system

Abstract

A system is provided.SOLUTION: The system includes a means for collecting and managing office data using a geographic information system, a means for collecting disaster information in real time from an external API and sensor data, an AI means for generating an appropriate initial action plan based on the collected disaster information, a means for notifying the office of the generated initial action plan, a means for compiling a situation after the occurrence of a disaster and creating a report for a management layer, a means for accumulating response data and generating a manual based on the response data, and a means for providing a dashboard and a web application for providing these pieces of information to a user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] When a disaster occurs, a fast and accurate initial response is required, but current methods require manual information gathering and response, which is inefficient. Furthermore, responses tend to vary and be delayed, potentially resulting in increased damage to business locations. To address this issue, a system is needed that provides optimal response measures in real time based on the location information and unique conditions of each business location. [Means for solving the problem]

[0005] This invention provides a system that streamlines initial disaster responses at business establishments. Specifically, it uses a geographic information system to collect and manage business data, and collects disaster information in real time from external APIs and sensor data. Furthermore, based on the collected disaster information, AI generates an optimal initial response plan and notifies the business establishment. After a disaster occurs, the situation is automatically summarized and a report is generated for management. Response data is accumulated and a manual is generated that can be used for future responses. This information is provided to users via a dashboard and web application. This system significantly improves response efficiency during disasters and minimizes damage.

[0006] A "geographic information system" is a system for collecting, managing, and displaying geographic information such as the location of a business establishment and its surrounding environment.

[0007] "Establishment data" refers to information such as the location of the establishment, business operations, number of employees, and important equipment.

[0008] "Disaster information" refers to real-time data on natural disasters such as earthquakes, floods, and typhoons, including their location, scale, and area of ​​impact.

[0009] "AI methods" refers to artificial intelligence technology that generates optimal initial response plans in the event of a disaster based on past data and the latest research results.

[0010] An "initial response plan" refers to the specific guidelines and procedures that should be taken first when a disaster occurs.

[0011] "Means of notification" refers to the method by which the generated initial response plan and other important information is communicated to the business via email, Slack, web apps, etc.

[0012] "Report creation method" refers to a system that compiles information on the situation after a disaster occurs and automatically generates a report for management.

[0013] A "management report" is a report that is provided to management and summarizes the damage situation, progress of the initial response, and future measures.

[0014] "Knowledge accumulation means" refers to a system that accumulates past disaster response data in a database and makes it available for analysis and future reference.

[0015] "Manual generation means" refers to the means of generating training and practical manuals for disaster response based on accumulated knowledge data.

[0016] A "dashboard" refers to an integrated screen that visually displays disaster information, damage status, response progress, etc. to users.

[0017] A "web application" refers to software that provides information and functions to users through a browser or mobile app. [Brief explanation of the drawings]

[0018] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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, a 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), and an APU (Accelerated Processing Unit).

[0022] 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.

[0023] 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.

[0024] 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), Bluetooth (registered trademark), etc.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0030] 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.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] This invention is a system for streamlining the initial response of a business in the event of a disaster, and is specifically composed of a geographic information system, external APIs, sensor data, AI technology, dashboards, and web applications. Below, we will explain in detail each component of this system and the processing flow based on them.

[0040] Geographic Information System (GIS) and Business Data Management

[0041] The server collects data on business establishments across the country from various data sources and inputs and manages it into a geographic information system (GIS). Business establishment data includes location information, business activities, number of employees, and information on important facilities. The GIS visualizes this data on a map and provides information on each business establishment in an easily accessible format for users.

[0042] Specific examples

[0043] The server calls the API to collect data on specific business locations in Tokyo. This data includes location (Shinjuku Ward, Tokyo), business type (IT company), number of employees (50), and critical facilities (data center). The collected data is registered in the GIS, and users can view it on a map via a web application.

[0044] Real-time disaster information collection

[0045] The server collects real-time disaster information from the Japan Meteorological Agency and various sensor data. Specifically, it obtains data on natural disasters such as earthquakes, typhoons, and floods via external APIs and sensor networks, and reflects this data in the GIS.

[0046] Specific examples

[0047] The server retrieves information from the Japan Meteorological Agency's API about a magnitude 6 earthquake with its epicenter in Tokyo Bay. This information is input into a GIS, and the affected areas around Tokyo Bay are highlighted in red on a map.

[0048] AI-powered disaster response advice

[0049] The server uses AI to generate optimal initial response plans based on the disaster information collected. This AI refers to past data, the latest research papers, and simulation results to propose specific countermeasures.

[0050] Specific examples

[0051] In the event of a magnitude 6 earthquake, the AI ​​will generate initial response plans, such as "conducting safety inspections of business buildings," "instructing employees to evacuate," and "procedures for emergency shutdowns of data centers." The server will then notify the business of this information.

[0052] Reporting and reporting to management

[0053] The server automatically compiles information on the situation after a disaster occurs and generates a report for management, summarizing the damage situation, the progress of the initial response, and future response measures.

[0054] Specific examples

[0055] The server uses the damage information from the GIS to create a damage report for a specific business location in Tokyo. This report includes information such as "Partial damage to the building," "All employees have safely evacuated," and "Data center emergency shutdown procedures have been completed." The report is then sent to management via email.

[0056] Knowledge accumulation and manual generation

[0057] The server accumulates disaster response data from each business location and generates a manual that will be useful for future responses. This manual is constantly updated to reflect the latest response experience.

[0058] Specific examples

[0059] The server will store the response data for the Tokyo Bay earthquake in a database and generate a new earthquake response manual, which will include building inspection methods, rapid evacuation procedures for employees, and measures to protect important equipment.

[0060] Dashboards and Web Applications

[0061] The server provides dashboards and web applications that visually display disaster information, damage status, response progress, etc. to users, allowing them to grasp the situation in real time and quickly take necessary action.

[0062] Specific examples

[0063] Users can access the dashboard through a web application to check current earthquake information, damage status, and the progress of initial responses in real time. For example, they can immediately see a list of employees who have completed evacuation and the status of ongoing building inspections.

[0064] The system of the present invention will improve the efficiency of initial responses when a disaster occurs, enable rapid assessment of damage, and enable appropriate countermeasures, thereby ensuring the safety of business establishments and minimizing damage.

[0065] The processing flow will be explained below.

[0066] Step 1: Collect and manage business data

[0067] The server collects data on businesses across the country through APIs and database connections and inputs it into a geographic information system (GIS).

[0068] Operation:

[0069] The server calls an external API to obtain business data.

[0070] The data is received in JSON format and stored in the database.

[0071] Extract business data from the database and send it to the GIS.

[0072] GIS maps and visualizes business locations on a map.

[0073] Step 2: Collecting real-time disaster information

[0074] The server collects real-time disaster information from the Japan Meteorological Agency and various sensor data.

[0075] Operation:

[0076] The server periodically polls the Japan Meteorological Agency API to obtain the latest earthquake information.

[0077] Sensor data is received in real time via WebSocket or MQTT protocols.

[0078] The acquired earthquake epicenter and seismic intensity information is sent to the GIS.

[0079] The GIS highlights affected areas on a map and matches them with business information.

[0080] Step 3: Generate initial response plans using AI

[0081] The server inputs the collected disaster information into an AI model and generates the optimal initial response plan.

[0082] Operation:

[0083] The server sends the collected disaster data to the AI ​​and activates the prediction module.

[0084] AI generates countermeasures based on past response data and the latest research results.

[0085] The server receives the initial response plan generated by the AI.

[0086] Step 4: Notify the business of the initial response plan

[0087] The server notifies each business office of the generated initial response plan.

[0088] Operation:

[0089] The server classifies the assigned countermeasures by business establishment.

[0090] Send messages to designated businesses using the Slack API or email services.

[0091] The message includes specific response measures such as evacuation instructions and equipment shutdown procedures.

[0092] Step 5: Compiling damage data and creating a report

[0093] The server compiles data on the situation after a disaster occurs and generates a report for management.

[0094] Operation:

[0095] The server compiles damage information based on the GIS damage information.

[0096] Enter data such as a list of affected businesses, response progress, and next steps into a report template.

[0097] Generate reports in PDF format and email them to management.

[0098] Step 6: Knowledge accumulation and manual creation

[0099] The server accumulates disaster response data from each business location and generates a dynamic manual.

[0100] Operation:

[0101] The server receives disaster response data sent from each business location and stores it in a database.

[0102] Past corresponding data is extracted from the database and a manual generation engine is started.

[0103] The manuals are updated based on the latest corresponding information and are generated as training and practical manuals.

[0104] Step 7: Serving the Dashboard and Web Application

[0105] The server provides information to users using a dashboard or web application.

[0106] Operation:

[0107] The server displays real-time disaster information, damage status, response progress, and other data on the dashboard.

[0108] Provides information to users through web applications and supports immediate response.

[0109] Example 1

[0110] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0111] In the past, when a disaster occurred, the initial response at a business site was difficult because it took time to collect and understand information. Furthermore, the management and analysis of collected information was often done manually, which led to problems such as errors and delays. This created major challenges in ensuring the safety of business sites and minimizing damage.

[0112] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0113] In this invention, the server includes means for collecting and managing business establishment data using a geographic information system, means for collecting disaster information in real time from external interfaces and sensor data, artificial intelligence means for generating appropriate initial response plans based on the collected disaster information, means for notifying the business establishment of the generated initial response plans, means for compiling the situation after a disaster occurs and creating reports for each level, means for accumulating response data and generating guides based on this, interface means for providing this information to users, means for visualizing business establishment data and disaster information on a map, means for tracking and managing the progress of countermeasures at each business establishment in real time, and means for proposing countermeasures related to the business establishment based on the collected data and disaster information. This enables rapid and accurate information collection and management, and enables business establishments to carry out initial responses efficiently and effectively.

[0114] A "geographic information system" is a system that collects, stores, analyzes, and visually displays geospatial data.

[0115] "Establishment data" refers to information related to a specific establishment, such as location information, business operations, number of employees, and important facilities.

[0116] An "external interface" is a means for communicating and exchanging data with external sources or systems.

[0117] "Sensor data" refers to information obtained from various sensors, including environmental data such as temperature, humidity, and seismic intensity.

[0118] "Disaster information" refers to information related to natural disasters such as earthquakes, typhoons, and floods, including the extent of damage and the timing of occurrence.

[0119] "Artificial intelligence tools" are algorithms or systems that use machine learning and data analysis techniques to solve specific problems.

[0120] An "initial response plan" is a set of specific guidelines and procedures that should be followed as the first response when a disaster occurs.

[0121] "Reports for higher levels" are reports provided to management and administrative levels that summarize the impact of the disaster and the progress of the response.

[0122] "Response data" refers to data on specific response measures taken in response to a disaster and their results.

[0123] A "guide" is a procedure or manual that outlines the desired actions or responses in a particular situation.

[0124] "Interface means" refers to the means by which a user can interact with a system and input or obtain information.

[0125] A "visualization tool" is a method for displaying data graphically and making it easier to understand intuitively.

[0126] "Means for tracking and managing progress of countermeasures in real time" refers to a system or method for monitoring and managing the implementation status of countermeasures in real time.

[0127] "Means for proposing countermeasures" are algorithms or systems that automatically suggest optimal countermeasures based on collected data.

[0128] This invention is a system for streamlining a business's initial response in the event of a disaster, and is composed of a geographic information system (GIS), external interfaces, sensor data, artificial intelligence (AI) technology, a dashboard, and a web application.

[0129] Geographic Information System (GIS) and Business Data Management

[0130] The server collects data on businesses across the country via API and stores it in a database. The collected data is then entered into a geographic information system (GIS) for visualization. The data registered in the GIS includes location information, business details, number of employees, and information on important facilities.

[0131] Specific examples

[0132] For example, the server calls the API to collect data on a specific business location in Tokyo (location: Shinjuku Ward, Tokyo). This data includes "Business Description: IT Company," "Number of Employees: 50," and "Key Equipment: Data Center." The collected data is stored in a database and reflected in the GIS, allowing users to view it on a map via a web application.

[0133] Real-time disaster information collection

[0134] The server collects disaster information in real time through external interfaces such as the Japan Meteorological Agency and sensor networks. By acquiring information on earthquakes, typhoons, floods, etc. and reflecting it in the GIS, affected areas can be visualized.

[0135] Specific examples

[0136] The server receives real-time information from the Japan Meteorological Agency's API about a magnitude 6 earthquake with its epicenter in Tokyo Bay. This information is entered into the GIS, and the affected areas around Tokyo Bay are highlighted in red on the map.

[0137] AI-powered disaster response advice

[0138] The server inputs the collected disaster information into an AI model to generate optimal initial response plans. This AI model proposes countermeasures based on past disaster response data and the latest research results.

[0139] Specific examples

[0140] For example, if a magnitude 6 earthquake occurs, the AI ​​model will generate initial response plans such as "conducting a building safety inspection," "instructing employees to evacuate," and "procedures for emergency shutdown of the data center." The server will then notify the person in charge at the business site.

[0141] Reporting and reporting to management

[0142] After a disaster occurs, the server compiles data and automatically generates a report for management, summarizing the damage situation, the progress of the initial response, and future countermeasures.

[0143] Specific examples

[0144] Based on the damage information from the GIS, the server creates a damage report for specific business locations in Tokyo. This report includes information such as "Partial damage to the building," "All employees have safely evacuated," and "Data center emergency shutdown procedures have been completed," and is sent to management via email.

[0145] Knowledge accumulation and manual generation

[0146] The server stores disaster response data in a database and generates a manual to be used in future responses. The manual is constantly updated to reflect the latest response experience.

[0147] Specific examples

[0148] The server will store the response data for the Tokyo Bay earthquake in a database and generate a new earthquake response manual, which will include building inspection methods, rapid evacuation procedures for employees, and measures to protect important equipment.

[0149] Providing dashboards and web applications

[0150] The server visually displays disaster information, damage status, and response progress to users via dashboards and web applications, allowing users to grasp the situation in real time and quickly take necessary action.

[0151] Specific examples

[0152] Users can access the dashboard through the web application to view current earthquake information, damage status, and the progress of initial responses. For example, they can see a list of employees who have completed evacuation and the status of ongoing building inspections.

[0153] With the above configuration, the present invention makes it possible to improve the efficiency of initial responses when a disaster occurs, and to ensure the safety of business establishments and minimize damage by realizing rapid and accurate information collection and management.

[0154] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0155] Step 1:

[0156] The server collects data on business establishments across the country via API and stores it in a database. Specifically, it sends an API request and receives response data including location information, business operations, number of employees, and information on important equipment. This data is then saved in the database. The input is the response data from the external API, and the output is the business establishment data stored in the database.

[0157] Step 2:

[0158] The server inputs business data from the database into a geographic information system (GIS) and visualizes it. By obtaining location information from the database and registering it in the GIS, the location and information of the business are displayed on a map. The input is business data from the database, and the output is visualized data on the GIS.

[0159] Step 3:

[0160] The server collects disaster information in real time through external interfaces such as the Japan Meteorological Agency and sensor networks. Information is obtained from APIs and sensors and reflected in the GIS. Specifically, data on earthquakes, typhoons, floods, etc. is obtained and input into the GIS to visualize the affected areas. The input is disaster information obtained from external interfaces, and the output is visualized information reflected in the GIS.

[0161] Step 4:

[0162] The server inputs the collected disaster information into an artificial intelligence (AI) model to generate the optimal initial response plan. The disaster information is input into the AI ​​model, and specific response measures are generated based on past disaster response data and the latest research results. The input is the collected disaster information, and the output is the initial response plan.

[0163] Step 5:

[0164] The server notifies the business person in charge of the generated initial response plan. The countermeasure plan is sent via emergency email or notification system so that the business can respond quickly. The input is the generated initial response plan, and the output is the notification to the business.

[0165] Step 6:

[0166] The server compiles information on the situation after a disaster occurs and creates reports for each level. It collects situation data from the GIS and database, and organizes and compiles the data according to a report format. The generated report is sent to management by email. The input is situation data from the GIS and database, and the output is the generated report.

[0167] Step 7:

[0168] The server accumulates disaster response data in a database and generates a guide that will be useful for future responses. Specifically, it saves the response data, analyzes and organizes it, and then creates new guidelines. The input is the disaster response data, and the output is the generated guide.

[0169] Step 8:

[0170] The server provides dashboards and web applications, allowing users to check this information in real time. The dashboard visually displays disaster information, damage status, response progress, etc. The input is the collected data, and the output is the visualized information on the dashboard.

[0171] Step 9:

[0172] Users access the dashboard through a web application to check the situation and take prompt action based on the displayed information, if necessary. The input is the visualized information on the dashboard, and the output is the user's decision on what to do.

[0173] (Application example 1)

[0174] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0175] At factories and other business establishments, the challenge is to quickly and efficiently respond to disasters. In particular, there is a need for an efficient system that can centralize the collection of disaster information, the creation of initial response plans, notification to managers, real-time progress monitoring, and report creation. Furthermore, there is a need for dynamic generation of manuals based on accumulated response data.

[0176] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0177] In this invention, the server includes a means for collecting and managing business data using a geographic information system, a means for collecting disaster information in real time from external APIs and sensor data, a means for using AI technology to have a robot in the factory generate an initial response plan when a disaster occurs, a means for notifying the factory manager of the generated initial response plan, a means for compiling the situation after the disaster occurs and creating a report for management, a means for accumulating response data and creating a manual based on this, and a means for providing a dashboard and web application that provides this information to users. This enables a quick and efficient initial response when a disaster occurs and subsequent situation assessment.

[0178] A "geographic information system" is an information system for collecting, managing, analyzing, and visualizing geographic data.

[0179] "Establishment data" refers to data that includes information such as the location, facilities, number of employees, and business operations of the establishment.

[0180] An "external API" is an interface for communicating with external systems and services.

[0181] "Sensor data" refers to data including environmental information and situation data obtained from sensors.

[0182] "Disaster information" refers to information about natural disasters such as earthquakes, typhoons, and floods.

[0183] "AI technology" is a technology that applies machine learning and data analysis techniques to automatically process data and make predictions.

[0184] An "initial response plan" is a proposal for the first response measures to be taken when a disaster occurs.

[0185] A "factory manager" is a person in charge of managing operations and facilities within a factory.

[0186] "Notification" is the act of conveying information to a person from a system or device.

[0187] A "report" is a written summary of the situation and results.

[0188] A "manual" is a set of instructions for a particular operation or procedure.

[0189] A "dashboard" is a visual display screen that allows multiple data and information to be viewed at a glance.

[0190] A "WEB application" is application software that can be used through a web browser.

[0191] This invention is a system for quickly and efficiently responding to disasters at factories and other business establishments. The system uses a geographic information system (GIS), external APIs, sensor data, and AI technology to notify and instruct factory robots and managers.

[0192] The system consists of the following components:

[0193] 1. Business Data Collection and Management:

[0194] The server collects data on business establishments across the country from a variety of data sources and inputs and manages it into a geographic information system (GIS). Business establishment data includes location information, business activities, number of employees, and information on important facilities. The GIS visualizes this data on a map and provides information on each business establishment in a format that users can easily check.

[0195] 2. Real-time disaster information collection:

[0196] The server collects real-time disaster information from external APIs and various sensor data. Specifically, it obtains data on natural disasters such as earthquakes, typhoons, and floods through the Japan Meteorological Agency's API and sensor network, and reflects this information in the GIS.

[0197] 3. AI-powered disaster response advice:

[0198] The server uses AI to generate optimal initial response plans based on the disaster information collected. This AI refers to past data, the latest research results, and simulation results to propose specific countermeasures.

[0199] 4. Factory robots implement initial response:

[0200] The initial response plan generated by the AI ​​is sent to the robots in the factory, which then automatically take action based on the plan, such as shutting down equipment, securing evacuation routes, and protecting important facilities.

[0201] 5. Notice and Administrator Information:

[0202] The server then notifies the administrator of the generated initial response plan via email or a dedicated application, allowing the administrator to grasp the situation in real time.

[0203] 6. Post-disaster reporting:

[0204] The server automatically compiles information on the situation after a disaster occurs and generates a report for management, summarizing the damage situation, the progress of the initial response, and future countermeasures, and sends it to the administrator.

[0205] 7. Knowledge accumulation and manual generation:

[0206] The server accumulates disaster response data from each business location and generates a manual that will be useful for future responses. This manual is constantly updated to reflect the latest response experience.

[0207] 8. Dashboards and Web Applications:

[0208] The server provides dashboards and web applications that visually display disaster information, damage status, response progress, etc. to users, allowing them to grasp the situation in real time and quickly take necessary action.

[0209] Specific examples

[0210] For example, if a magnitude 6 earthquake occurs, the server obtains earthquake information through an external API and reflects it in the GIS. At the same time, AI technology is used to generate initial response plans such as "stopping equipment," "evacuating employees," and "building safety inspections." These are then instructed to robots within the factory, which then quickly take action. Furthermore, the initial response plan is notified to the manager via email, allowing them to check the situation in real time. After a disaster occurs, the server automatically compiles the response status, compiles it into a report for management, and sends it to the manager.

[0211] Example prompts to input to the generative AI model

[0212] "An earthquake has occurred. The affected factory is in Shinjuku Ward, Tokyo. What initial response measures should be taken?"

[0213] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0214] Step 1:

[0215] The server collects data on business establishments across the country from a variety of data sources and inputs and manages it into a geographic information system (GIS). Specifically, the server calls an API to obtain information on the location, business activities, number of employees, and important equipment of each establishment. The input data is visualized on a map.

[0216] Input: "Business data" (location information, business details, number of employees, information on important facilities)

[0217] Output: "Business data visualized on GIS"

[0218] Step 2:

[0219] The server collects disaster information in real time from external APIs and various sensor data. The server obtains disaster information such as earthquakes, typhoons, and floods through APIs from the Japan Meteorological Agency. The collected information is reflected in the GIS.

[0220] Input: "External API and sensor data" (disaster information such as earthquakes, typhoons, and floods)

[0221] Output: "Disaster information reflected in GIS"

[0222] Step 3:

[0223] The server uses AI to generate optimal initial response plans based on the collected disaster information. The AI ​​refers to past data, the latest research results, and simulation results to propose response measures according to the type of disaster.

[0224] Input: "Disaster information" (information on earthquakes, typhoons, floods, etc.)

[0225] Output: "AI-generated initial response plan"

[0226] Step 4:

[0227] The server then sends the generated initial response plan to the factory robots, who then carry out the response according to the instructions. Specifically, the robots perform emergency shutdowns of equipment, secure evacuation routes, protect important facilities, etc.

[0228] Input: "AI-generated initial response plan"

[0229] Output: "Robot first response execution"

[0230] Step 5:

[0231] The server notifies the factory manager of the generated initial response plan via email or a dedicated application, allowing the manager to check the situation in real time.

