system

Abstract

Provide a system. A means for a user to report damage caused by a disaster using a terminal; means for receiving the damage report and retrieving a three-dimensional model of the target facility from a database; A means for identifying the location of damage on the 3D model; A means for estimating the extent of the impact centered on the damaged area; A means of estimating recovery costs and time using generative AI; A means of making estimates that take into account external environment forecasts in cooperation with other systems; means for generating and presenting a recovery plan to a user; A system including:

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 natural disaster such as an earthquake strikes a large factory or facility, it is necessary to identify the damaged areas and quickly formulate a recovery plan. However, currently, it takes a great deal of time and effort to identify the damaged areas and estimate the extent of the impact, and there are many uncertainties in estimating the cost and time of recovery, making it difficult to respond efficiently and quickly. In addition, it is not possible to formulate plans that take into account the external environment, which can lead to delays and additional costs due to unforeseen circumstances. [Means for solving the problem]

[0005] The present invention is a system that includes a means for users to report damage caused by a disaster using a terminal, a means for receiving the damage report and retrieving a 3D model of the target facility from a database, a means for identifying the damaged area on the 3D model using an image processing algorithm and sensor information analysis technology, a means for estimating the extent of the impact centered on the damaged area, a means for estimating the cost and time of recovery by referring to past disaster data and data on similar cases using generative AI, a means for making estimates that take into account external environment predictions in cooperation with other systems, and a means for generating a recovery plan and presenting it to the user. This enables the rapid identification of damaged areas and estimation of the extent of the impact, enabling the efficient and accurate formulation of recovery plans.

[0006] "User" refers to the employee or manager of a facility or factory who uses the terminal to submit a fault report.

[0007] "Terminal" refers to a device that can access the system via a network, such as a PC, tablet, or smartphone.

[0008] "Damage report" refers to information that a user records and sends to the system after a disaster, recording the location and condition of damage observed by the user.

[0009] A "3D model" refers to data that represents the structure and equipment of a factory or facility in three dimensions.

[0010] "Database" refers to a storage system for storing 3D models and other related information.

[0011] "Image processing algorithm" refers to a computational procedure for analyzing image data and identifying damaged areas.

[0012] "Sensor information analysis technology" refers to the technology for analyzing data obtained from IoT sensors and cameras within a facility.

[0013] "Affected area" refers to the area where damage may occur, centered on the damaged area.

[0014] "Generative AI" refers to artificial intelligence that makes new predictions and inferences based on past data and similar cases.

[0015] "Other systems" refers to external systems that provide external environmental data, such as weather forecast systems and traffic information systems.

[0016] "Recovery Plan" refers to a specific plan for the procedures, materials, personnel, and time required to repair the damaged area. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] System Overview

[0039] This invention is a system that assesses damage to building facilities caused by disasters such as earthquakes and estimates the extent of the impact and the cost and time required for restoration as a BCP (Business Continuity Plan) measure for large factories and facilities. This system uses 3D models to identify damaged areas, makes estimates using generative AI, and can also take external environment predictions into account by linking with other systems.

[0040] Key components of the system

[0041] 1. User Interface (Terminal)

[0042] The terminal is a device that can access the system via a network, such as a PC, tablet, or smartphone. Users use the terminal to report damage.

[0043] 2. Server

[0044] The server receives damage reports, obtains 3D models, identifies the damaged areas, estimates the extent of the impact, uses generative AI to estimate recovery costs and time, connects with other systems, generates recovery plans, and presents them to users.

[0045] 3. Database

[0046] The database is a storage system that stores 3D models, data on past disasters, and recovery case studies.

[0047] 4. Other Systems

[0048] The other systems are external systems that provide external environmental data, such as a weather forecast system or a traffic information system.

[0049] Program processing flow

[0050] 1. User reports damage

[0051] User: After a disaster occurs, enter the damaged areas of the factory or facility into the system. Using a terminal, access the system's damage report form and enter the damaged areas (e.g., second floor of the building, 3D printer area).

[0052] 2. The server retrieves the 3D model

[0053] Server: Receives the damage report and retrieves the 3D model data of the target facility from the database. For example, it loads the 3D model file of "Factory A."

[0054] 3. The server identifies the damage

[0055] Server: Identifies the damage locations reported by users on the 3D model. Using image processing algorithms and sensor information analysis technology, specific damage locations are mapped onto the 3D model.

[0056] 4. The server estimates the affected area

[0057] Server: Analyze the impact area around the damaged area. For example, use structural analysis software to simulate how damage to the second floor will affect the first floor and other rooms.

[0058] 5. The server uses generative AI to estimate recovery costs and time.

[0059] Server: Uses generative AI to estimate recovery costs and time. Refers to data from past disasters and similar cases to estimate repair costs and the number of days required for recovery.

[0060] 6. Integration with other systems

[0061] Server: Calls the API to obtain necessary data from other forecasting systems. For example, obtains future weather data from a weather forecast system and reflects it in adjusting construction schedules.

[0062] 7. Presentation of recovery plan

[0063] Server: Generates a specific recovery plan based on the estimation results and external data. The recovery plan includes the necessary materials, a schedule of workers, and a timeline of the start and end dates of construction. Once the plan is complete, it is presented to the user via their device.

[0064] Specific examples

[0065] Example 1: Earthquake damage at a parts manufacturing plant

[0066] 1. User: After the earthquake, report damage to the production line area on the second floor of the factory.

[0067] 2. Server: Obtain a 3D model of the factory and identify damaged areas in the production line area on the second floor.

[0068] 3. Server: Analyze the impact area around the damaged area and determine the potential impact on the logistics area on the first floor.

[0069] 4. Server: Using generative AI, we estimate that repair costs will be 5 million yen and recovery will take 10 days.

[0070] 5. Server: Works with the weather forecast system to make predictions that take into account the weather for the next week.

[0071] 6. Server: Provide users with a concrete recovery plan.

[0072] Example 2: Data center fire damage

[0073] 1. User: Reports that a fire has broken out in the data center and some server racks have been damaged.

[0074] 2. Server: Obtain a 3D model of the data center and identify areas of damage.

[0075] 3. Server: Analyze the extent of the impact around the damaged area and evaluate the impact on network equipment.

[0076] 4. Server: Using generative AI, we estimate that repair costs will be 2 million yen and recovery will take 5 days.

[0077] 5. Server: Obtains information on air conditioning and power supply status after a fire from the linked system and reflects it in the recovery plan.

[0078] 6. Server: Provide users with a concrete recovery plan.

[0079] The above is an embodiment of the present invention. This system makes it possible to quickly and efficiently assess damage and formulate a restoration plan in the event of a disaster.

[0080] The processing flow will be explained below.

[0081] Step 1:

[0082] After a disaster occurs, users report damage using a terminal. Specifically, users access the system's damage report form, enter location information and photos of the damaged area, and specific details of the damage (e.g., cracks in the wall, broken machinery), and send the information to the system.

[0083] Step 2:

[0084] The server receives the damage report sent by the user, analyzes the necessary data, such as the location of the damage and the timestamp of the report, and starts the damage assessment process.

[0085] Step 3:

[0086] The server retrieves the 3D model of the target facility from the database. Specifically, it loads the 3D model data corresponding to the building or area where the damage was reported via the database access API.

[0087] Step 4:

[0088] The server identifies the damaged areas on the 3D model, and uses image processing algorithms to match the reported photo data with the 3D model. Sensor information analysis technology is also used to ensure that the damaged areas are accurately identified.

[0089] Step 5:

[0090] The server estimates the extent of the impact around the damaged area, and uses structural analysis software to simulate how the damage will affect other parts and identify potential areas of impact.

[0091] Step 6:

[0092] The server uses generative AI to estimate recovery costs and time. Specifically, it references data from past disasters and similar recovery cases and uses an AI model to predict costs and time. This estimate also includes the necessary personnel, materials, and construction procedures.

[0093] Step 7:

[0094] The server works in conjunction with other systems to make estimates that take into account external environmental forecasts. Specifically, it calls the weather forecast system's API to obtain future weather data and adds factors that may affect construction schedules. It also obtains data from the traffic information system and performs risk assessments for material transportation.

[0095] Step 8:

[0096] The server generates a final recovery plan and presents it to the user, which includes a list of required materials, a schedule of repair personnel, and estimated start and completion dates for work. Once the plan is complete, the user is notified via push notification or email, allowing them to review the details.

[0097] The above are the specific processing steps.

[0098] Example 1

[0099] 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."

[0100] Rapid and accurate damage assessment and recovery plan formulation in the event of a disaster at large factories and facilities are important issues for ensuring business continuity. However, conventional methods require a great deal of time and effort to identify damaged areas and estimate the extent of the impact, and there is also a high degree of uncertainty in estimating recovery costs and time. It is also difficult to take external environmental predictions into account, and many challenges exist in formulating comprehensive recovery plans. A new system is needed to solve these problems.

[0101] 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.

[0102] In this invention, the server includes: means for a user to report damage caused by a disaster using a terminal; means for receiving the damage report and retrieving a 3D model of the target facility from a database; means for identifying the damaged area on the 3D model; means for estimating the extent of impact centered on the damaged area; means for estimating restoration costs and time using generative AI; means for linking with other systems to make estimates taking into account external environment predictions; means for generating a restoration plan and presenting it to the user; means for using image processing algorithms and sensor information analysis technology to map the damaged area onto a 3D model based on the damage report form; and means for inputting prompt statements to the generative AI to estimate repair costs and restoration time. This enables rapid and accurate assessment of damage caused by a disaster, detailed estimation of the extent of impact, reliable estimation of restoration costs and time, and the formulation of a comprehensive restoration plan taking into account the external environment.

[0103] "User" is a person or organization whose role is to operate the system and report damage caused by a disaster.

[0104] A "terminal" is a device such as a PC, tablet, or smartphone that can access the system via a network and operate it or input information.

[0105] A "damage report" is information entered by a user to the system about the location and extent of damage to facilities and equipment caused by a disaster.

[0106] "Server" is the central processing unit of the system, and is a collective term for the hardware and software components that are responsible for receiving damage reports, obtaining 3D models, identifying damaged areas, estimating the extent of the impact, estimating recovery costs and time, obtaining external environment forecasts, and generating and presenting recovery plans.

[0107] A "database" is an information management system for storing and managing 3D model data, past disaster data, recovery case data, etc.

[0108] A "3D model" is digital data that represents the physical structure of the target facility in three dimensions and is used to identify damaged areas and analyze the extent of the impact.

[0109] An "image processing algorithm" is a computational method for identifying specific features or patterns in a digital image and analyzing the location of damage.

[0110] "Sensor information analysis technology" is a technology for analyzing data obtained from various sensors and identifying damaged areas.

[0111] The "scope of impact" refers to the range and extent of the impact that the identified damaged area has on the surrounding area, and is simulated using structural analysis software, etc.

[0112] "Generative AI" is a type of artificial intelligence technology that generates output results (e.g., recovery costs and duration) based on specific input information (prompt statements).

[0113] A "prompt sentence" is an instruction sentence input to a generative AI, and contains information that forms the basis for the AI's analysis and inference.

[0114] "Restoration Plan" means the action plan required to repair and restore the damaged area, including specific materials, personnel, schedule, costs, etc.

[0115] "Other systems" are external data systems that provide external environmental data, such as weather forecast systems and traffic information systems.

[0116] System Overview

[0117] This invention is a system for assessing damage to building facilities caused by disasters (such as earthquakes and fires) and estimating the cost and time required for restoration as a business continuity plan (BCP) measure for large factories and facilities. This system uses 3D models to identify damaged areas, makes estimates using generative AI, and works with other systems to consider external environment predictions.

[0118] Key Components

[0119] 1. User Interface (Terminal)

[0120] The terminal is a device that can access the system via a network, such as a PC, tablet, or smartphone. Users use the terminal to report damage.

[0121] 2. Server

[0122] The server receives damage reports, obtains 3D models, identifies the damaged areas, estimates the extent of the impact, uses generative AI to estimate recovery costs and time, connects with other systems, generates recovery plans, and presents them to users.

[0123] 3. Database

[0124] The database is a storage system that stores 3D models, data on past disasters, and recovery case studies.

[0125] 4. Other Systems

[0126] The other systems are external systems that provide external environmental data, such as a weather forecast system or a traffic information system.

[0127] Program processing description

[0128] User reports damage

[0129] When a disaster occurs, users access the damage report form using a device (PC, tablet, smartphone). They enter the necessary information in the form and press the report button to send it to the system. For example, a user may report that "the 3D printer area on the second floor of the building has been damaged."

[0130] The server receives the damage report and obtains the 3D model.

[0131] When the server receives a damage report from a user, it retrieves the 3D model data of the target facility from the database. For example, it loads the 3D model file of "Factory A" (e.g., "Factory A_3DModel.obj").

[0132] The server identifies the damage

[0133] The server identifies the reported damage on the 3D model. Specifically, it uses image processing algorithms and sensor information analysis technology to map the damage reported by the user onto the digital model. For example, it identifies the location of the "3D printer area on the second floor."

[0134] The server estimates the scope of the impact

[0135] The server analyzes the extent of the damage, focusing on the damaged area. It uses structural analysis software (e.g., ANSYS) to simulate the impact of the damage on other areas and structural components. For example, it calculates how damage to the second floor will affect the logistics area on the first floor.

[0136] The server uses generative AI to estimate recovery costs and time.

[0137] The server inputs prompt text into the generative AI, which estimates the cost and time required for recovery. The prompt text includes information such as the extent of the damage, the extent of the impact, and similar past cases. For example, it might say, "The 3D printer area on the second floor has been damaged, affecting the logistics area on the first floor. Estimate the repair cost and number of days." Based on this, the AI ​​outputs an estimate such as "repair cost 5 million yen, recovery time 10 days" based on past disaster data and similar cases.

[0138] The server connects with other systems

[0139] The server connects with other systems to obtain external environmental data. For example, it extracts future weather data from a weather forecast system and reflects it in recovery plans. This data is then used to schedule outdoor work, etc.

[0140] The server presents a recovery plan

[0141] The server generates a specific restoration plan based on the estimation results and external data and presents it to the user. This plan includes information such as materials, personnel, schedule, and construction start and end dates. The user can then use their terminal to review the plan and make any necessary corrections or approvals.

[0142] Specific examples

[0143] Earthquake damage at a parts manufacturing plant

[0144] 1. User: After the earthquake, the user reports damage to the second floor production line area of ​​the factory. The user types "The second floor production line area is severely damaged" and submits the message.

[0145] 2. Server: Obtain a 3D model of the factory and identify damaged areas in the production line area on the second floor.

[0146] 3. Server: Image processing algorithms identify damage areas and map them onto the model.

[0147] 4. Server: Use structural analysis software to analyze the impact area and simulate the impact on the logistics area on the first floor.

[0148] 5. Server: The generative AI is given a prompt: "Estimate the repair costs and recovery time for damage caused by the earthquake." The server estimates that repair costs will be 5 million yen and recovery will take 10 days.

[0149] 6. Server: Obtains weather data for the next week from the weather forecast system to see if it will affect outdoor work.

[0150] 7. Server: Generates a recovery plan and presents it to the user's device, such as "Repair costs will be 5 million yen, and recovery will take 10 days." The user reviews it, makes any necessary corrections, and then approves it.

[0151] Data center fire damage

[0152] 1. User: Reports that a fire broke out in the data center and some server racks were damaged. Type "The fire damaged the server racks and also affected some network equipment" and submit.

[0153] 2. Server: Obtain a 3D model of the data center and identify areas of damage.

[0154] 3. Server: Using image processing technology, the damaged area is mapped onto a 3D model.

[0155] 4. Server: Use structural analysis software to analyze the scope of the impact and evaluate the impact on network equipment.

[0156] 5. Server: The generative AI is given a prompt: "If a server rack is damaged by fire, estimate the repair cost and recovery time." The AI ​​estimates that the repair cost will be 2 million yen and that it will take five days to recover.

[0157] 6. Server: Obtains information on air conditioning and power supply status after a fire from other systems and reflects it in recovery plans.

[0158] 7. Server: Generates a recovery plan and presents it to the user's device, such as "Repair costs will be 2 million yen, and recovery will take 5 days." The user can review the plan and make any changes or approvals.

[0159] This concludes the description of the "Mode for Carrying Out the Invention." This system enables damage assessment in the event of a disaster and the development of an efficient recovery plan.

[0160] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0161] Step 1: Enter your damage report

[0162] User: After a disaster occurs, the user accesses the damage report form using a device (PC, tablet, smartphone, etc.). They enter the necessary information, such as the location and extent of the damage, and contact information, and press the report button to submit. For example, they report that "the 3D printer area on the second floor of the building has been damaged."

[0163] Input: Report information such as location of damage, extent of damage, contact information, etc.

[0164] Output: Damage report data sent to the server (e.g. "The second floor of the building, the 3D printer area, was damaged")

[0165] Step 2: Receiving damage reports and obtaining 3D models

[0166] Server: Based on the received damage report, retrieves 3D model data from the database using the target facility's identification information. Executes a database query using the target facility's ID and name to load the 3D model file. For example, loads the 3D model file for "Factory A" (e.g., "Factory A_3DModel.obj").

[0167] Input: Damage report data, facility identification

[0168] Output: 3D model data of the facility (e.g. "FactoryA_3DModel.obj")

[0169] Step 3: Identify the damage

[0170] Server: Analyzes the damage information entered by the user and identifies the corresponding location on the 3D model. Using image processing algorithms and sensor information analysis technology, the reported damage is mapped onto the digital model. For example, the specific location of the "3D printer area on the second floor" is identified.

[0171] Input: Damage report data, 3D model data

[0172] Output: Damaged area on a 3D model (e.g., "3D printer area on the second floor")

[0173] Step 4: Estimate the impact area

[0174] Server: Analyze the impact area around the identified damaged area. Use structural analysis software (e.g., ANSYS) to simulate the impact of the damage on other areas and structural parts. For example, calculate how damage to the second floor will affect the logistics area on the first floor.

[0175] Input: Damage location, 3D model data

[0176] Output: Analysis results of the impact range (e.g., "Damage on the second floor affects the logistics area on the first floor")

[0177] Step 5: Estimate restoration costs and time

[0178] Server: A prompt is input into the generative AI, which estimates the cost and time required for recovery. The prompt includes information such as the extent of the damage, the extent of the impact, and past similar cases. For example, it might say, "The 3D printer area on the second floor has been damaged, affecting the logistics area on the first floor. Estimate the repair cost and number of days." Based on this, the AI ​​outputs an estimate such as "Repair cost 5 million yen, recovery time 10 days."

[0179] Input: Prompt statement (damage status, affected area, past similar cases)

[0180] Output: Estimated restoration cost and time (e.g., "Repair cost: 5 million yen, restoration time: 10 days")

[0181] Step 6: Integration with other systems

[0182] Server: Links with other systems (e.g., weather forecast systems and traffic information systems) to obtain external environmental data. Uses APIs to obtain necessary data and reflects this information in recovery plans. For example, obtains future weather data from a weather forecast system and uses it to adjust construction schedules.

[0183] Input: Request for external environment data from other systems

[0184] Output: External environmental data (e.g., "Weather forecast for the next week")

[0185] Step 7: Generate and present a recovery plan

[0186] Server: Generates a specific recovery plan based on the estimation results and acquired external data. This plan includes information such as the required materials, personnel, schedule, and construction start and end dates. The created recovery plan is presented on a terminal accessible to the user. The user can use the terminal to review the plan and make any necessary corrections or approvals.

[0187] Input: Estimated recovery costs and time, external environmental data

[0188] Output: A detailed restoration plan (e.g., "Repair cost: 5 million yen, restoration time: 10 days, materials required, personnel schedule, construction start and end dates")

[0189] (Application example 1)

[0190] 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."

[0191] When a disaster occurs and a robot installed in a factory or facility is damaged, it is difficult to quickly and accurately assess the damage and estimate the extent of the impact and the cost and time required for recovery. Conventional methods require a large amount of human resources and time, and there is a risk of delays in recovery plans or incorrect decisions. The purpose of this invention is to solve these problems and provide a system that enables efficient and rapid damage assessment and the generation of recovery plans.

[0192] 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.

[0193] In this invention, the server includes: means for a user to report damage caused by a disaster using a terminal; means for receiving the damage report and retrieving a 3D model of the target facility from a database; means for identifying the damaged area on the 3D model; means for estimating the impact area centered on the damaged area; means for estimating recovery costs and time using generative AI; means for making estimates taking into account external environment predictions in cooperation with other systems; means for generating and presenting a recovery plan to the user; and means for managing a robot that uses the 3D model to identify the damaged area and generate the impact area and recovery plan. This enables faster and more accurate damage assessment of robots and recovery plans in the event of a disaster.

[0194] A "user" is a person who uses this system to report damage caused by a disaster.

[0195] A "terminal" is a device that can be connected to a network, such as a PC, tablet, or smartphone used by a user.

[0196] A "disaster" is an event in which facilities or equipment are damaged due to natural phenomena such as earthquakes, fires, and typhoons, or due to human factors.

[0197] "Damaged area" refers to the specific location where facilities or equipment have been physically damaged by the disaster.

[0198] The "reporting means" refers to an interface that allows a user to input information about the damaged area into the system using a terminal.

[0199] "Means for receiving" refers to the function of the server receiving damage reports from users via the network.

