthermore, the collection unit can collect behavioral data of pets and detect abnormal behavior. In this way, by monitoring animal behavioral patterns, the collection unit can detect signs of disaster early and take appropriate measures.

[0090] The analysis unit can estimate the user's emotions and adjust the method of providing feedback on the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, positive feedback can be given priority. If the user is relaxed, detailed feedback can be provided. Furthermore, if the user is facing an emergency, feedback that can be quickly understood can be provided. In this way, the analysis unit can adjust the method of providing feedback on the analysis results according to the user's emotions and provide the user with the most appropriate information.

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

[0092] Step 1: The collection unit collects aerial photographs, maps, and actual disaster situations. For example, the collection unit can collect satellite photographs, drone photographs, topographical maps, etc. The collection unit can also collect situations of disasters such as floods, earthquakes, and fires. For example, the collection unit can collect data periodically. Step 2: The analysis unit analyzes the data collected by the collection unit and predicts disaster risks. For example, the analysis unit can analyze the data using a machine learning algorithm and predict disaster risks. The analysis unit can also refer to past disaster data to improve the accuracy of disaster risk predictions. Step 3: The promotion unit promotes evacuation based on the disaster risk predicted by the analysis unit. For example, the promotion unit can send a message to residents in areas with high disaster risk, urging them to evacuate early. The promotion unit can also provide evacuation route guidance. Step 4: The Consulting Department provides consultation to municipal governments based on the disaster risks predicted by the Analysis Department. For example, the Consulting Department can propose what kind of disaster prevention measures should be taken in areas with high disaster risk. The Consulting Department can also propose the installation of disaster prevention equipment and the implementation of disaster prevention drills.

[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 (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

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

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0140] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0142] 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.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects aerial photographs or maps and actual disaster situations; an analysis unit that analyzes the data collected by the collection unit and predicts disaster risks; a promotion unit that promotes evacuation based on the disaster risk predicted by the analysis unit; a consulting department that provides consulting to municipal governments based on the disaster risk predicted by the analysis department; A system characterized by:

2. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions The system of claim 1 .

3. The collecting unit Analyze past disaster data and select appropriate collection methods The system of claim 1 .

4. The collecting unit When collecting data, filter it based on local characteristics and topographical information. The system of claim 1 .

5. The collecting unit Estimate user emotions and prioritize data collection based on the estimated user emotions The system of claim 1 .

6. The collecting unit When collecting data, prioritize the collection of relevant data based on local weather information The system of claim 1 .

7. The collecting unit During data collection, social media posts will be analyzed to collect relevant disaster information. The system of claim 1 .

8. The analysis unit Inferring user emotions and adjusting the presentation of analysis based on the estimated user emotions The system of claim 1 .

Images