[0232] Input: "AI-generated initial response plan"

[0233] Output: "Notification to Administrator"

[0234] Step 6:

[0235] The server automatically compiles information on the situation after a disaster occurs and generates a report for management, summarizing the damage situation, the progress of the initial response, and future countermeasures.

[0236] Input: "Post-disaster response status data"

[0237] Output: "Report for management"

[0238] Step 7:

[0239] The server will store disaster response data from each business location and generate a manual that will be useful for future responses, including building inspection methods, rapid evacuation procedures for employees, and measures to protect important equipment.

[0240] Input: "Accumulated disaster response data"

[0241] Output: "A manual that will be useful for future reference"

[0242] Step 8:

[0243] The server provides dashboards and web applications that visually display disaster information, damage status, response progress, etc. Users can grasp the situation in real time through the dashboard and quickly take necessary action.

[0244] Input: "Disaster information, damage situation, response progress data"

[0245] Output: "Information visualized in a dashboard or web application"

[0246] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0247] This invention is a system for streamlining the initial response of a business in the event of a disaster, and is composed of a geographic information system, external APIs, sensor data, AI technology, an emotion engine, a dashboard, and a web application. Below, we will explain in detail each component of this system and the processing flow based on them.

[0248] Geographic Information System (GIS) and Business Data Management

[0249] The server collects data on business establishments across the country from various data sources and inputs and manages it into a geographic information system (GIS). Business establishment data includes location information, business activities, number of employees, and information on important facilities. The GIS visualizes this data on a map and provides information on each business establishment in an easily accessible format for users.

[0250] Specific examples

[0251] The server calls the API to collect data on specific business locations in Tokyo. This data includes location (Shinjuku Ward, Tokyo), business type (IT company), number of employees (50), and critical facilities (data center). The collected data is registered in the GIS, and users can view it on a map via a web application.

[0252] Real-time disaster information collection

[0253] The server collects real-time disaster information from the Japan Meteorological Agency and various sensor data. Specifically, it obtains data on natural disasters such as earthquakes, typhoons, and floods via external APIs and sensor networks, and reflects this data in the GIS.

[0254] Specific examples

[0255] The server retrieves information from the Japan Meteorological Agency's API about a magnitude 6 earthquake with its epicenter in Tokyo Bay. This information is input into a GIS, and the affected areas around Tokyo Bay are highlighted in red on a map.

[0256] AI-powered disaster response advice

[0257] The server uses AI to generate optimal initial response plans based on the disaster information collected. This AI refers to past data, the latest research papers, and simulation results to propose specific countermeasures.

[0258] Specific examples

[0259] In the event of a magnitude 6 earthquake, the AI ​​will generate initial response plans, such as "conducting safety inspections of business buildings," "instructing employees to evacuate," and "procedures for emergency shutdowns of data centers." The server will then notify the business of this information.

[0260] Reporting and reporting to management

[0261] The server automatically compiles information on the situation after a disaster occurs and generates a report for management, summarizing the damage situation, the progress of the initial response, and future response measures.

[0262] Specific examples

[0263] The server uses the damage information from the GIS to create a damage report for a specific business location in Tokyo. This report includes information such as "Partial damage to the building," "All employees have safely evacuated," and "Data center emergency shutdown procedures have been completed." The report is then sent to management via email.

[0264] Knowledge accumulation and manual generation

[0265] The server accumulates disaster response data from each business location and generates a manual that will be useful for future responses. This manual is constantly updated to reflect the latest response experience.

[0266] Specific examples

[0267] The server will store the response data for the Tokyo Bay earthquake in a database and generate a new earthquake response manual, which will include building inspection methods, rapid evacuation procedures for employees, and measures to protect important equipment.

[0268] Dashboards and Web Applications

[0269] The server provides dashboards and web applications that visually display disaster information, damage status, response progress, etc. to users, allowing them to grasp the situation in real time and quickly take necessary action.

[0270] Specific examples

[0271] Users can access the dashboard through a web application to check current earthquake information, damage status, and the progress of initial responses in real time. For example, they can immediately see a list of employees who have completed evacuation and the status of ongoing building inspections.

[0272] Emotion Engine

[0273] The server is equipped with an emotion engine that analyzes the user's emotional data. The emotion engine analyzes the user's voice and text data to assess their stress level, anxiety level, etc. Based on the results, the server adjusts the initial response plan and takes appropriate action.

[0274] Specific examples

[0275] The server receives the user's voice data and analyzes it using an emotion engine. As a result, it is determined that the user is in a state of high stress. Based on this, the AI ​​means generates initial response plans, including "support messages for rapid evacuation" and "provision of psychological support." The server notifies this information to the business.

[0276] The system of this invention will improve the efficiency of initial responses when a disaster occurs, enabling a rapid understanding of damage and appropriate countermeasures. Furthermore, the introduction of an emotion engine will enable measures to be taken to reduce employee stress and anxiety. This will ensure both workplace safety and employee mental health.

[0277] The processing flow will be explained below.

[0278] Step 1: Collect and manage business data

[0279] The server collects data on businesses across the country through APIs and database connections and inputs it into a geographic information system (GIS).

[0280] Operation:

[0281] The server calls an external API to obtain business data.

[0282] The data is received in JSON format and stored in the database.

[0283] Extract business data from the database and send it to the GIS.

[0284] GIS maps and visualizes business locations on a map.

[0285] Step 2: Collecting real-time disaster information

[0286] The server collects real-time disaster information from the Japan Meteorological Agency and various sensor data.

[0287] Operation:

[0288] The server periodically polls the Japan Meteorological Agency API to obtain the latest earthquake information.

[0289] Sensor data is received in real time via WebSocket or MQTT protocols.

[0290] The acquired earthquake epicenter and seismic intensity information is sent to the GIS.

[0291] The GIS highlights affected areas on a map and matches them with business information.

[0292] Step 3: Generate initial response plans using AI

[0293] The server inputs the collected disaster information into an AI model and generates the optimal initial response plan.

[0294] Operation:

[0295] The server sends the collected disaster data to the AI ​​and activates the prediction module.

[0296] AI generates countermeasures based on past response data and the latest research results.

[0297] The server receives the initial response plan generated by the AI.

[0298] Step 4: Collect and analyze user emotion data

[0299] The server uses an emotion engine to collect and analyze the user's emotion data.

[0300] Operation:

[0301] The server collects users' voice and text data through microphones and chat logs.

[0302] An emotion engine analyzes this data and assesses stress and anxiety levels.

[0303] Step 5: Adjust initial response plans based on sentiment data

[0304] The server adjusts the initial response plan based on the analysis results of the emotion engine.

[0305] Operation:

[0306] The server sends the emotional data to the AI, which generates a response that reflects the user's emotional state.

[0307] For example, if stress levels are high, the system generates response plans that include quick evacuation guidance and providing mental support.

[0308] The server notifies the establishment of this information.

[0309] Step 6: Notify the business of the initial response plan

[0310] The server notifies each business office of the generated initial response plan.

[0311] Operation:

[0312] The server classifies the assigned countermeasures by business establishment.

[0313] Send messages to designated businesses using the Slack API or email services.

[0314] The message includes specific response measures such as evacuation instructions and equipment shutdown procedures.

[0315] Step 7: Compiling damage data and creating a report

[0316] The server compiles data on the situation after a disaster occurs and generates a report for management.

[0317] Operation:

[0318] The server compiles damage information based on the GIS damage information.

[0319] Enter data such as a list of affected businesses, response progress, and next steps into a report template.

[0320] Generate reports in PDF format and email them to management.

[0321] Step 8: Knowledge accumulation and manual generation

[0322] The server accumulates disaster response data from each business location and generates a dynamic manual.

[0323] Operation:

[0324] The server receives disaster response data sent from each business location and stores it in a database.

[0325] Past corresponding data is extracted from the database and a manual generation engine is started.

[0326] The manuals are updated based on the latest corresponding information and are generated as training and practical manuals.

[0327] Step 9: Serving the Dashboard and Web Application

[0328] The server provides information to users using a dashboard or web application.

[0329] Operation:

[0330] The server displays real-time disaster information, damage status, response progress, and other data on the dashboard.

[0331] Provides information to users through web applications and supports immediate response.

[0332] Example 2

[0333] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0334] When a disaster strikes, a business's initial response requires the collection of a wide range of information and rapid decision-making, making it difficult to respond efficiently using conventional methods. Furthermore, employees' mental stress and anxiety can also hinder a rapid initial response. For this reason, a system that can easily aggregate information and generate and provide optimal initial response plans is needed.

[0335] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for collecting and managing business establishment data using a geographic information system; means for collecting disaster information in real time from external APIs and sensor data; AI means for generating appropriate initial response plans based on the collected disaster information; means for notifying the business establishment of the generated initial response plans; means for compiling the situation after a disaster occurs and creating a report for management; means for accumulating response data and generating manuals based on the data to always reflect the latest response experience; means for visually providing this information to users using a dashboard or web application; and means for analyzing user emotion data using an emotion engine and adjusting the initial response plans. This enables fast and efficient initial response and employee stress management.

[0336] A "geographic information system" is an information system for collecting, managing, analyzing, and visually displaying geospatial data.

[0337] "Establishment data" refers to information regarding the location, business activities, number of employees, and important facilities of a business establishment.

[0338] An "external API" is an interface for connecting with other systems and services, and is used to obtain external data in real time.

[0339] "Sensor data" refers to data such as environmental information and disaster information collected from various sensors.

[0340] "Disaster information" refers to data related to natural disasters such as earthquakes, typhoons, and floods.

[0341] "AI methods" are methods that use artificial intelligence to analyze data and generate optimal initial response plans.

[0342] An "initial response plan" is a specific plan for the course of action and procedures that a business should immediately implement in the event of a disaster.

[0343] "Notification means" refers to a means for communicating the generated initial response plan and other information to the business establishment.

[0344] The "report creation means" is a means for compiling the situation after a disaster occurs and automatically creating a report for management.

[0345] "Response data" refers to records and data related to past disaster responses.

[0346] "Manual generation means" refers to a means for creating a disaster response manual based on accumulated response data.

[0347] A "dashboard" is an interface that allows users to visually check disaster information, damage status, response progress, and so on.

[0348] A "web application" is an application that can be used by a user through a web browser.

[0349] The "emotion engine" is a system that analyzes a user's voice and text data to assess their emotional state, such as stress level and anxiety level.

[0350] This invention is a system for streamlining the initial response of businesses when disasters occur, and is composed of a geographic information system (GIS), external APIs, sensor data, AI technology, an emotion engine, a dashboard, and a web application.

[0351] 1. Geographic Information System (GIS) and Business Data Management

[0352] The server collects data on business establishments across the country from APIs and external databases and inputs it into the GIS. Business establishment data includes location information, business activities, number of employees, and information on important facilities. The GIS visualizes this data on a map and provides it in an easily accessible format for users. For example, the server uses an API to collect data on IT companies in Shinjuku Ward, Tokyo, and registers it in the GIS. This allows users to check detailed information about the establishments through a web application.

[0353] 2. Real-time disaster information collection

[0354] The server collects information on natural disasters such as earthquakes, typhoons, and floods in real time from the Japan Meteorological Agency's API and various sensor data. The collected disaster information is reflected in the GIS, and the affected area is displayed on a map. For example, the server uses the Japan Meteorological Agency's API to obtain information on a magnitude 6 earthquake with an epicenter in Tokyo Bay, and by immediately reflecting this information in the GIS, it is possible to visually display the affected areas around Tokyo Bay.

[0355] 3. AI-based disaster response advice

[0356] The server passes the collected disaster information to an AI model for analysis and generates an optimal initial response plan. This AI method proposes effective countermeasures based on past data, the latest research papers, and simulation results. For example, if a magnitude 6 earthquake occurs, the AI ​​method will generate an initial response plan including "safety inspection of the business building," "emergency evacuation instructions for employees," and "emergency shutdown procedures for the data center," and the server will notify the business.

[0357] 4. Report creation and reporting to management

[0358] The server compiles data on the situation after a disaster and automatically generates reports for management. These reports include information on the damage situation, the progress of the initial response, and future countermeasures. For example, the server can create a status report for a specific business location in Tokyo based on data obtained from the GIS and send it to management by email.

[0359] 5. Knowledge accumulation and manual generation

[0360] The server accumulates disaster response data from each business location and generates a manual based on that data that will be useful for future responses. This manual always reflects the latest response experience and is dynamically updated. For example, the server can store response data from the Tokyo Bay earthquake in a database and generate a new earthquake response manual.

[0361] 6. Dashboards and Web Applications

[0362] The server provides dashboards and web applications that visually display disaster information, damage status, and response progress to users. Users can use this to understand the situation in real time and quickly take necessary action. For example, users can access the dashboard through a web application to check earthquake information, damage status, and the status of ongoing initial responses.

[0363] 7. Emotion Engine

[0364] The server uses an emotion engine to analyze the user's emotional data. The emotion engine analyzes the voice and text data to evaluate the user's stress level and anxiety. Based on the results, the AI ​​means adjusts the initial response plan and proposes an appropriate response. For example, if the server acquires the user's voice data and analyzes it with the emotion engine and determines that the user is in a high stress state, the AI ​​means will generate an initial response plan including a "support message for rapid evacuation" and "provision of mental support," and the server will notify the business.

[0365] Specific prompt examples

[0366] Below are some examples of specific prompt sentences to input into the generative AI model.

[0367] "If an earthquake with a seismic intensity of 6 occurs with its epicenter in Tokyo Bay, please propose the initial response plan for your business."

[0368] "Please display a dashboard to check the real-time evacuation status of specific businesses in Tokyo."

[0369] "Analyze the user's emotional state based on their voice data and generate appropriate initial response plans."

[0370] The system of this invention will improve the efficiency of initial responses in the event of a disaster, enabling prompt and appropriate responses. In addition, by taking into consideration the mental health of employees through the emotion engine, it will be possible to ensure both the safety of the workplace and the mental health of employees.

[0371] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0372] Step 1: Collect and manage business data

[0373] The server collects data on business establishments across the country from APIs and external databases. The data collected here includes location information, business operations, number of employees, and information on important facilities. The input data is detailed information on business establishments obtained from the API, and the output is business establishment data registered in the GIS.

[0374] Specific operation: The server calls the API and retrieves data on IT companies located in Shinjuku Ward, Tokyo. This data includes "Location: Shinjuku Ward, Tokyo," "Business: IT company," "Number of employees: 50," and "Key facilities: Data center." The retrieved data is input into GIS and visualized on a map.

[0375] Step 2: Collecting real-time disaster information

[0376] The server collects real-time disaster information from the Japan Meteorological Agency's API and various sensors. The input data is disaster information obtained from external APIs and sensor data, and the output is the disaster situation reflected in the GIS.

[0377] Specific operation: The server uses an API provided by the Japan Meteorological Agency to obtain information on an earthquake with a seismic intensity of 6 and centered in Tokyo Bay. The obtained information is input into a GIS, and the affected areas around Tokyo Bay are highlighted in red on a map.

[0378] Step 3: AI-based disaster response advice

[0379] The server passes the collected disaster information to the AI ​​model for analysis. The input data is real-time disaster information obtained from the GIS, and the output is an initial response plan generated by the AI.

[0380] Specific operation: The server uses AI means to analyze earthquake information with a seismic intensity of 6. The AI ​​means generates an initial response plan including "safety inspection of the business building," "evacuation instructions for employees," and "emergency shutdown procedures for the data center," and the server notifies the business.

[0381] Step 4: Reporting and reporting to management

[0382] The server aggregates data on the situation after a disaster and automatically generates reports for management. The input data is information from GIS and AI models, and the output is a report sent to management.

[0383] Specific operation: The server creates a status report for specific business locations in Tokyo based on the damage information obtained from the GIS. This report includes information such as "Partial damage to the building," "All employees have safely evacuated," and "The data center has completed emergency shutdown procedures." The report is then sent to management via email.

[0384] Step 5: Knowledge accumulation and manual generation

[0385] The server accumulates disaster response data from each business location and generates a manual based on that data that will be useful for future responses. The input data is past disaster response information, and the output is an updated response manual.

[0386] Specific operation: The server saves the response data for the Tokyo Bay earthquake in a database and generates a new earthquake response manual. The new manual includes "building inspection methods," "quick evacuation procedures for employees," and "protection measures for important equipment."

[0387] Step 6: Dashboard and Web Applications

[0388] The server provides dashboards and web applications that visually display disaster information, damage status, and response progress to users. The input data is information from GIS and AI models, and the output is the dashboard or web application screen used by users.

[0389] How it works: Users can access a dashboard through a web application to check real-time earthquake information, damage status, and the progress of initial responses. It also displays a list of employees who have completed evacuation and the status of ongoing building inspections.

[0390] Step 7: Emotion Engine

[0391] The server uses an emotion engine to analyze the user's emotional data and adjust the initial response plan. The input data is the user's voice data and text data, and the output is a response plan based on the analysis results.

[0392] Specific operation: The server acquires the user's voice data and analyzes it using an emotion engine. If the analysis results indicate that the user is in a high stress state, the AI ​​means generates initial response plans, including "support messages for rapid evacuation" and "provision of psychological support," and the server notifies the business.

[0393] (Application example 2)

[0394] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0395] Improving the efficiency of a facility's initial response in the event of a disaster and implementing appropriate measures quickly are crucial for minimizing damage and quickly restoring operations. However, conventional systems do not adequately collect real-time information, generate appropriate response plans, or analyze employees' emotional states and reflect that information in their responses. As a result, there was a lack of an appropriate system for situations that require efficient initial responses and flexible responses that take into account employees' emotional states.

[0396] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0397] In this invention, the server includes means for collecting and managing facility data using a geographic information system, means for collecting disaster information in real time from external APIs and sensor data, AI means for generating appropriate initial response plans based on the collected disaster information, means for notifying the facility of the generated initial response plans, means for compiling post-disaster situations and creating reports for management, means for accumulating response data and generating manuals based on the data, means for providing a dashboard or web application that provides this information to users, means for analyzing user voice data and text data and using an emotion engine to evaluate the user's emotional state, and means for adjusting the initial response plans and taking appropriate action based on the analysis results of the emotion engine. This improves the efficiency of initial responses when a disaster occurs and enables flexible and appropriate responses that take into account the emotional states of employees.

[0398] A "geographic information system" is a system for collecting, managing, analyzing, and visually displaying geospatial data.

[0399] "Facility data" refers to data that includes location information, business operations, number of employees, and information on important equipment related to a specific facility.

[0400] An "external API" is a means of obtaining data using a program interface provided by an external system or service.

[0401] "Sensor data" refers to physical and environmental data obtained from various sensors.

[0402] "Disaster information" refers to real-time information about natural disasters such as earthquakes, typhoons, and floods.

[0403] "AI means" refers to a system that uses artificial intelligence technology to analyze data and generate output results such as response proposals.

[0404] An "emotion engine" is a technology that analyzes voice and text data to evaluate the user's emotional state.

[0405] A "dashboard" is an interface that visually displays real-time information and makes it easier for users to understand the situation.

[0406] A "WEB application" is application software provided using web technology.

[0407] "Notification means" refers to a function for transmitting generated information to users and systems.

[0408] A "report" refers to a report that organizes collected data and information and compiles it for specific users, such as management.

[0409] A "manual" is a document that describes specific procedures and measures that govern how to respond to disasters and how to carry out business operations.

[0410] This invention is a system for improving the efficiency of disaster response in logistics centers. This system is composed of a geographic information system (GIS), external APIs, sensor data, AI technology, an emotion engine, a dashboard, and a web application.

[0411] Geographic Information System (GIS) and Facility Data Management

[0412] The server collects facility data from various sources across the country and inputs and manages it into a geographic information system (GIS). Facility data includes location information, business operations, number of employees, and information on important equipment. The GIS visualizes this data on a map, providing information on each facility in an easily accessible format for users.

[0413] Specific examples

[0414] The server calls the API to collect data on a specific logistics center in Tokyo. This data includes the location (Shinjuku-ku, Tokyo), business operations (logistics management), number of employees (50), and critical equipment (warehouse). The collected data is registered in the GIS, and users can view it on a map via a web application.

[0415] Real-time disaster information collection

[0416] The server collects real-time disaster information from the Japan Meteorological Agency and various sensor data. Specifically, it obtains data on natural disasters such as earthquakes, typhoons, and floods via external APIs and sensor networks, and reflects this data in the GIS.

[0417] Specific examples

[0418] The server retrieves information from the Japan Meteorological Agency's API about a magnitude 6 earthquake with its epicenter in Tokyo Bay. This information is input into a GIS, and the affected areas around Tokyo Bay are highlighted in red on a map.

[0419] AI-powered disaster response advice

[0420] The server uses AI to generate optimal initial response plans based on the disaster information collected. This AI refers to past data, the latest research papers, and simulation results to propose specific countermeasures.

[0421] Specific examples

[0422] In the event of a magnitude 6 earthquake, the AI ​​will generate initial response plans, such as "conducting safety inspections of facility buildings," "instructing employees to evacuate," and "procedures for emergency shutdowns of warehouses." The server then notifies the facility of this information.

[0423] Emotion Engine

[0424] The server is equipped with an emotion engine that analyzes the user's emotional data. The emotion engine analyzes the user's voice and text data to assess their stress level, anxiety level, etc. Based on the results, the server adjusts the initial response plan and takes appropriate action.

[0425] Specific examples

[0426] The server receives the user's voice data and analyzes it using an emotion engine. As a result, it determines that the user is in a state of high stress. Based on this, the AI ​​generates initial response plans, including "support messages for rapid evacuation" and "provision of psychological support." The server then notifies the facility of this information.

[0427] Dashboards and Web Applications

[0428] The server provides dashboards and web applications that visually display disaster information, damage status, response progress, etc. to users, allowing them to grasp the situation in real time and quickly take necessary action.

[0429] Specific examples

[0430] Users can access the dashboard through a web application to check current earthquake information, damage status, and the progress of initial responses in real time. For example, they can immediately see a list of employees who have completed evacuation and the status of ongoing building inspections.

[0431] Reporting and reporting to management

[0432] The server automatically compiles information on the situation after a disaster occurs and generates a report for management, summarizing the damage situation, the progress of the initial response, and future response measures.

[0433] Specific examples

[0434] The server uses the damage information from the GIS to create a damage report for a specific logistics center in Tokyo. This report includes information such as "Partial damage to the building," "All employees have safely evacuated," and "Emergency shutdown procedures have been completed at the warehouse." The report is then sent to management via email.

[0435] Knowledge accumulation and manual generation

[0436] The server accumulates disaster response data from each facility and generates a manual that will be useful for future responses. This manual is constantly updated to reflect the latest response experience.