[0200] A "3D model" is digital data that reproduces the spatial structure of facilities and equipment.

[0201] A "database" is a storage system that allows the system to store 3D model data, past disaster data, and other data.

[0202] "Means of acquisition" refers to the function by which the server retrieves the necessary 3D model data from the database.

[0203] "Means for identification" refers to technology for identifying user-reported damage locations on a 3D model.

[0204] The "area of ​​impact" refers to the area centered on the reported damage location and indicates the extent to which surrounding facilities and equipment may be affected.

[0205] "Means of estimation" is a function for calculating the extent of the impact based on the location of damage, as well as the cost and time required for recovery.

[0206] "Generative AI" is artificial intelligence that uses machine learning algorithms and generative models to automatically estimate recovery costs and time.

[0207] A "recovery plan" is a plan that includes specific work procedures for repairing damage, necessary materials, work personnel, construction schedule, etc.

[0208] "Means for presenting" refers to a function for displaying the recovery plan generated by the server on the user's terminal.

[0209] "Means of management" refers to the ability to control and monitor the robot using 3D models, and use them to identify damaged areas and generate restoration plans.

[0210] "Other systems" are systems that provide external environmental data, such as weather forecast systems and traffic information systems.

[0211] "External environment forecast" refers to environmental data provided by external systems, such as weather forecasts and traffic conditions, and is a factor taken into account in recovery plans.

[0212] System Overview

[0213] This invention is a system that quickly assesses damage and estimates the extent of the impact and the cost and time required for restoration when a disaster such as an earthquake or fire occurs in a factory or facility. Based on damage information reported by the user via a terminal, this system identifies the damaged area using a 3D model, makes estimates using generative AI, and cooperates with other systems to consider external environment predictions, thereby efficiently and quickly generating restoration plans.

[0214] Key components of the system

[0215] 1. User Interface (Terminal)

[0216] The terminal is a device that can access the system via a network, such as a PC, tablet, or smartphone. Users use the terminal to report damage.

[0217] 2. Server

[0218] The server receives damage reports, obtains 3D models, identifies the damaged areas, estimates the extent of the impact, uses generative AI to estimate recovery costs and time, connects with other systems, generates recovery plans, and presents them to users.

[0219] 3. Database

[0220] The database is a storage system that stores 3D models, data on past disasters, and recovery case studies.

[0221] 4. Other Systems

[0222] Other systems refer to external systems that provide external environmental data, such as weather forecast systems and traffic information systems.

[0223] About Torsion

[0224] Hardware / Software used

[0225] Hardware: Use a smartphone or tablet.

[0226] Software: Python 3.8 or higher, generative AI modules (e.g., TENSORFLOW (registered trademark) or PyTorch), 3D model processing libraries (e.g., trimesh), and external API access libraries (e.g., requests).

[0227] Data processing and calculation

[0228] 1. Damage report

[0229] A user uses a terminal to report damage caused by a disaster to the system, including detailed information about the damage (for example, "damage to the robot arm").

[0230] 2. Obtaining a 3D model

[0231] Based on the damage report received, the server retrieves 3D model data of the target facility from the database. This is digital data that reproduces the spatial structure of the target facility.

[0232] 3. Identifying the damaged area

[0233] The server identifies the reported damage locations on the 3D model using image processing algorithms and sensor information analysis techniques.

[0234] 4. Estimation of the impact range

[0235] The server analyzes the extent of the impact around the damaged area, using structural analysis software to simulate how the damaged area will affect the surrounding area.

[0236] 5. Utilizing generative AI

[0237] The server uses generative AI to estimate recovery costs and time. The AI ​​model references data from past disasters and similar cases to estimate the necessary repair costs and recovery time.

[0238] 6. Integration with other systems

[0239] The server connects with other systems, such as weather forecast systems and traffic information systems, to obtain external environment forecasts, enabling predictions that take future weather and traffic conditions into account.

[0240] 7. Presentation of recovery plan

[0241] The server generates a specific recovery plan based on the results of these analyses, including the necessary materials, a schedule of workers, and a timeline of the start and end dates of the work.The server then presents the final recovery plan to the user.

[0242] Specific examples

[0243] Earthquake damage assessment and restoration planning app for factory robots

[0244] If a robotic arm installed in a factory is damaged in an earthquake, the user can use their terminal to report, "The robotic arm on the second floor is damaged." The server retrieves a 3D model of the factory from the database and identifies the damaged area. It analyzes the extent of the impact and uses generative AI to estimate the cost and time of restoration. It also retrieves data from the weather forecast system, generates an optimal restoration plan, and presents it to the user.

[0245] Example prompt statement

[0246] "An earthquake has occurred in Factory A. The robot arm on the second floor has been damaged. Please make a recovery plan."

[0247] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0248] Step 1:

[0249] The user uses a terminal to report damage caused by the disaster. The user enters specific damage information, such as "The robot arm on the second floor is damaged," into the form displayed on the terminal. The entered damage report is sent to the server via the network.

[0250] Step 2:

[0251] The server receives the damage report, analyzes the received data, and extracts information about the damaged location (for example, which part of the facility was damaged). It then retrieves a 3D model of the target facility from the database. If the target facility is "Factory A," the server loads the 3D model data of Factory A from the database.

[0252] Step 3:

[0253] The server identifies the damaged area on the 3D model. The server maps the received information on the damaged area (e.g., "second floor" or "robot arm area") onto the 3D model and identifies the specific damaged area using image processing algorithms and sensor information analysis technology. During this process, the server generates coordinate data for the damaged area.

[0254] Step 4:

[0255] The server estimates the extent of the impact around the damaged area. Using structural analysis software, the server simulates the impact of the damaged area on surrounding structures and equipment. For example, it analyzes how damage to the second floor will affect the first floor area and other rooms, and outputs the specific extent of the impact.

[0256] Step 5:

[0257] The server uses generative AI to estimate recovery costs and time. The server runs a generative AI model that uses data from past disasters and similar cases to estimate repair costs and the number of days required for recovery based on the location of damage and the extent of the impact. The input in this step is coordinate data of the damaged area and data on the extent of the impact, and the output is the estimated recovery cost and time.

[0258] Step 6:

[0259] The server connects with other systems to obtain external environment forecasts. The server then calls the APIs of weather forecast systems and traffic information systems to obtain future weather and traffic data. This data is used to schedule restoration work.

[0260] Step 7:

[0261] The server generates a recovery plan and presents it to the user. The server creates a specific recovery plan based on estimated recovery costs, time, impact area, and external environmental data. This plan includes the necessary materials, a schedule of work personnel, and a timeline of construction start and end dates. The generated recovery plan is displayed on the user's terminal via a user interface.

[0262] 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.

[0263] System Overview

[0264] This invention is a system that assesses damage to building facilities caused by disasters such as earthquakes and estimates the extent of the impact and the cost and time required for restoration as a BCP (Business Continuity Plan) measure for large factories and facilities. This system identifies damaged areas using 3D models, makes estimates using generative AI, and provides efficient restoration plans by taking into account external environment predictions. In addition, by combining it with an emotion engine that recognizes user emotions, more effective responses are possible.

[0265] Key components of the system

[0266] 1. User Interface (Terminal)

[0267] The terminal is a device that can access the system via a network, such as a PC, tablet, or smartphone. Users use the terminal to report damage.

[0268] 2. Server

[0269] The server receives damage reports, obtains 3D models, identifies damaged areas, estimates the extent of the impact, uses generative AI to estimate recovery costs and time, connects with other systems, generates recovery plans, and presents them to users. It also uses an emotion engine to analyze user emotions and adjust response measures based on that information.

[0270] 3. Database

[0271] The database is a storage system that stores 3D models, data on past disasters, and recovery case studies.

[0272] 4. Other Systems

[0273] The other systems are external systems that provide external environmental data, such as a weather forecast system or a traffic information system.

[0274] 5. Emotion Engine

[0275] The emotion engine extracts emotional information from user reports and operational logs during use, which is used for recovery planning and customer support resource allocation.

[0276] Program processing flow

[0277] 1. User reports damage

[0278] User: After a disaster occurs, the user enters the damaged areas of the factory or facility into the system. Using a terminal, the user accesses the system's damage report form, enters the location information and photos of the damaged areas, and specific details of the damage (e.g., cracks in the wall, broken machinery), and sends the form to the system.

[0279] 2. The server receives the damage report

[0280] Server: Receives damage reports sent by users. Based on the received information, it analyzes the necessary data, such as the location of the damage and the timestamp of the report, and starts the damage assessment process.

[0281] 3. The server retrieves the 3D model

[0282] Server: Retrieves 3D model data of the target facility from the database. Specifically, it loads the 3D model data corresponding to the building or area where the damage was reported via the database access API.

[0283] 4. The server identifies the damage

[0284] Server: Identifies the damage areas reported by users on the 3D model. Image processing algorithms are used to match the reported photo data with the 3D model. Sensor information analysis technology is also used to ensure that the damage areas are accurately identified.

[0285] 5. The server estimates the affected area

[0286] Server: Analyze the impact area around the damaged area. Use structural analysis software to simulate how the damage will affect other parts and identify potential areas of impact.

[0287] 6. The server uses generative AI to estimate recovery costs and time.

[0288] Server: Uses generative AI to estimate recovery costs and time. It references data from past disasters and similar cases and uses AI models to predict costs and time. This estimate also includes the required personnel, materials, and construction procedures.

[0289] 7. The server connects with other systems

[0290] Server: Calls the API to obtain necessary data from other forecasting systems. For example, it obtains future weather data from a weather forecast system and adds factors that may affect construction schedules. It also obtains data from a traffic information system to perform risk assessments for material transportation.

[0291] 8. The server uses the emotion engine

[0292] Server: Analyzes the user's report and extracts emotional information using an emotion engine. Identifies the stress or anxiety the user is feeling when reporting and adjusts the response accordingly.

[0293] 9. The server generates a recovery plan and presents it to the user.

[0294] Server: Generates and presents the final recovery plan to the user. The recovery plan includes a list of needed materials, a schedule of repair personnel, and estimated start and completion dates for work. Once the plan is complete, the user is notified via push notification or email so they can review the details. The server also takes into account the user's emotional state and presents appropriate language and support options.

[0295] Specific examples

[0296] Example 1: Earthquake damage at a parts manufacturing plant

[0297] 1. User: After the earthquake, report damage to the production line area on the second floor of the factory.

[0298] 2. Server: Obtain a 3D model of the factory and identify damaged areas in the production line area on the second floor.

[0299] 3. Server: Analyze the impact area around the damaged area and determine the potential impact on the logistics area on the first floor.

[0300] 4. Server: Using generative AI, we estimate that repair costs will be 5 million yen and recovery will take 10 days.

[0301] 5. Server: Works with the weather forecast system to make predictions that take into account the weather for the next week.

[0302] 6. Server: Analyzes the stress level from the user's report using an emotion engine. If high stress is detected, allocate additional resources and include them in the recovery plan.

[0303] 7. Server: Provide users with a concrete recovery plan.

[0304] Example 2: Data center fire damage

[0305] 1. User: Reports that a fire has broken out in the data center and some server racks have been damaged.

[0306] 2. Server: Obtain a 3D model of the data center and identify areas of damage.

[0307] 3. Server: Analyze the extent of the impact around the damaged area and evaluate the impact on network equipment.

[0308] 4. Server: Using generative AI, we estimate that repair costs will be 2 million yen and recovery will take 5 days.

[0309] 5. Server: Obtains information on air conditioning and power supply status after a fire from the linked system and reflects it in the recovery plan.

[0310] 6. Server: Analyzes the user's report using an emotion engine to determine whether there is any anxiety. If a serious anxiety is detected, a special support plan is presented.

[0311] 7. Server: Provide users with a concrete recovery plan.

[0312] The above is an embodiment of the present invention. This system makes it possible to quickly and efficiently assess damage and formulate recovery plans in the event of a disaster, and also takes into account the emotions of users.

[0313] The processing flow will be explained below.

[0314] Step 1:

[0315] After a disaster occurs, users report damage using a terminal. Specifically, users access the system's damage report form, enter location information and photos of the damaged area, and specific details of the damage (e.g., cracks in the wall, broken machinery), and send the information to the system.

[0316] Step 2:

[0317] The server receives the damage report sent by the user, which includes the location of the damage, details of the damage, and a timestamp of the report.

[0318] Step 3:

[0319] The server retrieves the 3D model data of the target facility from the database, and based on the damage report, loads the 3D model file of the target building or facility via the database access API.

[0320] Step 4:

[0321] The server identifies damaged areas on the 3D model. It uses image processing algorithms to analyze the photo data sent by the user and match it with the damaged areas in the 3D model. It also uses sensor information analysis technology to accurately identify damaged areas based on IoT sensor and camera data.

[0322] Step 5:

[0323] The server estimates the extent of the damage from the center, and uses structural analysis software to simulate the impact of the damage on other parts, thereby identifying areas that may be affected by the damage.

[0324] Step 6:

[0325] The server uses generative AI to estimate recovery costs and time. Specifically, data from past disasters and similar recovery cases is input into the AI ​​model to predict costs and time, including the required personnel, materials, and construction procedures. The AI ​​uses neural networks to generate highly accurate estimates.

[0326] Step 7:

[0327] The server works in conjunction with other systems to make estimates that take into account external environmental forecasts. It calls the weather forecast system's API to obtain future weather data and calculates factors that will affect construction schedules. It also obtains data from the traffic information system and performs risk assessments for material transportation.

[0328] Step 8:

[0329] The server uses an emotion engine to recognize the user's emotions. It analyzes the user's report content and the operation log at the time of sending, and extracts emotional information such as stress, anxiety, and satisfaction. This information is analyzed based on the wording in the report content and the timing of sending.

[0330] Step 9:

[0331] The server tailors the recovery plan it presents based on emotional information, for example automatically assigning additional customer support if high stress levels are detected, or generating a plan with detailed, easy-to-understand explanations if the user is feeling anxious.

[0332] Step 10:

[0333] The server generates a final recovery plan and presents it to the user, which includes a list of required materials, a schedule of repair personnel, and estimated start and completion dates for work. Once the plan is complete, the user is notified via push notification or email, allowing them to review the details.

[0334] The above are the specific processing steps.

[0335] Example 2

[0336] 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."

[0337] It has been difficult to effectively carry out rapid damage assessment and recovery plan formulation for factories and facilities in the event of a disaster using conventional methods. In addition, it is necessary to take into account the psychological stress of users, but there has been a lack of concrete methods for doing so.

[0338] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0339] In this invention, the server includes means for a user to report damage caused by a disaster using a terminal, means for receiving the damage report and retrieving a 3D model of the target facility from a database, means for identifying the damaged area on the 3D model, means for estimating the extent of the impact centered on the damaged area, means for estimating recovery costs and time using generative AI, means for making estimates taking into account external environment predictions in cooperation with other systems, means for analyzing the user's emotional information and using an emotion engine to adjust the response based on that information, and means for generating a recovery plan and presenting it to the user. This enables quick and effective damage assessment and formulation of a recovery plan, as well as responses that take into account the user's psychological stress.

[0340] "User" refers to the person or organization that operates the system to report damage caused by a disaster.

[0341] "Terminal" refers to a device (PC, tablet, smartphone, etc.) that can access the system via a network.

[0342] "Damage report" refers to a report from a user that includes location information, photos, and specific details of damage to building facilities caused by a disaster.

[0343] A "3D model" refers to digital data that shows the three-dimensional structure of the target facility.

[0344] "Database" refers to a storage system for storing information required by the system.

[0345] "Image processing algorithm" refers to a computational method for analyzing digital images to extract useful information.

[0346] "Sensor information analysis technology" refers to the technology for analyzing data obtained from sensors and converting it into meaningful information.

[0347] "Affected area" refers to the extent of the impact on other areas and facilities centered on the damaged area.

[0348] "Generative AI" refers to artificial intelligence models that perform inference and generative tasks.

[0349] "External environment forecast" refers to forecast data obtained from external sources, such as weather forecasts and traffic information.

[0350] An "emotion engine" refers to a system that analyzes a user's emotional information and adjusts the response based on that information.

[0351] "Restoration plan" refers to a plan that includes repair procedures for damaged areas, necessary materials, work schedules, etc.

[0352] System Overview

[0353] This invention is a system that assesses damage to building facilities caused by disasters such as earthquakes and estimates the extent of the impact and the cost and time required for restoration as a BCP (Business Continuity Plan) measure for large factories and facilities. This system identifies damaged areas using a 3D model, makes estimates using a generative AI model, and provides an efficient restoration plan by taking into account external environment predictions. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, more effective responses are possible.

[0354] Hardware and software used

[0355] Hardware

[0356] 1. Devices (PC, tablet, smartphone)

[0357] A device that can be accessed by the user to report damage.

[0358] 2. Server

[0359] Responsible for data processing for the entire system, receiving damage reports, acquiring 3D models, identifying damaged areas, estimating the extent of the impact, estimating recovery costs and time using generative AI, linking with other systems, and generating and presenting recovery plans to users.

[0360] 3. Database

[0361] A storage system that stores 3D models, data on past disasters, and recovery case studies.

[0362] software

[0363] 1. Database Access API

[0364] Software for retrieving 3D models and past disaster data from databases.

[0365] 2. Image Processing Algorithm

[0366] Software for identifying user-reported damage locations on 3D models.

[0367] 3. Sensor Information Analysis Technology

[0368] Software that analyzes sensor information within the facility and accurately identifies damaged areas.

[0369] 4. Structural Analysis Software

[0370] Software for analyzing the impact range centered on the damaged area and simulating the impact of the damage on other parts.

[0371] 5. Generative AI Models

[0372] An artificial intelligence model for estimating recovery costs and time. Estimates are made by referencing data from past disasters and similar cases.

[0373] 6. Emotion Engine

[0374] Software that analyzes user reports and extracts emotional information. It is used to adjust the response based on the extracted emotional information.

[0375] 7. Integration API

[0376] Software that connects with external systems such as weather forecast systems and traffic information systems to obtain the necessary data.

[0377] Specific examples

[0378] Example 1: Earthquake damage at a parts manufacturing plant

[0379] 1. User: After the earthquake, report damage to the production line area on the second floor of the factory.

[0380] Fill out and submit the damage report form from your device.

[0381] 2. Server: Retrieves the 3D model corresponding to the second floor production line area of ​​the factory from the database.

[0382] 3. Server: Matches the 3D model with the reported photo data to identify the damage.

[0383] 4. Server: Use structural analysis software to simulate how damage could affect the logistics area on the first floor.

[0384] 5. Server: Using a generative AI model, we estimate that repair costs will be 5 million yen and recovery will take 10 days.

[0385] 6. Server: Obtains weather data for the next week from the weather forecast system and incorporates it into recovery plans.

[0386] 7. Server: Analyzes user reports using an emotion engine, and if stress levels are high, allocates additional resources and includes them in the recovery plan.

[0387] 8. Server: Provide users with a concrete recovery plan.

[0388] Example 2: Data center fire damage

[0389] 1. User: Reports that a fire has broken out in the data center and some server racks have been damaged.

[0390] Fill out and submit the damage report form from your device.

[0391] 2. Server: Retrieves the 3D model of the data center from the database.

[0392] 3. Server: Matches the 3D model with the reported photo data to identify the damage.

[0393] 4. Server: The impact on network equipment is also assessed using structural analysis software.

[0394] 5. Server: Using a generative AI model, we estimate that repair costs will be 2 million yen and recovery will take 5 days.

[0395] 6. Server: Obtains information on air conditioning and power supply status after a fire from the linked system and reflects it in the recovery plan.

[0396] 7. Server: Analyzes the user's report using an emotion engine and presents a special support plan if there are any concerns.

[0397] 8. Server: Provide users with a concrete recovery plan.

[0398] This system enables quick and efficient damage assessment and recovery planning in the event of a disaster. It also takes user emotions into consideration, providing greater reliability and satisfaction.

[0399] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0400] Step 1:

[0401] User: After a disaster occurs, report damage to a factory or facility.

[0402] Specific actions: Use the terminal to access the system's damage report form and enter the location of the damaged area, a photo, and specific details of the damage.

[0403] Input: Location, photo data, damage details.

[0404] Output: Damage report data is sent to the system.

[0405] Step 2:

[0406] Server: Receives damage reports.

[0407] Specific operation: Analyzes damage report data sent by users and extracts information on the damage location and the report timestamp.

[0408] Input: Damage report data (location, photo data, damage details).

[0409] Output: Extracted damage data (damage location, timestamp).

[0410] Step 3:

[0411] Server: Retrieves the 3D model of the target facility from the database.

[0412] Specific operation: Uses the database access API to load 3D model data of the facility corresponding to the reported damage location.

[0413] Input: Damage report data.

[0414] Output: 3D model data.

[0415] Step 4:

[0416] Server: Identify the damaged area on the 3D model.

[0417] What it does: It uses image processing algorithms to compare reported photographic data with 3D models to map damage, and then analyzes sensor information to improve the accuracy of damage identification.

[0418] Input: 3D model data, photo data, sensor information.

[0419] Output: Location data of identified damage areas.

[0420] Step 5:

[0421] Server: Estimate the affected area centered on the damaged area.

[0422] Specific behavior: Use structural analysis software to simulate the impact of damage on other parts and identify the extent of the impact.

[0423] Input: Damage location data, 3D model data.

[0424] Output: Impact area data.