[0437] Specific examples

[0438] The server will store the response data for the Tokyo Bay earthquake in a database and generate a new earthquake response manual, which will include building inspection methods, rapid evacuation procedures for employees, and measures to protect important equipment.

[0439] Prompt Sentence Examples

[0440] Generate a plan for initial response in the event of a disaster based on the following facility data:

[0441] Earthquake (Epicenter: Tokyo, Seismic Intensity: 6)

[0442] Damaged area: Logistics center (Shinjuku-ku, Tokyo)

[0443] Important information: Warehouse safety inspections, employee evacuation instructions, warehouse emergency shutdown procedures

[0444] As a result, the system of the present invention will improve the efficiency of initial responses when a disaster occurs, and will enable flexible and appropriate responses that take into account the emotional state of employees.

[0445] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0446] Step 1:

[0447] Geographic Information System (GIS) and Facility Data Management

[0448] The server collects facility data from various sources across the country. The collected data includes location information, business operations, number of employees, and important facilities, and inputs and manages this data into a GIS. The server visualizes this GIS data on a map and makes it accessible to users through a web application.

[0449] Input: Facility data retrieved from data sources

[0450] Output: Visualized data displayed on a GIS

[0451] Specific operations: Collecting data by calling API, saving to database, and drawing data on map

[0452] Step 2:

[0453] Real-time disaster information collection

[0454] The server collects real-time disaster information from external APIs and various sensor data. Specifically, it uses APIs from the Japan Meteorological Agency and other sources to obtain information on earthquakes, typhoons, floods, etc., and reflects this information in the GIS.

[0455] Input: Japan Meteorological Agency API and sensor data

[0456] Output: Disaster information reflected in GIS

[0457] Specific operations: Information acquisition by API calls, processing of sensor data, input into GIS database

[0458] Step 3:

[0459] AI-powered disaster response advice

[0460] The server uses AI technology to generate optimal initial response plans based on the collected disaster information. This AI refers to past disaster data and the latest research results to propose specific countermeasures.

[0461] Input: Real-time disaster information

[0462] Output: Initial response plan

[0463] Specific operations: Analysis of disaster information, generation of countermeasures using AI models, notification of countermeasures

[0464] Step 4:

[0465] Generated initial response plan is notified to the facility

[0466] The server then notifies each facility of the generated initial response plan via a variety of methods, including email and app notifications.

[0467] Input: AI-generated initial response plan

[0468] Output: Action suggestions sent via notification channels

[0469] Specific actions: Generate notification content and send it via communication means

[0470] Step 5:

[0471] User analysis using emotion engine

[0472] The server receives the user's voice and text data and analyzes it using an emotion engine to evaluate their stress and anxiety levels, and reflects the analysis results in the initial response plan.

[0473] Input: User voice or text data

[0474] Output: Emotion analysis results

[0475] Specific actions: Data collection, processing of emotion analysis algorithms, and reflection of analysis results in initial response plans

[0476] Step 6:

[0477] Providing information through dashboards and web applications

[0478] The server provides users with visual information on disasters, damage situations, response progress, etc. via dashboards and web applications, allowing users to grasp the situation in real time and quickly take necessary action.

[0479] Input: GIS data, real-time disaster information, initial response plan, emotion analysis results

[0480] Output: Visualizations on dashboards and web applications

[0481] Specific actions: Data collection, real-time visualization, and user interface updates

[0482] Step 7:

[0483] Reporting and reporting to management

[0484] The server automatically compiles information on the situation after a disaster occurs and generates a report for management, summarizing the damage situation, the progress of the initial response, and future countermeasures, and sends it to management via email or other means.

[0485] Input: Disaster information, progress of initial response, emotion analysis results

[0486] Output: Report for management

[0487] Specific actions: Data collection, report generation, and sending of report contents

[0488] Step 8:

[0489] Knowledge accumulation and manual generation

[0490] The server accumulates disaster response data from each facility and generates a manual that will be useful for future responses. This manual is constantly updated to reflect the latest response experience.

[0491] Input: Disaster response data

[0492] Output: Updated manual

[0493] Specific operations: saving to database, executing manual generation algorithm, updating manual

[0494] 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.

[0495] 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> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0496] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0497] [Second embodiment]

[0498] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0499] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0500] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0501] 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.

[0502] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0503] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0504] 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. 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.

[0505] 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.

[0506] 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 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.

[0507] 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.

[0508] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0509] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0510] This invention is a system for streamlining the initial response of a business in the event of a disaster, and is specifically composed of a geographic information system, external APIs, sensor data, AI technology, dashboards, and web applications. Below, we will explain in detail each component of this system and the processing flow based on them.

[0511] Geographic Information System (GIS) and Business Data Management

[0512] The server collects data on business establishments across the country from various data sources and inputs and manages it into a geographic information system (GIS). Business establishment data includes location information, business activities, number of employees, and information on important facilities. The GIS visualizes this data on a map and provides information on each business establishment in an easily accessible format for users.

[0513] Specific examples

[0514] The server calls the API to collect data on specific business locations in Tokyo. This data includes location (Shinjuku Ward, Tokyo), business type (IT company), number of employees (50), and critical facilities (data center). The collected data is registered in the GIS, and users can view it on a map via a web application.

[0515] Real-time disaster information collection

[0516] The server collects real-time disaster information from the Japan Meteorological Agency and various sensor data. Specifically, it obtains data on natural disasters such as earthquakes, typhoons, and floods via external APIs and sensor networks, and reflects this data in the GIS.

[0517] Specific examples

[0518] The server retrieves information from the Japan Meteorological Agency's API about a magnitude 6 earthquake with its epicenter in Tokyo Bay. This information is input into a GIS, and the affected areas around Tokyo Bay are highlighted in red on a map.

[0519] AI-powered disaster response advice

[0520] The server uses AI to generate optimal initial response plans based on the disaster information collected. This AI refers to past data, the latest research papers, and simulation results to propose specific countermeasures.

[0521] Specific examples

[0522] In the event of a magnitude 6 earthquake, the AI ​​will generate initial response plans, such as "conducting safety inspections of business buildings," "instructing employees to evacuate," and "procedures for emergency shutdowns of data centers." The server will then notify the business of this information.

[0523] Reporting and reporting to management

[0524] The server automatically compiles information on the situation after a disaster occurs and generates a report for management, summarizing the damage situation, the progress of the initial response, and future response measures.

[0525] Specific examples

[0526] The server uses the damage information from the GIS to create a damage report for a specific business location in Tokyo. This report includes information such as "Partial damage to the building," "All employees have safely evacuated," and "Data center emergency shutdown procedures have been completed." The report is then sent to management via email.

[0527] Knowledge accumulation and manual generation

[0528] The server accumulates disaster response data from each business location and generates a manual that will be useful for future responses. This manual is constantly updated to reflect the latest response experience.

[0529] Specific examples

[0530] The server will store the response data for the Tokyo Bay earthquake in a database and generate a new earthquake response manual, which will include building inspection methods, rapid evacuation procedures for employees, and measures to protect important equipment.

[0531] Dashboards and Web Applications

[0532] The server provides dashboards and web applications that visually display disaster information, damage status, response progress, etc. to users, allowing them to grasp the situation in real time and quickly take necessary action.

[0533] Specific examples

[0534] Users can access the dashboard through a web application to check current earthquake information, damage status, and the progress of initial responses in real time. For example, they can immediately see a list of employees who have completed evacuation and the status of ongoing building inspections.

[0535] The system of the present invention will improve the efficiency of initial responses when a disaster occurs, enable rapid assessment of damage, and enable appropriate countermeasures, thereby ensuring the safety of business establishments and minimizing damage.

[0536] The processing flow will be explained below.

[0537] Step 1: Collect and manage business data

[0538] The server collects data on businesses across the country through APIs and database connections and inputs it into a geographic information system (GIS).

[0539] Operation:

[0540] The server calls an external API to obtain business data.

[0541] The data is received in JSON format and stored in the database.

[0542] Extract business data from the database and send it to the GIS.

[0543] GIS maps and visualizes business locations on a map.

[0544] Step 2: Collecting real-time disaster information

[0545] The server collects real-time disaster information from the Japan Meteorological Agency and various sensor data.

[0546] Operation:

[0547] The server periodically polls the Japan Meteorological Agency API to obtain the latest earthquake information.

[0548] Sensor data is received in real time via WebSocket or MQTT protocols.

[0549] The acquired earthquake epicenter and seismic intensity information is sent to the GIS.

[0550] The GIS highlights affected areas on a map and matches them with business information.

[0551] Step 3: Generate initial response plans using AI

[0552] The server inputs the collected disaster information into an AI model and generates the optimal initial response plan.

[0553] Operation:

[0554] The server sends the collected disaster data to the AI ​​and activates the prediction module.

[0555] AI generates countermeasures based on past response data and the latest research results.

[0556] The server receives the initial response plan generated by the AI.

[0557] Step 4: Notify the business of the initial response plan

[0558] The server notifies each business office of the generated initial response plan.

[0559] Operation:

[0560] The server classifies the assigned countermeasures by business establishment.

[0561] Send messages to designated businesses using the Slack API or email services.

[0562] The message includes specific response measures such as evacuation instructions and equipment shutdown procedures.

[0563] Step 5: Compiling damage data and creating a report

[0564] The server compiles data on the situation after a disaster occurs and generates a report for management.

[0565] Operation:

[0566] The server compiles damage information based on the GIS damage information.

[0567] Enter data such as a list of affected businesses, response progress, and next steps into a report template.

[0568] Generate reports in PDF format and email them to management.

[0569] Step 6: Knowledge accumulation and manual creation

[0570] The server accumulates disaster response data from each business location and generates a dynamic manual.

[0571] Operation:

[0572] The server receives disaster response data sent from each business location and stores it in a database.

[0573] Past corresponding data is extracted from the database and a manual generation engine is started.

[0574] The manuals are updated based on the latest corresponding information and are generated as training and practical manuals.

[0575] Step 7: Serving the Dashboard and Web Application

[0576] The server provides information to users using a dashboard or web application.

[0577] Operation:

[0578] The server displays real-time disaster information, damage status, response progress, and other data on the dashboard.

[0579] Provides information to users through web applications and supports immediate response.

[0580] Example 1

[0581] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0582] In the past, when a disaster occurred, the initial response at a business site was difficult because it took time to collect and understand information. Furthermore, the management and analysis of collected information was often done manually, which led to problems such as errors and delays. This created major challenges in ensuring the safety of business sites and minimizing damage.

[0583] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0584] In this invention, the server includes means for collecting and managing business establishment data using a geographic information system, means for collecting disaster information in real time from external interfaces and sensor data, artificial intelligence means for generating appropriate initial response plans based on the collected disaster information, means for notifying the business establishment of the generated initial response plans, means for compiling the situation after a disaster occurs and creating reports for each level, means for accumulating response data and generating guides based on this, interface means for providing this information to users, means for visualizing business establishment data and disaster information on a map, means for tracking and managing the progress of countermeasures at each business establishment in real time, and means for proposing countermeasures related to the business establishment based on the collected data and disaster information. This enables rapid and accurate information collection and management, and enables business establishments to carry out initial responses efficiently and effectively.

[0585] A "geographic information system" is a system that collects, stores, analyzes, and visually displays geospatial data.

[0586] "Establishment data" refers to information related to a specific establishment, such as location information, business operations, number of employees, and important facilities.

[0587] An "external interface" is a means for communicating and exchanging data with external sources or systems.

[0588] "Sensor data" refers to information obtained from various sensors, including environmental data such as temperature, humidity, and seismic intensity.

[0589] "Disaster information" refers to information related to natural disasters such as earthquakes, typhoons, and floods, including the extent of damage and the timing of occurrence.

[0590] "Artificial intelligence tools" are algorithms or systems that use machine learning and data analysis techniques to solve specific problems.

[0591] An "initial response plan" is a set of specific guidelines and procedures that should be followed as the first response when a disaster occurs.

[0592] "Reports for higher levels" are reports provided to management and administrative levels that summarize the impact of the disaster and the progress of the response.

[0593] "Response data" refers to data on specific response measures taken in response to a disaster and their results.

[0594] A "guide" is a procedure or manual that outlines the desired actions or responses in a particular situation.

[0595] "Interface means" refers to the means by which a user can interact with a system and input or obtain information.

[0596] A "visualization tool" is a method for displaying data graphically and making it easier to understand intuitively.

[0597] "Means for tracking and managing progress of countermeasures in real time" refers to a system or method for monitoring and managing the implementation status of countermeasures in real time.

[0598] "Means for proposing countermeasures" are algorithms or systems that automatically suggest optimal countermeasures based on collected data.

[0599] This invention is a system for streamlining a business's initial response in the event of a disaster, and is composed of a geographic information system (GIS), external interfaces, sensor data, artificial intelligence (AI) technology, a dashboard, and a web application.

[0600] Geographic Information System (GIS) and Business Data Management

[0601] The server collects data on businesses across the country via API and stores it in a database. The collected data is then entered into a geographic information system (GIS) for visualization. The data registered in the GIS includes location information, business details, number of employees, and information on important facilities.

[0602] Specific examples

[0603] For example, the server calls the API to collect data on a specific business location in Tokyo (location: Shinjuku Ward, Tokyo). This data includes "Business Description: IT Company," "Number of Employees: 50," and "Key Equipment: Data Center." The collected data is stored in a database and reflected in the GIS, allowing users to view it on a map via a web application.

[0604] Real-time disaster information collection

[0605] The server collects disaster information in real time through external interfaces such as the Japan Meteorological Agency and sensor networks. By acquiring information on earthquakes, typhoons, floods, etc. and reflecting it in the GIS, affected areas can be visualized.

[0606] Specific examples

[0607] The server receives real-time information from the Japan Meteorological Agency's API about a magnitude 6 earthquake with its epicenter in Tokyo Bay. This information is entered into the GIS, and the affected areas around Tokyo Bay are highlighted in red on the map.

[0608] AI-powered disaster response advice

[0609] The server inputs the collected disaster information into an AI model to generate optimal initial response plans. This AI model proposes countermeasures based on past disaster response data and the latest research results.

[0610] Specific examples

[0611] For example, if a magnitude 6 earthquake occurs, the AI ​​model will generate initial response plans such as "conducting a building safety inspection," "instructing employees to evacuate," and "procedures for emergency shutdown of the data center." The server will then notify the person in charge at the business site.

[0612] Reporting and reporting to management

[0613] After a disaster occurs, the server compiles data and automatically generates a report for management, summarizing the damage situation, the progress of the initial response, and future countermeasures.

[0614] Specific examples

[0615] Based on the damage information from the GIS, the server creates a damage report for specific business locations in Tokyo. This report includes information such as "Partial damage to the building," "All employees have safely evacuated," and "Data center emergency shutdown procedures have been completed," and is sent to management via email.

[0616] Knowledge accumulation and manual generation

[0617] The server stores disaster response data in a database and generates a manual to be used in future responses. The manual is constantly updated to reflect the latest response experience.

[0618] Specific examples

[0619] The server will store the response data for the Tokyo Bay earthquake in a database and generate a new earthquake response manual, which will include building inspection methods, rapid evacuation procedures for employees, and measures to protect important equipment.

[0620] Providing dashboards and web applications

[0621] The server visually displays disaster information, damage status, and response progress to users via dashboards and web applications, allowing users to grasp the situation in real time and quickly take necessary action.

[0622] Specific examples

[0623] Users can access the dashboard through the web application to view current earthquake information, damage status, and the progress of initial responses. For example, they can see a list of employees who have completed evacuation and the status of ongoing building inspections.

[0624] With the above configuration, the present invention makes it possible to improve the efficiency of initial responses when a disaster occurs, and to ensure the safety of business establishments and minimize damage by realizing rapid and accurate information collection and management.

[0625] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0626] Step 1:

[0627] The server collects data on business establishments across the country via API and stores it in a database. Specifically, it sends an API request and receives response data including location information, business operations, number of employees, and information on important equipment. This data is then saved in the database. The input is the response data from the external API, and the output is the business establishment data stored in the database.

[0628] Step 2:

[0629] The server inputs business data from the database into a geographic information system (GIS) and visualizes it. By obtaining location information from the database and registering it in the GIS, the location and information of the business are displayed on a map. The input is business data from the database, and the output is visualized data on the GIS.

[0630] Step 3:

[0631] The server collects disaster information in real time through external interfaces such as the Japan Meteorological Agency and sensor networks. Information is obtained from APIs and sensors and reflected in the GIS. Specifically, data on earthquakes, typhoons, floods, etc. is obtained and input into the GIS to visualize the affected areas. The input is disaster information obtained from external interfaces, and the output is visualized information reflected in the GIS.

[0632] Step 4:

[0633] The server inputs the collected disaster information into an artificial intelligence (AI) model to generate the optimal initial response plan. The disaster information is input into the AI ​​model, and specific response measures are generated based on past disaster response data and the latest research results. The input is the collected disaster information, and the output is the initial response plan.

[0634] Step 5:

[0635] The server notifies the business person in charge of the generated initial response plan. The countermeasure plan is sent via emergency email or notification system so that the business can respond quickly. The input is the generated initial response plan, and the output is the notification to the business.

[0636] Step 6:

[0637] The server compiles information on the situation after a disaster occurs and creates reports for each level. It collects situation data from the GIS and database, and organizes and compiles the data according to a report format. The generated report is sent to management by email. The input is situation data from the GIS and database, and the output is the generated report.

[0638] Step 7:

[0639] The server accumulates disaster response data in a database and generates a guide that will be useful for future responses. Specifically, it saves the response data, analyzes and organizes it, and then creates new guidelines. The input is the disaster response data, and the output is the generated guide.

[0640] Step 8:

[0641] The server provides dashboards and web applications, allowing users to check this information in real time. The dashboard visually displays disaster information, damage status, response progress, etc. The input is the collected data, and the output is the visualized information on the dashboard.

[0642] Step 9:

[0643] Users access the dashboard through a web application to check the situation and take prompt action based on the displayed information, if necessary. The input is the visualized information on the dashboard, and the output is the user's decision on what to do.

[0644] (Application example 1)

[0645] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0646] At factories and other business establishments, the challenge is to quickly and efficiently respond to disasters. In particular, there is a need for an efficient system that can centralize the collection of disaster information, the creation of initial response plans, notification to managers, real-time progress monitoring, and report creation. Furthermore, there is a need for dynamic generation of manuals based on accumulated response data.

[0647] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0648] In this invention, the server includes a means for collecting and managing business data using a geographic information system, a means for collecting disaster information in real time from external APIs and sensor data, a means for using AI technology to have a robot in the factory generate an initial response plan when a disaster occurs, a means for notifying the factory manager of the generated initial response plan, a means for compiling the situation after the disaster occurs and creating a report for management, a means for accumulating response data and creating a manual based on this, and a means for providing a dashboard and web application that provides this information to users. This enables a quick and efficient initial response when a disaster occurs and subsequent situation assessment.

[0649] A "geographic information system" is an information system for collecting, managing, analyzing, and visualizing geographic data.

[0650] "Establishment data" refers to data that includes information such as the location, facilities, number of employees, and business operations of the establishment.

[0651] An "external API" is an interface for communicating with external systems and services.

[0652] "Sensor data" refers to data including environmental information and situation data obtained from sensors.

[0653] "Disaster information" refers to information about natural disasters such as earthquakes, typhoons, and floods.

[0654] "AI technology" is a technology that applies machine learning and data analysis techniques to automatically process data and make predictions.

[0655] An "initial response plan" is a proposal for the first response measures to be taken when a disaster occurs.

[0656] A "factory manager" is a person in charge of managing operations and facilities within a factory.

[0657] "Notification" is the act of conveying information to a person from a system or device.

[0658] A "report" is a written summary of the situation and results.

[0659] A "manual" is a set of instructions for a particular operation or procedure.

[0660] A "dashboard" is a visual display screen that allows multiple data and information to be viewed at a glance.

[0661] A "WEB application" is application software that can be used through a web browser.

[0662] This invention is a system for quickly and efficiently responding to disasters at factories and other business establishments. The system uses a geographic information system (GIS), external APIs, sensor data, and AI technology to notify and instruct factory robots and managers.

[0663] The system consists of the following components:

[0664] 1. Business Data Collection and Management:

[0665] The server collects data on business establishments across the country from a variety of data sources and inputs and manages it into a geographic information system (GIS). Business establishment data includes location information, business activities, number of employees, and information on important facilities. The GIS visualizes this data on a map and provides information on each business establishment in a format that users can easily check.

[0666] 2. Real-time disaster information collection:

[0667] The server collects real-time disaster information from external APIs and various sensor data. Specifically, it obtains data on natural disasters such as earthquakes, typhoons, and floods through the Japan Meteorological Agency's API and sensor network, and reflects this information in the GIS.

[0668] 3. AI-powered disaster response advice:

[0669] The server uses AI to generate optimal initial response plans based on the disaster information collected. This AI refers to past data, the latest research results, and simulation results to propose specific countermeasures.

[0670] 4. Factory robots implement initial response:

[0671] The initial response plan generated by the AI ​​is sent to the robots in the factory, which then automatically take action based on the plan, such as shutting down equipment, securing evacuation routes, and protecting important facilities.

[0672] 5. Notice and Administrator Information:

[0673] The server then notifies the administrator of the generated initial response plan via email or a dedicated application, allowing the administrator to grasp the situation in real time.

[0674] 6. Post-disaster reporting:

[0675] The server automatically compiles information on the situation after a disaster occurs and generates a report for management, summarizing the damage situation, the progress of the initial response, and future countermeasures, and sends it to the administrator.

[0676] 7. Knowledge accumulation and manual generation:

[0677] The server accumulates disaster response data from each business location and generates a manual that will be useful for future responses. This manual is constantly updated to reflect the latest response experience.

[0678] 8. Dashboards and Web Applications:

[0679] The server provides dashboards and web applications that visually display disaster information, damage status, response progress, etc. to users, allowing them to grasp the situation in real time and quickly take necessary action.

[0680] Specific examples

[0681] For example, if a magnitude 6 earthquake occurs, the server obtains earthquake information through an external API and reflects it in the GIS. At the same time, AI technology is used to generate initial response plans such as "stopping equipment," "evacuating employees," and "building safety inspections." These are then instructed to robots within the factory, which then quickly take action. Furthermore, the initial response plan is notified to the manager via email, allowing them to check the situation in real time. After a disaster occurs, the server automatically compiles the response status, compiles it into a report for management, and sends it to the manager.

[0682] Example prompts to input to the generative AI model

[0683] "An earthquake has occurred. The affected factory is in Shinjuku Ward, Tokyo. What initial response measures should be taken?"

[0684] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0685] Step 1:

[0686] The server collects data on business establishments across the country from a variety of data sources and inputs and manages it into a geographic information system (GIS). Specifically, the server calls an API to obtain information on the location, business activities, number of employees, and important equipment of each establishment. The input data is visualized on a map.

[0687] Input: "Business data" (location information, business details, number of employees, information on important facilities)

[0688] Output: "Business data visualized on GIS"

[0689] Step 2:

[0690] The server collects disaster information in real time from external APIs and various sensor data. The server obtains disaster information such as earthquakes, typhoons, and floods through APIs from the Japan Meteorological Agency. The collected information is reflected in the GIS.

[0691] Input: "External API and sensor data" (disaster information such as earthquakes, typhoons, and floods)

[0692] Output: "Disaster information reflected in GIS"

[0693] Step 3:

[0694] The server uses AI to generate optimal initial response plans based on the collected disaster information. The AI ​​refers to past data, the latest research results, and simulation results to propose response measures according to the type of disaster.

[0695] Input: "Disaster information" (information on earthquakes, typhoons, floods, etc.)

[0696] Output: "AI-generated initial response plan"

[0697] Step 4:

[0698] The server then sends the generated initial response plan to the factory robots, who then carry out the response according to the instructions. Specifically, the robots perform emergency shutdowns of equipment, secure evacuation routes, protect important facilities, etc.

[0699] Input: "AI-generated initial response plan"

[0700] Output: "Robot first response execution"

[0701] Step 5:

[0702] The server notifies the factory manager of the generated initial response plan via email or a dedicated application, allowing the manager to check the situation in real time.

[0703] Input: "AI-generated initial response plan"

[0704] Output: "Notification to Administrator"

[0705] Step 6:

[0706] The server automatically compiles information on the situation after a disaster occurs and generates a report for management, summarizing the damage situation, the progress of the initial response, and future countermeasures.

[0707] Input: "Post-disaster response status data"

[0708] Output: "Report for management"

[0709] Step 7:

[0710] The server will store disaster response data from each business location and generate a manual that will be useful for future responses, including building inspection methods, rapid evacuation procedures for employees, and measures to protect important equipment.

[0711] Input: "Accumulated disaster response data"

[0712] Output: "A manual that will be useful for future reference"

[0713] Step 8:

[0714] The server provides dashboards and web applications that visually display disaster information, damage status, response progress, etc. Users can grasp the situation in real time through the dashboard and quickly take necessary action.

[0715] Input: "Disaster information, damage situation, response progress data"

[0716] Output: "Information visualized in a dashboard or web application"

[0717] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0718] This invention is a system for streamlining the initial response of a business in the event of a disaster, and is composed of a geographic information system, external APIs, sensor data, AI technology, an emotion engine, a dashboard, and a web application. Below, we will explain in detail each component of this system and the processing flow based on them.

[0719] Geographic Information System (GIS) and Business Data Management

[0720] The server collects data on business establishments across the country from various data sources and inputs and manages it into a geographic information system (GIS). Business establishment data includes location information, business activities, number of employees, and information on important facilities. The GIS visualizes this data on a map and provides information on each business establishment in an easily accessible format for users.

[0721] Specific examples

[0722] The server calls the API to collect data on specific business locations in Tokyo. This data includes location (Shinjuku Ward, Tokyo), business type (IT company), number of employees (50), and critical facilities (data center). The collected data is registered in the GIS, and users can view it on a map via a web application.

[0723] Real-time disaster information collection

[0724] The server collects real-time disaster information from the Japan Meteorological Agency and various sensor data. Specifically, it obtains data on natural disasters such as earthquakes, typhoons, and floods via external APIs and sensor networks, and reflects this data in the GIS.

[0725] Specific examples

[0726] The server retrieves information from the Japan Meteorological Agency's API about a magnitude 6 earthquake with its epicenter in Tokyo Bay. This information is input into a GIS, and the affected areas around Tokyo Bay are highlighted in red on a map.

[0727] AI-powered disaster response advice

[0728] The server uses AI to generate optimal initial response plans based on the disaster information collected. This AI refers to past data, the latest research papers, and simulation results to propose specific countermeasures.

[0729] Specific examples

[0730] In the event of a magnitude 6 earthquake, the AI ​​will generate initial response plans, such as "conducting safety inspections of business buildings," "instructing employees to evacuate," and "procedures for emergency shutdowns of data centers." The server will then notify the business of this information.

[0731] Reporting and reporting to management

[0732] The server automatically compiles information on the situation after a disaster occurs and generates a report for management, summarizing the damage situation, the progress of the initial response, and future response measures.

[0733] Specific examples

[0734] The server uses the damage information from the GIS to create a damage report for a specific business location in Tokyo. This report includes information such as "Partial damage to the building," "All employees have safely evacuated," and "Data center emergency shutdown procedures have been completed." The report is then sent to management via email.

[0735] Knowledge accumulation and manual generation

[0736] The server accumulates disaster response data from each business location and generates a manual that will be useful for future responses. This manual is constantly updated to reflect the latest response experience.

[0737] Specific examples

[0738] The server will store the response data for the Tokyo Bay earthquake in a database and generate a new earthquake response manual, which will include building inspection methods, rapid evacuation procedures for employees, and measures to protect important equipment.

[0739] Dashboards and Web Applications

[0740] The server provides dashboards and web applications that visually display disaster information, damage status, response progress, etc. to users, allowing them to grasp the situation in real time and quickly take necessary action.

[0741] Specific examples

[0742] Users can access the dashboard through a web application to check current earthquake information, damage status, and the progress of initial responses in real time. For example, they can immediately see a list of employees who have completed evacuation and the status of ongoing building inspections.

[0743] Emotion Engine

[0744] The server is equipped with an emotion engine that analyzes the user's emotional data. The emotion engine analyzes the user's voice and text data to assess their stress level, anxiety level, etc. Based on the results, the server adjusts the initial response plan and takes appropriate action.

[0745] Specific examples

[0746] The server receives the user's voice data and analyzes it using an emotion engine. As a result, it is determined that the user is in a state of high stress. Based on this, the AI ​​means generates initial response plans, including "support messages for rapid evacuation" and "provision of psychological support." The server notifies this information to the business.

[0747] The system of this invention will improve the efficiency of initial responses when a disaster occurs, enabling a rapid understanding of damage and appropriate countermeasures. Furthermore, the introduction of an emotion engine will enable measures to be taken to reduce employee stress and anxiety. This will ensure both workplace safety and employee mental health.

[0748] The processing flow will be explained below.

[0749] Step 1: Collect and manage business data

[0750] The server collects data on businesses across the country through APIs and database connections and inputs it into a geographic information system (GIS).

[0751] Operation:

[0752] The server calls an external API to obtain business data.

[0753] The data is received in JSON format and stored in the database.

[0754] Extract business data from the database and send it to the GIS.

[0755] GIS maps and visualizes business locations on a map.

[0756] Step 2: Collecting real-time disaster information

[0757] The server collects real-time disaster information from the Japan Meteorological Agency and various sensor data.

[0758] Operation:

[0759] The server periodically polls the Japan Meteorological Agency API to obtain the latest earthquake information.

[0760] Sensor data is received in real time via WebSocket or MQTT protocols.

[0761] The acquired earthquake epicenter and seismic intensity information is sent to the GIS.

[0762] The GIS highlights affected areas on a map and matches them with business information.

[0763] Step 3: Generate initial response plans using AI

[0764] The server inputs the collected disaster information into an AI model and generates the optimal initial response plan.

[0765] Operation:

[0766] The server sends the collected disaster data to the AI ​​and activates the prediction module.

[0767] AI generates countermeasures based on past response data and the latest research results.

[0768] The server receives the initial response plan generated by the AI.

[0769] Step 4: Collect and analyze user emotion data

[0770] The server uses an emotion engine to collect and analyze the user's emotion data.

[0771] Operation:

[0772] The server collects users' voice and text data through microphones and chat logs.

[0773] An emotion engine analyzes this data and assesses stress and anxiety levels.

[0774] Step 5: Adjust initial response plans based on sentiment data

[0775] The server adjusts the initial response plan based on the analysis results of the emotion engine.

[0776] Operation:

[0777] The server sends the emotional data to the AI, which generates a response that reflects the user's emotional state.

[0778] For example, if stress levels are high, the system generates response plans that include quick evacuation guidance and providing mental support.

[0779] The server notifies the establishment of this information.

[0780] Step 6: Notify the business of the initial response plan

[0781] The server notifies each business office of the generated initial response plan.

[0782] Operation:

[0783] The server classifies the assigned countermeasures by business establishment.

[0784] Send messages to designated businesses using the Slack API or email services.

[0785] The message includes specific response measures such as evacuation instructions and equipment shutdown procedures.

[0786] Step 7: Compiling damage data and creating a report

[0787] The server compiles data on the situation after a disaster occurs and generates a report for management.

[0788] Operation:

[0789] The server compiles damage information based on the GIS damage information.

[0790] Enter data such as a list of affected businesses, response progress, and next steps into a report template.

[0791] Generate reports in PDF format and email them to management.

[0792] Step 8: Knowledge accumulation and manual generation

[0793] The server accumulates disaster response data from each business location and generates a dynamic manual.

[0794] Operation:

[0795] The server receives disaster response data sent from each business location and stores it in a database.

[0796] Past corresponding data is extracted from the database and a manual generation engine is started.

[0797] The manuals are updated based on the latest corresponding information and are generated as training and practical manuals.

[0798] Step 9: Serving the Dashboard and Web Application

[0799] The server provides information to users using a dashboard or web application.

[0800] Operation:

[0801] The server displays real-time disaster information, damage status, response progress, and other data on the dashboard.

[0802] Provides information to users through web applications and supports immediate response.

[0803] Example 2

[0804] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0805] When a disaster strikes, a business's initial response requires the collection of a wide range of information and rapid decision-making, making it difficult to respond efficiently using conventional methods. Furthermore, employees' mental stress and anxiety can also hinder a rapid initial response. For this reason, a system that can easily aggregate information and generate and provide optimal initial response plans is needed.

[0806] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for collecting and managing business establishment data using a geographic information system; means for collecting disaster information in real time from external APIs and sensor data; AI means for generating appropriate initial response plans based on the collected disaster information; means for notifying the business establishment of the generated initial response plans; means for compiling the situation after a disaster occurs and creating a report for management; means for accumulating response data and generating manuals based on the data to always reflect the latest response experience; means for visually providing this information to users using a dashboard or web application; and means for analyzing user emotion data using an emotion engine and adjusting the initial response plans. This enables fast and efficient initial response and employee stress management.

[0807] A "geographic information system" is an information system for collecting, managing, analyzing, and visually displaying geospatial data.

[0808] "Establishment data" refers to information regarding the location, business activities, number of employees, and important facilities of a business establishment.

[0809] An "external API" is an interface for connecting with other systems and services, and is used to obtain external data in real time.

[0810] "Sensor data" refers to data such as environmental information and disaster information collected from various sensors.

[0811] "Disaster information" refers to data related to natural disasters such as earthquakes, typhoons, and floods.

[0812] "AI methods" are methods that use artificial intelligence to analyze data and generate optimal initial response plans.

[0813] An "initial response plan" is a specific plan for the course of action and procedures that a business should immediately implement in the event of a disaster.

[0814] "Notification means" refers to a means for communicating the generated initial response plan and other information to the business establishment.

[0815] The "report creation means" is a means for compiling the situation after a disaster occurs and automatically creating a report for management.

[0816] "Response data" refers to records and data related to past disaster responses.

[0817] "Manual generation means" refers to a means for creating a disaster response manual based on accumulated response data.

[0818] A "dashboard" is an interface that allows users to visually check disaster information, damage status, response progress, and so on.

[0819] A "web application" is an application that can be used by a user through a web browser.

[0820] The "emotion engine" is a system that analyzes a user's voice and text data to assess their emotional state, such as stress level and anxiety level.

[0821] This invention is a system for streamlining the initial response of businesses when disasters occur, and is composed of a geographic information system (GIS), external APIs, sensor data, AI technology, an emotion engine, a dashboard, and a web application.

[0822] 1. Geographic Information System (GIS) and Business Data Management

[0823] The server collects data on business establishments across the country from APIs and external databases and inputs it into the GIS. Business establishment data includes location information, business activities, number of employees, and information on important facilities. The GIS visualizes this data on a map and provides it in an easily accessible format for users. For example, the server uses an API to collect data on IT companies in Shinjuku Ward, Tokyo, and registers it in the GIS. This allows users to check detailed information about the establishments through a web application.

[0824] 2. Real-time disaster information collection

[0825] The server collects information on natural disasters such as earthquakes, typhoons, and floods in real time from the Japan Meteorological Agency's API and various sensor data. The collected disaster information is reflected in the GIS, and the affected area is displayed on a map. For example, the server uses the Japan Meteorological Agency's API to obtain information on a magnitude 6 earthquake with an epicenter in Tokyo Bay, and by immediately reflecting this information in the GIS, it is possible to visually display the affected areas around Tokyo Bay.

[0826] 3. AI-based disaster response advice

[0827] The server passes the collected disaster information to an AI model for analysis and generates an optimal initial response plan. This AI method proposes effective countermeasures based on past data, the latest research papers, and simulation results. For example, if a magnitude 6 earthquake occurs, the AI ​​method will generate an initial response plan including "safety inspection of the business building," "emergency evacuation instructions for employees," and "emergency shutdown procedures for the data center," and the server will notify the business.

[0828] 4. Report creation and reporting to management

[0829] The server compiles data on the situation after a disaster and automatically generates reports for management. These reports include information on the damage situation, the progress of the initial response, and future countermeasures. For example, the server can create a status report for a specific business location in Tokyo based on data obtained from the GIS and send it to management by email.

[0830] 5. Knowledge accumulation and manual generation

[0831] The server accumulates disaster response data from each business location and generates a manual based on that data that will be useful for future responses. This manual always reflects the latest response experience and is dynamically updated. For example, the server can store response data from the Tokyo Bay earthquake in a database and generate a new earthquake response manual.

[0832] 6. Dashboards and Web Applications

[0833] The server provides dashboards and web applications that visually display disaster information, damage status, and response progress to users. Users can use this to understand the situation in real time and quickly take necessary action. For example, users can access the dashboard through a web application to check earthquake information, damage status, and the status of ongoing initial responses.

[0834] 7. Emotion Engine

[0835] The server uses an emotion engine to analyze the user's emotional data. The emotion engine analyzes the voice and text data to evaluate the user's stress level and anxiety. Based on the results, the AI ​​means adjusts the initial response plan and proposes an appropriate response. For example, if the server acquires the user's voice data and analyzes it with the emotion engine and determines that the user is in a high stress state, the AI ​​means will generate an initial response plan including a "support message for rapid evacuation" and "provision of mental support," and the server will notify the business.

[0836] Specific prompt examples

[0837] Below are some examples of specific prompt sentences to input into the generative AI model.

[0838] "If an earthquake with a seismic intensity of 6 occurs with its epicenter in Tokyo Bay, please propose the initial response plan for your business."

[0839] "Please display a dashboard to check the real-time evacuation status of specific businesses in Tokyo."

[0840] "Analyze the user's emotional state based on their voice data and generate appropriate initial response plans."

[0841] The system of this invention will improve the efficiency of initial responses in the event of a disaster, enabling prompt and appropriate responses. In addition, by taking into consideration the mental health of employees through the emotion engine, it will be possible to ensure both the safety of the workplace and the mental health of employees.

[0842] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0843] Step 1: Collect and manage business data

[0844] The server collects data on business establishments across the country from APIs and external databases. The data collected here includes location information, business operations, number of employees, and information on important facilities. The input data is detailed information on business establishments obtained from the API, and the output is business establishment data registered in the GIS.

[0845] Specific operation: The server calls the API and retrieves data on IT companies located in Shinjuku Ward, Tokyo. This data includes "Location: Shinjuku Ward, Tokyo," "Business: IT company," "Number of employees: 50," and "Key facilities: Data center." The retrieved data is input into GIS and visualized on a map.

[0846] Step 2: Collecting real-time disaster information

[0847] The server collects real-time disaster information from the Japan Meteorological Agency's API and various sensors. The input data is disaster information obtained from external APIs and sensor data, and the output is the disaster situation reflected in the GIS.

[0848] Specific operation: The server uses an API provided by the Japan Meteorological Agency to obtain information on an earthquake with a seismic intensity of 6 and centered in Tokyo Bay. The obtained information is input into a GIS, and the affected areas around Tokyo Bay are highlighted in red on a map.

[0849] Step 3: AI-based disaster response advice

[0850] The server passes the collected disaster information to the AI ​​model for analysis. The input data is real-time disaster information obtained from the GIS, and the output is an initial response plan generated by the AI.

[0851] Specific operation: The server uses AI means to analyze earthquake information with a seismic intensity of 6. The AI ​​means generates an initial response plan including "safety inspection of the business building," "evacuation instructions for employees," and "emergency shutdown procedures for the data center," and the server notifies the business.

[0852] Step 4: Reporting and reporting to management

[0853] The server aggregates data on the situation after a disaster and automatically generates reports for management. The input data is information from GIS and AI models, and the output is a report sent to management.

[0854] Specific operation: The server creates a status report for specific business locations in Tokyo based on the damage information obtained from the GIS. This report includes information such as "Partial damage to the building," "All employees have safely evacuated," and "The data center has completed emergency shutdown procedures." The report is then sent to management via email.

[0855] Step 5: Knowledge accumulation and manual generation

[0856] The server accumulates disaster response data from each business location and generates a manual based on that data that will be useful for future responses. The input data is past disaster response information, and the output is an updated response manual.

[0857] Specific operation: The server saves the response data for the Tokyo Bay earthquake in a database and generates a new earthquake response manual. The new manual includes "building inspection methods," "quick evacuation procedures for employees," and "protection measures for important equipment."

[0858] Step 6: Dashboard and Web Applications

[0859] The server provides dashboards and web applications that visually display disaster information, damage status, and response progress to users. The input data is information from GIS and AI models, and the output is the dashboard or web application screen used by users.

[0860] How it works: Users can access a dashboard through a web application to check real-time earthquake information, damage status, and the progress of initial responses. It also displays a list of employees who have completed evacuation and the status of ongoing building inspections.

[0861] Step 7: Emotion Engine

[0862] The server uses an emotion engine to analyze the user's emotional data and adjust the initial response plan. The input data is the user's voice data and text data, and the output is a response plan based on the analysis results.

[0863] Specific operation: The server acquires the user's voice data and analyzes it using an emotion engine. If the analysis results indicate that the user is in a high stress state, the AI ​​means generates initial response plans, including "support messages for rapid evacuation" and "provision of psychological support," and the server notifies the business.

[0864] (Application example 2)

[0865] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0866] Improving the efficiency of a facility's initial response in the event of a disaster and implementing appropriate measures quickly are crucial for minimizing damage and quickly restoring operations. However, conventional systems do not adequately collect real-time information, generate appropriate response plans, or analyze employees' emotional states and reflect that information in their responses. As a result, there was a lack of an appropriate system for situations that require efficient initial responses and flexible responses that take into account employees' emotional states.

[0867] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0868] In this invention, the server includes means for collecting and managing facility data using a geographic information system, means for collecting disaster information in real time from external APIs and sensor data, AI means for generating appropriate initial response plans based on the collected disaster information, means for notifying the facility of the generated initial response plans, means for compiling post-disaster situations and creating reports for management, means for accumulating response data and generating manuals based on the data, means for providing a dashboard or web application that provides this information to users, means for analyzing user voice data and text data and using an emotion engine to evaluate the user's emotional state, and means for adjusting the initial response plans and taking appropriate action based on the analysis results of the emotion engine. This improves the efficiency of initial responses when a disaster occurs and enables flexible and appropriate responses that take into account the emotional states of employees.

[0869] A "geographic information system" is a system for collecting, managing, analyzing, and visually displaying geospatial data.

[0870] "Facility data" refers to data that includes location information, business operations, number of employees, and information on important equipment related to a specific facility.

[0871] An "external API" is a means of obtaining data using a program interface provided by an external system or service.

[0872] "Sensor data" refers to physical and environmental data obtained from various sensors.

[0873] "Disaster information" refers to real-time information about natural disasters such as earthquakes, typhoons, and floods.

[0874] "AI means" refers to a system that uses artificial intelligence technology to analyze data and generate output results such as response proposals.

[0875] An "emotion engine" is a technology that analyzes voice and text data to evaluate the user's emotional state.

[0876] A "dashboard" is an interface that visually displays real-time information and makes it easier for users to understand the situation.

[0877] A "WEB application" is application software provided using web technology.

[0878] "Notification means" refers to a function for transmitting generated information to users and systems.

[0879] A "report" refers to a report that organizes collected data and information and compiles it for specific users, such as management.

[0880] A "manual" is a document that describes specific procedures and measures that govern how to respond to disasters and how to carry out business operations.

[0881] This invention is a system for improving the efficiency of disaster response in logistics centers. This system is composed of a geographic information system (GIS), external APIs, sensor data, AI technology, an emotion engine, a dashboard, and a web application.

[0882] Geographic Information System (GIS) and Facility Data Management

[0883] The server collects facility data from various sources across the country and inputs and manages it into a geographic information system (GIS). Facility data includes location information, business operations, number of employees, and information on important equipment. The GIS visualizes this data on a map, providing information on each facility in an easily accessible format for users.

[0884] Specific examples

[0885] The server calls the API to collect data on a specific logistics center in Tokyo. This data includes the location (Shinjuku-ku, Tokyo), business operations (logistics management), number of employees (50), and critical equipment (warehouse). The collected data is registered in the GIS, and users can view it on a map via a web application.

[0886] Real-time disaster information collection

[0887] The server collects real-time disaster information from the Japan Meteorological Agency and various sensor data. Specifically, it obtains data on natural disasters such as earthquakes, typhoons, and floods via external APIs and sensor networks, and reflects this data in the GIS.

[0888] Specific examples

[0889] The server retrieves information from the Japan Meteorological Agency's API about a magnitude 6 earthquake with its epicenter in Tokyo Bay. This information is input into a GIS, and the affected areas around Tokyo Bay are highlighted in red on a map.

[0890] AI-powered disaster response advice

[0891] The server uses AI to generate optimal initial response plans based on the disaster information collected. This AI refers to past data, the latest research papers, and simulation results to propose specific countermeasures.

[0892] Specific examples

[0893] In the event of a magnitude 6 earthquake, the AI ​​will generate initial response plans, such as "conducting safety inspections of facility buildings," "instructing employees to evacuate," and "procedures for emergency shutdowns of warehouses." The server then notifies the facility of this information.