[0425] Step 6:

[0426] Server: Uses generative AI to estimate recovery costs and time.

[0427] Specific operation: By referencing data from past disasters and similar cases, recovery costs and time are predicted through a generative AI model.

[0428] Input: Damage location data, affected area data, past disaster data, similar case data.

[0429] Output: Estimated restoration cost and duration data.

[0430] Step 7:

[0431] Server: Works with other systems to consider external environment predictions.

[0432] Specific operation: Using the integrated API, weather data is obtained from the weather forecast system and risk assessment data on material transportation is obtained from the traffic information system, and this data is reflected in damage assessment and restoration plans.

[0433] Input: Weather data, traffic information from external systems.

[0434] Output: A proposed recovery plan that takes into account the external environment.

[0435] Step 8:

[0436] Server: Analyzes the user's emotional information using the emotion engine.

[0437] Specific operation: The system analyzes the user's report using natural language processing technology to extract emotional information (stress, anxiety levels), and adjusts the response accordingly.

[0438] Input: User report.

[0439] Output: Extracted emotion information.

[0440] Step 9:

[0441] Server: Generates a recovery plan and presents it to the user.

[0442] Specific behavior: Generates a restoration plan including a list of required materials, a schedule of repair personnel, and estimated start and completion dates for work. Once the plan is complete, the user is notified via push notification or email with detailed information. It also takes emotional information into account to provide appropriate language and additional support.

[0443] Inputs: Estimated recovery cost and duration data, impact area data, external environment data, and sentiment information.

[0444] Output: Completed recovery plan, notification to users.

[0445] (Application example 2)

[0446] 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."

[0447] Although existing systems exist for quickly and accurately assessing damage to building facilities caused by earthquakes and other disasters and estimating the extent of the impact and the cost and time required for restoration, they have limitations in their accuracy and response capabilities. For example, users often fail to fully report the location of damage, or restoration plans are formulated without sufficient consideration of external environmental forecasts. Furthermore, most systems ignore the user's emotional state, and are unable to fully alleviate the user's stress and anxiety. To solve these problems, a comprehensive and emotion-sensitive disaster response system was needed.

[0448] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to report damage caused by a disaster using a terminal, means for receiving the damage report and retrieving a 3D model of the target facility from a database, means for identifying the damaged location on the 3D model, means for estimating the impact area centered on the damaged location, means for estimating recovery costs and time using generative AI, means for coordinating with other systems to make estimates taking into account external environment predictions, means for analyzing emotional information based on the user's report, means for adjusting a recovery plan and support system based on the emotional information, and means for generating a recovery plan and presenting it to the user. This not only enables improved accuracy in damage assessment and recovery plans during a disaster, but also enables flexible responses according to the user's emotional state.

[0449] The "means for a user to report a damaged location using a terminal" is a function that allows a user to report a damaged location caused by a disaster using a terminal.

[0450] The "means for receiving a damage report" is a function that allows the server to receive a damage report sent from a user.

[0451] "Means for obtaining a 3D model from a database" refers to a function for calling up and obtaining a 3D model of the target facility from a database.

[0452] "Means for identifying damaged areas on a 3D model" refers to a function for accurately identifying damaged areas on a 3D model.

[0453] "Means for estimating the extent of the impact centered on the damaged location" is a function for analyzing and estimating the extent of the impact centered on the identified damaged location.

[0454] "Means for estimating recovery costs and time using generative AI" refers to a function for estimating the cost and time required for recovery using generative AI.

[0455] "Means of making estimates that take into account external environment forecasts in cooperation with other systems" is a function that takes into account the external environment by using data from other external systems to make more accurate estimates.

[0456] The "means for analyzing emotional information based on the contents of a user's report" is a function for analyzing the contents of a user's report and extracting emotional information.

[0457] The "means for adjusting a recovery plan or support system based on emotional information" is a function for adjusting a recovery plan or support system based on the extracted emotional information.

[0458] The "means for generating a recovery plan and presenting it to the user" is a function for generating a recovery plan and presenting it to the user.

[0459] The present invention is a system for efficiently evaluating and planning restoration of building facilities damaged by earthquakes or other disasters. Detailed explanations for carrying out the present invention are provided below.

[0460] System Configuration

[0461] The system of the present invention comprises a terminal used by a user, a server, a database, and other systems (systems that provide external environment data).

[0462] 1. Device:

[0463] The devices include PCs, tablets, smartphones, etc. Users use these devices to report damage.

[0464] 2. Server:

[0465] The server receives damage reports, obtains 3D models, identifies the damaged areas, estimates the extent of the impact, uses generative AI to estimate recovery costs and time, connects with other systems, analyzes emotional information, and generates and presents recovery plans to users.

[0466] 3. Database:

[0467] The database stores 3D model data of the target facility, data on past disasters, and data on similar cases.

[0468] 4. Other Systems:

[0469] It works in conjunction with systems that provide external environmental data, such as weather forecast systems and traffic information systems.

[0470] Processing Overview

[0471] 1. Collecting damage reports

[0472] The user reports the damaged area using a terminal. Through this process, the user inputs detailed information about the damaged area of ​​the target facility (location, photo, specific damage content).

[0473] 2. Obtaining a 3D model

[0474] The server receives the damage report and retrieves the 3D model of the target facility from the database. The server loads the necessary 3D model data through the database access API.

[0475] 3. Identifying the damaged area

[0476] The server identifies the damage location on the 3D model and uses image processing algorithms and sensor information analysis techniques to match the reported photo data with the 3D model.

[0477] 4. Estimation of the impact range

[0478] The server analyzes the impact area around the damaged area, using structural analysis software to simulate how the damage will affect other parts and identify potential areas of impact.

[0479] 5. Estimated restoration costs and time

[0480] The server uses generative AI to estimate recovery costs and time, referencing data from past disasters and similar cases, and predicting the necessary personnel, materials, and construction procedures.

[0481] 6. Consideration of external environment forecasts

[0482] By linking with other systems, weather data is obtained from the weather forecast system and traffic data from the traffic information system, and recovery plans are adjusted taking into account this external environmental data.

[0483] 7. Emotional information analysis and response adjustment

[0484] The server analyzes the emotional information from the user's report, uses an emotion engine to identify the user's stress and anxiety, and adjusts recovery plans and support systems based on the emotional information.

[0485] 8. Generate and present a recovery plan

[0486] A final recovery plan is generated and presented to the user, including a list of required materials, a schedule of repair personnel, and estimated start and completion dates for work. Once the plan is complete, the user is notified via push notification or email.

[0487] Hardware and Software

[0488] Hardware used: smartphone, tablet, PC

[0489] Software used: Database access API, image processing algorithms, sensor information analysis technology, structural analysis software, generative AI models, emotion engines

[0490] Specific examples

[0491] Example 1: Earthquake damage to a store

[0492] 1. A user reports damage to an entrance after a disaster occurs.

[0493] 2. The server acquires the 3D model and identifies the damaged area.

[0494] 3. The server analyzes the impact range and identifies the affected area.

[0495] 4. The server uses generative AI to calculate the repair cost and number of days.

[0496] 5. The server creates a final recovery plan, taking into account weather and traffic forecasts.

[0497] 6. Analyze emotional information from user reports and provide support according to stress levels.

[0498] 7. Provide users with a concrete recovery plan.

[0499] Prompt Sentence Examples

[0500] Damage Report: Entrance

[0501] Damage: Cracks in the wall

[0502] Photo: entrance-crack.jpg

[0503] Emotion analysis:

[0504] A user reported: "There is a large crack at the entrance that is likely to cause anxiety for guests."

[0505] Estimate:

[0506] Repair cost: 500,000 yen

[0507] Recovery time: 3 days

[0508] External Data:

[0509] Weather forecast: Sunny for the next three days

[0510] Traffic Information: Normal traffic conditions

[0511] Emotion engine analysis results:

[0512] Stress level: 8

[0513] Recommended Action: Allocate additional resources

[0514] Final Recovery Plan:

[0515] List of required materials

[0516] Repair technician schedule

[0517] Planned start and completion dates for construction

[0518] In this way, the present invention makes it possible to carry out damage assessment and restoration planning with high accuracy in the event of a disaster, and realizes flexible responses that also take into account the feelings of users.

[0519] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0520] Step 1:

[0521] Collecting damage reports

[0522] Users report damage caused by a disaster using a terminal. Specifically, they use a smartphone or PC to input the location information of the damaged area, details of the damage, photos, etc., and send them to the server.

[0523] Input: Location of damaged area, damage details, photo

[0524] Output: Damage report data sent to the server

[0525] Step 2:

[0526] Receiving damage reports

[0527] The server receives the damage report sent by the user and analyzes the data, such as the location of the damage and the timestamp of the report, based on the received information.

[0528] Input: User-submitted damage report data

[0529] Output: Parsed damage report data

[0530] Step 3:

[0531] Acquiring a 3D model

[0532] The server retrieves a 3D model of the target facility from the database based on the damage report, and loads the 3D model data corresponding to the building or area where the damage was reported via the database access API.

[0533] Input: Damage report data, building information

[0534] Output: Acquired 3D model data

[0535] Step 4:

[0536] Identifying the damage

[0537] The server identifies the damaged areas on the acquired 3D model, uses image processing algorithms to match the photo data sent by the user with the 3D model data, and also uses sensor information to accurately identify the damaged areas.

[0538] Input: 3D model data, photo data of damage report

[0539] Output: Information on identified damage locations

[0540] Step 5:

[0541] Estimation of the impact range

[0542] The server then analyzes the impact area around the identified damage, using structural analysis software to simulate how the damage will affect other parts and identify potential areas of impact.

[0543] Input: Identified damage information, structural analysis software

[0544] Output: Estimated impact area data

[0545] Step 6:

[0546] Estimated restoration costs and time

[0547] The server uses generative AI to estimate recovery costs and time. It references data from past disasters and similar cases and uses an AI model to predict costs and time. It also includes the required personnel, material lists, and construction procedures.

[0548] Input: Affected area data, past disaster data, similar case data

[0549] Output: Estimated restoration costs and times

[0550] Step 7:

[0551] Consideration of external environment forecasts

[0552] The server works with other systems to make estimates that take into account external environmental forecasts, obtains weather data from the weather forecast system, and traffic data from the traffic information system, and adjusts recovery plans based on this data.

[0553] Input: Weather data, traffic data

[0554] Output: Estimated data taking into account the external environment

[0555] Step 8:

[0556] Emotional information analysis

[0557] The server analyzes the emotional information based on the user's report, uses an emotion engine to identify the user's stress and anxiety, and adjusts recovery plans and support systems based on that information.

[0558] Input: User report

[0559] Output: Parsed emotion information

[0560] Step 9:

[0561] Generate and present a recovery plan

[0562] The server generates a final recovery plan and presents it to the user, including a list of required materials, a schedule of repair personnel, and estimated start and completion dates for the work. Once the plan is complete, the user is notified via push notification or email.

[0563] Input: Emotional information, external environment forecast data, estimated recovery cost and time data

[0564] Output: A recovery plan presented to the user

[0565] 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.

[0566] 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.

[0567] 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.

[0568] [Second embodiment]

[0569] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0570] 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.

[0571] 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).

[0572] 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.

[0573] 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.

[0574] 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).

[0575] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0576] 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.

[0577] 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.

[0578] 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.

[0579] 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.

[0580] 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."

[0581] System Overview

[0582] This invention is a system that assesses damage to building facilities caused by disasters such as earthquakes and estimates the extent of the impact and the cost and time required for restoration as a BCP (Business Continuity Plan) measure for large factories and facilities. This system uses 3D models to identify damaged areas, makes estimates using generative AI, and can also take external environment predictions into account by linking with other systems.

[0583] Key components of the system

[0584] 1. User Interface (Terminal)

[0585] The terminal is a device that can access the system via a network, such as a PC, tablet, or smartphone. Users use the terminal to report damage.

[0586] 2. Server

[0587] The server receives damage reports, obtains 3D models, identifies the damaged areas, estimates the extent of the impact, uses generative AI to estimate recovery costs and time, connects with other systems, generates recovery plans, and presents them to users.

[0588] 3. Database

[0589] The database is a storage system that stores 3D models, data on past disasters, and recovery case studies.

[0590] 4. Other Systems

[0591] The other systems are external systems that provide external environmental data, such as a weather forecast system or a traffic information system.

[0592] Program processing flow

[0593] 1. User reports damage

[0594] User: After a disaster occurs, enter the damaged areas of the factory or facility into the system. Using a terminal, access the system's damage report form and enter the damaged areas (e.g., second floor of the building, 3D printer area).

[0595] 2. The server retrieves the 3D model

[0596] Server: Receives the damage report and retrieves the 3D model data of the target facility from the database. For example, it loads the 3D model file of "Factory A."

[0597] 3. The server identifies the damage

[0598] Server: Identifies the damage locations reported by users on the 3D model. Using image processing algorithms and sensor information analysis technology, specific damage locations are mapped onto the 3D model.

[0599] 4. The server estimates the affected area

[0600] Server: Analyze the impact area around the damaged area. For example, use structural analysis software to simulate how damage to the second floor will affect the first floor and other rooms.

[0601] 5. The server uses generative AI to estimate recovery costs and time.

[0602] Server: Uses generative AI to estimate recovery costs and time. Refers to data from past disasters and similar cases to estimate repair costs and the number of days required for recovery.

[0603] 6. Integration with other systems

[0604] Server: Calls the API to obtain necessary data from other forecasting systems. For example, obtains future weather data from a weather forecast system and reflects it in adjusting construction schedules.

[0605] 7. Presentation of recovery plan

[0606] Server: Generates a specific recovery plan based on the estimation results and external data. The recovery plan includes the necessary materials, a schedule of workers, and a timeline of the start and end dates of construction. Once the plan is complete, it is presented to the user via their device.

[0607] Specific examples

[0608] Example 1: Earthquake damage at a parts manufacturing plant

[0609] 1. User: After the earthquake, report damage to the production line area on the second floor of the factory.

[0610] 2. Server: Obtain a 3D model of the factory and identify damaged areas in the production line area on the second floor.

[0611] 3. Server: Analyze the impact area around the damaged area and determine the potential impact on the logistics area on the first floor.

[0612] 4. Server: Using generative AI, we estimate that repair costs will be 5 million yen and recovery will take 10 days.

[0613] 5. Server: Works with the weather forecast system to make predictions that take into account the weather for the next week.

[0614] 6. Server: Provide users with a concrete recovery plan.

[0615] Example 2: Data center fire damage

[0616] 1. User: Reports that a fire has broken out in the data center and some server racks have been damaged.

[0617] 2. Server: Obtain a 3D model of the data center and identify areas of damage.

[0618] 3. Server: Analyze the extent of the impact around the damaged area and evaluate the impact on network equipment.

[0619] 4. Server: Using generative AI, we estimate that repair costs will be 2 million yen and recovery will take 5 days.

[0620] 5. Server: Obtains information on air conditioning and power supply status after a fire from the linked system and reflects it in the recovery plan.

[0621] 6. Server: Provide users with a concrete recovery plan.

[0622] The above is an embodiment of the present invention. This system makes it possible to quickly and efficiently assess damage and formulate a restoration plan in the event of a disaster.

[0623] The processing flow will be explained below.

[0624] Step 1:

[0625] After a disaster occurs, users report damage using a terminal. Specifically, users access the system's damage report form, enter location information and photos of the damaged area, and specific details of the damage (e.g., cracks in the wall, broken machinery), and send the information to the system.

[0626] Step 2:

[0627] The server receives the damage report sent by the user, analyzes the necessary data, such as the location of the damage and the timestamp of the report, and starts the damage assessment process.

[0628] Step 3:

[0629] The server retrieves the 3D model of the target facility from the database. Specifically, it loads the 3D model data corresponding to the building or area where the damage was reported via the database access API.

[0630] Step 4:

[0631] The server identifies the damaged areas on the 3D model, and uses image processing algorithms to match the reported photo data with the 3D model. Sensor information analysis technology is also used to ensure that the damaged areas are accurately identified.

[0632] Step 5:

[0633] The server estimates the extent of the impact around the damaged area, and uses structural analysis software to simulate how the damage will affect other parts and identify potential areas of impact.

[0634] Step 6:

[0635] The server uses generative AI to estimate recovery costs and time. Specifically, it references data from past disasters and similar recovery cases and uses an AI model to predict costs and time. This estimate also includes the necessary personnel, materials, and construction procedures.

[0636] Step 7:

[0637] The server works in conjunction with other systems to make estimates that take into account external environmental forecasts. Specifically, it calls the weather forecast system's API to obtain future weather data and adds factors that may affect construction schedules. It also obtains data from the traffic information system and performs risk assessments for material transportation.

[0638] Step 8:

[0639] The server generates a final recovery plan and presents it to the user, which includes a list of required materials, a schedule of repair personnel, and estimated start and completion dates for work. Once the plan is complete, the user is notified via push notification or email, allowing them to review the details.

[0640] The above are the specific processing steps.

[0641] Example 1

[0642] 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."

[0643] Rapid and accurate damage assessment and recovery plan formulation in the event of a disaster at large factories and facilities are important issues for ensuring business continuity. However, conventional methods require a great deal of time and effort to identify damaged areas and estimate the extent of the impact, and there is also a high degree of uncertainty in estimating recovery costs and time. It is also difficult to take external environmental predictions into account, and many challenges exist in formulating comprehensive recovery plans. A new system is needed to solve these problems.

[0644] 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.

[0645] In this invention, the server includes: means for a user to report damage caused by a disaster using a terminal; means for receiving the damage report and retrieving a 3D model of the target facility from a database; means for identifying the damaged area on the 3D model; means for estimating the extent of impact centered on the damaged area; means for estimating restoration costs and time using generative AI; means for linking with other systems to make estimates taking into account external environment predictions; means for generating a restoration plan and presenting it to the user; means for using image processing algorithms and sensor information analysis technology to map the damaged area onto a 3D model based on the damage report form; and means for inputting prompt statements to the generative AI to estimate repair costs and restoration time. This enables rapid and accurate assessment of damage caused by a disaster, detailed estimation of the extent of impact, reliable estimation of restoration costs and time, and the formulation of a comprehensive restoration plan taking into account the external environment.

[0646] "User" is a person or organization whose role is to operate the system and report damage caused by a disaster.

[0647] A "terminal" is a device such as a PC, tablet, or smartphone that can access the system via a network and operate it or input information.

[0648] A "damage report" is information entered by a user to the system about the location and extent of damage to facilities and equipment caused by a disaster.

[0649] "Server" is the central processing unit of the system, and is a collective term for the hardware and software components that are responsible for receiving damage reports, obtaining 3D models, identifying damaged areas, estimating the extent of the impact, estimating recovery costs and time, obtaining external environment forecasts, and generating and presenting recovery plans.

[0650] A "database" is an information management system for storing and managing 3D model data, past disaster data, recovery case data, etc.

[0651] A "3D model" is digital data that represents the physical structure of the target facility in three dimensions and is used to identify damaged areas and analyze the extent of the impact.

[0652] An "image processing algorithm" is a computational method for identifying specific features or patterns in a digital image and analyzing the location of damage.

[0653] "Sensor information analysis technology" is a technology for analyzing data obtained from various sensors and identifying damaged areas.

[0654] The "scope of impact" refers to the range and extent of the impact that the identified damaged area has on the surrounding area, and is simulated using structural analysis software, etc.

[0655] "Generative AI" is a type of artificial intelligence technology that generates output results (e.g., recovery costs and duration) based on specific input information (prompt statements).

[0656] A "prompt sentence" is an instruction sentence input to a generative AI, and contains information that forms the basis for the AI's analysis and inference.

[0657] "Restoration Plan" means the action plan required to repair and restore the damaged area, including specific materials, personnel, schedule, costs, etc.

[0658] "Other systems" are external data systems that provide external environmental data, such as weather forecast systems and traffic information systems.

[0659] System Overview

[0660] This invention is a system for assessing damage to building facilities caused by disasters (such as earthquakes and fires) and estimating the cost and time required for restoration as a business continuity plan (BCP) measure for large factories and facilities. This system uses 3D models to identify damaged areas, makes estimates using generative AI, and works with other systems to consider external environment predictions.

[0661] Key Components

[0662] 1. User Interface (Terminal)

[0663] The terminal is a device that can access the system via a network, such as a PC, tablet, or smartphone. Users use the terminal to report damage.

[0664] 2. Server

[0665] The server receives damage reports, obtains 3D models, identifies the damaged areas, estimates the extent of the impact, uses generative AI to estimate recovery costs and time, connects with other systems, generates recovery plans, and presents them to users.

[0666] 3. Database

[0667] The database is a storage system that stores 3D models, data on past disasters, and recovery case studies.

[0668] 4. Other Systems

[0669] The other systems are external systems that provide external environmental data, such as a weather forecast system or a traffic information system.

[0670] Program processing description

[0671] User reports damage

[0672] When a disaster occurs, users access the damage report form using a device (PC, tablet, smartphone). They enter the necessary information in the form and press the report button to send it to the system. For example, a user may report that "the 3D printer area on the second floor of the building has been damaged."

[0673] The server receives the damage report and obtains the 3D model.

[0674] When the server receives a damage report from a user, it retrieves the 3D model data of the target facility from the database. For example, it loads the 3D model file of "Factory A" (e.g., "Factory A_3DModel.obj").