[0894] Emotion Engine

[0895] The server is equipped with an emotion engine that analyzes the user's emotional data. The emotion engine analyzes the user's voice and text data to assess their stress level, anxiety level, etc. Based on the results, the server adjusts the initial response plan and takes appropriate action.

[0896] Specific examples

[0897] The server receives the user's voice data and analyzes it using an emotion engine. As a result, it determines that the user is in a state of high stress. Based on this, the AI ​​generates initial response plans, including "support messages for rapid evacuation" and "provision of psychological support." The server then notifies the facility of this information.

[0898] Dashboards and Web Applications

[0899] The server provides dashboards and web applications that visually display disaster information, damage status, response progress, etc. to users, allowing them to grasp the situation in real time and quickly take necessary action.

[0900] Specific examples

[0901] Users can access the dashboard through a web application to check current earthquake information, damage status, and the progress of initial responses in real time. For example, they can immediately see a list of employees who have completed evacuation and the status of ongoing building inspections.

[0902] Reporting and reporting to management

[0903] The server automatically compiles information on the situation after a disaster occurs and generates a report for management, summarizing the damage situation, the progress of the initial response, and future response measures.

[0904] Specific examples

[0905] The server uses the damage information from the GIS to create a damage report for a specific logistics center in Tokyo. This report includes information such as "Partial damage to the building," "All employees have safely evacuated," and "Emergency shutdown procedures have been completed at the warehouse." The report is then sent to management via email.

[0906] Knowledge accumulation and manual generation

[0907] The server accumulates disaster response data from each facility and generates a manual that will be useful for future responses. This manual is constantly updated to reflect the latest response experience.

[0908] Specific examples

[0909] The server will store the response data for the Tokyo Bay earthquake in a database and generate a new earthquake response manual, which will include building inspection methods, rapid evacuation procedures for employees, and measures to protect important equipment.

[0910] Prompt Sentence Examples

[0911] Generate a plan for initial response in the event of a disaster based on the following facility data:

[0912] Earthquake (Epicenter: Tokyo, Seismic Intensity: 6)

[0913] Damaged area: Logistics center (Shinjuku-ku, Tokyo)

[0914] Important information: Warehouse safety inspections, employee evacuation instructions, warehouse emergency shutdown procedures

[0915] As a result, the system of the present invention will improve the efficiency of initial responses when a disaster occurs, and will enable flexible and appropriate responses that take into account the emotional state of employees.

[0916] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0917] Step 1:

[0918] Geographic Information System (GIS) and Facility Data Management

[0919] The server collects facility data from various sources across the country. The collected data includes location information, business operations, number of employees, and important facilities, and inputs and manages this data into a GIS. The server visualizes this GIS data on a map and makes it accessible to users through a web application.

[0920] Input: Facility data retrieved from data sources

[0921] Output: Visualized data displayed on a GIS

[0922] Specific operations: Collecting data by calling API, saving to database, and drawing data on map

[0923] Step 2:

[0924] Real-time disaster information collection

[0925] The server collects real-time disaster information from external APIs and various sensor data. Specifically, it uses APIs from the Japan Meteorological Agency and other sources to obtain information on earthquakes, typhoons, floods, etc., and reflects this information in the GIS.

[0926] Input: Japan Meteorological Agency API and sensor data

[0927] Output: Disaster information reflected in GIS

[0928] Specific operations: Information acquisition by API calls, processing of sensor data, input into GIS database

[0929] Step 3:

[0930] AI-powered disaster response advice

[0931] The server uses AI technology to generate optimal initial response plans based on the collected disaster information. This AI refers to past disaster data and the latest research results to propose specific countermeasures.

[0932] Input: Real-time disaster information

[0933] Output: Initial response plan

[0934] Specific operations: Analysis of disaster information, generation of countermeasures using AI models, notification of countermeasures

[0935] Step 4:

[0936] Generated initial response plan is notified to the facility

[0937] The server then notifies each facility of the generated initial response plan via a variety of methods, including email and app notifications.

[0938] Input: AI-generated initial response plan

[0939] Output: Action suggestions sent via notification channels

[0940] Specific actions: Generate notification content and send it via communication means

[0941] Step 5:

[0942] User analysis using emotion engine

[0943] The server receives the user's voice and text data and analyzes it using an emotion engine to evaluate their stress and anxiety levels, and reflects the analysis results in the initial response plan.

[0944] Input: User voice or text data

[0945] Output: Emotion analysis results

[0946] Specific actions: Data collection, processing of emotion analysis algorithms, and reflection of analysis results in initial response plans

[0947] Step 6:

[0948] Providing information through dashboards and web applications

[0949] The server provides users with visual information on disasters, damage situations, response progress, etc. via dashboards and web applications, allowing users to grasp the situation in real time and quickly take necessary action.

[0950] Input: GIS data, real-time disaster information, initial response plan, emotion analysis results

[0951] Output: Visualizations on dashboards and web applications

[0952] Specific actions: Data collection, real-time visualization, and user interface updates

[0953] Step 7:

[0954] Reporting and reporting to management

[0955] The server automatically compiles information on the situation after a disaster occurs and generates a report for management, summarizing the damage situation, the progress of the initial response, and future countermeasures, and sends it to management via email or other means.

[0956] Input: Disaster information, progress of initial response, emotion analysis results

[0957] Output: Report for management

[0958] Specific actions: Data collection, report generation, and sending of report contents

[0959] Step 8:

[0960] Knowledge accumulation and manual generation

[0961] The server accumulates disaster response data from each facility and generates a manual that will be useful for future responses. This manual is constantly updated to reflect the latest response experience.

[0962] Input: Disaster response data

[0963] Output: Updated manual

[0964] Specific operations: saving to database, executing manual generation algorithm, updating manual

[0965] 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.

[0966] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0967] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0968] [Third embodiment]

[0969] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0970] 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.

[0971] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0972] 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.

[0973] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0974] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0975] 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. 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.

[0976] 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.

[0977] 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 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.

[0978] 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.

[0979] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0980] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0981] This invention is a system for streamlining the initial response of a business in the event of a disaster, and is specifically composed of a geographic information system, external APIs, sensor data, AI technology, dashboards, and web applications. Below, we will explain in detail each component of this system and the processing flow based on them.

[0982] Geographic Information System (GIS) and Business Data Management

[0983] The server collects data on business establishments across the country from various data sources and inputs and manages it into a geographic information system (GIS). Business establishment data includes location information, business activities, number of employees, and information on important facilities. The GIS visualizes this data on a map and provides information on each business establishment in an easily accessible format for users.

[0984] Specific examples

[0985] The server calls the API to collect data on specific business locations in Tokyo. This data includes location (Shinjuku Ward, Tokyo), business type (IT company), number of employees (50), and critical facilities (data center). The collected data is registered in the GIS, and users can view it on a map via a web application.

[0986] Real-time disaster information collection

[0987] The server collects real-time disaster information from the Japan Meteorological Agency and various sensor data. Specifically, it obtains data on natural disasters such as earthquakes, typhoons, and floods via external APIs and sensor networks, and reflects this data in the GIS.

[0988] Specific examples

[0989] The server retrieves information from the Japan Meteorological Agency's API about a magnitude 6 earthquake with its epicenter in Tokyo Bay. This information is input into a GIS, and the affected areas around Tokyo Bay are highlighted in red on a map.

[0990] AI-powered disaster response advice

[0991] The server uses AI to generate optimal initial response plans based on the disaster information collected. This AI refers to past data, the latest research papers, and simulation results to propose specific countermeasures.

[0992] Specific examples

[0993] In the event of a magnitude 6 earthquake, the AI ​​will generate initial response plans, such as "conducting safety inspections of business buildings," "instructing employees to evacuate," and "procedures for emergency shutdowns of data centers." The server will then notify the business of this information.

[0994] Reporting and reporting to management

[0995] The server automatically compiles information on the situation after a disaster occurs and generates a report for management, summarizing the damage situation, the progress of the initial response, and future response measures.

[0996] Specific examples

[0997] The server uses the damage information from the GIS to create a damage report for a specific business location in Tokyo. This report includes information such as "Partial damage to the building," "All employees have safely evacuated," and "Data center emergency shutdown procedures have been completed." The report is then sent to management via email.

[0998] Knowledge accumulation and manual generation

[0999] The server accumulates disaster response data from each business location and generates a manual that will be useful for future responses. This manual is constantly updated to reflect the latest response experience.

[1000] Specific examples

[1001] The server will store the response data for the Tokyo Bay earthquake in a database and generate a new earthquake response manual, which will include building inspection methods, rapid evacuation procedures for employees, and measures to protect important equipment.

[1002] Dashboards and Web Applications

[1003] The server provides dashboards and web applications that visually display disaster information, damage status, response progress, etc. to users, allowing them to grasp the situation in real time and quickly take necessary action.

[1004] Specific examples

[1005] Users can access the dashboard through a web application to check current earthquake information, damage status, and the progress of initial responses in real time. For example, they can immediately see a list of employees who have completed evacuation and the status of ongoing building inspections.

[1006] The system of the present invention will improve the efficiency of initial responses when a disaster occurs, enable rapid assessment of damage, and enable appropriate countermeasures, thereby ensuring the safety of business establishments and minimizing damage.

[1007] The processing flow will be explained below.

[1008] Step 1: Collect and manage business data

[1009] The server collects data on businesses across the country through APIs and database connections and inputs it into a geographic information system (GIS).

[1010] Operation:

[1011] The server calls an external API to obtain business data.

[1012] The data is received in JSON format and stored in the database.

[1013] Extract business data from the database and send it to the GIS.

[1014] GIS maps and visualizes business locations on a map.

[1015] Step 2: Collecting real-time disaster information

[1016] The server collects real-time disaster information from the Japan Meteorological Agency and various sensor data.

[1017] Operation:

[1018] The server periodically polls the Japan Meteorological Agency API to obtain the latest earthquake information.

[1019] Sensor data is received in real time via WebSocket or MQTT protocols.

[1020] The acquired earthquake epicenter and seismic intensity information is sent to the GIS.

[1021] The GIS highlights affected areas on a map and matches them with business information.

[1022] Step 3: Generate initial response plans using AI

[1023] The server inputs the collected disaster information into an AI model and generates the optimal initial response plan.

[1024] Operation:

[1025] The server sends the collected disaster data to the AI ​​and activates the prediction module.

[1026] AI generates countermeasures based on past response data and the latest research results.

[1027] The server receives the initial response plan generated by the AI.

[1028] Step 4: Notify the business of the initial response plan

[1029] The server notifies each business office of the generated initial response plan.

[1030] Operation:

[1031] The server classifies the assigned countermeasures by business establishment.

[1032] Send messages to designated businesses using the Slack API or email services.

[1033] The message includes specific response measures such as evacuation instructions and equipment shutdown procedures.

[1034] Step 5: Compiling damage data and creating a report

[1035] The server compiles data on the situation after a disaster occurs and generates a report for management.

[1036] Operation:

[1037] The server compiles damage information based on the GIS damage information.

[1038] Enter data such as a list of affected businesses, response progress, and next steps into a report template.

[1039] Generate reports in PDF format and email them to management.

[1040] Step 6: Knowledge accumulation and manual creation

[1041] The server accumulates disaster response data from each business location and generates a dynamic manual.

[1042] Operation:

[1043] The server receives disaster response data sent from each business location and stores it in a database.

[1044] Past corresponding data is extracted from the database and a manual generation engine is started.

[1045] The manuals are updated based on the latest corresponding information and are generated as training and practical manuals.

[1046] Step 7: Serving the Dashboard and Web Application

[1047] The server provides information to users using a dashboard or web application.

[1048] Operation:

[1049] The server displays real-time disaster information, damage status, response progress, and other data on the dashboard.

[1050] Provides information to users through web applications and supports immediate response.

[1051] Example 1

[1052] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1053] In the past, when a disaster occurred, the initial response at a business site was difficult because it took time to collect and understand information. Furthermore, the management and analysis of collected information was often done manually, which led to problems such as errors and delays. This created major challenges in ensuring the safety of business sites and minimizing damage.

[1054] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1055] In this invention, the server includes means for collecting and managing business establishment data using a geographic information system, means for collecting disaster information in real time from external interfaces and sensor data, artificial intelligence means for generating appropriate initial response plans based on the collected disaster information, means for notifying the business establishment of the generated initial response plans, means for compiling the situation after a disaster occurs and creating reports for each level, means for accumulating response data and generating guides based on this, interface means for providing this information to users, means for visualizing business establishment data and disaster information on a map, means for tracking and managing the progress of countermeasures at each business establishment in real time, and means for proposing countermeasures related to the business establishment based on the collected data and disaster information. This enables rapid and accurate information collection and management, and enables business establishments to carry out initial responses efficiently and effectively.

[1056] A "geographic information system" is a system that collects, stores, analyzes, and visually displays geospatial data.

[1057] "Establishment data" refers to information related to a specific establishment, such as location information, business operations, number of employees, and important facilities.

[1058] An "external interface" is a means for communicating and exchanging data with external sources or systems.

[1059] "Sensor data" refers to information obtained from various sensors, including environmental data such as temperature, humidity, and seismic intensity.

[1060] "Disaster information" refers to information related to natural disasters such as earthquakes, typhoons, and floods, including the extent of damage and the timing of occurrence.

[1061] "Artificial intelligence tools" are algorithms or systems that use machine learning and data analysis techniques to solve specific problems.

[1062] An "initial response plan" is a set of specific guidelines and procedures that should be followed as the first response when a disaster occurs.

[1063] "Reports for higher levels" are reports provided to management and administrative levels that summarize the impact of the disaster and the progress of the response.

[1064] "Response data" refers to data on specific response measures taken in response to a disaster and their results.

[1065] A "guide" is a procedure or manual that outlines the desired actions or responses in a particular situation.

[1066] "Interface means" refers to the means by which a user can interact with a system and input or obtain information.

[1067] A "visualization tool" is a method for displaying data graphically and making it easier to understand intuitively.

[1068] "Means for tracking and managing progress of countermeasures in real time" refers to a system or method for monitoring and managing the implementation status of countermeasures in real time.

[1069] "Means for proposing countermeasures" are algorithms or systems that automatically suggest optimal countermeasures based on collected data.

[1070] This invention is a system for streamlining a business's initial response in the event of a disaster, and is composed of a geographic information system (GIS), external interfaces, sensor data, artificial intelligence (AI) technology, a dashboard, and a web application.

[1071] Geographic Information System (GIS) and Business Data Management

[1072] The server collects data on businesses across the country via API and stores it in a database. The collected data is then entered into a geographic information system (GIS) for visualization. The data registered in the GIS includes location information, business details, number of employees, and information on important facilities.

[1073] Specific examples

[1074] For example, the server calls the API to collect data on a specific business location in Tokyo (location: Shinjuku Ward, Tokyo). This data includes "Business Description: IT Company," "Number of Employees: 50," and "Key Equipment: Data Center." The collected data is stored in a database and reflected in the GIS, allowing users to view it on a map via a web application.

[1075] Real-time disaster information collection

[1076] The server collects disaster information in real time through external interfaces such as the Japan Meteorological Agency and sensor networks. By acquiring information on earthquakes, typhoons, floods, etc. and reflecting it in the GIS, affected areas can be visualized.

[1077] Specific examples

[1078] The server receives real-time information from the Japan Meteorological Agency's API about a magnitude 6 earthquake with its epicenter in Tokyo Bay. This information is entered into the GIS, and the affected areas around Tokyo Bay are highlighted in red on the map.

[1079] AI-powered disaster response advice

[1080] The server inputs the collected disaster information into an AI model to generate optimal initial response plans. This AI model proposes countermeasures based on past disaster response data and the latest research results.

[1081] Specific examples

[1082] For example, if a magnitude 6 earthquake occurs, the AI ​​model will generate initial response plans such as "conducting a building safety inspection," "instructing employees to evacuate," and "procedures for emergency shutdown of the data center." The server will then notify the person in charge at the business site.

[1083] Reporting and reporting to management

[1084] After a disaster occurs, the server compiles data and automatically generates a report for management, summarizing the damage situation, the progress of the initial response, and future countermeasures.

[1085] Specific examples

[1086] Based on the damage information from the GIS, the server creates a damage report for specific business locations in Tokyo. This report includes information such as "Partial damage to the building," "All employees have safely evacuated," and "Data center emergency shutdown procedures have been completed," and is sent to management via email.

[1087] Knowledge accumulation and manual generation

[1088] The server stores disaster response data in a database and generates a manual to be used in future responses. The manual is constantly updated to reflect the latest response experience.

[1089] Specific examples

[1090] The server will store the response data for the Tokyo Bay earthquake in a database and generate a new earthquake response manual, which will include building inspection methods, rapid evacuation procedures for employees, and measures to protect important equipment.

[1091] Providing dashboards and web applications

[1092] The server visually displays disaster information, damage status, and response progress to users via dashboards and web applications, allowing users to grasp the situation in real time and quickly take necessary action.

[1093] Specific examples

[1094] Users can access the dashboard through the web application to view current earthquake information, damage status, and the progress of initial responses. For example, they can see a list of employees who have completed evacuation and the status of ongoing building inspections.

[1095] With the above configuration, the present invention makes it possible to improve the efficiency of initial responses when a disaster occurs, and to ensure the safety of business establishments and minimize damage by realizing rapid and accurate information collection and management.

[1096] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1097] Step 1:

[1098] The server collects data on business establishments across the country via API and stores it in a database. Specifically, it sends an API request and receives response data including location information, business operations, number of employees, and information on important equipment. This data is then saved in the database. The input is the response data from the external API, and the output is the business establishment data stored in the database.

[1099] Step 2:

[1100] The server inputs business data from the database into a geographic information system (GIS) and visualizes it. By obtaining location information from the database and registering it in the GIS, the location and information of the business are displayed on a map. The input is business data from the database, and the output is visualized data on the GIS.

[1101] Step 3:

[1102] The server collects disaster information in real time through external interfaces such as the Japan Meteorological Agency and sensor networks. Information is obtained from APIs and sensors and reflected in the GIS. Specifically, data on earthquakes, typhoons, floods, etc. is obtained and input into the GIS to visualize the affected areas. The input is disaster information obtained from external interfaces, and the output is visualized information reflected in the GIS.

[1103] Step 4:

[1104] The server inputs the collected disaster information into an artificial intelligence (AI) model to generate the optimal initial response plan. The disaster information is input into the AI ​​model, and specific response measures are generated based on past disaster response data and the latest research results. The input is the collected disaster information, and the output is the initial response plan.

[1105] Step 5:

[1106] The server notifies the business person in charge of the generated initial response plan. The countermeasure plan is sent via emergency email or notification system so that the business can respond quickly. The input is the generated initial response plan, and the output is the notification to the business.

[1107] Step 6:

[1108] The server compiles information on the situation after a disaster occurs and creates reports for each level. It collects situation data from the GIS and database, and organizes and compiles the data according to a report format. The generated report is sent to management by email. The input is situation data from the GIS and database, and the output is the generated report.

[1109] Step 7:

[1110] The server accumulates disaster response data in a database and generates a guide that will be useful for future responses. Specifically, it saves the response data, analyzes and organizes it, and then creates new guidelines. The input is the disaster response data, and the output is the generated guide.

[1111] Step 8:

[1112] The server provides dashboards and web applications, allowing users to check this information in real time. The dashboard visually displays disaster information, damage status, response progress, etc. The input is the collected data, and the output is the visualized information on the dashboard.

[1113] Step 9:

[1114] Users access the dashboard through a web application to check the situation and take prompt action based on the displayed information, if necessary. The input is the visualized information on the dashboard, and the output is the user's decision on what to do.

[1115] (Application example 1)

[1116] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1117] At factories and other business establishments, the challenge is to quickly and efficiently respond to disasters. In particular, there is a need for an efficient system that can centralize the collection of disaster information, the creation of initial response plans, notification to managers, real-time progress monitoring, and report creation. Furthermore, there is a need for dynamic generation of manuals based on accumulated response data.

[1118] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1119] In this invention, the server includes a means for collecting and managing business data using a geographic information system, a means for collecting disaster information in real time from external APIs and sensor data, a means for using AI technology to have a robot in the factory generate an initial response plan when a disaster occurs, a means for notifying the factory manager of the generated initial response plan, a means for compiling the situation after the disaster occurs and creating a report for management, a means for accumulating response data and creating a manual based on this, and a means for providing a dashboard and web application that provides this information to users. This enables a quick and efficient initial response when a disaster occurs and subsequent situation assessment.

[1120] A "geographic information system" is an information system for collecting, managing, analyzing, and visualizing geographic data.

[1121] "Establishment data" refers to data that includes information such as the location, facilities, number of employees, and business operations of the establishment.

[1122] An "external API" is an interface for communicating with external systems and services.

[1123] "Sensor data" refers to data including environmental information and situation data obtained from sensors.

[1124] "Disaster information" refers to information about natural disasters such as earthquakes, typhoons, and floods.

[1125] "AI technology" is a technology that applies machine learning and data analysis techniques to automatically process data and make predictions.

[1126] An "initial response plan" is a proposal for the first response measures to be taken when a disaster occurs.

[1127] A "factory manager" is a person in charge of managing operations and facilities within a factory.

[1128] "Notification" is the act of conveying information to a person from a system or device.

[1129] A "report" is a written summary of the situation and results.

[1130] A "manual" is a set of instructions for a particular operation or procedure.

[1131] A "dashboard" is a visual display screen that allows multiple data and information to be viewed at a glance.

[1132] A "WEB application" is application software that can be used through a web browser.

[1133] This invention is a system for quickly and efficiently responding to disasters at factories and other business establishments. The system uses a geographic information system (GIS), external APIs, sensor data, and AI technology to notify and instruct factory robots and managers.

[1134] The system consists of the following components:

[1135] 1. Business Data Collection and Management:

[1136] The server collects data on business establishments across the country from a variety of data sources and inputs and manages it into a geographic information system (GIS). Business establishment data includes location information, business activities, number of employees, and information on important facilities. The GIS visualizes this data on a map and provides information on each business establishment in a format that users can easily check.

[1137] 2. Real-time disaster information collection:

[1138] The server collects real-time disaster information from external APIs and various sensor data. Specifically, it obtains data on natural disasters such as earthquakes, typhoons, and floods through the Japan Meteorological Agency's API and sensor network, and reflects this information in the GIS.

[1139] 3. AI-powered disaster response advice:

[1140] The server uses AI to generate optimal initial response plans based on the disaster information collected. This AI refers to past data, the latest research results, and simulation results to propose specific countermeasures.

[1141] 4. Factory robots implement initial response:

[1142] The initial response plan generated by the AI ​​is sent to the robots in the factory, which then automatically take action based on the plan, such as shutting down equipment, securing evacuation routes, and protecting important facilities.