[0675] The server identifies the damage

[0676] The server identifies the reported damage on the 3D model. Specifically, it uses image processing algorithms and sensor information analysis technology to map the damage reported by the user onto the digital model. For example, it identifies the location of the "3D printer area on the second floor."

[0677] The server estimates the scope of the impact

[0678] The server analyzes the extent of the damage, focusing on the damaged area. It uses structural analysis software (e.g., ANSYS) to simulate the impact of the damage on other areas and structural components. For example, it calculates how damage to the second floor will affect the logistics area on the first floor.

[0679] The server uses generative AI to estimate recovery costs and time.

[0680] The server inputs prompt text into the generative AI, which estimates the cost and time required for recovery. The prompt text includes information such as the extent of the damage, the extent of the impact, and similar past cases. For example, it might say, "The 3D printer area on the second floor has been damaged, affecting the logistics area on the first floor. Estimate the repair cost and number of days." Based on this, the AI ​​outputs an estimate such as "repair cost 5 million yen, recovery time 10 days" based on past disaster data and similar cases.

[0681] The server connects with other systems

[0682] The server connects with other systems to obtain external environmental data. For example, it extracts future weather data from a weather forecast system and reflects it in recovery plans. This data is then used to schedule outdoor work, etc.

[0683] The server presents a recovery plan

[0684] The server generates a specific restoration plan based on the estimation results and external data and presents it to the user. This plan includes information such as materials, personnel, schedule, and construction start and end dates. The user can then use their terminal to review the plan and make any necessary corrections or approvals.

[0685] Specific examples

[0686] Earthquake damage at a parts manufacturing plant

[0687] 1. User: After the earthquake, the user reports damage to the second floor production line area of ​​the factory. The user types "The second floor production line area is severely damaged" and submits the message.

[0688] 2. Server: Obtain a 3D model of the factory and identify damaged areas in the production line area on the second floor.

[0689] 3. Server: Image processing algorithms identify damage areas and map them onto the model.

[0690] 4. Server: Use structural analysis software to analyze the impact area and simulate the impact on the logistics area on the first floor.

[0691] 5. Server: The generative AI is given a prompt: "Estimate the repair costs and recovery time for damage caused by the earthquake." The server estimates that repair costs will be 5 million yen and recovery will take 10 days.

[0692] 6. Server: Obtains weather data for the next week from the weather forecast system to see if it will affect outdoor work.

[0693] 7. Server: Generates a recovery plan and presents it to the user's device, such as "Repair costs will be 5 million yen, and recovery will take 10 days." The user reviews it, makes any necessary corrections, and then approves it.

[0694] Data center fire damage

[0695] 1. User: Reports that a fire broke out in the data center and some server racks were damaged. Type "The fire damaged the server racks and also affected some network equipment" and submit.

[0696] 2. Server: Obtain a 3D model of the data center and identify areas of damage.

[0697] 3. Server: Using image processing technology, the damaged area is mapped onto a 3D model.

[0698] 4. Server: Use structural analysis software to analyze the scope of the impact and evaluate the impact on network equipment.

[0699] 5. Server: The generative AI is given a prompt: "If a server rack is damaged by fire, estimate the repair cost and recovery time." The AI ​​estimates that the repair cost will be 2 million yen and that it will take five days to recover.

[0700] 6. Server: Obtains information on air conditioning and power supply status after a fire from other systems and reflects it in recovery plans.

[0701] 7. Server: Generates a recovery plan and presents it to the user's device, such as "Repair costs will be 2 million yen, and recovery will take 5 days." The user can review the plan and make any changes or approvals.

[0702] This concludes the description of the "Mode for Carrying Out the Invention." This system enables damage assessment in the event of a disaster and the development of an efficient recovery plan.

[0703] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0704] Step 1: Enter your damage report

[0705] User: After a disaster occurs, the user accesses the damage report form using a device (PC, tablet, smartphone, etc.). They enter the necessary information, such as the location and extent of the damage, and contact information, and press the report button to submit. For example, they report that "the 3D printer area on the second floor of the building has been damaged."

[0706] Input: Report information such as location of damage, extent of damage, contact information, etc.

[0707] Output: Damage report data sent to the server (e.g. "The second floor of the building, the 3D printer area, was damaged")

[0708] Step 2: Receiving damage reports and obtaining 3D models

[0709] Server: Based on the received damage report, retrieves 3D model data from the database using the target facility's identification information. Executes a database query using the target facility's ID and name to load the 3D model file. For example, loads the 3D model file for "Factory A" (e.g., "Factory A_3DModel.obj").

[0710] Input: Damage report data, facility identification

[0711] Output: 3D model data of the facility (e.g. "FactoryA_3DModel.obj")

[0712] Step 3: Identify the damage

[0713] Server: Analyzes the damage information entered by the user and identifies the corresponding location on the 3D model. Using image processing algorithms and sensor information analysis technology, the reported damage is mapped onto the digital model. For example, the specific location of the "3D printer area on the second floor" is identified.

[0714] Input: Damage report data, 3D model data

[0715] Output: Damaged area on a 3D model (e.g., "3D printer area on the second floor")

[0716] Step 4: Estimate the impact area

[0717] Server: Analyze the impact area around the identified damaged area. Use structural analysis software (e.g., ANSYS) to simulate the impact of the damage on other areas and structural parts. For example, calculate how damage to the second floor will affect the logistics area on the first floor.

[0718] Input: Damage location, 3D model data

[0719] Output: Analysis results of the impact range (e.g., "Damage on the second floor affects the logistics area on the first floor")

[0720] Step 5: Estimate restoration costs and time

[0721] Server: A prompt is input into the generative AI, which estimates the cost and time required for recovery. The prompt includes information such as the extent of the damage, the extent of the impact, and past similar cases. For example, it might say, "The 3D printer area on the second floor has been damaged, affecting the logistics area on the first floor. Estimate the repair cost and number of days." Based on this, the AI ​​outputs an estimate such as "Repair cost 5 million yen, recovery time 10 days."

[0722] Input: Prompt statement (damage status, affected area, past similar cases)

[0723] Output: Estimated restoration cost and time (e.g., "Repair cost: 5 million yen, restoration time: 10 days")

[0724] Step 6: Integration with other systems

[0725] Server: Links with other systems (e.g., weather forecast systems and traffic information systems) to obtain external environmental data. Uses APIs to obtain necessary data and reflects this information in recovery plans. For example, obtains future weather data from a weather forecast system and uses it to adjust construction schedules.

[0726] Input: Request for external environment data from other systems

[0727] Output: External environmental data (e.g., "Weather forecast for the next week")

[0728] Step 7: Generate and present a recovery plan

[0729] Server: Generates a specific recovery plan based on the estimation results and acquired external data. This plan includes information such as the required materials, personnel, schedule, and construction start and end dates. The created recovery plan is presented on a terminal accessible to the user. The user can use the terminal to review the plan and make any necessary corrections or approvals.

[0730] Input: Estimated recovery costs and time, external environmental data

[0731] Output: A detailed restoration plan (e.g., "Repair cost: 5 million yen, restoration time: 10 days, materials required, personnel schedule, construction start and end dates")

[0732] (Application example 1)

[0733] 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."

[0734] When a disaster occurs and a robot installed in a factory or facility is damaged, it is difficult to quickly and accurately assess the damage and estimate the extent of the impact and the cost and time required for recovery. Conventional methods require a large amount of human resources and time, and there is a risk of delays in recovery plans or incorrect decisions. The purpose of this invention is to solve these problems and provide a system that enables efficient and rapid damage assessment and the generation of recovery plans.

[0735] 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.

[0736] In this invention, the server includes: means for a user to report damage caused by a disaster using a terminal; means for receiving the damage report and retrieving a 3D model of the target facility from a database; means for identifying the damaged area on the 3D model; means for estimating the impact area centered on the damaged area; means for estimating recovery costs and time using generative AI; means for making estimates taking into account external environment predictions in cooperation with other systems; means for generating and presenting a recovery plan to the user; and means for managing a robot that uses the 3D model to identify the damaged area and generate the impact area and recovery plan. This enables faster and more accurate damage assessment of robots and recovery plans in the event of a disaster.

[0737] A "user" is a person who uses this system to report damage caused by a disaster.

[0738] A "terminal" is a device that can be connected to a network, such as a PC, tablet, or smartphone used by a user.

[0739] A "disaster" is an event in which facilities or equipment are damaged due to natural phenomena such as earthquakes, fires, and typhoons, or due to human factors.

[0740] "Damaged area" refers to the specific location where facilities or equipment have been physically damaged by the disaster.

[0741] The "reporting means" refers to an interface that allows a user to input information about the damaged area into the system using a terminal.

[0742] "Means for receiving" refers to the function of the server receiving damage reports from users via the network.

[0743] A "3D model" is digital data that reproduces the spatial structure of facilities and equipment.

[0744] A "database" is a storage system that allows the system to store 3D model data, past disaster data, and other data.

[0745] "Means of acquisition" refers to the function by which the server retrieves the necessary 3D model data from the database.

[0746] "Means for identification" refers to technology for identifying user-reported damage locations on a 3D model.

[0747] The "area of ​​impact" refers to the area centered on the reported damage location and indicates the extent to which surrounding facilities and equipment may be affected.

[0748] "Means of estimation" is a function for calculating the extent of the impact based on the location of damage, as well as the cost and time required for recovery.

[0749] "Generative AI" is artificial intelligence that uses machine learning algorithms and generative models to automatically estimate recovery costs and time.

[0750] A "recovery plan" is a plan that includes specific work procedures for repairing damage, necessary materials, work personnel, construction schedule, etc.

[0751] "Means for presenting" refers to a function for displaying the recovery plan generated by the server on the user's terminal.

[0752] "Means of management" refers to the ability to control and monitor the robot using 3D models, and use them to identify damaged areas and generate restoration plans.

[0753] "Other systems" are systems that provide external environmental data, such as weather forecast systems and traffic information systems.

[0754] "External environment forecast" refers to environmental data provided by external systems, such as weather forecasts and traffic conditions, and is a factor taken into account in recovery plans.

[0755] System Overview

[0756] This invention is a system that quickly assesses damage and estimates the extent of the impact and the cost and time required for restoration when a disaster such as an earthquake or fire occurs in a factory or facility. Based on damage information reported by the user via a terminal, this system identifies the damaged area using a 3D model, makes estimates using generative AI, and cooperates with other systems to consider external environment predictions, thereby efficiently and quickly generating restoration plans.

[0757] Key components of the system

[0758] 1. User Interface (Terminal)

[0759] The terminal is a device that can access the system via a network, such as a PC, tablet, or smartphone. Users use the terminal to report damage.

[0760] 2. Server

[0761] The server receives damage reports, obtains 3D models, identifies the damaged areas, estimates the extent of the impact, uses generative AI to estimate recovery costs and time, connects with other systems, generates recovery plans, and presents them to users.

[0762] 3. Database

[0763] The database is a storage system that stores 3D models, data on past disasters, and recovery case studies.

[0764] 4. Other Systems

[0765] Other systems refer to external systems that provide external environmental data, such as weather forecast systems and traffic information systems.

[0766] About Torsion

[0767] Hardware / Software used

[0768] Hardware: Use a smartphone or tablet.

[0769] Software: Python 3.8 or higher, generative AI modules (e.g., TensorFlow or PyTorch), 3D model processing libraries (e.g., trimesh), and external API access libraries (e.g., requests).

[0770] Data processing and calculation

[0771] 1. Damage report

[0772] A user uses a terminal to report damage caused by a disaster to the system, including detailed information about the damage (for example, "damage to the robot arm").

[0773] 2. Obtaining a 3D model

[0774] Based on the damage report received, the server retrieves 3D model data of the target facility from the database. This is digital data that reproduces the spatial structure of the target facility.

[0775] 3. Identifying the damaged area

[0776] The server identifies the reported damage locations on the 3D model using image processing algorithms and sensor information analysis techniques.

[0777] 4. Estimation of the impact range

[0778] The server analyzes the extent of the impact around the damaged area, using structural analysis software to simulate how the damaged area will affect the surrounding area.

[0779] 5. Utilizing generative AI

[0780] The server uses generative AI to estimate recovery costs and time. The AI ​​model references data from past disasters and similar cases to estimate the necessary repair costs and recovery time.

[0781] 6. Integration with other systems

[0782] The server connects with other systems, such as weather forecast systems and traffic information systems, to obtain external environment forecasts, enabling predictions that take future weather and traffic conditions into account.

[0783] 7. Presentation of recovery plan

[0784] The server generates a specific recovery plan based on the results of these analyses, including the necessary materials, a schedule of workers, and a timeline of the start and end dates of the work.The server then presents the final recovery plan to the user.

[0785] Specific examples

[0786] Earthquake damage assessment and restoration planning app for factory robots

[0787] If a robotic arm installed in a factory is damaged in an earthquake, the user can use their terminal to report, "The robotic arm on the second floor is damaged." The server retrieves a 3D model of the factory from the database and identifies the damaged area. It analyzes the extent of the impact and uses generative AI to estimate the cost and time of restoration. It also retrieves data from the weather forecast system, generates an optimal restoration plan, and presents it to the user.

[0788] Example prompt statement

[0789] "An earthquake has occurred in Factory A. The robot arm on the second floor has been damaged. Please make a recovery plan."

[0790] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0791] Step 1:

[0792] The user uses a terminal to report damage caused by the disaster. The user enters specific damage information, such as "The robot arm on the second floor is damaged," into the form displayed on the terminal. The entered damage report is sent to the server via the network.

[0793] Step 2:

[0794] The server receives the damage report, analyzes the received data, and extracts information about the damaged location (for example, which part of the facility was damaged). It then retrieves a 3D model of the target facility from the database. If the target facility is "Factory A," the server loads the 3D model data of Factory A from the database.

[0795] Step 3:

[0796] The server identifies the damaged area on the 3D model. The server maps the received information on the damaged area (e.g., "second floor" or "robot arm area") onto the 3D model and identifies the specific damaged area using image processing algorithms and sensor information analysis technology. During this process, the server generates coordinate data for the damaged area.

[0797] Step 4:

[0798] The server estimates the extent of the impact around the damaged area. Using structural analysis software, the server simulates the impact of the damaged area on surrounding structures and equipment. For example, it analyzes how damage to the second floor will affect the first floor area and other rooms, and outputs the specific extent of the impact.

[0799] Step 5:

[0800] The server uses generative AI to estimate recovery costs and time. The server runs a generative AI model that uses data from past disasters and similar cases to estimate repair costs and the number of days required for recovery based on the location of damage and the extent of the impact. The input in this step is coordinate data of the damaged area and data on the extent of the impact, and the output is the estimated recovery cost and time.

[0801] Step 6:

[0802] The server connects with other systems to obtain external environment forecasts. The server then calls the APIs of weather forecast systems and traffic information systems to obtain future weather and traffic data. This data is used to schedule restoration work.

[0803] Step 7:

[0804] The server generates a recovery plan and presents it to the user. The server creates a specific recovery plan based on estimated recovery costs, time, impact area, and external environmental data. This plan includes the necessary materials, a schedule of work personnel, and a timeline of construction start and end dates. The generated recovery plan is displayed on the user's terminal via a user interface.

[0805] 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.

[0806] System Overview

[0807] This invention is a system that assesses damage to building facilities caused by disasters such as earthquakes and estimates the extent of the impact and the cost and time required for restoration as a BCP (Business Continuity Plan) measure for large factories and facilities. This system identifies damaged areas using 3D models, makes estimates using generative AI, and provides efficient restoration plans by taking into account external environment predictions. In addition, by combining it with an emotion engine that recognizes user emotions, more effective responses are possible.

[0808] Key components of the system

[0809] 1. User Interface (Terminal)

[0810] The terminal is a device that can access the system via a network, such as a PC, tablet, or smartphone. Users use the terminal to report damage.

[0811] 2. Server

[0812] The server receives damage reports, obtains 3D models, identifies damaged areas, estimates the extent of the impact, uses generative AI to estimate recovery costs and time, connects with other systems, generates recovery plans, and presents them to users. It also uses an emotion engine to analyze user emotions and adjust response measures based on that information.

[0813] 3. Database

[0814] The database is a storage system that stores 3D models, data on past disasters, and recovery case studies.

[0815] 4. Other Systems

[0816] The other systems are external systems that provide external environmental data, such as a weather forecast system or a traffic information system.

[0817] 5. Emotion Engine

[0818] The emotion engine extracts emotional information from user reports and operational logs during use, which is used for recovery planning and customer support resource allocation.

[0819] Program processing flow

[0820] 1. User reports damage

[0821] User: After a disaster occurs, the user enters the damaged areas of the factory or facility into the system. Using a terminal, the user accesses the system's damage report form, enters the location information and photos of the damaged areas, and specific details of the damage (e.g., cracks in the wall, broken machinery), and sends the form to the system.

[0822] 2. The server receives the damage report

[0823] Server: Receives damage reports sent by users. Based on the received information, it analyzes the necessary data, such as the location of the damage and the timestamp of the report, and starts the damage assessment process.

[0824] 3. The server retrieves the 3D model

[0825] Server: Retrieves 3D model data of the target facility from the database. Specifically, it loads the 3D model data corresponding to the building or area where the damage was reported via the database access API.

[0826] 4. The server identifies the damage

[0827] Server: Identifies the damage areas reported by users on the 3D model. Image processing algorithms are used to match the reported photo data with the 3D model. Sensor information analysis technology is also used to ensure that the damage areas are accurately identified.

[0828] 5. The server estimates the affected area

[0829] Server: Analyze the impact area around the damaged area. Use structural analysis software to simulate how the damage will affect other parts and identify potential areas of impact.

[0830] 6. The server uses generative AI to estimate recovery costs and time.

[0831] Server: Uses generative AI to estimate recovery costs and time. It references data from past disasters and similar cases and uses AI models to predict costs and time. This estimate also includes the required personnel, materials, and construction procedures.

[0832] 7. The server connects with other systems

[0833] Server: Calls the API to obtain necessary data from other forecasting systems. For example, it obtains future weather data from a weather forecast system and adds factors that may affect construction schedules. It also obtains data from a traffic information system to perform risk assessments for material transportation.

[0834] 8. The server uses the emotion engine

[0835] Server: Analyzes the user's report and extracts emotional information using an emotion engine. Identifies the stress or anxiety the user is feeling when reporting and adjusts the response accordingly.

[0836] 9. The server generates a recovery plan and presents it to the user.

[0837] Server: Generates and presents the final recovery plan to the user. The recovery plan includes a list of needed materials, a schedule of repair personnel, and estimated start and completion dates for work. Once the plan is complete, the user is notified via push notification or email so they can review the details. The server also takes into account the user's emotional state and presents appropriate language and support options.

[0838] Specific examples

[0839] Example 1: Earthquake damage at a parts manufacturing plant

[0840] 1. User: After the earthquake, report damage to the production line area on the second floor of the factory.

[0841] 2. Server: Obtain a 3D model of the factory and identify damaged areas in the production line area on the second floor.

[0842] 3. Server: Analyze the impact area around the damaged area and determine the potential impact on the logistics area on the first floor.

[0843] 4. Server: Using generative AI, we estimate that repair costs will be 5 million yen and recovery will take 10 days.

[0844] 5. Server: Works with the weather forecast system to make predictions that take into account the weather for the next week.

[0845] 6. Server: Analyzes the stress level from the user's report using an emotion engine. If high stress is detected, allocate additional resources and include them in the recovery plan.

[0846] 7. Server: Provide users with a concrete recovery plan.

[0847] Example 2: Data center fire damage

[0848] 1. User: Reports that a fire has broken out in the data center and some server racks have been damaged.

[0849] 2. Server: Obtain a 3D model of the data center and identify areas of damage.

[0850] 3. Server: Analyze the extent of the impact around the damaged area and evaluate the impact on network equipment.

[0851] 4. Server: Using generative AI, we estimate that repair costs will be 2 million yen and recovery will take 5 days.

[0852] 5. Server: Obtains information on air conditioning and power supply status after a fire from the linked system and reflects it in the recovery plan.

[0853] 6. Server: Analyzes the user's report using an emotion engine to determine whether there is any anxiety. If a serious anxiety is detected, a special support plan is presented.

[0854] 7. Server: Provide users with a concrete recovery plan.

[0855] The above is an embodiment of the present invention. This system makes it possible to quickly and efficiently assess damage and formulate recovery plans in the event of a disaster, and also takes into account the emotions of users.

[0856] The processing flow will be explained below.

[0857] Step 1:

[0858] After a disaster occurs, users report damage using a terminal. Specifically, users access the system's damage report form, enter location information and photos of the damaged area, and specific details of the damage (e.g., cracks in the wall, broken machinery), and send the information to the system.

[0859] Step 2:

[0860] The server receives the damage report sent by the user, which includes the location of the damage, details of the damage, and a timestamp of the report.

[0861] Step 3:

[0862] The server retrieves the 3D model data of the target facility from the database, and based on the damage report, loads the 3D model file of the target building or facility via the database access API.

[0863] Step 4:

[0864] The server identifies damaged areas on the 3D model. It uses image processing algorithms to analyze the photo data sent by the user and match it with the damaged areas in the 3D model. It also uses sensor information analysis technology to accurately identify damaged areas based on IoT sensor and camera data.