[1143] 5. Notice and Administrator Information:

[1144] The server then notifies the administrator of the generated initial response plan via email or a dedicated application, allowing the administrator to grasp the situation in real time.

[1145] 6. Post-disaster reporting:

[1146] The server automatically compiles information on the situation after a disaster occurs and generates a report for management, summarizing the damage situation, the progress of the initial response, and future countermeasures, and sends it to the administrator.

[1147] 7. Knowledge accumulation and manual generation:

[1148] The server accumulates disaster response data from each business location and generates a manual that will be useful for future responses. This manual is constantly updated to reflect the latest response experience.

[1149] 8. Dashboards and Web Applications:

[1150] The server provides dashboards and web applications that visually display disaster information, damage status, response progress, etc. to users, allowing them to grasp the situation in real time and quickly take necessary action.

[1151] Specific examples

[1152] For example, if a magnitude 6 earthquake occurs, the server obtains earthquake information through an external API and reflects it in the GIS. At the same time, AI technology is used to generate initial response plans such as "stopping equipment," "evacuating employees," and "building safety inspections." These are then instructed to robots within the factory, which then quickly take action. Furthermore, the initial response plan is notified to the manager via email, allowing them to check the situation in real time. After a disaster occurs, the server automatically compiles the response status, compiles it into a report for management, and sends it to the manager.

[1153] Example prompts to input to the generative AI model

[1154] "An earthquake has occurred. The affected factory is in Shinjuku Ward, Tokyo. What initial response measures should be taken?"

[1155] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1156] Step 1:

[1157] The server collects data on business establishments across the country from a variety of data sources and inputs and manages it into a geographic information system (GIS). Specifically, the server calls an API to obtain information on the location, business activities, number of employees, and important equipment of each establishment. The input data is visualized on a map.

[1158] Input: "Business data" (location information, business details, number of employees, information on important facilities)

[1159] Output: "Business data visualized on GIS"

[1160] Step 2:

[1161] The server collects disaster information in real time from external APIs and various sensor data. The server obtains disaster information such as earthquakes, typhoons, and floods through APIs from the Japan Meteorological Agency. The collected information is reflected in the GIS.

[1162] Input: "External API and sensor data" (disaster information such as earthquakes, typhoons, and floods)

[1163] Output: "Disaster information reflected in GIS"

[1164] Step 3:

[1165] The server uses AI to generate optimal initial response plans based on the collected disaster information. The AI ​​refers to past data, the latest research results, and simulation results to propose response measures according to the type of disaster.

[1166] Input: "Disaster information" (information on earthquakes, typhoons, floods, etc.)

[1167] Output: "AI-generated initial response plan"

[1168] Step 4:

[1169] The server then sends the generated initial response plan to the factory robots, who then carry out the response according to the instructions. Specifically, the robots perform emergency shutdowns of equipment, secure evacuation routes, protect important facilities, etc.

[1170] Input: "AI-generated initial response plan"

[1171] Output: "Robot first response execution"

[1172] Step 5:

[1173] The server notifies the factory manager of the generated initial response plan via email or a dedicated application, allowing the manager to check the situation in real time.

[1174] Input: "AI-generated initial response plan"

[1175] Output: "Notification to Administrator"

[1176] Step 6:

[1177] The server automatically compiles information on the situation after a disaster occurs and generates a report for management, summarizing the damage situation, the progress of the initial response, and future countermeasures.

[1178] Input: "Post-disaster response status data"

[1179] Output: "Report for management"

[1180] Step 7:

[1181] The server will store disaster response data from each business location and generate a manual that will be useful for future responses, including building inspection methods, rapid evacuation procedures for employees, and measures to protect important equipment.

[1182] Input: "Accumulated disaster response data"

[1183] Output: "A manual that will be useful for future reference"

[1184] Step 8:

[1185] The server provides dashboards and web applications that visually display disaster information, damage status, response progress, etc. Users can grasp the situation in real time through the dashboard and quickly take necessary action.

[1186] Input: "Disaster information, damage situation, response progress data"

[1187] Output: "Information visualized in a dashboard or web application"

[1188] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1189] This invention is a system for streamlining the initial response of a business in the event of a disaster, and is composed of a geographic information system, external APIs, sensor data, AI technology, an emotion engine, a dashboard, and a web application. Below, we will explain in detail each component of this system and the processing flow based on them.

[1190] Geographic Information System (GIS) and Business Data Management

[1191] The server collects data on business establishments across the country from various data sources and inputs and manages it into a geographic information system (GIS). Business establishment data includes location information, business activities, number of employees, and information on important facilities. The GIS visualizes this data on a map and provides information on each business establishment in an easily accessible format for users.

[1192] Specific examples

[1193] The server calls the API to collect data on specific business locations in Tokyo. This data includes location (Shinjuku Ward, Tokyo), business type (IT company), number of employees (50), and critical facilities (data center). The collected data is registered in the GIS, and users can view it on a map via a web application.

[1194] Real-time disaster information collection

[1195] The server collects real-time disaster information from the Japan Meteorological Agency and various sensor data. Specifically, it obtains data on natural disasters such as earthquakes, typhoons, and floods via external APIs and sensor networks, and reflects this data in the GIS.

[1196] Specific examples

[1197] The server retrieves information from the Japan Meteorological Agency's API about a magnitude 6 earthquake with its epicenter in Tokyo Bay. This information is input into a GIS, and the affected areas around Tokyo Bay are highlighted in red on a map.

[1198] AI-powered disaster response advice

[1199] The server uses AI to generate optimal initial response plans based on the disaster information collected. This AI refers to past data, the latest research papers, and simulation results to propose specific countermeasures.

[1200] Specific examples

[1201] In the event of a magnitude 6 earthquake, the AI ​​will generate initial response plans, such as "conducting safety inspections of business buildings," "instructing employees to evacuate," and "procedures for emergency shutdowns of data centers." The server will then notify the business of this information.

[1202] Reporting and reporting to management

[1203] The server automatically compiles information on the situation after a disaster occurs and generates a report for management, summarizing the damage situation, the progress of the initial response, and future response measures.

[1204] Specific examples

[1205] The server uses the damage information from the GIS to create a damage report for a specific business location in Tokyo. This report includes information such as "Partial damage to the building," "All employees have safely evacuated," and "Data center emergency shutdown procedures have been completed." The report is then sent to management via email.

[1206] Knowledge accumulation and manual generation

[1207] The server accumulates disaster response data from each business location and generates a manual that will be useful for future responses. This manual is constantly updated to reflect the latest response experience.

[1208] Specific examples

[1209] The server will store the response data for the Tokyo Bay earthquake in a database and generate a new earthquake response manual, which will include building inspection methods, rapid evacuation procedures for employees, and measures to protect important equipment.

[1210] Dashboards and Web Applications

[1211] The server provides dashboards and web applications that visually display disaster information, damage status, response progress, etc. to users, allowing them to grasp the situation in real time and quickly take necessary action.

[1212] Specific examples

[1213] Users can access the dashboard through a web application to check current earthquake information, damage status, and the progress of initial responses in real time. For example, they can immediately see a list of employees who have completed evacuation and the status of ongoing building inspections.

[1214] Emotion Engine

[1215] The server is equipped with an emotion engine that analyzes the user's emotional data. The emotion engine analyzes the user's voice and text data to assess their stress level, anxiety level, etc. Based on the results, the server adjusts the initial response plan and takes appropriate action.

[1216] Specific examples

[1217] The server receives the user's voice data and analyzes it using an emotion engine. As a result, it is determined that the user is in a state of high stress. Based on this, the AI ​​means generates initial response plans, including "support messages for rapid evacuation" and "provision of psychological support." The server notifies this information to the business.

[1218] The system of this invention will improve the efficiency of initial responses when a disaster occurs, enabling a rapid understanding of damage and appropriate countermeasures. Furthermore, the introduction of an emotion engine will enable measures to be taken to reduce employee stress and anxiety. This will ensure both workplace safety and employee mental health.

[1219] The processing flow will be explained below.

[1220] Step 1: Collect and manage business data

[1221] The server collects data on businesses across the country through APIs and database connections and inputs it into a geographic information system (GIS).

[1222] Operation:

[1223] The server calls an external API to obtain business data.

[1224] The data is received in JSON format and stored in the database.

[1225] Extract business data from the database and send it to the GIS.

[1226] GIS maps and visualizes business locations on a map.

[1227] Step 2: Collecting real-time disaster information

[1228] The server collects real-time disaster information from the Japan Meteorological Agency and various sensor data.

[1229] Operation:

[1230] The server periodically polls the Japan Meteorological Agency API to obtain the latest earthquake information.

[1231] Sensor data is received in real time via WebSocket or MQTT protocols.

[1232] The acquired earthquake epicenter and seismic intensity information is sent to the GIS.

[1233] The GIS highlights affected areas on a map and matches them with business information.

[1234] Step 3: Generate initial response plans using AI

[1235] The server inputs the collected disaster information into an AI model and generates the optimal initial response plan.

[1236] Operation:

[1237] The server sends the collected disaster data to the AI ​​and activates the prediction module.

[1238] AI generates countermeasures based on past response data and the latest research results.

[1239] The server receives the initial response plan generated by the AI.

[1240] Step 4: Collect and analyze user emotion data

[1241] The server uses an emotion engine to collect and analyze the user's emotion data.

[1242] Operation:

[1243] The server collects users' voice and text data through microphones and chat logs.

[1244] An emotion engine analyzes this data and assesses stress and anxiety levels.

[1245] Step 5: Adjust initial response plans based on sentiment data

[1246] The server adjusts the initial response plan based on the analysis results of the emotion engine.

[1247] Operation:

[1248] The server sends the emotional data to the AI, which generates a response that reflects the user's emotional state.

[1249] For example, if stress levels are high, the system generates response plans that include quick evacuation guidance and providing mental support.

[1250] The server notifies the establishment of this information.

[1251] Step 6: Notify the business of the initial response plan

[1252] The server notifies each business office of the generated initial response plan.

[1253] Operation:

[1254] The server classifies the assigned countermeasures by business establishment.

[1255] Send messages to designated businesses using the Slack API or email services.

[1256] The message includes specific response measures such as evacuation instructions and equipment shutdown procedures.

[1257] Step 7: Compiling damage data and creating a report

[1258] The server compiles data on the situation after a disaster occurs and generates a report for management.

[1259] Operation:

[1260] The server compiles damage information based on the GIS damage information.

[1261] Enter data such as a list of affected businesses, response progress, and next steps into a report template.

[1262] Generate reports in PDF format and email them to management.

[1263] Step 8: Knowledge accumulation and manual generation

[1264] The server accumulates disaster response data from each business location and generates a dynamic manual.

[1265] Operation:

[1266] The server receives disaster response data sent from each business location and stores it in a database.

[1267] Past corresponding data is extracted from the database and a manual generation engine is started.

[1268] The manuals are updated based on the latest corresponding information and are generated as training and practical manuals.

[1269] Step 9: Serving the Dashboard and Web Application

[1270] The server provides information to users using a dashboard or web application.

[1271] Operation:

[1272] The server displays real-time disaster information, damage status, response progress, and other data on the dashboard.

[1273] Provides information to users through web applications and supports immediate response.

[1274] Example 2

[1275] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1276] When a disaster strikes, a business's initial response requires the collection of a wide range of information and rapid decision-making, making it difficult to respond efficiently using conventional methods. Furthermore, employees' mental stress and anxiety can also hinder a rapid initial response. For this reason, a system that can easily aggregate information and generate and provide optimal initial response plans is needed.

[1277] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for collecting and managing business establishment data using a geographic information system; means for collecting disaster information in real time from external APIs and sensor data; AI means for generating appropriate initial response plans based on the collected disaster information; means for notifying the business establishment of the generated initial response plans; means for compiling the situation after a disaster occurs and creating a report for management; means for accumulating response data and generating manuals based on the data to always reflect the latest response experience; means for visually providing this information to users using a dashboard or web application; and means for analyzing user emotion data using an emotion engine and adjusting the initial response plans. This enables fast and efficient initial response and employee stress management.

[1278] A "geographic information system" is an information system for collecting, managing, analyzing, and visually displaying geospatial data.

[1279] "Establishment data" refers to information regarding the location, business activities, number of employees, and important facilities of a business establishment.

[1280] An "external API" is an interface for connecting with other systems and services, and is used to obtain external data in real time.

[1281] "Sensor data" refers to data such as environmental information and disaster information collected from various sensors.

[1282] "Disaster information" refers to data related to natural disasters such as earthquakes, typhoons, and floods.

[1283] "AI methods" are methods that use artificial intelligence to analyze data and generate optimal initial response plans.

[1284] An "initial response plan" is a specific plan for the course of action and procedures that a business should immediately implement in the event of a disaster.

[1285] "Notification means" refers to a means for communicating the generated initial response plan and other information to the business establishment.

[1286] The "report creation means" is a means for compiling the situation after a disaster occurs and automatically creating a report for management.

[1287] "Response data" refers to records and data related to past disaster responses.

[1288] "Manual generation means" refers to a means for creating a disaster response manual based on accumulated response data.

[1289] A "dashboard" is an interface that allows users to visually check disaster information, damage status, response progress, and so on.

[1290] A "web application" is an application that can be used by a user through a web browser.

[1291] The "emotion engine" is a system that analyzes a user's voice and text data to assess their emotional state, such as stress level and anxiety level.

[1292] This invention is a system for streamlining the initial response of businesses when disasters occur, and is composed of a geographic information system (GIS), external APIs, sensor data, AI technology, an emotion engine, a dashboard, and a web application.

[1293] 1. Geographic Information System (GIS) and Business Data Management

[1294] The server collects data on business establishments across the country from APIs and external databases and inputs it into the GIS. Business establishment data includes location information, business activities, number of employees, and information on important facilities. The GIS visualizes this data on a map and provides it in an easily accessible format for users. For example, the server uses an API to collect data on IT companies in Shinjuku Ward, Tokyo, and registers it in the GIS. This allows users to check detailed information about the establishments through a web application.

[1295] 2. Real-time disaster information collection

[1296] The server collects information on natural disasters such as earthquakes, typhoons, and floods in real time from the Japan Meteorological Agency's API and various sensor data. The collected disaster information is reflected in the GIS, and the affected area is displayed on a map. For example, the server uses the Japan Meteorological Agency's API to obtain information on a magnitude 6 earthquake with an epicenter in Tokyo Bay, and by immediately reflecting this information in the GIS, it is possible to visually display the affected areas around Tokyo Bay.

[1297] 3. AI-based disaster response advice

[1298] The server passes the collected disaster information to an AI model for analysis and generates an optimal initial response plan. This AI method proposes effective countermeasures based on past data, the latest research papers, and simulation results. For example, if a magnitude 6 earthquake occurs, the AI ​​method will generate an initial response plan including "safety inspection of the business building," "emergency evacuation instructions for employees," and "emergency shutdown procedures for the data center," and the server will notify the business.

[1299] 4. Report creation and reporting to management

[1300] The server compiles data on the situation after a disaster and automatically generates reports for management. These reports include information on the damage situation, the progress of the initial response, and future countermeasures. For example, the server can create a status report for a specific business location in Tokyo based on data obtained from the GIS and send it to management by email.

[1301] 5. Knowledge accumulation and manual generation

[1302] The server accumulates disaster response data from each business location and generates a manual based on that data that will be useful for future responses. This manual always reflects the latest response experience and is dynamically updated. For example, the server can store response data from the Tokyo Bay earthquake in a database and generate a new earthquake response manual.

[1303] 6. Dashboards and Web Applications

[1304] The server provides dashboards and web applications that visually display disaster information, damage status, and response progress to users. Users can use this to understand the situation in real time and quickly take necessary action. For example, users can access the dashboard through a web application to check earthquake information, damage status, and the status of ongoing initial responses.

[1305] 7. Emotion Engine

[1306] The server uses an emotion engine to analyze the user's emotional data. The emotion engine analyzes the voice and text data to evaluate the user's stress level and anxiety. Based on the results, the AI ​​means adjusts the initial response plan and proposes an appropriate response. For example, if the server acquires the user's voice data and analyzes it with the emotion engine and determines that the user is in a high stress state, the AI ​​means will generate an initial response plan including a "support message for rapid evacuation" and "provision of mental support," and the server will notify the business.

[1307] Specific prompt examples

[1308] Below are some examples of specific prompt sentences to input into the generative AI model.

[1309] "If an earthquake with a seismic intensity of 6 occurs with its epicenter in Tokyo Bay, please propose the initial response plan for your business."

[1310] "Please display a dashboard to check the real-time evacuation status of specific businesses in Tokyo."

[1311] "Analyze the user's emotional state based on their voice data and generate appropriate initial response plans."

[1312] The system of this invention will improve the efficiency of initial responses in the event of a disaster, enabling prompt and appropriate responses. In addition, by taking into consideration the mental health of employees through the emotion engine, it will be possible to ensure both the safety of the workplace and the mental health of employees.

[1313] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1314] Step 1: Collect and manage business data

[1315] The server collects data on business establishments across the country from APIs and external databases. The data collected here includes location information, business operations, number of employees, and information on important facilities. The input data is detailed information on business establishments obtained from the API, and the output is business establishment data registered in the GIS.

[1316] Specific operation: The server calls the API and retrieves data on IT companies located in Shinjuku Ward, Tokyo. This data includes "Location: Shinjuku Ward, Tokyo," "Business: IT company," "Number of employees: 50," and "Key facilities: Data center." The retrieved data is input into GIS and visualized on a map.

[1317] Step 2: Collecting real-time disaster information

[1318] The server collects real-time disaster information from the Japan Meteorological Agency's API and various sensors. The input data is disaster information obtained from external APIs and sensor data, and the output is the disaster situation reflected in the GIS.

[1319] Specific operation: The server uses an API provided by the Japan Meteorological Agency to obtain information on an earthquake with a seismic intensity of 6 and centered in Tokyo Bay. The obtained information is input into a GIS, and the affected areas around Tokyo Bay are highlighted in red on a map.

[1320] Step 3: AI-based disaster response advice

[1321] The server passes the collected disaster information to the AI ​​model for analysis. The input data is real-time disaster information obtained from the GIS, and the output is an initial response plan generated by the AI.

[1322] Specific operation: The server uses AI means to analyze earthquake information with a seismic intensity of 6. The AI ​​means generates an initial response plan including "safety inspection of the business building," "evacuation instructions for employees," and "emergency shutdown procedures for the data center," and the server notifies the business.

[1323] Step 4: Reporting and reporting to management

[1324] The server aggregates data on the situation after a disaster and automatically generates reports for management. The input data is information from GIS and AI models, and the output is a report sent to management.

[1325] Specific operation: The server creates a status report for specific business locations in Tokyo based on the damage information obtained from the GIS. This report includes information such as "Partial damage to the building," "All employees have safely evacuated," and "The data center has completed emergency shutdown procedures." The report is then sent to management via email.

[1326] Step 5: Knowledge accumulation and manual generation

[1327] The server accumulates disaster response data from each business location and generates a manual based on that data that will be useful for future responses. The input data is past disaster response information, and the output is an updated response manual.

[1328] Specific operation: The server saves the response data for the Tokyo Bay earthquake in a database and generates a new earthquake response manual. The new manual includes "building inspection methods," "quick evacuation procedures for employees," and "protection measures for important equipment."

[1329] Step 6: Dashboard and Web Applications

[1330] The server provides dashboards and web applications that visually display disaster information, damage status, and response progress to users. The input data is information from GIS and AI models, and the output is the dashboard or web application screen used by users.

[1331] How it works: Users can access a dashboard through a web application to check real-time earthquake information, damage status, and the progress of initial responses. It also displays a list of employees who have completed evacuation and the status of ongoing building inspections.

[1332] Step 7: Emotion Engine

[1333] The server uses an emotion engine to analyze the user's emotional data and adjust the initial response plan. The input data is the user's voice data and text data, and the output is a response plan based on the analysis results.

[1334] Specific operation: The server acquires the user's voice data and analyzes it using an emotion engine. If the analysis results indicate that the user is in a high stress state, the AI ​​means generates initial response plans, including "support messages for rapid evacuation" and "provision of psychological support," and the server notifies the business.

[1335] (Application example 2)

[1336] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1337] Improving the efficiency of a facility's initial response in the event of a disaster and implementing appropriate measures quickly are crucial for minimizing damage and quickly restoring operations. However, conventional systems do not adequately collect real-time information, generate appropriate response plans, or analyze employees' emotional states and reflect that information in their responses. As a result, there was a lack of an appropriate system for situations that require efficient initial responses and flexible responses that take into account employees' emotional states.

[1338] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1339] In this invention, the server includes means for collecting and managing facility data using a geographic information system, means for collecting disaster information in real time from external APIs and sensor data, AI means for generating appropriate initial response plans based on the collected disaster information, means for notifying the facility of the generated initial response plans, means for compiling post-disaster situations and creating reports for management, means for accumulating response data and generating manuals based on the data, means for providing a dashboard or web application that provides this information to users, means for analyzing user voice data and text data and using an emotion engine to evaluate the user's emotional state, and means for adjusting the initial response plans and taking appropriate action based on the analysis results of the emotion engine. This improves the efficiency of initial responses when a disaster occurs and enables flexible and appropriate responses that take into account the emotional states of employees.

[1340] A "geographic information system" is a system for collecting, managing, analyzing, and visually displaying geospatial data.

[1341] "Facility data" refers to data that includes location information, business operations, number of employees, and information on important equipment related to a specific facility.

[1342] An "external API" is a means of obtaining data using a program interface provided by an external system or service.

[1343] "Sensor data" refers to physical and environmental data obtained from various sensors.

[1344] "Disaster information" refers to real-time information about natural disasters such as earthquakes, typhoons, and floods.

[1345] "AI means" refers to a system that uses artificial intelligence technology to analyze data and generate output results such as response proposals.

[1346] An "emotion engine" is a technology that analyzes voice and text data to evaluate the user's emotional state.

[1347] A "dashboard" is an interface that visually displays real-time information and makes it easier for users to understand the situation.

[1348] A "WEB application" is application software provided using web technology.

[1349] "Notification means" refers to a function for transmitting generated information to users and systems.

[1350] A "report" refers to a report that organizes collected data and information and compiles it for specific users, such as management.

[1351] A "manual" is a document that describes specific procedures and measures that govern how to respond to disasters and how to carry out business operations.

[1352] This invention is a system for improving the efficiency of disaster response in logistics centers. This system is composed of a geographic information system (GIS), external APIs, sensor data, AI technology, an emotion engine, a dashboard, and a web application.