[0865] Step 5:

[0866] The server estimates the extent of the damage from the center, and uses structural analysis software to simulate the impact of the damage on other parts, thereby identifying areas that may be affected by the damage.

[0867] Step 6:

[0868] The server uses generative AI to estimate recovery costs and time. Specifically, data from past disasters and similar recovery cases is input into the AI ​​model to predict costs and time, including the required personnel, materials, and construction procedures. The AI ​​uses neural networks to generate highly accurate estimates.

[0869] Step 7:

[0870] The server works in conjunction with other systems to make estimates that take into account external environmental forecasts. It calls the weather forecast system's API to obtain future weather data and calculates factors that will affect construction schedules. It also obtains data from the traffic information system and performs risk assessments for material transportation.

[0871] Step 8:

[0872] The server uses an emotion engine to recognize the user's emotions. It analyzes the user's report content and the operation log at the time of sending, and extracts emotional information such as stress, anxiety, and satisfaction. This information is analyzed based on the wording in the report content and the timing of sending.

[0873] Step 9:

[0874] The server tailors the recovery plan it presents based on emotional information, for example automatically assigning additional customer support if high stress levels are detected, or generating a plan with detailed, easy-to-understand explanations if the user is feeling anxious.

[0875] Step 10:

[0876] The server generates a final recovery plan and presents it to the user, which includes a list of required materials, a schedule of repair personnel, and estimated start and completion dates for work. Once the plan is complete, the user is notified via push notification or email, allowing them to review the details.

[0877] The above are the specific processing steps.

[0878] Example 2

[0879] 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."

[0880] It has been difficult to effectively carry out rapid damage assessment and recovery plan formulation for factories and facilities in the event of a disaster using conventional methods. In addition, it is necessary to take into account the psychological stress of users, but there has been a lack of concrete methods for doing so.

[0881] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0882] In this invention, the server includes means for a user to report damage caused by a disaster using a terminal, means for receiving the damage report and retrieving a 3D model of the target facility from a database, means for identifying the damaged area on the 3D model, means for estimating the extent of the impact centered on the damaged area, means for estimating recovery costs and time using generative AI, means for making estimates taking into account external environment predictions in cooperation with other systems, means for analyzing the user's emotional information and using an emotion engine to adjust the response based on that information, and means for generating a recovery plan and presenting it to the user. This enables quick and effective damage assessment and formulation of a recovery plan, as well as responses that take into account the user's psychological stress.

[0883] "User" refers to the person or organization that operates the system to report damage caused by a disaster.

[0884] "Terminal" refers to a device (PC, tablet, smartphone, etc.) that can access the system via a network.

[0885] "Damage report" refers to a report from a user that includes location information, photos, and specific details of damage to building facilities caused by a disaster.

[0886] A "3D model" refers to digital data that shows the three-dimensional structure of the target facility.

[0887] "Database" refers to a storage system for storing information required by the system.

[0888] "Image processing algorithm" refers to a computational method for analyzing digital images to extract useful information.

[0889] "Sensor information analysis technology" refers to the technology for analyzing data obtained from sensors and converting it into meaningful information.

[0890] "Affected area" refers to the extent of the impact on other areas and facilities centered on the damaged area.

[0891] "Generative AI" refers to artificial intelligence models that perform inference and generative tasks.

[0892] "External environment forecast" refers to forecast data obtained from external sources, such as weather forecasts and traffic information.

[0893] An "emotion engine" refers to a system that analyzes a user's emotional information and adjusts the response based on that information.

[0894] "Restoration plan" refers to a plan that includes repair procedures for damaged areas, necessary materials, work schedules, etc.

[0895] System Overview

[0896] This invention is a system that assesses damage to building facilities caused by disasters such as earthquakes and estimates the extent of the impact and the cost and time required for restoration as a BCP (Business Continuity Plan) measure for large factories and facilities. This system identifies damaged areas using a 3D model, makes estimates using a generative AI model, and provides an efficient restoration plan by taking into account external environment predictions. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, more effective responses are possible.

[0897] Hardware and software used

[0898] Hardware

[0899] 1. Devices (PC, tablet, smartphone)

[0900] A device that can be accessed by the user to report damage.

[0901] 2. Server

[0902] Responsible for data processing for the entire system, receiving damage reports, acquiring 3D models, identifying damaged areas, estimating the extent of the impact, estimating recovery costs and time using generative AI, linking with other systems, and generating and presenting recovery plans to users.

[0903] 3. Database

[0904] A storage system that stores 3D models, data on past disasters, and recovery case studies.

[0905] software

[0906] 1. Database Access API

[0907] Software for retrieving 3D models and past disaster data from databases.

[0908] 2. Image Processing Algorithm

[0909] Software for identifying user-reported damage locations on 3D models.

[0910] 3. Sensor Information Analysis Technology

[0911] Software that analyzes sensor information within the facility and accurately identifies damaged areas.

[0912] 4. Structural Analysis Software

[0913] Software for analyzing the impact range centered on the damaged area and simulating the impact of the damage on other parts.

[0914] 5. Generative AI Models

[0915] An artificial intelligence model for estimating recovery costs and time. Estimates are made by referencing data from past disasters and similar cases.

[0916] 6. Emotion Engine

[0917] Software that analyzes user reports and extracts emotional information. It is used to adjust the response based on the extracted emotional information.

[0918] 7. Integration API

[0919] Software that connects with external systems such as weather forecast systems and traffic information systems to obtain the necessary data.

[0920] Specific examples

[0921] Example 1: Earthquake damage at a parts manufacturing plant

[0922] 1. User: After the earthquake, report damage to the production line area on the second floor of the factory.

[0923] Fill out and submit the damage report form from your device.

[0924] 2. Server: Retrieves the 3D model corresponding to the second floor production line area of ​​the factory from the database.

[0925] 3. Server: Matches the 3D model with the reported photo data to identify the damage.

[0926] 4. Server: Use structural analysis software to simulate how damage could affect the logistics area on the first floor.

[0927] 5. Server: Using a generative AI model, we estimate that repair costs will be 5 million yen and recovery will take 10 days.

[0928] 6. Server: Obtains weather data for the next week from the weather forecast system and incorporates it into recovery plans.

[0929] 7. Server: Analyzes user reports using an emotion engine, and if stress levels are high, allocates additional resources and includes them in the recovery plan.

[0930] 8. Server: Provide users with a concrete recovery plan.

[0931] Example 2: Data center fire damage

[0932] 1. User: Reports that a fire has broken out in the data center and some server racks have been damaged.

[0933] Fill out and submit the damage report form from your device.

[0934] 2. Server: Retrieves the 3D model of the data center from the database.

[0935] 3. Server: Matches the 3D model with the reported photo data to identify the damage.

[0936] 4. Server: The impact on network equipment is also assessed using structural analysis software.

[0937] 5. Server: Using a generative AI model, we estimate that repair costs will be 2 million yen and recovery will take 5 days.

[0938] 6. Server: Obtains information on air conditioning and power supply status after a fire from the linked system and reflects it in the recovery plan.

[0939] 7. Server: Analyzes the user's report using an emotion engine and presents a special support plan if there are any concerns.

[0940] 8. Server: Provide users with a concrete recovery plan.

[0941] This system enables quick and efficient damage assessment and recovery planning in the event of a disaster. It also takes user emotions into consideration, providing greater reliability and satisfaction.

[0942] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0943] Step 1:

[0944] User: After a disaster occurs, report damage to a factory or facility.

[0945] Specific actions: Use the terminal to access the system's damage report form and enter the location of the damaged area, a photo, and specific details of the damage.

[0946] Input: Location, photo data, damage details.

[0947] Output: Damage report data is sent to the system.

[0948] Step 2:

[0949] Server: Receives damage reports.

[0950] Specific operation: Analyzes damage report data sent by users and extracts information on the damage location and the report timestamp.

[0951] Input: Damage report data (location, photo data, damage details).

[0952] Output: Extracted damage data (damage location, timestamp).

[0953] Step 3:

[0954] Server: Retrieves the 3D model of the target facility from the database.

[0955] Specific operation: Uses the database access API to load 3D model data of the facility corresponding to the reported damage location.

[0956] Input: Damage report data.

[0957] Output: 3D model data.

[0958] Step 4:

[0959] Server: Identify the damaged area on the 3D model.

[0960] What it does: It uses image processing algorithms to compare reported photographic data with 3D models to map damage, and then analyzes sensor information to improve the accuracy of damage identification.

[0961] Input: 3D model data, photo data, sensor information.

[0962] Output: Location data of identified damage areas.

[0963] Step 5:

[0964] Server: Estimate the affected area centered on the damaged area.

[0965] Specific behavior: Use structural analysis software to simulate the impact of damage on other parts and identify the extent of the impact.

[0966] Input: Damage location data, 3D model data.

[0967] Output: Impact area data.

[0968] Step 6:

[0969] Server: Uses generative AI to estimate recovery costs and time.

[0970] Specific operation: By referencing data from past disasters and similar cases, recovery costs and time are predicted through a generative AI model.

[0971] Input: Damage location data, affected area data, past disaster data, similar case data.

[0972] Output: Estimated restoration cost and duration data.

[0973] Step 7:

[0974] Server: Works with other systems to consider external environment predictions.

[0975] Specific operation: Using the integrated API, weather data is obtained from the weather forecast system and risk assessment data on material transportation is obtained from the traffic information system, and this data is reflected in damage assessment and restoration plans.

[0976] Input: Weather data, traffic information from external systems.

[0977] Output: A proposed recovery plan that takes into account the external environment.

[0978] Step 8:

[0979] Server: Analyzes the user's emotional information using the emotion engine.

[0980] Specific operation: The system analyzes the user's report using natural language processing technology to extract emotional information (stress, anxiety levels), and adjusts the response accordingly.

[0981] Input: User report.

[0982] Output: Extracted emotion information.

[0983] Step 9:

[0984] Server: Generates a recovery plan and presents it to the user.

[0985] Specific behavior: Generates a restoration plan including a list of required materials, a schedule of repair personnel, and estimated start and completion dates for work. Once the plan is complete, the user is notified via push notification or email with detailed information. It also takes emotional information into account to provide appropriate language and additional support.

[0986] Inputs: Estimated recovery cost and duration data, impact area data, external environment data, and sentiment information.

[0987] Output: Completed recovery plan, notification to users.

[0988] (Application example 2)

[0989] 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."

[0990] Although existing systems exist for quickly and accurately assessing damage to building facilities caused by earthquakes and other disasters and estimating the extent of the impact and the cost and time required for restoration, they have limitations in their accuracy and response capabilities. For example, users often fail to fully report the location of damage, or restoration plans are formulated without sufficient consideration of external environmental forecasts. Furthermore, most systems ignore the user's emotional state, and are unable to fully alleviate the user's stress and anxiety. To solve these problems, a comprehensive and emotion-sensitive disaster response system was needed.

[0991] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to report damage caused by a disaster using a terminal, means for receiving the damage report and retrieving a 3D model of the target facility from a database, means for identifying the damaged location on the 3D model, means for estimating the impact area centered on the damaged location, means for estimating recovery costs and time using generative AI, means for coordinating with other systems to make estimates taking into account external environment predictions, means for analyzing emotional information based on the user's report, means for adjusting a recovery plan and support system based on the emotional information, and means for generating a recovery plan and presenting it to the user. This not only enables improved accuracy in damage assessment and recovery plans during a disaster, but also enables flexible responses according to the user's emotional state.

[0992] The "means for a user to report a damaged location using a terminal" is a function that allows a user to report a damaged location caused by a disaster using a terminal.

[0993] The "means for receiving a damage report" is a function that allows the server to receive a damage report sent from a user.

[0994] "Means for obtaining a 3D model from a database" refers to a function for calling up and obtaining a 3D model of the target facility from a database.

[0995] "Means for identifying damaged areas on a 3D model" refers to a function for accurately identifying damaged areas on a 3D model.

[0996] "Means for estimating the extent of the impact centered on the damaged location" is a function for analyzing and estimating the extent of the impact centered on the identified damaged location.

[0997] "Means for estimating recovery costs and time using generative AI" refers to a function for estimating the cost and time required for recovery using generative AI.

[0998] "Means of making estimates that take into account external environment forecasts in cooperation with other systems" is a function that takes into account the external environment by using data from other external systems to make more accurate estimates.

[0999] The "means for analyzing emotional information based on the contents of a user's report" is a function for analyzing the contents of a user's report and extracting emotional information.

[1000] The "means for adjusting a recovery plan or support system based on emotional information" is a function for adjusting a recovery plan or support system based on the extracted emotional information.

[1001] The "means for generating a recovery plan and presenting it to the user" is a function for generating a recovery plan and presenting it to the user.

[1002] The present invention is a system for efficiently evaluating and planning restoration of building facilities damaged by earthquakes or other disasters. Detailed explanations for carrying out the present invention are provided below.

[1003] System Configuration

[1004] The system of the present invention comprises a terminal used by a user, a server, a database, and other systems (systems that provide external environment data).

[1005] 1. Device:

[1006] The devices include PCs, tablets, smartphones, etc. Users use these devices to report damage.

[1007] 2. Server:

[1008] The server receives damage reports, obtains 3D models, identifies the damaged areas, estimates the extent of the impact, uses generative AI to estimate recovery costs and time, connects with other systems, analyzes emotional information, and generates and presents recovery plans to users.

[1009] 3. Database:

[1010] The database stores 3D model data of the target facility, data on past disasters, and data on similar cases.

[1011] 4. Other Systems:

[1012] It works in conjunction with systems that provide external environmental data, such as weather forecast systems and traffic information systems.

[1013] Processing Overview

[1014] 1. Collecting damage reports

[1015] The user reports the damaged area using a terminal. Through this process, the user inputs detailed information about the damaged area of ​​the target facility (location, photo, specific damage content).

[1016] 2. Obtaining a 3D model

[1017] The server receives the damage report and retrieves the 3D model of the target facility from the database. The server loads the necessary 3D model data through the database access API.

[1018] 3. Identifying the damaged area

[1019] The server identifies the damage location on the 3D model and uses image processing algorithms and sensor information analysis techniques to match the reported photo data with the 3D model.

[1020] 4. Estimation of the impact range

[1021] The server analyzes the impact area around the damaged area, using structural analysis software to simulate how the damage will affect other parts and identify potential areas of impact.

[1022] 5. Estimated restoration costs and time

[1023] The server uses generative AI to estimate recovery costs and time, referencing data from past disasters and similar cases, and predicting the necessary personnel, materials, and construction procedures.

[1024] 6. Consideration of external environment forecasts

[1025] By linking with other systems, weather data is obtained from the weather forecast system and traffic data from the traffic information system, and recovery plans are adjusted taking into account this external environmental data.

[1026] 7. Emotional information analysis and response adjustment

[1027] The server analyzes the emotional information from the user's report, uses an emotion engine to identify the user's stress and anxiety, and adjusts recovery plans and support systems based on the emotional information.

[1028] 8. Generate and present a recovery plan

[1029] A final recovery plan is generated and presented to the user, including a list of required materials, a schedule of repair personnel, and estimated start and completion dates for work. Once the plan is complete, the user is notified via push notification or email.

[1030] Hardware and Software

[1031] Hardware used: smartphone, tablet, PC

[1032] Software used: Database access API, image processing algorithms, sensor information analysis technology, structural analysis software, generative AI models, emotion engines

[1033] Specific examples

[1034] Example 1: Earthquake damage to a store

[1035] 1. A user reports damage to an entrance after a disaster occurs.

[1036] 2. The server acquires the 3D model and identifies the damaged area.

[1037] 3. The server analyzes the impact range and identifies the affected area.

[1038] 4. The server uses generative AI to calculate the repair cost and number of days.

[1039] 5. The server creates a final recovery plan, taking into account weather and traffic forecasts.

[1040] 6. Analyze emotional information from user reports and provide support according to stress levels.

[1041] 7. Provide users with a concrete recovery plan.

[1042] Prompt Sentence Examples

[1043] Damage Report: Entrance

[1044] Damage: Cracks in the wall

[1045] Photo: entrance-crack.jpg

[1046] Emotion analysis:

[1047] A user reported: "There is a large crack at the entrance that is likely to cause anxiety for guests."

[1048] Estimate:

[1049] Repair cost: 500,000 yen

[1050] Recovery time: 3 days

[1051] External Data:

[1052] Weather forecast: Sunny for the next three days

[1053] Traffic Information: Normal traffic conditions

[1054] Emotion engine analysis results:

[1055] Stress level: 8

[1056] Recommended Action: Allocate additional resources

[1057] Final Recovery Plan:

[1058] List of required materials

[1059] Repair technician schedule

[1060] Planned start and completion dates for construction

[1061] In this way, the present invention makes it possible to carry out damage assessment and restoration planning with high accuracy in the event of a disaster, and realizes flexible responses that also take into account the feelings of users.

[1062] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1063] Step 1:

[1064] Collecting damage reports

[1065] Users report damage caused by a disaster using a terminal. Specifically, they use a smartphone or PC to input the location information of the damaged area, details of the damage, photos, etc., and send them to the server.

[1066] Input: Location of damaged area, damage details, photo

[1067] Output: Damage report data sent to the server

[1068] Step 2:

[1069] Receiving damage reports

[1070] The server receives the damage report sent by the user and analyzes the data, such as the location of the damage and the timestamp of the report, based on the received information.

[1071] Input: User-submitted damage report data

[1072] Output: Parsed damage report data

[1073] Step 3:

[1074] Acquiring a 3D model

[1075] The server retrieves a 3D model of the target facility from the database based on the damage report, and loads the 3D model data corresponding to the building or area where the damage was reported via the database access API.

[1076] Input: Damage report data, building information

[1077] Output: Acquired 3D model data

[1078] Step 4:

[1079] Identifying the damage

[1080] The server identifies the damaged areas on the acquired 3D model, uses image processing algorithms to match the photo data sent by the user with the 3D model data, and also uses sensor information to accurately identify the damaged areas.

[1081] Input: 3D model data, photo data of damage report

[1082] Output: Information on identified damage locations

[1083] Step 5:

[1084] Estimation of the impact range

[1085] The server then analyzes the impact area around the identified damage, using structural analysis software to simulate how the damage will affect other parts and identify potential areas of impact.

[1086] Input: Identified damage information, structural analysis software

[1087] Output: Estimated impact area data

[1088] Step 6:

[1089] Estimated restoration costs and time

[1090] The server uses generative AI to estimate recovery costs and time. It references data from past disasters and similar cases and uses an AI model to predict costs and time. It also includes the required personnel, material lists, and construction procedures.

[1091] Input: Affected area data, past disaster data, similar case data

[1092] Output: Estimated restoration costs and times

[1093] Step 7:

[1094] Consideration of external environment forecasts

[1095] The server works with other systems to make estimates that take into account external environmental forecasts, obtains weather data from the weather forecast system, and traffic data from the traffic information system, and adjusts recovery plans based on this data.

[1096] Input: Weather data, traffic data

[1097] Output: Estimated data taking into account the external environment

[1098] Step 8:

[1099] Emotional information analysis

[1100] The server analyzes the emotional information based on the user's report, uses an emotion engine to identify the user's stress and anxiety, and adjusts recovery plans and support systems based on that information.

[1101] Input: User report

[1102] Output: Parsed emotion information

[1103] Step 9:

[1104] Generate and present a recovery plan

[1105] The server generates a final recovery plan and presents it to the user, including a list of required materials, a schedule of repair personnel, and estimated start and completion dates for the work. Once the plan is complete, the user is notified via push notification or email.

[1106] Input: Emotional information, external environment forecast data, estimated recovery cost and time data

[1107] Output: A recovery plan presented to the user

[1108] 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.

[1109] 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.

[1110] 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.

[1111] [Third embodiment]

[1112] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1113] 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.

[1114] 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).

[1115] 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.

[1116] 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.

[1117] 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).

[1118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1119] 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.

[1120] 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.

[1121] 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.

[1122] 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.

[1123] 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."

[1124] System Overview

[1125] This invention is a system that assesses damage to building facilities caused by disasters such as earthquakes and estimates the extent of the impact and the cost and time required for restoration as a BCP (Business Continuity Plan) measure for large factories and facilities. This system uses 3D models to identify damaged areas, makes estimates using generative AI, and can also take external environment predictions into account by linking with other systems.

[1126] Key components of the system

[1127] 1. User Interface (Terminal)

[1128] The terminal is a device that can access the system via a network, such as a PC, tablet, or smartphone. Users use the terminal to report damage.

[1129] 2. Server

[1130] The server receives damage reports, obtains 3D models, identifies the damaged areas, estimates the extent of the impact, uses generative AI to estimate recovery costs and time, connects with other systems, generates recovery plans, and presents them to users.

[1131] 3. Database

[1132] The database is a storage system that stores 3D models, data on past disasters, and recovery case studies.

[1133] 4. Other Systems

[1134] The other systems are external systems that provide external environmental data, such as a weather forecast system or a traffic information system.

[1135] Program processing flow

[1136] 1. User reports damage

[1137] User: After a disaster occurs, enter the damaged areas of the factory or facility into the system. Using a terminal, access the system's damage report form and enter the damaged areas (e.g., second floor of the building, 3D printer area).

[1138] 2. The server retrieves the 3D model

[1139] Server: Receives the damage report and retrieves the 3D model data of the target facility from the database. For example, it loads the 3D model file of "Factory A."

[1140] 3. The server identifies the damage

[1141] Server: Identifies the damage locations reported by users on the 3D model. Using image processing algorithms and sensor information analysis technology, specific damage locations are mapped onto the 3D model.

[1142] 4. The server estimates the affected area

[1143] Server: Analyze the impact area around the damaged area. For example, use structural analysis software to simulate how damage to the second floor will affect the first floor and other rooms.

[1144] 5. The server uses generative AI to estimate recovery costs and time.

[1145] Server: Uses generative AI to estimate recovery costs and time. Refers to data from past disasters and similar cases to estimate repair costs and the number of days required for recovery.

[1146] 6. Integration with other systems

[1147] Server: Calls the API to obtain necessary data from other forecasting systems. For example, obtains future weather data from a weather forecast system and reflects it in adjusting construction schedules.

[1148] 7. Presentation of recovery plan

[1149] Server: Generates a specific recovery plan based on the estimation results and external data. The recovery plan includes the necessary materials, a schedule of workers, and a timeline of the start and end dates of construction. Once the plan is complete, it is presented to the user via their device.

[1150] Specific examples

[1151] Example 1: Earthquake damage at a parts manufacturing plant

[1152] 1. User: After the earthquake, report damage to the production line area on the second floor of the factory.

[1153] 2. Server: Obtain a 3D model of the factory and identify damaged areas in the production line area on the second floor.

[1154] 3. Server: Analyze the impact area around the damaged area and determine the potential impact on the logistics area on the first floor.

[1155] 4. Server: Using generative AI, we estimate that repair costs will be 5 million yen and recovery will take 10 days.

[1156] 5. Server: Works with the weather forecast system to make predictions that take into account the weather for the next week.

[1157] 6. Server: Provide users with a concrete recovery plan.

[1158] Example 2: Data center fire damage

[1159] 1. User: Reports that a fire has broken out in the data center and some server racks have been damaged.

[1160] 2. Server: Obtain a 3D model of the data center and identify areas of damage.

[1161] 3. Server: Analyze the extent of the impact around the damaged area and evaluate the impact on network equipment.

[1162] 4. Server: Using generative AI, we estimate that repair costs will be 2 million yen and recovery will take 5 days.

[1163] 5. Server: Obtains information on air conditioning and power supply status after a fire from the linked system and reflects it in the recovery plan.

[1164] 6. Server: Provide users with a concrete recovery plan.

[1165] The above is an embodiment of the present invention. This system makes it possible to quickly and efficiently assess damage and formulate a restoration plan in the event of a disaster.

[1166] The processing flow will be explained below.

[1167] Step 1:

[1168] After a disaster occurs, users report damage using a terminal. Specifically, users access the system's damage report form, enter location information and photos of the damaged area, and specific details of the damage (e.g., cracks in the wall, broken machinery), and send the information to the system.

[1169] Step 2:

[1170] The server receives the damage report sent by the user, analyzes the necessary data, such as the location of the damage and the timestamp of the report, and starts the damage assessment process.

[1171] Step 3:

[1172] The server retrieves the 3D model of the target facility from the database. Specifically, it loads the 3D model data corresponding to the building or area where the damage was reported via the database access API.

[1173] Step 4:

[1174] The server identifies the damaged areas on the 3D model, and uses image processing algorithms to match the reported photo data with the 3D model. Sensor information analysis technology is also used to ensure that the damaged areas are accurately identified.

[1175] Step 5:

[1176] The server estimates the extent of the impact around the damaged area, and uses structural analysis software to simulate how the damage will affect other parts and identify potential areas of impact.

[1177] Step 6:

[1178] The server uses generative AI to estimate recovery costs and time. Specifically, it references data from past disasters and similar recovery cases and uses an AI model to predict costs and time. This estimate also includes the necessary personnel, materials, and construction procedures.

[1179] Step 7:

[1180] The server works in conjunction with other systems to make estimates that take into account external environmental forecasts. Specifically, it calls the weather forecast system's API to obtain future weather data and adds factors that may affect construction schedules. It also obtains data from the traffic information system and performs risk assessments for material transportation.

[1181] Step 8:

[1182] The server generates a final recovery plan and presents it to the user, which includes a list of required materials, a schedule of repair personnel, and estimated start and completion dates for work. Once the plan is complete, the user is notified via push notification or email, allowing them to review the details.

[1183] The above are the specific processing steps.

[1184] Example 1

[1185] 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."

[1186] Rapid and accurate damage assessment and recovery plan formulation in the event of a disaster at large factories and facilities are important issues for ensuring business continuity. However, conventional methods require a great deal of time and effort to identify damaged areas and estimate the extent of the impact, and there is also a high degree of uncertainty in estimating recovery costs and time. It is also difficult to take external environmental predictions into account, and many challenges exist in formulating comprehensive recovery plans. A new system is needed to solve these problems.

[1187] 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.

[1188] In this invention, the server includes: means for a user to report damage caused by a disaster using a terminal; means for receiving the damage report and retrieving a 3D model of the target facility from a database; means for identifying the damaged area on the 3D model; means for estimating the extent of impact centered on the damaged area; means for estimating restoration costs and time using generative AI; means for linking with other systems to make estimates taking into account external environment predictions; means for generating a restoration plan and presenting it to the user; means for using image processing algorithms and sensor information analysis technology to map the damaged area onto a 3D model based on the damage report form; and means for inputting prompt statements to the generative AI to estimate repair costs and restoration time. This enables rapid and accurate assessment of damage caused by a disaster, detailed estimation of the extent of impact, reliable estimation of restoration costs and time, and the formulation of a comprehensive restoration plan taking into account the external environment.

[1189] "User" is a person or organization whose role is to operate the system and report damage caused by a disaster.

[1190] A "terminal" is a device such as a PC, tablet, or smartphone that can access the system via a network and operate it or input information.

[1191] A "damage report" is information entered by a user to the system about the location and extent of damage to facilities and equipment caused by a disaster.

[1192] "Server" is the central processing unit of the system, and is a collective term for the hardware and software components that are responsible for receiving damage reports, obtaining 3D models, identifying damaged areas, estimating the extent of the impact, estimating recovery costs and time, obtaining external environment forecasts, and generating and presenting recovery plans.

[1193] A "database" is an information management system for storing and managing 3D model data, past disaster data, recovery case data, etc.

[1194] A "3D model" is digital data that represents the physical structure of the target facility in three dimensions and is used to identify damaged areas and analyze the extent of the impact.

[1195] An "image processing algorithm" is a computational method for identifying specific features or patterns in a digital image and analyzing the location of damage.

[1196] "Sensor information analysis technology" is a technology for analyzing data obtained from various sensors and identifying damaged areas.

[1197] The "scope of impact" refers to the range and extent of the impact that the identified damaged area has on the surrounding area, and is simulated using structural analysis software, etc.

[1198] "Generative AI" is a type of artificial intelligence technology that generates output results (e.g., recovery costs and duration) based on specific input information (prompt statements).

[1199] A "prompt sentence" is an instruction sentence input to a generative AI, and contains information that forms the basis for the AI's analysis and inference.

[1200] "Restoration Plan" means the action plan required to repair and restore the damaged area, including specific materials, personnel, schedule, costs, etc.

[1201] "Other systems" are external data systems that provide external environmental data, such as weather forecast systems and traffic information systems.

[1202] System Overview

[1203] This invention is a system for assessing damage to building facilities caused by disasters (such as earthquakes and fires) and estimating the cost and time required for restoration as a business continuity plan (BCP) measure for large factories and facilities. This system uses 3D models to identify damaged areas, makes estimates using generative AI, and works with other systems to consider external environment predictions.

[1204] Key Components

[1205] 1. User Interface (Terminal)

[1206] The terminal is a device that can access the system via a network, such as a PC, tablet, or smartphone. Users use the terminal to report damage.

[1207] 2. Server

[1208] The server receives damage reports, obtains 3D models, identifies the damaged areas, estimates the extent of the impact, uses generative AI to estimate recovery costs and time, connects with other systems, generates recovery plans, and presents them to users.

[1209] 3. Database

[1210] The database is a storage system that stores 3D models, data on past disasters, and recovery case studies.

[1211] 4. Other Systems

[1212] The other systems are external systems that provide external environmental data, such as a weather forecast system or a traffic information system.

[1213] Program processing description

[1214] User reports damage

[1215] When a disaster occurs, users access the damage report form using a device (PC, tablet, smartphone). They enter the necessary information in the form and press the report button to send it to the system. For example, a user may report that "the 3D printer area on the second floor of the building has been damaged."

[1216] The server receives the damage report and obtains the 3D model.

[1217] When the server receives a damage report from a user, it retrieves the 3D model data of the target facility from the database. For example, it loads the 3D model file of "Factory A" (e.g., "Factory A_3DModel.obj").

[1218] The server identifies the damage

[1219] The server identifies the reported damage on the 3D model. Specifically, it uses image processing algorithms and sensor information analysis technology to map the damage reported by the user onto the digital model. For example, it identifies the location of the "3D printer area on the second floor."

[1220] The server estimates the scope of the impact

[1221] The server analyzes the extent of the damage, focusing on the damaged area. It uses structural analysis software (e.g., ANSYS) to simulate the impact of the damage on other areas and structural components. For example, it calculates how damage to the second floor will affect the logistics area on the first floor.

[1222] The server uses generative AI to estimate recovery costs and time.

[1223] The server inputs prompt text into the generative AI, which estimates the cost and time required for recovery. The prompt text includes information such as the extent of the damage, the extent of the impact, and similar past cases. For example, it might say, "The 3D printer area on the second floor has been damaged, affecting the logistics area on the first floor. Estimate the repair cost and number of days." Based on this, the AI ​​outputs an estimate such as "repair cost 5 million yen, recovery time 10 days" based on past disaster data and similar cases.

[1224] The server connects with other systems

[1225] The server connects with other systems to obtain external environmental data. For example, it extracts future weather data from a weather forecast system and reflects it in recovery plans. This data is then used to schedule outdoor work, etc.

[1226] The server presents a recovery plan

[1227] The server generates a specific restoration plan based on the estimation results and external data and presents it to the user. This plan includes information such as materials, personnel, schedule, and construction start and end dates. The user can then use their terminal to review the plan and make any necessary corrections or approvals.

[1228] Specific examples

[1229] Earthquake damage at a parts manufacturing plant

[1230] 1. User: After the earthquake, the user reports damage to the second floor production line area of ​​the factory. The user types "The second floor production line area is severely damaged" and submits the message.

[1231] 2. Server: Obtain a 3D model of the factory and identify damaged areas in the production line area on the second floor.

[1232] 3. Server: Image processing algorithms identify damage areas and map them onto the model.

[1233] 4. Server: Use structural analysis software to analyze the impact area and simulate the impact on the logistics area on the first floor.

[1234] 5. Server: The generative AI is given a prompt: "Estimate the repair costs and recovery time for damage caused by the earthquake." The server estimates that repair costs will be 5 million yen and recovery will take 10 days.

[1235] 6. Server: Obtains weather data for the next week from the weather forecast system to see if it will affect outdoor work.

[1236] 7. Server: Generates a recovery plan and presents it to the user's device, such as "Repair costs will be 5 million yen, and recovery will take 10 days." The user reviews it, makes any necessary corrections, and then approves it.

[1237] Data center fire damage

[1238] 1. User: Reports that a fire broke out in the data center and some server racks were damaged. Type "The fire damaged the server racks and also affected some network equipment" and submit.

[1239] 2. Server: Obtain a 3D model of the data center and identify areas of damage.

[1240] 3. Server: Using image processing technology, the damaged area is mapped onto a 3D model.

[1241] 4. Server: Use structural analysis software to analyze the scope of the impact and evaluate the impact on network equipment.

[1242] 5. Server: The generative AI is given a prompt: "If a server rack is damaged by fire, estimate the repair cost and recovery time." The AI ​​estimates that the repair cost will be 2 million yen and that it will take five days to recover.

[1243] 6. Server: Obtains information on air conditioning and power supply status after a fire from other systems and reflects it in recovery plans.

[1244] 7. Server: Generates a recovery plan and presents it to the user's device, such as "Repair costs will be 2 million yen, and recovery will take 5 days." The user can review the plan and make any changes or approvals.

[1245] This concludes the description of the "Mode for Carrying Out the Invention." This system enables damage assessment in the event of a disaster and the development of an efficient recovery plan.

[1246] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1247] Step 1: Enter your damage report

[1248] User: After a disaster occurs, the user accesses the damage report form using a device (PC, tablet, smartphone, etc.). They enter the necessary information, such as the location and extent of the damage, and contact information, and press the report button to submit. For example, they report that "the 3D printer area on the second floor of the building has been damaged."

[1249] Input: Report information such as location of damage, extent of damage, contact information, etc.

[1250] Output: Damage report data sent to the server (e.g. "The second floor of the building, the 3D printer area, was damaged")

[1251] Step 2: Receiving damage reports and obtaining 3D models

[1252] Server: Based on the received damage report, retrieves 3D model data from the database using the target facility's identification information. Executes a database query using the target facility's ID and name to load the 3D model file. For example, loads the 3D model file for "Factory A" (e.g., "Factory A_3DModel.obj").

[1253] Input: Damage report data, facility identification

[1254] Output: 3D model data of the facility (e.g. "FactoryA_3DModel.obj")

[1255] Step 3: Identify the damage

[1256] Server: Analyzes the damage information entered by the user and identifies the corresponding location on the 3D model. Using image processing algorithms and sensor information analysis technology, the reported damage is mapped onto the digital model. For example, the specific location of the "3D printer area on the second floor" is identified.

[1257] Input: Damage report data, 3D model data

[1258] Output: Damaged area on a 3D model (e.g., "3D printer area on the second floor")

[1259] Step 4: Estimate the impact area

[1260] Server: Analyze the impact area around the identified damaged area. Use structural analysis software (e.g., ANSYS) to simulate the impact of the damage on other areas and structural parts. For example, calculate how damage to the second floor will affect the logistics area on the first floor.

[1261] Input: Damage location, 3D model data

[1262] Output: Analysis results of the impact range (e.g., "Damage on the second floor affects the logistics area on the first floor")

[1263] Step 5: Estimate restoration costs and time

[1264] Server: A prompt is input into the generative AI, which estimates the cost and time required for recovery. The prompt includes information such as the extent of the damage, the extent of the impact, and past similar cases. For example, it might say, "The 3D printer area on the second floor has been damaged, affecting the logistics area on the first floor. Estimate the repair cost and number of days." Based on this, the AI ​​outputs an estimate such as "Repair cost 5 million yen, recovery time 10 days."

[1265] Input: Prompt statement (damage status, affected area, past similar cases)

[1266] Output: Estimated restoration cost and time (e.g., "Repair cost: 5 million yen, restoration time: 10 days")

[1267] Step 6: Integration with other systems

[1268] Server: Links with other systems (e.g., weather forecast systems and traffic information systems) to obtain external environmental data. Uses APIs to obtain necessary data and reflects this information in recovery plans. For example, obtains future weather data from a weather forecast system and uses it to adjust construction schedules.

[1269] Input: Request for external environment data from other systems

[1270] Output: External environmental data (e.g., "Weather forecast for the next week")

[1271] Step 7: Generate and present a recovery plan

[1272] Server: Generates a specific recovery plan based on the estimation results and acquired external data. This plan includes information such as the required materials, personnel, schedule, and construction start and end dates. The created recovery plan is presented on a terminal accessible to the user. The user can use the terminal to review the plan and make any necessary corrections or approvals.

[1273] Input: Estimated recovery costs and time, external environmental data

[1274] Output: A detailed restoration plan (e.g., "Repair cost: 5 million yen, restoration time: 10 days, materials required, personnel schedule, construction start and end dates")

[1275] (Application example 1)

[1276] 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."

[1277] When a disaster occurs and a robot installed in a factory or facility is damaged, it is difficult to quickly and accurately assess the damage and estimate the extent of the impact and the cost and time required for recovery. Conventional methods require a large amount of human resources and time, and there is a risk of delays in recovery plans or incorrect decisions. The purpose of this invention is to solve these problems and provide a system that enables efficient and rapid damage assessment and the generation of recovery plans.

[1278] 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.

[1279] In this invention, the server includes: means for a user to report damage caused by a disaster using a terminal; means for receiving the damage report and retrieving a 3D model of the target facility from a database; means for identifying the damaged area on the 3D model; means for estimating the impact area centered on the damaged area; means for estimating recovery costs and time using generative AI; means for making estimates taking into account external environment predictions in cooperation with other systems; means for generating and presenting a recovery plan to the user; and means for managing a robot that uses the 3D model to identify the damaged area and generate the impact area and recovery plan. This enables faster and more accurate damage assessment of robots and recovery plans in the event of a disaster.

[1280] A "user" is a person who uses this system to report damage caused by a disaster.

[1281] A "terminal" is a device that can be connected to a network, such as a PC, tablet, or smartphone used by a user.

[1282] A "disaster" is an event in which facilities or equipment are damaged due to natural phenomena such as earthquakes, fires, and typhoons, or due to human factors.

[1283] "Damaged area" refers to the specific location where facilities or equipment have been physically damaged by the disaster.

[1284] The "reporting means" refers to an interface that allows a user to input information about the damaged area into the system using a terminal.

[1285] "Means for receiving" refers to the function of the server receiving damage reports from users via the network.

[1286] A "3D model" is digital data that reproduces the spatial structure of facilities and equipment.

[1287] A "database" is a storage system that allows the system to store 3D model data, past disaster data, and other data.

[1288] "Means of acquisition" refers to the function by which the server retrieves the necessary 3D model data from the database.

[1289] "Means for identification" refers to technology for identifying user-reported damage locations on a 3D model.

[1290] The "area of ​​impact" refers to the area centered on the reported damage location and indicates the extent to which surrounding facilities and equipment may be affected.

[1291] "Means of estimation" is a function for calculating the extent of the impact based on the location of damage, as well as the cost and time required for recovery.

[1292] "Generative AI" is artificial intelligence that uses machine learning algorithms and generative models to automatically estimate recovery costs and time.

[1293] A "recovery plan" is a plan that includes specific work procedures for repairing damage, necessary materials, work personnel, construction schedule, etc.

[1294] "Means for presenting" refers to a function for displaying the recovery plan generated by the server on the user's terminal.

[1295] "Means of management" refers to the ability to control and monitor the robot using 3D models, and use them to identify damaged areas and generate restoration plans.

[1296] "Other systems" are systems that provide external environmental data, such as weather forecast systems and traffic information systems.

[1297] "External environment forecast" refers to environmental data provided by external systems, such as weather forecasts and traffic conditions, and is a factor taken into account in recovery plans.

[1298] System Overview

[1299] This invention is a system that quickly assesses damage and estimates the extent of the impact and the cost and time required for restoration when a disaster such as an earthquake or fire occurs in a factory or facility. Based on damage information reported by the user via a terminal, this system identifies the damaged area using a 3D model, makes estimates using generative AI, and cooperates with other systems to consider external environment predictions, thereby efficiently and quickly generating restoration plans.

[1300] Key components of the system

[1301] 1. User Interface (Terminal)

[1302] The terminal is a device that can access the system via a network, such as a PC, tablet, or smartphone. Users use the terminal to report damage.

[1303] 2. Server

[1304] The server receives damage reports, obtains 3D models, identifies the damaged areas, estimates the extent of the impact, uses generative AI to estimate recovery costs and time, connects with other systems, generates recovery plans, and presents them to users.

[1305] 3. Database

[1306] The database is a storage system that stores 3D models, data on past disasters, and recovery case studies.

[1307] 4. Other Systems

[1308] Other systems refer to external systems that provide external environmental data, such as weather forecast systems and traffic information systems.

[1309] About Torsion

[1310] Hardware / Software used

[1311] Hardware: Use a smartphone or tablet.

[1312] Software: Python 3.8 or higher, generative AI modules (e.g., TensorFlow or PyTorch), 3D model processing libraries (e.g., trimesh), and external API access libraries (e.g., requests).

[1313] Data processing and calculation

[1314] 1. Damage report

[1315] A user uses a terminal to report damage caused by a disaster to the system, including detailed information about the damage (for example, "damage to the robot arm").

[1316] 2. Obtaining a 3D model

[1317] Based on the damage report received, the server retrieves 3D model data of the target facility from the database. This is digital data that reproduces the spatial structure of the target facility.

[1318] 3. Identifying the damaged area

[1319] The server identifies the reported damage locations on the 3D model using image processing algorithms and sensor information analysis techniques.

[1320] 4. Estimation of the impact range

[1321] The server analyzes the extent of the impact around the damaged area, using structural analysis software to simulate how the damaged area will affect the surrounding area.

[1322] 5. Utilizing generative AI

[1323] The server uses generative AI to estimate recovery costs and time. The AI ​​model references data from past disasters and similar cases to estimate the necessary repair costs and recovery time.

[1324] 6. Integration with other systems

[1325] The server connects with other systems, such as weather forecast systems and traffic information systems, to obtain external environment forecasts, enabling predictions that take future weather and traffic conditions into account.

[1326] 7. Presentation of recovery plan

[1327] The server generates a specific recovery plan based on the results of these analyses, including the necessary materials, a schedule of workers, and a timeline of the start and end dates of the work.The server then presents the final recovery plan to the user.