[1353] Geographic Information System (GIS) and Facility Data Management

[1354] The server collects facility data from various sources across the country and inputs and manages it into a geographic information system (GIS). Facility data includes location information, business operations, number of employees, and information on important equipment. The GIS visualizes this data on a map, providing information on each facility in an easily accessible format for users.

[1355] Specific examples

[1356] The server calls the API to collect data on a specific logistics center in Tokyo. This data includes the location (Shinjuku-ku, Tokyo), business operations (logistics management), number of employees (50), and critical equipment (warehouse). The collected data is registered in the GIS, and users can view it on a map via a web application.

[1357] Real-time disaster information collection

[1358] The server collects real-time disaster information from the Japan Meteorological Agency and various sensor data. Specifically, it obtains data on natural disasters such as earthquakes, typhoons, and floods via external APIs and sensor networks, and reflects this data in the GIS.

[1359] Specific examples

[1360] The server retrieves information from the Japan Meteorological Agency's API about a magnitude 6 earthquake with its epicenter in Tokyo Bay. This information is input into a GIS, and the affected areas around Tokyo Bay are highlighted in red on a map.

[1361] AI-powered disaster response advice

[1362] The server uses AI to generate optimal initial response plans based on the disaster information collected. This AI refers to past data, the latest research papers, and simulation results to propose specific countermeasures.

[1363] Specific examples

[1364] In the event of a magnitude 6 earthquake, the AI ​​will generate initial response plans, such as "conducting safety inspections of facility buildings," "instructing employees to evacuate," and "procedures for emergency shutdowns of warehouses." The server then notifies the facility of this information.

[1365] Emotion Engine

[1366] The server is equipped with an emotion engine that analyzes the user's emotional data. The emotion engine analyzes the user's voice and text data to assess their stress level, anxiety level, etc. Based on the results, the server adjusts the initial response plan and takes appropriate action.

[1367] Specific examples

[1368] The server receives the user's voice data and analyzes it using an emotion engine. As a result, it determines that the user is in a state of high stress. Based on this, the AI ​​generates initial response plans, including "support messages for rapid evacuation" and "provision of psychological support." The server then notifies the facility of this information.

[1369] Dashboards and Web Applications

[1370] The server provides dashboards and web applications that visually display disaster information, damage status, response progress, etc. to users, allowing them to grasp the situation in real time and quickly take necessary action.

[1371] Specific examples

[1372] Users can access the dashboard through a web application to check current earthquake information, damage status, and the progress of initial responses in real time. For example, they can immediately see a list of employees who have completed evacuation and the status of ongoing building inspections.

[1373] Reporting and reporting to management

[1374] The server automatically compiles information on the situation after a disaster occurs and generates a report for management, summarizing the damage situation, the progress of the initial response, and future response measures.

[1375] Specific examples

[1376] The server uses the damage information from the GIS to create a damage report for a specific logistics center in Tokyo. This report includes information such as "Partial damage to the building," "All employees have safely evacuated," and "Emergency shutdown procedures have been completed at the warehouse." The report is then sent to management via email.

[1377] Knowledge accumulation and manual generation

[1378] The server accumulates disaster response data from each facility and generates a manual that will be useful for future responses. This manual is constantly updated to reflect the latest response experience.

[1379] Specific examples

[1380] The server will store the response data for the Tokyo Bay earthquake in a database and generate a new earthquake response manual, which will include building inspection methods, rapid evacuation procedures for employees, and measures to protect important equipment.

[1381] Prompt Sentence Examples

[1382] Generate a plan for initial response in the event of a disaster based on the following facility data:

[1383] Earthquake (Epicenter: Tokyo, Seismic Intensity: 6)

[1384] Damaged area: Logistics center (Shinjuku-ku, Tokyo)

[1385] Important information: Warehouse safety inspections, employee evacuation instructions, warehouse emergency shutdown procedures

[1386] As a result, the system of the present invention will improve the efficiency of initial responses when a disaster occurs, and will enable flexible and appropriate responses that take into account the emotional state of employees.

[1387] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1388] Step 1:

[1389] Geographic Information System (GIS) and Facility Data Management

[1390] The server collects facility data from various sources across the country. The collected data includes location information, business operations, number of employees, and important facilities, and inputs and manages this data into a GIS. The server visualizes this GIS data on a map and makes it accessible to users through a web application.

[1391] Input: Facility data retrieved from data sources

[1392] Output: Visualized data displayed on a GIS

[1393] Specific operations: Collecting data by calling API, saving to database, and drawing data on map

[1394] Step 2:

[1395] Real-time disaster information collection

[1396] The server collects real-time disaster information from external APIs and various sensor data. Specifically, it uses APIs from the Japan Meteorological Agency and other sources to obtain information on earthquakes, typhoons, floods, etc., and reflects this information in the GIS.

[1397] Input: Japan Meteorological Agency API and sensor data

[1398] Output: Disaster information reflected in GIS

[1399] Specific operations: Information acquisition by API calls, processing of sensor data, input into GIS database

[1400] Step 3:

[1401] AI-powered disaster response advice

[1402] The server uses AI technology to generate optimal initial response plans based on the collected disaster information. This AI refers to past disaster data and the latest research results to propose specific countermeasures.

[1403] Input: Real-time disaster information

[1404] Output: Initial response plan

[1405] Specific operations: Analysis of disaster information, generation of countermeasures using AI models, notification of countermeasures

[1406] Step 4:

[1407] Generated initial response plan is notified to the facility

[1408] The server then notifies each facility of the generated initial response plan via a variety of methods, including email and app notifications.

[1409] Input: AI-generated initial response plan

[1410] Output: Action suggestions sent via notification channels

[1411] Specific actions: Generate notification content and send it via communication means

[1412] Step 5:

[1413] User analysis using emotion engine

[1414] The server receives the user's voice and text data and analyzes it using an emotion engine to evaluate their stress and anxiety levels, and reflects the analysis results in the initial response plan.

[1415] Input: User voice or text data

[1416] Output: Emotion analysis results

[1417] Specific actions: Data collection, processing of emotion analysis algorithms, and reflection of analysis results in initial response plans

[1418] Step 6:

[1419] Providing information through dashboards and web applications

[1420] The server provides users with visual information on disasters, damage situations, response progress, etc. via dashboards and web applications, allowing users to grasp the situation in real time and quickly take necessary action.

[1421] Input: GIS data, real-time disaster information, initial response plan, emotion analysis results

[1422] Output: Visualizations on dashboards and web applications

[1423] Specific actions: Data collection, real-time visualization, and user interface updates

[1424] Step 7:

[1425] Reporting and reporting to management

[1426] The server automatically compiles information on the situation after a disaster occurs and generates a report for management, summarizing the damage situation, the progress of the initial response, and future countermeasures, and sends it to management via email or other means.

[1427] Input: Disaster information, progress of initial response, emotion analysis results

[1428] Output: Report for management

[1429] Specific actions: Data collection, report generation, and sending of report contents

[1430] Step 8:

[1431] Knowledge accumulation and manual generation

[1432] The server accumulates disaster response data from each facility and generates a manual that will be useful for future responses. This manual is constantly updated to reflect the latest response experience.

[1433] Input: Disaster response data

[1434] Output: Updated manual

[1435] Specific operations: saving to database, executing manual generation algorithm, updating manual

[1436] 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.

[1437] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1438] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1439] [Fourth embodiment]

[1440] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1441] 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.

[1442] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[1443] 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.

[1444] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1445] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1446] 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. 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.

[1447] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1448] 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.

[1449] 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 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.

[1450] 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.

[1451] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1452] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1453] This invention is a system for streamlining the initial response of a business in the event of a disaster, and is specifically composed of a geographic information system, external APIs, sensor data, AI technology, dashboards, and web applications. Below, we will explain in detail each component of this system and the processing flow based on them.

[1454] Geographic Information System (GIS) and Business Data Management

[1455] The server collects data on business establishments across the country from various data sources and inputs and manages it into a geographic information system (GIS). Business establishment data includes location information, business activities, number of employees, and information on important facilities. The GIS visualizes this data on a map and provides information on each business establishment in an easily accessible format for users.

[1456] Specific examples

[1457] The server calls the API to collect data on specific business locations in Tokyo. This data includes location (Shinjuku Ward, Tokyo), business type (IT company), number of employees (50), and critical facilities (data center). The collected data is registered in the GIS, and users can view it on a map via a web application.

[1458] Real-time disaster information collection

[1459] The server collects real-time disaster information from the Japan Meteorological Agency and various sensor data. Specifically, it obtains data on natural disasters such as earthquakes, typhoons, and floods via external APIs and sensor networks, and reflects this data in the GIS.

[1460] Specific examples

[1461] The server retrieves information from the Japan Meteorological Agency's API about a magnitude 6 earthquake with its epicenter in Tokyo Bay. This information is input into a GIS, and the affected areas around Tokyo Bay are highlighted in red on a map.

[1462] AI-powered disaster response advice

[1463] The server uses AI to generate optimal initial response plans based on the disaster information collected. This AI refers to past data, the latest research papers, and simulation results to propose specific countermeasures.

[1464] Specific examples

[1465] In the event of a magnitude 6 earthquake, the AI ​​will generate initial response plans, such as "conducting safety inspections of business buildings," "instructing employees to evacuate," and "procedures for emergency shutdowns of data centers." The server will then notify the business of this information.

[1466] Reporting and reporting to management

[1467] The server automatically compiles information on the situation after a disaster occurs and generates a report for management, summarizing the damage situation, the progress of the initial response, and future response measures.

[1468] Specific examples

[1469] The server uses the damage information from the GIS to create a damage report for a specific business location in Tokyo. This report includes information such as "Partial damage to the building," "All employees have safely evacuated," and "Data center emergency shutdown procedures have been completed." The report is then sent to management via email.

[1470] Knowledge accumulation and manual generation

[1471] The server accumulates disaster response data from each business location and generates a manual that will be useful for future responses. This manual is constantly updated to reflect the latest response experience.

[1472] Specific examples

[1473] The server will store the response data for the Tokyo Bay earthquake in a database and generate a new earthquake response manual, which will include building inspection methods, rapid evacuation procedures for employees, and measures to protect important equipment.

[1474] Dashboards and Web Applications

[1475] The server provides dashboards and web applications that visually display disaster information, damage status, response progress, etc. to users, allowing them to grasp the situation in real time and quickly take necessary action.

[1476] Specific examples

[1477] Users can access the dashboard through a web application to check current earthquake information, damage status, and the progress of initial responses in real time. For example, they can immediately see a list of employees who have completed evacuation and the status of ongoing building inspections.

[1478] The system of the present invention will improve the efficiency of initial responses when a disaster occurs, enable rapid assessment of damage, and enable appropriate countermeasures, thereby ensuring the safety of business establishments and minimizing damage.

[1479] The processing flow will be explained below.

[1480] Step 1: Collect and manage business data

[1481] The server collects data on businesses across the country through APIs and database connections and inputs it into a geographic information system (GIS).

[1482] Operation:

[1483] The server calls an external API to obtain business data.

[1484] The data is received in JSON format and stored in the database.

[1485] Extract business data from the database and send it to the GIS.

[1486] GIS maps and visualizes business locations on a map.

[1487] Step 2: Collecting real-time disaster information

[1488] The server collects real-time disaster information from the Japan Meteorological Agency and various sensor data.

[1489] Operation:

[1490] The server periodically polls the Japan Meteorological Agency API to obtain the latest earthquake information.

[1491] Sensor data is received in real time via WebSocket or MQTT protocols.

[1492] The acquired earthquake epicenter and seismic intensity information is sent to the GIS.

[1493] The GIS highlights affected areas on a map and matches them with business information.

[1494] Step 3: Generate initial response plans using AI

[1495] The server inputs the collected disaster information into an AI model and generates the optimal initial response plan.

[1496] Operation:

[1497] The server sends the collected disaster data to the AI ​​and activates the prediction module.

[1498] AI generates countermeasures based on past response data and the latest research results.

[1499] The server receives the initial response plan generated by the AI.

[1500] Step 4: Notify the business of the initial response plan

[1501] The server notifies each business office of the generated initial response plan.

[1502] Operation:

[1503] The server classifies the assigned countermeasures by business establishment.

[1504] Send messages to designated businesses using the Slack API or email services.

[1505] The message includes specific response measures such as evacuation instructions and equipment shutdown procedures.

[1506] Step 5: Compiling damage data and creating a report

[1507] The server compiles data on the situation after a disaster occurs and generates a report for management.

[1508] Operation:

[1509] The server compiles damage information based on the GIS damage information.

[1510] Enter data such as a list of affected businesses, response progress, and next steps into a report template.

[1511] Generate reports in PDF format and email them to management.

[1512] Step 6: Knowledge accumulation and manual creation

[1513] The server accumulates disaster response data from each business location and generates a dynamic manual.

[1514] Operation:

[1515] The server receives disaster response data sent from each business location and stores it in a database.

[1516] Past corresponding data is extracted from the database and a manual generation engine is started.

[1517] The manuals are updated based on the latest corresponding information and are generated as training and practical manuals.

[1518] Step 7: Serving the Dashboard and Web Application

[1519] The server provides information to users using a dashboard or web application.

[1520] Operation:

[1521] The server displays real-time disaster information, damage status, response progress, and other data on the dashboard.

[1522] Provides information to users through web applications and supports immediate response.

[1523] Example 1

[1524] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1525] In the past, when a disaster occurred, the initial response at a business site was difficult because it took time to collect and understand information. Furthermore, the management and analysis of collected information was often done manually, which led to problems such as errors and delays. This created major challenges in ensuring the safety of business sites and minimizing damage.

[1526] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1527] In this invention, the server includes means for collecting and managing business establishment data using a geographic information system, means for collecting disaster information in real time from external interfaces and sensor data, artificial intelligence means for generating appropriate initial response plans based on the collected disaster information, means for notifying the business establishment of the generated initial response plans, means for compiling the situation after a disaster occurs and creating reports for each level, means for accumulating response data and generating guides based on this, interface means for providing this information to users, means for visualizing business establishment data and disaster information on a map, means for tracking and managing the progress of countermeasures at each business establishment in real time, and means for proposing countermeasures related to the business establishment based on the collected data and disaster information. This enables rapid and accurate information collection and management, and enables business establishments to carry out initial responses efficiently and effectively.

[1528] A "geographic information system" is a system that collects, stores, analyzes, and visually displays geospatial data.

[1529] "Establishment data" refers to information related to a specific establishment, such as location information, business operations, number of employees, and important facilities.

[1530] An "external interface" is a means for communicating and exchanging data with external sources or systems.

[1531] "Sensor data" refers to information obtained from various sensors, including environmental data such as temperature, humidity, and seismic intensity.

[1532] "Disaster information" refers to information related to natural disasters such as earthquakes, typhoons, and floods, including the extent of damage and the timing of occurrence.

[1533] "Artificial intelligence tools" are algorithms or systems that use machine learning and data analysis techniques to solve specific problems.

[1534] An "initial response plan" is a set of specific guidelines and procedures that should be followed as the first response when a disaster occurs.

[1535] "Reports for higher levels" are reports provided to management and administrative levels that summarize the impact of the disaster and the progress of the response.

[1536] "Response data" refers to data on specific response measures taken in response to a disaster and their results.

[1537] A "guide" is a procedure or manual that outlines the desired actions or responses in a particular situation.

[1538] "Interface means" refers to the means by which a user can interact with a system and input or obtain information.

[1539] A "visualization tool" is a method for displaying data graphically and making it easier to understand intuitively.

[1540] "Means for tracking and managing progress of countermeasures in real time" refers to a system or method for monitoring and managing the implementation status of countermeasures in real time.

[1541] "Means for proposing countermeasures" are algorithms or systems that automatically suggest optimal countermeasures based on collected data.

[1542] This invention is a system for streamlining a business's initial response in the event of a disaster, and is composed of a geographic information system (GIS), external interfaces, sensor data, artificial intelligence (AI) technology, a dashboard, and a web application.

[1543] Geographic Information System (GIS) and Business Data Management

[1544] The server collects data on businesses across the country via API and stores it in a database. The collected data is then entered into a geographic information system (GIS) for visualization. The data registered in the GIS includes location information, business details, number of employees, and information on important facilities.

[1545] Specific examples

[1546] For example, the server calls the API to collect data on a specific business location in Tokyo (location: Shinjuku Ward, Tokyo). This data includes "Business Description: IT Company," "Number of Employees: 50," and "Key Equipment: Data Center." The collected data is stored in a database and reflected in the GIS, allowing users to view it on a map via a web application.

[1547] Real-time disaster information collection

[1548] The server collects disaster information in real time through external interfaces such as the Japan Meteorological Agency and sensor networks. By acquiring information on earthquakes, typhoons, floods, etc. and reflecting it in the GIS, affected areas can be visualized.

[1549] Specific examples

[1550] The server receives real-time information from the Japan Meteorological Agency's API about a magnitude 6 earthquake with its epicenter in Tokyo Bay. This information is entered into the GIS, and the affected areas around Tokyo Bay are highlighted in red on the map.

[1551] AI-powered disaster response advice

[1552] The server inputs the collected disaster information into an AI model to generate optimal initial response plans. This AI model proposes countermeasures based on past disaster response data and the latest research results.

[1553] Specific examples

[1554] For example, if a magnitude 6 earthquake occurs, the AI ​​model will generate initial response plans such as "conducting a building safety inspection," "instructing employees to evacuate," and "procedures for emergency shutdown of the data center." The server will then notify the person in charge at the business site.

[1555] Reporting and reporting to management

[1556] After a disaster occurs, the server compiles data and automatically generates a report for management, summarizing the damage situation, the progress of the initial response, and future countermeasures.

[1557] Specific examples

[1558] Based on the damage information from the GIS, the server creates a damage report for specific business locations in Tokyo. This report includes information such as "Partial damage to the building," "All employees have safely evacuated," and "Data center emergency shutdown procedures have been completed," and is sent to management via email.

[1559] Knowledge accumulation and manual generation

[1560] The server stores disaster response data in a database and generates a manual to be used in future responses. The manual is constantly updated to reflect the latest response experience.

[1561] Specific examples

[1562] The server will store the response data for the Tokyo Bay earthquake in a database and generate a new earthquake response manual, which will include building inspection methods, rapid evacuation procedures for employees, and measures to protect important equipment.

[1563] Providing dashboards and web applications

[1564] The server visually displays disaster information, damage status, and response progress to users via dashboards and web applications, allowing users to grasp the situation in real time and quickly take necessary action.

[1565] Specific examples

[1566] Users can access the dashboard through the web application to view current earthquake information, damage status, and the progress of initial responses. For example, they can see a list of employees who have completed evacuation and the status of ongoing building inspections.

[1567] With the above configuration, the present invention makes it possible to improve the efficiency of initial responses when a disaster occurs, and to ensure the safety of business establishments and minimize damage by realizing rapid and accurate information collection and management.

[1568] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1569] Step 1:

[1570] The server collects data on business establishments across the country via API and stores it in a database. Specifically, it sends an API request and receives response data including location information, business operations, number of employees, and information on important equipment. This data is then saved in the database. The input is the response data from the external API, and the output is the business establishment data stored in the database.

[1571] Step 2:

[1572] The server inputs business data from the database into a geographic information system (GIS) and visualizes it. By obtaining location information from the database and registering it in the GIS, the location and information of the business are displayed on a map. The input is business data from the database, and the output is visualized data on the GIS.

[1573] Step 3:

[1574] The server collects disaster information in real time through external interfaces such as the Japan Meteorological Agency and sensor networks. Information is obtained from APIs and sensors and reflected in the GIS. Specifically, data on earthquakes, typhoons, floods, etc. is obtained and input into the GIS to visualize the affected areas. The input is disaster information obtained from external interfaces, and the output is visualized information reflected in the GIS.

[1575] Step 4:

[1576] The server inputs the collected disaster information into an artificial intelligence (AI) model to generate the optimal initial response plan. The disaster information is input into the AI ​​model, and specific response measures are generated based on past disaster response data and the latest research results. The input is the collected disaster information, and the output is the initial response plan.

[1577] Step 5:

[1578] The server notifies the business person in charge of the generated initial response plan. The countermeasure plan is sent via emergency email or notification system so that the business can respond quickly. The input is the generated initial response plan, and the output is the notification to the business.

[1579] Step 6:

[1580] The server compiles information on the situation after a disaster occurs and creates reports for each level. It collects situation data from the GIS and database, and organizes and compiles the data according to a report format. The generated report is sent to management by email. The input is situation data from the GIS and database, and the output is the generated report.

[1581] Step 7:

[1582] The server accumulates disaster response data in a database and generates a guide that will be useful for future responses. Specifically, it saves the response data, analyzes and organizes it, and then creates new guidelines. The input is the disaster response data, and the output is the generated guide.

[1583] Step 8:

[1584] The server provides dashboards and web applications, allowing users to check this information in real time. The dashboard visually displays disaster information, damage status, response progress, etc. The input is the collected data, and the output is the visualized information on the dashboard.

[1585] Step 9:

[1586] Users access the dashboard through a web application to check the situation and take prompt action based on the displayed information, if necessary. The input is the visualized information on the dashboard, and the output is the user's decision on what to do.

[1587] (Application example 1)

[1588] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1589] At factories and other business establishments, the challenge is to quickly and efficiently respond to disasters. In particular, there is a need for an efficient system that can centralize the collection of disaster information, the creation of initial response plans, notification to managers, real-time progress monitoring, and report creation. Furthermore, there is a need for dynamic generation of manuals based on accumulated response data.

[1590] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1591] In this invention, the server includes a means for collecting and managing business data using a geographic information system, a means for collecting disaster information in real time from external APIs and sensor data, a means for using AI technology to have a robot in the factory generate an initial response plan when a disaster occurs, a means for notifying the factory manager of the generated initial response plan, a means for compiling the situation after the disaster occurs and creating a report for management, a means for accumulating response data and creating a manual based on this, and a means for providing a dashboard and web application that provides this information to users. This enables a quick and efficient initial response when a disaster occurs and subsequent situation assessment.

[1592] A "geographic information system" is an information system for collecting, managing, analyzing, and visualizing geographic data.

[1593] "Establishment data" refers to data that includes information such as the location, facilities, number of employees, and business operations of the establishment.

[1594] An "external API" is an interface for communicating with external systems and services.

[1595] "Sensor data" refers to data including environmental information and situation data obtained from sensors.

[1596] "Disaster information" refers to information about natural disasters such as earthquakes, typhoons, and floods.

[1597] "AI technology" is a technology that applies machine learning and data analysis techniques to automatically process data and make predictions.

[1598] An "initial response plan" is a proposal for the first response measures to be taken when a disaster occurs.

[1599] A "factory manager" is a person in charge of managing operations and facilities within a factory.

[1600] "Notification" is the act of conveying information to a person from a system or device.