[1328] Specific examples

[1329] Earthquake damage assessment and restoration planning app for factory robots

[1330] If a robotic arm installed in a factory is damaged in an earthquake, the user can use their terminal to report, "The robotic arm on the second floor is damaged." The server retrieves a 3D model of the factory from the database and identifies the damaged area. It analyzes the extent of the impact and uses generative AI to estimate the cost and time of restoration. It also retrieves data from the weather forecast system, generates an optimal restoration plan, and presents it to the user.

[1331] Example prompt statement

[1332] "An earthquake has occurred in Factory A. The robot arm on the second floor has been damaged. Please make a recovery plan."

[1333] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1334] Step 1:

[1335] The user uses a terminal to report damage caused by the disaster. The user enters specific damage information, such as "The robot arm on the second floor is damaged," into the form displayed on the terminal. The entered damage report is sent to the server via the network.

[1336] Step 2:

[1337] The server receives the damage report, analyzes the received data, and extracts information about the damaged location (for example, which part of the facility was damaged). It then retrieves a 3D model of the target facility from the database. If the target facility is "Factory A," the server loads the 3D model data of Factory A from the database.

[1338] Step 3:

[1339] The server identifies the damaged area on the 3D model. The server maps the received information on the damaged area (e.g., "second floor" or "robot arm area") onto the 3D model and identifies the specific damaged area using image processing algorithms and sensor information analysis technology. During this process, the server generates coordinate data for the damaged area.

[1340] Step 4:

[1341] The server estimates the extent of the impact around the damaged area. Using structural analysis software, the server simulates the impact of the damaged area on surrounding structures and equipment. For example, it analyzes how damage to the second floor will affect the first floor area and other rooms, and outputs the specific extent of the impact.

[1342] Step 5:

[1343] The server uses generative AI to estimate recovery costs and time. The server runs a generative AI model that uses data from past disasters and similar cases to estimate repair costs and the number of days required for recovery based on the location of damage and the extent of the impact. The input in this step is coordinate data of the damaged area and data on the extent of the impact, and the output is the estimated recovery cost and time.

[1344] Step 6:

[1345] The server connects with other systems to obtain external environment forecasts. The server then calls the APIs of weather forecast systems and traffic information systems to obtain future weather and traffic data. This data is used to schedule restoration work.

[1346] Step 7:

[1347] The server generates a recovery plan and presents it to the user. The server creates a specific recovery plan based on estimated recovery costs, time, impact area, and external environmental data. This plan includes the necessary materials, a schedule of work personnel, and a timeline of construction start and end dates. The generated recovery plan is displayed on the user's terminal via a user interface.

[1348] 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.

[1349] System Overview

[1350] This invention is a system that assesses damage to building facilities caused by disasters such as earthquakes and estimates the extent of the impact and the cost and time required for restoration as a BCP (Business Continuity Plan) measure for large factories and facilities. This system identifies damaged areas using 3D models, makes estimates using generative AI, and provides efficient restoration plans by taking into account external environment predictions. In addition, by combining it with an emotion engine that recognizes user emotions, more effective responses are possible.

[1351] Key components of the system

[1352] 1. User Interface (Terminal)

[1353] The terminal is a device that can access the system via a network, such as a PC, tablet, or smartphone. Users use the terminal to report damage.

[1354] 2. Server

[1355] The server receives damage reports, obtains 3D models, identifies damaged areas, estimates the extent of the impact, uses generative AI to estimate recovery costs and time, connects with other systems, generates recovery plans, and presents them to users. It also uses an emotion engine to analyze user emotions and adjust response measures based on that information.

[1356] 3. Database

[1357] The database is a storage system that stores 3D models, data on past disasters, and recovery case studies.

[1358] 4. Other Systems

[1359] The other systems are external systems that provide external environmental data, such as a weather forecast system or a traffic information system.

[1360] 5. Emotion Engine

[1361] The emotion engine extracts emotional information from user reports and operational logs during use, which is used for recovery planning and customer support resource allocation.

[1362] Program processing flow

[1363] 1. User reports damage

[1364] User: After a disaster occurs, the user enters the damaged areas of the factory or facility into the system. Using a terminal, the user accesses the system's damage report form, enters the location information and photos of the damaged areas, and specific details of the damage (e.g., cracks in the wall, broken machinery), and sends the form to the system.

[1365] 2. The server receives the damage report

[1366] Server: Receives damage reports sent by users. Based on the received information, it analyzes the necessary data, such as the location of the damage and the timestamp of the report, and starts the damage assessment process.

[1367] 3. The server retrieves the 3D model

[1368] Server: Retrieves 3D model data of the target facility from the database. Specifically, it loads the 3D model data corresponding to the building or area where the damage was reported via the database access API.

[1369] 4. The server identifies the damage

[1370] Server: Identifies the damage areas reported by users on the 3D model. Image processing algorithms are used to match the reported photo data with the 3D model. Sensor information analysis technology is also used to ensure that the damage areas are accurately identified.

[1371] 5. The server estimates the affected area

[1372] Server: Analyze the impact area around the damaged area. Use structural analysis software to simulate how the damage will affect other parts and identify potential areas of impact.

[1373] 6. The server uses generative AI to estimate recovery costs and time.

[1374] Server: Uses generative AI to estimate recovery costs and time. It references data from past disasters and similar cases and uses AI models to predict costs and time. This estimate also includes the required personnel, materials, and construction procedures.

[1375] 7. The server connects with other systems

[1376] Server: Calls the API to obtain necessary data from other forecasting systems. For example, it obtains future weather data from a weather forecast system and adds factors that may affect construction schedules. It also obtains data from a traffic information system to perform risk assessments for material transportation.

[1377] 8. The server uses the emotion engine

[1378] Server: Analyzes the user's report and extracts emotional information using an emotion engine. Identifies the stress or anxiety the user is feeling when reporting and adjusts the response accordingly.

[1379] 9. The server generates a recovery plan and presents it to the user.

[1380] Server: Generates and presents the final recovery plan to the user. The recovery plan includes a list of needed materials, a schedule of repair personnel, and estimated start and completion dates for work. Once the plan is complete, the user is notified via push notification or email so they can review the details. The server also takes into account the user's emotional state and presents appropriate language and support options.

[1381] Specific examples

[1382] Example 1: Earthquake damage at a parts manufacturing plant

[1383] 1. User: After the earthquake, report damage to the production line area on the second floor of the factory.

[1384] 2. Server: Obtain a 3D model of the factory and identify damaged areas in the production line area on the second floor.

[1385] 3. Server: Analyze the impact area around the damaged area and determine the potential impact on the logistics area on the first floor.

[1386] 4. Server: Using generative AI, we estimate that repair costs will be 5 million yen and recovery will take 10 days.

[1387] 5. Server: Works with the weather forecast system to make predictions that take into account the weather for the next week.

[1388] 6. Server: Analyzes the stress level from the user's report using an emotion engine. If high stress is detected, allocate additional resources and include them in the recovery plan.

[1389] 7. Server: Provide users with a concrete recovery plan.

[1390] Example 2: Data center fire damage

[1391] 1. User: Reports that a fire has broken out in the data center and some server racks have been damaged.

[1392] 2. Server: Obtain a 3D model of the data center and identify areas of damage.

[1393] 3. Server: Analyze the extent of the impact around the damaged area and evaluate the impact on network equipment.

[1394] 4. Server: Using generative AI, we estimate that repair costs will be 2 million yen and recovery will take 5 days.

[1395] 5. Server: Obtains information on air conditioning and power supply status after a fire from the linked system and reflects it in the recovery plan.

[1396] 6. Server: Analyzes the user's report using an emotion engine to determine whether there is any anxiety. If a serious anxiety is detected, a special support plan is presented.

[1397] 7. Server: Provide users with a concrete recovery plan.

[1398] The above is an embodiment of the present invention. This system makes it possible to quickly and efficiently assess damage and formulate recovery plans in the event of a disaster, and also takes into account the emotions of users.

[1399] The processing flow will be explained below.

[1400] Step 1:

[1401] After a disaster occurs, users report damage using a terminal. Specifically, users access the system's damage report form, enter location information and photos of the damaged area, and specific details of the damage (e.g., cracks in the wall, broken machinery), and send the information to the system.

[1402] Step 2:

[1403] The server receives the damage report sent by the user, which includes the location of the damage, details of the damage, and a timestamp of the report.

[1404] Step 3:

[1405] The server retrieves the 3D model data of the target facility from the database, and based on the damage report, loads the 3D model file of the target building or facility via the database access API.

[1406] Step 4:

[1407] The server identifies damaged areas on the 3D model. It uses image processing algorithms to analyze the photo data sent by the user and match it with the damaged areas in the 3D model. It also uses sensor information analysis technology to accurately identify damaged areas based on IoT sensor and camera data.

[1408] Step 5:

[1409] The server estimates the extent of the damage from the center, and uses structural analysis software to simulate the impact of the damage on other parts, thereby identifying areas that may be affected by the damage.

[1410] Step 6:

[1411] The server uses generative AI to estimate recovery costs and time. Specifically, data from past disasters and similar recovery cases is input into the AI ​​model to predict costs and time, including the required personnel, materials, and construction procedures. The AI ​​uses neural networks to generate highly accurate estimates.

[1412] Step 7:

[1413] The server works in conjunction with other systems to make estimates that take into account external environmental forecasts. It calls the weather forecast system's API to obtain future weather data and calculates factors that will affect construction schedules. It also obtains data from the traffic information system and performs risk assessments for material transportation.

[1414] Step 8:

[1415] The server uses an emotion engine to recognize the user's emotions. It analyzes the user's report content and the operation log at the time of sending, and extracts emotional information such as stress, anxiety, and satisfaction. This information is analyzed based on the wording in the report content and the timing of sending.

[1416] Step 9:

[1417] The server tailors the recovery plan it presents based on emotional information, for example automatically assigning additional customer support if high stress levels are detected, or generating a plan with detailed, easy-to-understand explanations if the user is feeling anxious.

[1418] Step 10:

[1419] The server generates a final recovery plan and presents it to the user, which includes a list of required materials, a schedule of repair personnel, and estimated start and completion dates for work. Once the plan is complete, the user is notified via push notification or email, allowing them to review the details.

[1420] The above are the specific processing steps.

[1421] Example 2

[1422] 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."

[1423] It has been difficult to effectively carry out rapid damage assessment and recovery plan formulation for factories and facilities in the event of a disaster using conventional methods. In addition, it is necessary to take into account the psychological stress of users, but there has been a lack of concrete methods for doing so.

[1424] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1425] In this invention, the server includes means for a user to report damage caused by a disaster using a terminal, means for receiving the damage report and retrieving a 3D model of the target facility from a database, means for identifying the damaged area on the 3D model, means for estimating the extent of the impact centered on the damaged area, means for estimating recovery costs and time using generative AI, means for making estimates taking into account external environment predictions in cooperation with other systems, means for analyzing the user's emotional information and using an emotion engine to adjust the response based on that information, and means for generating a recovery plan and presenting it to the user. This enables quick and effective damage assessment and formulation of a recovery plan, as well as responses that take into account the user's psychological stress.

[1426] "User" refers to the person or organization that operates the system to report damage caused by a disaster.

[1427] "Terminal" refers to a device (PC, tablet, smartphone, etc.) that can access the system via a network.

[1428] "Damage report" refers to a report from a user that includes location information, photos, and specific details of damage to building facilities caused by a disaster.

[1429] A "3D model" refers to digital data that shows the three-dimensional structure of the target facility.

[1430] "Database" refers to a storage system for storing information required by the system.

[1431] "Image processing algorithm" refers to a computational method for analyzing digital images to extract useful information.

[1432] "Sensor information analysis technology" refers to the technology for analyzing data obtained from sensors and converting it into meaningful information.

[1433] "Affected area" refers to the extent of the impact on other areas and facilities centered on the damaged area.

[1434] "Generative AI" refers to artificial intelligence models that perform inference and generative tasks.

[1435] "External environment forecast" refers to forecast data obtained from external sources, such as weather forecasts and traffic information.

[1436] An "emotion engine" refers to a system that analyzes a user's emotional information and adjusts the response based on that information.

[1437] "Restoration plan" refers to a plan that includes repair procedures for damaged areas, necessary materials, work schedules, etc.

[1438] System Overview

[1439] This invention is a system that assesses damage to building facilities caused by disasters such as earthquakes and estimates the extent of the impact and the cost and time required for restoration as a BCP (Business Continuity Plan) measure for large factories and facilities. This system identifies damaged areas using a 3D model, makes estimates using a generative AI model, and provides an efficient restoration plan by taking into account external environment predictions. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, more effective responses are possible.

[1440] Hardware and software used

[1441] Hardware

[1442] 1. Devices (PC, tablet, smartphone)

[1443] A device that can be accessed by the user to report damage.

[1444] 2. Server

[1445] Responsible for data processing for the entire system, receiving damage reports, acquiring 3D models, identifying damaged areas, estimating the extent of the impact, estimating recovery costs and time using generative AI, linking with other systems, and generating and presenting recovery plans to users.

[1446] 3. Database

[1447] A storage system that stores 3D models, data on past disasters, and recovery case studies.

[1448] software

[1449] 1. Database Access API

[1450] Software for retrieving 3D models and past disaster data from databases.

[1451] 2. Image Processing Algorithm

[1452] Software for identifying user-reported damage locations on 3D models.

[1453] 3. Sensor Information Analysis Technology

[1454] Software that analyzes sensor information within the facility and accurately identifies damaged areas.

[1455] 4. Structural Analysis Software

[1456] Software for analyzing the impact range centered on the damaged area and simulating the impact of the damage on other parts.

[1457] 5. Generative AI Models

[1458] An artificial intelligence model for estimating recovery costs and time. Estimates are made by referencing data from past disasters and similar cases.

[1459] 6. Emotion Engine

[1460] Software that analyzes user reports and extracts emotional information. It is used to adjust the response based on the extracted emotional information.

[1461] 7. Integration API

[1462] Software that connects with external systems such as weather forecast systems and traffic information systems to obtain the necessary data.

[1463] Specific examples

[1464] Example 1: Earthquake damage at a parts manufacturing plant

[1465] 1. User: After the earthquake, report damage to the production line area on the second floor of the factory.

[1466] Fill out and submit the damage report form from your device.

[1467] 2. Server: Retrieves the 3D model corresponding to the second floor production line area of ​​the factory from the database.

[1468] 3. Server: Matches the 3D model with the reported photo data to identify the damage.

[1469] 4. Server: Use structural analysis software to simulate how damage could affect the logistics area on the first floor.

[1470] 5. Server: Using a generative AI model, we estimate that repair costs will be 5 million yen and recovery will take 10 days.

[1471] 6. Server: Obtains weather data for the next week from the weather forecast system and incorporates it into recovery plans.

[1472] 7. Server: Analyzes user reports using an emotion engine, and if stress levels are high, allocates additional resources and includes them in the recovery plan.

[1473] 8. Server: Provide users with a concrete recovery plan.

[1474] Example 2: Data center fire damage

[1475] 1. User: Reports that a fire has broken out in the data center and some server racks have been damaged.

[1476] Fill out and submit the damage report form from your device.

[1477] 2. Server: Retrieves the 3D model of the data center from the database.

[1478] 3. Server: Matches the 3D model with the reported photo data to identify the damage.

[1479] 4. Server: The impact on network equipment is also assessed using structural analysis software.

[1480] 5. Server: Using a generative AI model, we estimate that repair costs will be 2 million yen and recovery will take 5 days.

[1481] 6. Server: Obtains information on air conditioning and power supply status after a fire from the linked system and reflects it in the recovery plan.

[1482] 7. Server: Analyzes the user's report using an emotion engine and presents a special support plan if there are any concerns.

[1483] 8. Server: Provide users with a concrete recovery plan.

[1484] This system enables quick and efficient damage assessment and recovery planning in the event of a disaster. It also takes user emotions into consideration, providing greater reliability and satisfaction.

[1485] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1486] Step 1:

[1487] User: After a disaster occurs, report damage to a factory or facility.

[1488] Specific actions: Use the terminal to access the system's damage report form and enter the location of the damaged area, a photo, and specific details of the damage.

[1489] Input: Location, photo data, damage details.

[1490] Output: Damage report data is sent to the system.

[1491] Step 2:

[1492] Server: Receives damage reports.

[1493] Specific operation: Analyzes damage report data sent by users and extracts information on the damage location and the report timestamp.

[1494] Input: Damage report data (location, photo data, damage details).

[1495] Output: Extracted damage data (damage location, timestamp).

[1496] Step 3:

[1497] Server: Retrieves the 3D model of the target facility from the database.

[1498] Specific operation: Uses the database access API to load 3D model data of the facility corresponding to the reported damage location.

[1499] Input: Damage report data.

[1500] Output: 3D model data.

[1501] Step 4:

[1502] Server: Identify the damaged area on the 3D model.

[1503] What it does: It uses image processing algorithms to compare reported photographic data with 3D models to map damage, and then analyzes sensor information to improve the accuracy of damage identification.

[1504] Input: 3D model data, photo data, sensor information.

[1505] Output: Location data of identified damage areas.

[1506] Step 5:

[1507] Server: Estimate the affected area centered on the damaged area.

[1508] Specific behavior: Use structural analysis software to simulate the impact of damage on other parts and identify the extent of the impact.

[1509] Input: Damage location data, 3D model data.

[1510] Output: Impact area data.

[1511] Step 6:

[1512] Server: Uses generative AI to estimate recovery costs and time.

[1513] Specific operation: By referencing data from past disasters and similar cases, recovery costs and time are predicted through a generative AI model.

[1514] Input: Damage location data, affected area data, past disaster data, similar case data.

[1515] Output: Estimated restoration cost and duration data.

[1516] Step 7:

[1517] Server: Works with other systems to consider external environment predictions.

[1518] Specific operation: Using the integrated API, weather data is obtained from the weather forecast system and risk assessment data on material transportation is obtained from the traffic information system, and this data is reflected in damage assessment and restoration plans.

[1519] Input: Weather data, traffic information from external systems.

[1520] Output: A proposed recovery plan that takes into account the external environment.

[1521] Step 8:

[1522] Server: Analyzes the user's emotional information using the emotion engine.

[1523] Specific operation: The system analyzes the user's report using natural language processing technology to extract emotional information (stress, anxiety levels), and adjusts the response accordingly.

[1524] Input: User report.

[1525] Output: Extracted emotion information.

[1526] Step 9:

[1527] Server: Generates a recovery plan and presents it to the user.

[1528] Specific behavior: Generates a restoration plan including a list of required materials, a schedule of repair personnel, and estimated start and completion dates for work. Once the plan is complete, the user is notified via push notification or email with detailed information. It also takes emotional information into account to provide appropriate language and additional support.

[1529] Inputs: Estimated recovery cost and duration data, impact area data, external environment data, and sentiment information.

[1530] Output: Completed recovery plan, notification to users.

[1531] (Application example 2)

[1532] 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."

[1533] Although existing systems exist for quickly and accurately assessing damage to building facilities caused by earthquakes and other disasters and estimating the extent of the impact and the cost and time required for restoration, they have limitations in their accuracy and response capabilities. For example, users often fail to fully report the location of damage, or restoration plans are formulated without sufficient consideration of external environmental forecasts. Furthermore, most systems ignore the user's emotional state, and are unable to fully alleviate the user's stress and anxiety. To solve these problems, a comprehensive and emotion-sensitive disaster response system was needed.

[1534] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to report damage caused by a disaster using a terminal, means for receiving the damage report and retrieving a 3D model of the target facility from a database, means for identifying the damaged location on the 3D model, means for estimating the impact area centered on the damaged location, means for estimating recovery costs and time using generative AI, means for coordinating with other systems to make estimates taking into account external environment predictions, means for analyzing emotional information based on the user's report, means for adjusting a recovery plan and support system based on the emotional information, and means for generating a recovery plan and presenting it to the user. This not only enables improved accuracy in damage assessment and recovery plans during a disaster, but also enables flexible responses according to the user's emotional state.

[1535] The "means for a user to report a damaged location using a terminal" is a function that allows a user to report a damaged location caused by a disaster using a terminal.

[1536] The "means for receiving a damage report" is a function that allows the server to receive a damage report sent from a user.

[1537] "Means for obtaining a 3D model from a database" refers to a function for calling up and obtaining a 3D model of the target facility from a database.

[1538] "Means for identifying damaged areas on a 3D model" refers to a function for accurately identifying damaged areas on a 3D model.

[1539] "Means for estimating the extent of the impact centered on the damaged location" is a function for analyzing and estimating the extent of the impact centered on the identified damaged location.

[1540] "Means for estimating recovery costs and time using generative AI" refers to a function for estimating the cost and time required for recovery using generative AI.

[1541] "Means of making estimates that take into account external environment forecasts in cooperation with other systems" is a function that takes into account the external environment by using data from other external systems to make more accurate estimates.

[1542] The "means for analyzing emotional information based on the contents of a user's report" is a function for analyzing the contents of a user's report and extracting emotional information.

[1543] The "means for adjusting a recovery plan or support system based on emotional information" is a function for adjusting a recovery plan or support system based on the extracted emotional information.

[1544] The "means for generating a recovery plan and presenting it to the user" is a function for generating a recovery plan and presenting it to the user.

[1545] The present invention is a system for efficiently evaluating and planning restoration of building facilities damaged by earthquakes or other disasters. Detailed explanations for carrying out the present invention are provided below.