[1601] A "report" is a written summary of the situation and results.

[1602] A "manual" is a set of instructions for a particular operation or procedure.

[1603] A "dashboard" is a visual display screen that allows multiple data and information to be viewed at a glance.

[1604] A "WEB application" is application software that can be used through a web browser.

[1605] This invention is a system for quickly and efficiently responding to disasters at factories and other business establishments. The system uses a geographic information system (GIS), external APIs, sensor data, and AI technology to notify and instruct factory robots and managers.

[1606] The system consists of the following components:

[1607] 1. Business Data Collection and Management:

[1608] The server collects data on business establishments across the country from a variety of data sources and inputs and manages it into a geographic information system (GIS). Business establishment data includes location information, business activities, number of employees, and information on important facilities. The GIS visualizes this data on a map and provides information on each business establishment in a format that users can easily check.

[1609] 2. Real-time disaster information collection:

[1610] The server collects real-time disaster information from external APIs and various sensor data. Specifically, it obtains data on natural disasters such as earthquakes, typhoons, and floods through the Japan Meteorological Agency's API and sensor network, and reflects this information in the GIS.

[1611] 3. AI-powered disaster response advice:

[1612] The server uses AI to generate optimal initial response plans based on the disaster information collected. This AI refers to past data, the latest research results, and simulation results to propose specific countermeasures.

[1613] 4. Factory robots implement initial response:

[1614] The initial response plan generated by the AI ​​is sent to the robots in the factory, which then automatically take action based on the plan, such as shutting down equipment, securing evacuation routes, and protecting important facilities.

[1615] 5. Notice and Administrator Information:

[1616] The server then notifies the administrator of the generated initial response plan via email or a dedicated application, allowing the administrator to grasp the situation in real time.

[1617] 6. Post-disaster reporting:

[1618] The server automatically compiles information on the situation after a disaster occurs and generates a report for management, summarizing the damage situation, the progress of the initial response, and future countermeasures, and sends it to the administrator.

[1619] 7. Knowledge accumulation and manual generation:

[1620] The server accumulates disaster response data from each business location and generates a manual that will be useful for future responses. This manual is constantly updated to reflect the latest response experience.

[1621] 8. Dashboards and Web Applications:

[1622] The server provides dashboards and web applications that visually display disaster information, damage status, response progress, etc. to users, allowing them to grasp the situation in real time and quickly take necessary action.

[1623] Specific examples

[1624] For example, if a magnitude 6 earthquake occurs, the server obtains earthquake information through an external API and reflects it in the GIS. At the same time, AI technology is used to generate initial response plans such as "stopping equipment," "evacuating employees," and "building safety inspections." These are then instructed to robots within the factory, which then quickly take action. Furthermore, the initial response plan is notified to the manager via email, allowing them to check the situation in real time. After a disaster occurs, the server automatically compiles the response status, compiles it into a report for management, and sends it to the manager.

[1625] Example prompts to input to the generative AI model

[1626] "An earthquake has occurred. The affected factory is in Shinjuku Ward, Tokyo. What initial response measures should be taken?"

[1627] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1628] Step 1:

[1629] The server collects data on business establishments across the country from a variety of data sources and inputs and manages it into a geographic information system (GIS). Specifically, the server calls an API to obtain information on the location, business activities, number of employees, and important equipment of each establishment. The input data is visualized on a map.

[1630] Input: "Business data" (location information, business details, number of employees, information on important facilities)

[1631] Output: "Business data visualized on GIS"

[1632] Step 2:

[1633] The server collects disaster information in real time from external APIs and various sensor data. The server obtains disaster information such as earthquakes, typhoons, and floods through APIs from the Japan Meteorological Agency. The collected information is reflected in the GIS.

[1634] Input: "External API and sensor data" (disaster information such as earthquakes, typhoons, and floods)

[1635] Output: "Disaster information reflected in GIS"

[1636] Step 3:

[1637] The server uses AI to generate optimal initial response plans based on the collected disaster information. The AI ​​refers to past data, the latest research results, and simulation results to propose response measures according to the type of disaster.

[1638] Input: "Disaster information" (information on earthquakes, typhoons, floods, etc.)

[1639] Output: "AI-generated initial response plan"

[1640] Step 4:

[1641] The server then sends the generated initial response plan to the factory robots, who then carry out the response according to the instructions. Specifically, the robots perform emergency shutdowns of equipment, secure evacuation routes, protect important facilities, etc.

[1642] Input: "AI-generated initial response plan"

[1643] Output: "Robot first response execution"

[1644] Step 5:

[1645] The server notifies the factory manager of the generated initial response plan via email or a dedicated application, allowing the manager to check the situation in real time.

[1646] Input: "AI-generated initial response plan"

[1647] Output: "Notification to Administrator"

[1648] Step 6:

[1649] The server automatically compiles information on the situation after a disaster occurs and generates a report for management, summarizing the damage situation, the progress of the initial response, and future countermeasures.

[1650] Input: "Post-disaster response status data"

[1651] Output: "Report for management"

[1652] Step 7:

[1653] The server will store disaster response data from each business location and generate a manual that will be useful for future responses, including building inspection methods, rapid evacuation procedures for employees, and measures to protect important equipment.

[1654] Input: "Accumulated disaster response data"

[1655] Output: "A manual that will be useful for future reference"

[1656] Step 8:

[1657] The server provides dashboards and web applications that visually display disaster information, damage status, response progress, etc. Users can grasp the situation in real time through the dashboard and quickly take necessary action.

[1658] Input: "Disaster information, damage situation, response progress data"

[1659] Output: "Information visualized in a dashboard or web application"

[1660] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1661] This invention is a system for streamlining the initial response of a business in the event of a disaster, and is composed of a geographic information system, external APIs, sensor data, AI technology, an emotion engine, a dashboard, and a web application. Below, we will explain in detail each component of this system and the processing flow based on them.

[1662] Geographic Information System (GIS) and Business Data Management

[1663] The server collects data on business establishments across the country from various data sources and inputs and manages it into a geographic information system (GIS). Business establishment data includes location information, business activities, number of employees, and information on important facilities. The GIS visualizes this data on a map and provides information on each business establishment in an easily accessible format for users.

[1664] Specific examples

[1665] The server calls the API to collect data on specific business locations in Tokyo. This data includes location (Shinjuku Ward, Tokyo), business type (IT company), number of employees (50), and critical facilities (data center). The collected data is registered in the GIS, and users can view it on a map via a web application.

[1666] Real-time disaster information collection

[1667] The server collects real-time disaster information from the Japan Meteorological Agency and various sensor data. Specifically, it obtains data on natural disasters such as earthquakes, typhoons, and floods via external APIs and sensor networks, and reflects this data in the GIS.

[1668] Specific examples

[1669] The server retrieves information from the Japan Meteorological Agency's API about a magnitude 6 earthquake with its epicenter in Tokyo Bay. This information is input into a GIS, and the affected areas around Tokyo Bay are highlighted in red on a map.

[1670] AI-powered disaster response advice

[1671] The server uses AI to generate optimal initial response plans based on the disaster information collected. This AI refers to past data, the latest research papers, and simulation results to propose specific countermeasures.

[1672] Specific examples

[1673] In the event of a magnitude 6 earthquake, the AI ​​will generate initial response plans, such as "conducting safety inspections of business buildings," "instructing employees to evacuate," and "procedures for emergency shutdowns of data centers." The server will then notify the business of this information.

[1674] Reporting and reporting to management

[1675] The server automatically compiles information on the situation after a disaster occurs and generates a report for management, summarizing the damage situation, the progress of the initial response, and future response measures.

[1676] Specific examples

[1677] The server uses the damage information from the GIS to create a damage report for a specific business location in Tokyo. This report includes information such as "Partial damage to the building," "All employees have safely evacuated," and "Data center emergency shutdown procedures have been completed." The report is then sent to management via email.

[1678] Knowledge accumulation and manual generation

[1679] The server accumulates disaster response data from each business location and generates a manual that will be useful for future responses. This manual is constantly updated to reflect the latest response experience.

[1680] Specific examples

[1681] The server will store the response data for the Tokyo Bay earthquake in a database and generate a new earthquake response manual, which will include building inspection methods, rapid evacuation procedures for employees, and measures to protect important equipment.

[1682] Dashboards and Web Applications

[1683] The server provides dashboards and web applications that visually display disaster information, damage status, response progress, etc. to users, allowing them to grasp the situation in real time and quickly take necessary action.

[1684] Specific examples

[1685] Users can access the dashboard through a web application to check current earthquake information, damage status, and the progress of initial responses in real time. For example, they can immediately see a list of employees who have completed evacuation and the status of ongoing building inspections.

[1686] Emotion Engine

[1687] The server is equipped with an emotion engine that analyzes the user's emotional data. The emotion engine analyzes the user's voice and text data to assess their stress level, anxiety level, etc. Based on the results, the server adjusts the initial response plan and takes appropriate action.

[1688] Specific examples

[1689] The server receives the user's voice data and analyzes it using an emotion engine. As a result, it is determined that the user is in a state of high stress. Based on this, the AI ​​means generates initial response plans, including "support messages for rapid evacuation" and "provision of psychological support." The server notifies this information to the business.

[1690] The system of this invention will improve the efficiency of initial responses when a disaster occurs, enabling a rapid understanding of damage and appropriate countermeasures. Furthermore, the introduction of an emotion engine will enable measures to be taken to reduce employee stress and anxiety. This will ensure both workplace safety and employee mental health.

[1691] The processing flow will be explained below.

[1692] Step 1: Collect and manage business data

[1693] The server collects data on businesses across the country through APIs and database connections and inputs it into a geographic information system (GIS).

[1694] Operation:

[1695] The server calls an external API to obtain business data.

[1696] The data is received in JSON format and stored in the database.

[1697] Extract business data from the database and send it to the GIS.

[1698] GIS maps and visualizes business locations on a map.

[1699] Step 2: Collecting real-time disaster information

[1700] The server collects real-time disaster information from the Japan Meteorological Agency and various sensor data.

[1701] Operation:

[1702] The server periodically polls the Japan Meteorological Agency API to obtain the latest earthquake information.

[1703] Sensor data is received in real time via WebSocket or MQTT protocols.

[1704] The acquired earthquake epicenter and seismic intensity information is sent to the GIS.

[1705] The GIS highlights affected areas on a map and matches them with business information.

[1706] Step 3: Generate initial response plans using AI

[1707] The server inputs the collected disaster information into an AI model and generates the optimal initial response plan.

[1708] Operation:

[1709] The server sends the collected disaster data to the AI ​​and activates the prediction module.

[1710] AI generates countermeasures based on past response data and the latest research results.

[1711] The server receives the initial response plan generated by the AI.

[1712] Step 4: Collect and analyze user emotion data

[1713] The server uses an emotion engine to collect and analyze the user's emotion data.

[1714] Operation:

[1715] The server collects users' voice and text data through microphones and chat logs.

[1716] An emotion engine analyzes this data and assesses stress and anxiety levels.

[1717] Step 5: Adjust initial response plans based on sentiment data

[1718] The server adjusts the initial response plan based on the analysis results of the emotion engine.

[1719] Operation:

[1720] The server sends the emotional data to the AI, which generates a response that reflects the user's emotional state.

[1721] For example, if stress levels are high, the system generates response plans that include quick evacuation guidance and providing mental support.

[1722] The server notifies the establishment of this information.

[1723] Step 6: Notify the business of the initial response plan

[1724] The server notifies each business office of the generated initial response plan.

[1725] Operation:

[1726] The server classifies the assigned countermeasures by business establishment.

[1727] Send messages to designated businesses using the Slack API or email services.

[1728] The message includes specific response measures such as evacuation instructions and equipment shutdown procedures.

[1729] Step 7: Compiling damage data and creating a report

[1730] The server compiles data on the situation after a disaster occurs and generates a report for management.

[1731] Operation:

[1732] The server compiles damage information based on the GIS damage information.

[1733] Enter data such as a list of affected businesses, response progress, and next steps into a report template.

[1734] Generate reports in PDF format and email them to management.

[1735] Step 8: Knowledge accumulation and manual generation

[1736] The server accumulates disaster response data from each business location and generates a dynamic manual.

[1737] Operation:

[1738] The server receives disaster response data sent from each business location and stores it in a database.

[1739] Past corresponding data is extracted from the database and a manual generation engine is started.

[1740] The manuals are updated based on the latest corresponding information and are generated as training and practical manuals.

[1741] Step 9: Serving the Dashboard and Web Application

[1742] The server provides information to users using a dashboard or web application.

[1743] Operation:

[1744] The server displays real-time disaster information, damage status, response progress, and other data on the dashboard.

[1745] Provides information to users through web applications and supports immediate response.

[1746] Example 2

[1747] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1748] When a disaster strikes, a business's initial response requires the collection of a wide range of information and rapid decision-making, making it difficult to respond efficiently using conventional methods. Furthermore, employees' mental stress and anxiety can also hinder a rapid initial response. For this reason, a system that can easily aggregate information and generate and provide optimal initial response plans is needed.

[1749] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for collecting and managing business establishment data using a geographic information system; means for collecting disaster information in real time from external APIs and sensor data; AI means for generating appropriate initial response plans based on the collected disaster information; means for notifying the business establishment of the generated initial response plans; means for compiling the situation after a disaster occurs and creating a report for management; means for accumulating response data and generating manuals based on the data to always reflect the latest response experience; means for visually providing this information to users using a dashboard or web application; and means for analyzing user emotion data using an emotion engine and adjusting the initial response plans. This enables fast and efficient initial response and employee stress management.

[1750] A "geographic information system" is an information system for collecting, managing, analyzing, and visually displaying geospatial data.

[1751] "Establishment data" refers to information regarding the location, business activities, number of employees, and important facilities of a business establishment.

[1752] An "external API" is an interface for connecting with other systems and services, and is used to obtain external data in real time.

[1753] "Sensor data" refers to data such as environmental information and disaster information collected from various sensors.

[1754] "Disaster information" refers to data related to natural disasters such as earthquakes, typhoons, and floods.

[1755] "AI methods" are methods that use artificial intelligence to analyze data and generate optimal initial response plans.

[1756] An "initial response plan" is a specific plan for the course of action and procedures that a business should immediately implement in the event of a disaster.

[1757] "Notification means" refers to a means for communicating the generated initial response plan and other information to the business establishment.

[1758] The "report creation means" is a means for compiling the situation after a disaster occurs and automatically creating a report for management.

[1759] "Response data" refers to records and data related to past disaster responses.

[1760] "Manual generation means" refers to a means for creating a disaster response manual based on accumulated response data.

[1761] A "dashboard" is an interface that allows users to visually check disaster information, damage status, response progress, and so on.

[1762] A "web application" is an application that can be used by a user through a web browser.

[1763] The "emotion engine" is a system that analyzes a user's voice and text data to assess their emotional state, such as stress level and anxiety level.

[1764] This invention is a system for streamlining the initial response of businesses when disasters occur, and is composed of a geographic information system (GIS), external APIs, sensor data, AI technology, an emotion engine, a dashboard, and a web application.

[1765] 1. Geographic Information System (GIS) and Business Data Management

[1766] The server collects data on business establishments across the country from APIs and external databases and inputs it into the GIS. Business establishment data includes location information, business activities, number of employees, and information on important facilities. The GIS visualizes this data on a map and provides it in an easily accessible format for users. For example, the server uses an API to collect data on IT companies in Shinjuku Ward, Tokyo, and registers it in the GIS. This allows users to check detailed information about the establishments through a web application.

[1767] 2. Real-time disaster information collection

[1768] The server collects information on natural disasters such as earthquakes, typhoons, and floods in real time from the Japan Meteorological Agency's API and various sensor data. The collected disaster information is reflected in the GIS, and the affected area is displayed on a map. For example, the server uses the Japan Meteorological Agency's API to obtain information on a magnitude 6 earthquake with an epicenter in Tokyo Bay, and by immediately reflecting this information in the GIS, it is possible to visually display the affected areas around Tokyo Bay.

[1769] 3. AI-based disaster response advice

[1770] The server passes the collected disaster information to an AI model for analysis and generates an optimal initial response plan. This AI method proposes effective countermeasures based on past data, the latest research papers, and simulation results. For example, if a magnitude 6 earthquake occurs, the AI ​​method will generate an initial response plan including "safety inspection of the business building," "emergency evacuation instructions for employees," and "emergency shutdown procedures for the data center," and the server will notify the business.

[1771] 4. Report creation and reporting to management

[1772] The server compiles data on the situation after a disaster and automatically generates reports for management. These reports include information on the damage situation, the progress of the initial response, and future countermeasures. For example, the server can create a status report for a specific business location in Tokyo based on data obtained from the GIS and send it to management by email.

[1773] 5. Knowledge accumulation and manual generation

[1774] The server accumulates disaster response data from each business location and generates a manual based on that data that will be useful for future responses. This manual always reflects the latest response experience and is dynamically updated. For example, the server can store response data from the Tokyo Bay earthquake in a database and generate a new earthquake response manual.

[1775] 6. Dashboards and Web Applications

[1776] The server provides dashboards and web applications that visually display disaster information, damage status, and response progress to users. Users can use this to understand the situation in real time and quickly take necessary action. For example, users can access the dashboard through a web application to check earthquake information, damage status, and the status of ongoing initial responses.

[1777] 7. Emotion Engine

[1778] The server uses an emotion engine to analyze the user's emotional data. The emotion engine analyzes the voice and text data to evaluate the user's stress level and anxiety. Based on the results, the AI ​​means adjusts the initial response plan and proposes an appropriate response. For example, if the server acquires the user's voice data and analyzes it with the emotion engine and determines that the user is in a high stress state, the AI ​​means will generate an initial response plan including a "support message for rapid evacuation" and "provision of mental support," and the server will notify the business.

[1779] Specific prompt examples

[1780] Below are some examples of specific prompt sentences to input into the generative AI model.

[1781] "If an earthquake with a seismic intensity of 6 occurs with its epicenter in Tokyo Bay, please propose the initial response plan for your business."

[1782] "Please display a dashboard to check the real-time evacuation status of specific businesses in Tokyo."

[1783] "Analyze the user's emotional state based on their voice data and generate appropriate initial response plans."

[1784] The system of this invention will improve the efficiency of initial responses in the event of a disaster, enabling prompt and appropriate responses. In addition, by taking into consideration the mental health of employees through the emotion engine, it will be possible to ensure both the safety of the workplace and the mental health of employees.

[1785] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1786] Step 1: Collect and manage business data

[1787] The server collects data on business establishments across the country from APIs and external databases. The data collected here includes location information, business operations, number of employees, and information on important facilities. The input data is detailed information on business establishments obtained from the API, and the output is business establishment data registered in the GIS.

[1788] Specific operation: The server calls the API and retrieves data on IT companies located in Shinjuku Ward, Tokyo. This data includes "Location: Shinjuku Ward, Tokyo," "Business: IT company," "Number of employees: 50," and "Key facilities: Data center." The retrieved data is input into GIS and visualized on a map.

[1789] Step 2: Collecting real-time disaster information

[1790] The server collects real-time disaster information from the Japan Meteorological Agency's API and various sensors. The input data is disaster information obtained from external APIs and sensor data, and the output is the disaster situation reflected in the GIS.

[1791] Specific operation: The server uses an API provided by the Japan Meteorological Agency to obtain information on an earthquake with a seismic intensity of 6 and centered in Tokyo Bay. The obtained information is input into a GIS, and the affected areas around Tokyo Bay are highlighted in red on a map.

[1792] Step 3: AI-based disaster response advice

[1793] The server passes the collected disaster information to the AI ​​model for analysis. The input data is real-time disaster information obtained from the GIS, and the output is an initial response plan generated by the AI.

[1794] Specific operation: The server uses AI means to analyze earthquake information with a seismic intensity of 6. The AI ​​means generates an initial response plan including "safety inspection of the business building," "evacuation instructions for employees," and "emergency shutdown procedures for the data center," and the server notifies the business.

[1795] Step 4: Reporting and reporting to management

[1796] The server aggregates data on the situation after a disaster and automatically generates reports for management. The input data is information from GIS and AI models, and the output is a report sent to management.

[1797] Specific operation: The server creates a status report for specific business locations in Tokyo based on the damage information obtained from the GIS. This report includes information such as "Partial damage to the building," "All employees have safely evacuated," and "The data center has completed emergency shutdown procedures." The report is then sent to management via email.

[1798] Step 5: Knowledge accumulation and manual generation

[1799] The server accumulates disaster response data from each business location and generates a manual based on that data that will be useful for future responses. The input data is past disaster response information, and the output is an updated response manual.

[1800] Specific operation: The server saves the response data for the Tokyo Bay earthquake in a database and generates a new earthquake response manual. The new manual includes "building inspection methods," "quick evacuation procedures for employees," and "protection measures for important equipment."

[1801] Step 6: Dashboard and Web Applications

[1802] The server provides dashboards and web applications that visually display disaster information, damage status, and response progress to users. The input data is information from GIS and AI models, and the output is the dashboard or web application screen used by users.

[1803] How it works: Users can access a dashboard through a web application to check real-time earthquake information, damage status, and the progress of initial responses. It also displays a list of employees who have completed evacuation and the status of ongoing building inspections.

[1804] Step 7: Emotion Engine

[1805] The server uses an emotion engine to analyze the user's emotional data and adjust the initial response plan. The input data is the user's voice data and text data, and the output is a response plan based on the analysis results.

[1806] Specific operation: The server acquires the user's voice data and analyzes it using an emotion engine. If the analysis results indicate that the user is in a high stress state, the AI ​​means generates initial response plans, including "support messages for rapid evacuation" and "provision of psychological support," and the server notifies the business.

[1807] (Application example 2)

[1808] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1809] Improving the efficiency of a facility's initial response in the event of a disaster and implementing appropriate measures quickly are crucial for minimizing damage and quickly restoring operations. However, conventional systems do not adequately col...

Claims

1. A means for collecting and managing business establishment data using a geographic information system; A means of collecting disaster information in real time from external APIs and sensor data; An AI method to generate appropriate initial response plans based on collected disaster information; A means for notifying the business establishment of the generated initial response plan; A means of compiling the situation after a disaster and creating a report for management, A means for accumulating corresponding data and generating manuals based on the data; A means to provide a dashboard or web application that provides this information to users A system including:

2. The system according to claim 1, wherein the AI ​​means for generating an initial response plan generates the response plan based on past disaster response data and the latest research results.

3. 2. The system according to claim 1, further comprising means for compiling in real time the progress of the initial response plan notified to each business establishment, and automatically generating a report for management.

4. 2. The system according to claim 1, further comprising means for accumulating disaster response data from each business establishment and generating a dynamic manual based on the data that will be useful for future use.

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