[1546] System Configuration

[1547] The system of the present invention comprises a terminal used by a user, a server, a database, and other systems (systems that provide external environment data).

[1548] 1. Device:

[1549] The devices include PCs, tablets, smartphones, etc. Users use these devices to report damage.

[1550] 2. Server:

[1551] The server receives damage reports, obtains 3D models, identifies the damaged areas, estimates the extent of the impact, uses generative AI to estimate recovery costs and time, connects with other systems, analyzes emotional information, and generates and presents recovery plans to users.

[1552] 3. Database:

[1553] The database stores 3D model data of the target facility, data on past disasters, and data on similar cases.

[1554] 4. Other Systems:

[1555] It works in conjunction with systems that provide external environmental data, such as weather forecast systems and traffic information systems.

[1556] Processing Overview

[1557] 1. Collecting damage reports

[1558] The user reports the damaged area using a terminal. Through this process, the user inputs detailed information about the damaged area of ​​the target facility (location, photo, specific damage content).

[1559] 2. Obtaining a 3D model

[1560] The server receives the damage report and retrieves the 3D model of the target facility from the database. The server loads the necessary 3D model data through the database access API.

[1561] 3. Identifying the damaged area

[1562] The server identifies the damage location on the 3D model and uses image processing algorithms and sensor information analysis techniques to match the reported photo data with the 3D model.

[1563] 4. Estimation of the impact range

[1564] The server analyzes the impact area around the damaged area, using structural analysis software to simulate how the damage will affect other parts and identify potential areas of impact.

[1565] 5. Estimated restoration costs and time

[1566] The server uses generative AI to estimate recovery costs and time, referencing data from past disasters and similar cases, and predicting the necessary personnel, materials, and construction procedures.

[1567] 6. Consideration of external environment forecasts

[1568] By linking with other systems, weather data is obtained from the weather forecast system and traffic data from the traffic information system, and recovery plans are adjusted taking into account this external environmental data.

[1569] 7. Emotional information analysis and response adjustment

[1570] The server analyzes the emotional information from the user's report, uses an emotion engine to identify the user's stress and anxiety, and adjusts recovery plans and support systems based on the emotional information.

[1571] 8. Generate and present a recovery plan

[1572] A final recovery plan is generated and presented to the user, including a list of required materials, a schedule of repair personnel, and estimated start and completion dates for work. Once the plan is complete, the user is notified via push notification or email.

[1573] Hardware and Software

[1574] Hardware used: smartphone, tablet, PC

[1575] Software used: Database access API, image processing algorithms, sensor information analysis technology, structural analysis software, generative AI models, emotion engines

[1576] Specific examples

[1577] Example 1: Earthquake damage to a store

[1578] 1. A user reports damage to an entrance after a disaster occurs.

[1579] 2. The server acquires the 3D model and identifies the damaged area.

[1580] 3. The server analyzes the impact range and identifies the affected area.

[1581] 4. The server uses generative AI to calculate the repair cost and number of days.

[1582] 5. The server creates a final recovery plan, taking into account weather and traffic forecasts.

[1583] 6. Analyze emotional information from user reports and provide support according to stress levels.

[1584] 7. Provide users with a concrete recovery plan.

[1585] Prompt Sentence Examples

[1586] Damage Report: Entrance

[1587] Damage: Cracks in the wall

[1588] Photo: entrance-crack.jpg

[1589] Emotion analysis:

[1590] A user reported: "There is a large crack at the entrance that is likely to cause anxiety for guests."

[1591] Estimate:

[1592] Repair cost: 500,000 yen

[1593] Recovery time: 3 days

[1594] External Data:

[1595] Weather forecast: Sunny for the next three days

[1596] Traffic Information: Normal traffic conditions

[1597] Emotion engine analysis results:

[1598] Stress level: 8

[1599] Recommended Action: Allocate additional resources

[1600] Final Recovery Plan:

[1601] List of required materials

[1602] Repair technician schedule

[1603] Planned start and completion dates for construction

[1604] In this way, the present invention makes it possible to carry out damage assessment and restoration planning with high accuracy in the event of a disaster, and realizes flexible responses that also take into account the feelings of users.

[1605] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1606] Step 1:

[1607] Collecting damage reports

[1608] Users report damage caused by a disaster using a terminal. Specifically, they use a smartphone or PC to input the location information of the damaged area, details of the damage, photos, etc., and send them to the server.

[1609] Input: Location of damaged area, damage details, photo

[1610] Output: Damage report data sent to the server

[1611] Step 2:

[1612] Receiving damage reports

[1613] The server receives the damage report sent by the user and analyzes the data, such as the location of the damage and the timestamp of the report, based on the received information.

[1614] Input: User-submitted damage report data

[1615] Output: Parsed damage report data

[1616] Step 3:

[1617] Acquiring a 3D model

[1618] The server retrieves a 3D model of the target facility from the database based on the damage report, and loads the 3D model data corresponding to the building or area where the damage was reported via the database access API.

[1619] Input: Damage report data, building information

[1620] Output: Acquired 3D model data

[1621] Step 4:

[1622] Identifying the damage

[1623] The server identifies the damaged areas on the acquired 3D model, uses image processing algorithms to match the photo data sent by the user with the 3D model data, and also uses sensor information to accurately identify the damaged areas.

[1624] Input: 3D model data, photo data of damage report

[1625] Output: Information on identified damage locations

[1626] Step 5:

[1627] Estimation of the impact range

[1628] The server then analyzes the impact area around the identified damage, using structural analysis software to simulate how the damage will affect other parts and identify potential areas of impact.

[1629] Input: Identified damage information, structural analysis software

[1630] Output: Estimated impact area data

[1631] Step 6:

[1632] Estimated restoration costs and time

[1633] The server uses generative AI to estimate recovery costs and time. It references data from past disasters and similar cases and uses an AI model to predict costs and time. It also includes the required personnel, material lists, and construction procedures.

[1634] Input: Affected area data, past disaster data, similar case data

[1635] Output: Estimated restoration costs and times

[1636] Step 7:

[1637] Consideration of external environment forecasts

[1638] The server works with other systems to make estimates that take into account external environmental forecasts, obtains weather data from the weather forecast system, and traffic data from the traffic information system, and adjusts recovery plans based on this data.

[1639] Input: Weather data, traffic data

[1640] Output: Estimated data taking into account the external environment

[1641] Step 8:

[1642] Emotional information analysis

[1643] The server analyzes the emotional information based on the user's report, uses an emotion engine to identify the user's stress and anxiety, and adjusts recovery plans and support systems based on that information.

[1644] Input: User report

[1645] Output: Parsed emotion information

[1646] Step 9:

[1647] Generate and present a recovery plan

[1648] The server generates a final recovery plan and presents it to the user, including a list of required materials, a schedule of repair personnel, and estimated start and completion dates for the work. Once the plan is complete, the user is notified via push notification or email.

[1649] Input: Emotional information, external environment forecast data, estimated recovery cost and time data

[1650] Output: A recovery plan presented to the user

[1651] 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.

[1652] 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.

[1653] 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.

[1654] [Fourth embodiment]

[1655] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1656] 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.

[1657] 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).

[1658] 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.

[1659] 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.

[1660] 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).

[1661] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1662] 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.

[1663] 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.

[1664] 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.

[1665] 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.

[1666] 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.

[1667] 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."

[1668] System Overview

[1669] This invention is a system that assesses damage to building facilities caused by disasters such as earthquakes and estimates the extent of the impact and the cost and time required for restoration as a BCP (Business Continuity Plan) measure for large factories and facilities. This system uses 3D models to identify damaged areas, makes estimates using generative AI, and can also take external environment predictions into account by linking with other systems.

[1670] Key components of the system

[1671] 1. User Interface (Terminal)

[1672] The terminal is a device that can access the system via a network, such as a PC, tablet, or smartphone. Users use the terminal to report damage.

[1673] 2. Server

[1674] The server receives damage reports, obtains 3D models, identifies the damaged areas, estimates the extent of the impact, uses generative AI to estimate recovery costs and time, connects with other systems, generates recovery plans, and presents them to users.

[1675] 3. Database

[1676] The database is a storage system that stores 3D models, data on past disasters, and recovery case studies.

[1677] 4. Other Systems

[1678] The other systems are external systems that provide external environmental data, such as a weather forecast system or a traffic information system.

[1679] Program processing flow

[1680] 1. User reports damage

[1681] User: After a disaster occurs, enter the damaged areas of the factory or facility into the system. Using a terminal, access the system's damage report form and enter the damaged areas (e.g., second floor of the building, 3D printer area).

[1682] 2. The server retrieves the 3D model

[1683] Server: Receives the damage report and retrieves the 3D model data of the target facility from the database. For example, it loads the 3D model file of "Factory A."

[1684] 3. The server identifies the damage

[1685] Server: Identifies the damage locations reported by users on the 3D model. Using image processing algorithms and sensor information analysis technology, specific damage locations are mapped onto the 3D model.

[1686] 4. The server estimates the affected area

[1687] Server: Analyze the impact area around the damaged area. For example, use structural analysis software to simulate how damage to the second floor will affect the first floor and other rooms.

[1688] 5. The server uses generative AI to estimate recovery costs and time.

[1689] Server: Uses generative AI to estimate recovery costs and time. Refers to data from past disasters and similar cases to estimate repair costs and the number of days required for recovery.

[1690] 6. Integration with other systems

[1691] Server: Calls the API to obtain necessary data from other forecasting systems. For example, obtains future weather data from a weather forecast system and reflects it in adjusting construction schedules.

[1692] 7. Presentation of recovery plan

[1693] Server: Generates a specific recovery plan based on the estimation results and external data. The recovery plan includes the necessary materials, a schedule of workers, and a timeline of the start and end dates of construction. Once the plan is complete, it is presented to the user via their device.

[1694] Specific examples

[1695] Example 1: Earthquake damage at a parts manufacturing plant

[1696] 1. User: After the earthquake, report damage to the production line area on the second floor of the factory.

[1697] 2. Server: Obtain a 3D model of the factory and identify damaged areas in the production line area on the second floor.

[1698] 3. Server: Analyze the impact area around the damaged area and determine the potential impact on the logistics area on the first floor.

[1699] 4. Server: Using generative AI, we estimate that repair costs will be 5 million yen and recovery will take 10 days.

[1700] 5. Server: Works with the weather forecast system to make predictions that take into account the weather for the next week.

[1701] 6. Server: Provide users with a concrete recovery plan.

[1702] Example 2: Data center fire damage

[1703] 1. User: Reports that a fire has broken out in the data center and some server racks have been damaged.

[1704] 2. Server: Obtain a 3D model of the data center and identify areas of damage.

[1705] 3. Server: Analyze the extent of the impact around the damaged area and evaluate the impact on network equipment.

[1706] 4. Server: Using generative AI, we estimate that repair costs will be 2 million yen and recovery will take 5 days.

[1707] 5. Server: Obtains information on air conditioning and power supply status after a fire from the linked system and reflects it in the recovery plan.

[1708] 6. Server: Provide users with a concrete recovery plan.

[1709] The above is an embodiment of the present invention. This system makes it possible to quickly and efficiently assess damage and formulate a restoration plan in the event of a disaster.

[1710] The processing flow will be explained below.

[1711] Step 1:

[1712] After a disaster occurs, users report damage using a terminal. Specifically, users access the system's damage report form, enter location information and photos of the damaged area, and specific details of the damage (e.g., cracks in the wall, broken machinery), and send the information to the system.

[1713] Step 2:

[1714] The server receives the damage report sent by the user, analyzes the necessary data, such as the location of the damage and the timestamp of the report, and starts the damage assessment process.

[1715] Step 3:

[1716] The server retrieves the 3D model of the target facility from the database. Specifically, it loads the 3D model data corresponding to the building or area where the damage was reported via the database access API.

[1717] Step 4:

[1718] The server identifies the damaged areas on the 3D model, and uses image processing algorithms to match the reported photo data with the 3D model. Sensor information analysis technology is also used to ensure that the damaged areas are accurately identified.

[1719] Step 5:

[1720] The server estimates the extent of the impact around the damaged area, and uses structural analysis software to simulate how the damage will affect other parts and identify potential areas of impact.

[1721] Step 6:

[1722] The server uses generative AI to estimate recovery costs and time. Specifically, it references data from past disasters and similar recovery cases and uses an AI model to predict costs and time. This estimate also includes the necessary personnel, materials, and construction procedures.

[1723] Step 7:

[1724] The server works in conjunction with other systems to make estimates that take into account external environmental forecasts. Specifically, it calls the weather forecast system's API to obtain future weather data and adds factors that may affect construction schedules. It also obtains data from the traffic information system and performs risk assessments for material transportation.

[1725] Step 8:

[1726] The server generates a final recovery plan and presents it to the user, which includes a list of required materials, a schedule of repair personnel, and estimated start and completion dates for work. Once the plan is complete, the user is notified via push notification or email, allowing them to review the details.

[1727] The above are the specific processing steps.

[1728] Example 1

[1729] 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."

[1730] Rapid and accurate damage assessment and recovery plan formulation in the event of a disaster at large factories and facilities are important issues for ensuring business continuity. However, conventional methods require a great deal of time and effort to identify damaged areas and estimate the extent of the impact, and there is also a high degree of uncertainty in estimating recovery costs and time. It is also difficult to take external environmental predictions into account, and many challenges exist in formulating comprehensive recovery plans. A new system is needed to solve these problems.

[1731] 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.

[1732] In this invention, the server includes: means for a user to report damage caused by a disaster using a terminal; means for receiving the damage report and retrieving a 3D model of the target facility from a database; means for identifying the damaged area on the 3D model; means for estimating the extent of impact centered on the damaged area; means for estimating restoration costs and time using generative AI; means for linking with other systems to make estimates taking into account external environment predictions; means for generating a restoration plan and presenting it to the user; means for using image processing algorithms and sensor information analysis technology to map the damaged area onto a 3D model based on the damage report form; and means for inputting prompt statements to the generative AI to estimate repair costs and restoration time. This enables rapid and accurate assessment of damage caused by a disaster, detailed estimation of the extent of impact, reliable estimation of restoration costs and time, and the formulation of a comprehensive restoration plan taking into account the external environment.

[1733] "User" is a person or organization whose role is to operate the system and report damage caused by a disaster.

[1734] A "terminal" is a device such as a PC, tablet, or smartphone that can access the system via a network and operate it or input information.

[1735] A "damage report" is information entered by a user to the system about the location and extent of damage to facilities and equipment caused by a disaster.

[1736] "Server" is the central processing unit of the system, and is a collective term for the hardware and software components that are responsible for receiving damage reports, obtaining 3D models, identifying damaged areas, estimating the extent of the impact, estimating recovery costs and time, obtaining external environment forecasts, and generating and presenting recovery plans.

[1737] A "database" is an information management system for storing and managing 3D model data, past disaster data, recovery case data, etc.

[1738] A "3D model" is digital data that represents the physical structure of the target facility in three dimensions and is used to identify damaged areas and analyze the extent of the impact.

[1739] An "image processing algorithm" is a computational method for identifying specific features or patterns in a digital image and analyzing the location of damage.

[1740] "Sensor information analysis technology" is a technology for analyzing data obtained from various sensors and identifying damaged areas.

[1741] The "scope of impact" refers to the range and extent of the impact that the identified damaged area has on the surrounding area, and is simulated using structural analysis software, etc.

[1742] "Generative AI" is a type of artificial intelligence technology that generates output results (e.g., recovery costs and duration) based on specific input information (prompt statements).

[1743] A "prompt sentence" is an instruction sentence input to a generative AI, and contains information that forms the basis for the AI's analysis and inference.

[1744] "Restoration Plan" means the action plan required to repair and restore the damaged area, including specific materials, personnel, schedule, costs, etc.

[1745] "Other systems" are external data systems that provide external environmental data, such as weather forecast systems and traffic information systems.

[1746] System Overview

[1747] This invention is a system for assessing damage to building facilities caused by disasters (such as earthquakes and fires) and estimating the cost and time required for restoration as a business continuity plan (BCP) measure for large factories and facilities. This system uses 3D models to identify damaged areas, makes estimates using generative AI, and works with other systems to consider external environment predictions.

[1748] Key Components

[1749] 1. User Interface (Terminal)

[1750] The terminal is a device that can access the system via a network, such as a PC, tablet, or smartphone. Users use the terminal to report damage.

[1751] 2. Server

[1752] The server receives damage reports, obtains 3D models, identifies the damaged areas, estimates the extent of the impact, uses generative AI to estimate recovery costs and time, connects with other systems, generates recovery plans, and presents them to users.

[1753] 3. Database

[1754] The database is a storage system that stores 3D models, data on past disasters, and recovery case studies.

[1755] 4. Other Systems

[1756] The other systems are external systems that provide external environmental data, such as a weather forecast system or a traffic information system.

[1757] Program processing description

[1758] User reports damage

[1759] When a disaster occurs, users access the damage report form using a device (PC, tablet, smartphone). They enter the necessary information in the form and press the report button to send it to the system. For example, a user may report that "the 3D printer area on the second floor of the building has been damaged."

[1760] The server receives the damage report and obtains the 3D model.

[1761] When the server receives a damage report from a user, it retrieves the 3D model data of the target facility from the database. For example, it loads the 3D model file of "Factory A" (e.g., "Factory A_3DModel.obj").

[1762] The server identifies the damage

[1763] The server identifies the reported damage on the 3D model. Specifically, it uses image processing algorithms and sensor information analysis technology to map the damage reported by the user onto the digital model. For example, it identifies the location of the "3D printer area on the second floor."

[1764] The server estimates the scope of the impact

[1765] The server analyzes the extent of the damage, focusing on the damaged area. It uses structural analysis software (e.g., ANSYS) to simulate the impact of the damage on other areas and structural components. For example, it calculates how damage to the second floor will affect the logistics area on the first floor.

[1766] The server uses generative AI to estimate recovery costs and time.

[1767] The server inputs prompt text into the generative AI, which estimates the cost and time required for recovery. The prompt text includes information such as the extent of the damage, the extent of the impact, and similar past cases. For example, it might say, "The 3D printer area on the second floor has been damaged, affecting the logistics area on the first floor. Estimate the repair cost and number of days." Based on this, the AI ​​outputs an estimate such as "repair cost 5 million yen, recovery time 10 days" based on past disaster data and similar cases.

[1768] The server connects with other systems

[1769] The server connects with other systems to obtain external environmental data. For example, it extracts future weather data from a weather forecast system and reflects it in recovery plans. This data is then used to schedule outdoor work, etc.

[1770] The server presents a recovery plan

[1771] The server generates a specific restoration plan based on the estimation results and external data and presents it to the user. This plan includes information such as materials, personnel, schedule, and construction start and end dates. The user can then use their terminal to review the plan and make any necessary corrections or approvals.

[1772] Specific examples

[1773] Earthquake damage at a parts manufacturing plant

[1774] 1. User: After the earthquake, the user reports damage to the second floor production line area of ​​the factory. The user types "The second floor production line area is severely damaged" and submits the message.

[1775] 2. Server: Obtain a 3D model of the factory and identify damaged areas in the production line area on the second floor.

[1776] 3. Server: Image processing algorithms identify damage areas and map them onto the model.

[1777] 4. Server: Use structural analysis software to analyze the impact area and simulate the impact on the logistics area on the first floor.

[1778] 5. Server: The generative AI is given a prompt: "Estimate the repair costs and recovery time for damage caused by the earthquake." The server estimates that repair costs will be 5 million yen and recovery will take 10 days.

[1779] 6. Server: Obtains weather data for the next week from the weather forecast system to see if it will affect outdoor work.

[1780] 7. Server: Generates a recovery plan and presents it to the user's device, such as "Repair costs will be 5 million yen, and recovery will take 10 days." The user reviews it, makes any necessary corrections, and then approves it.

[1781] Data center fire damage

[1782] 1. User: Reports that a fire broke out in the data center and some server racks were damaged. Type "The fire damaged the server racks and also affected some network equipment" and submit.

[1783] 2. Server: Obtain a 3D model of the data center and identify areas of damage.

[1784] 3. Server: Using image processing technology, the damaged area is mapped onto a 3D model.

[1785] 4. Server: Use structural analysis software to analyze the scope of the impact and evaluate the impact on network equipment.

[1786] 5. Server: The generative AI is given a prompt: "If a server rack is damaged by fire, estimate the repair cost and recovery time." The AI ​​estimates that the repair cost will be 2 million yen and that it will take five days to recover.

[1787] 6. Server: Obtains information on air conditioning and power supply status after a fire from other systems and reflects it in recovery plans.

[1788] 7. Server: Generates a recovery plan and presents it to the user's device, such as "Repair costs will be 2 million yen, and recovery will take 5 days." The user can review the plan and make any changes or approvals.

[1789] This concludes the description of the "Mode for Carrying Out the Invention." This system enables damage assessment in the event of a disaster and the development of an efficient recovery plan.

[1790] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1791] Step 1: Enter your damage report

[1792] User: After a disaster occurs, the user accesses the damage report form using a device (PC, tablet, smartphone, etc.). They enter the necessary information, such as the location and extent of the damage, and contact information, and press the report button to submit. For example, they report that "the 3D printer area on the second floor of the building has been damaged."

[1793] Input: Report information such as location of damage, extent of damage, contact information, etc.

[1794] Output: Damage report data sent to the server (e.g. "The second floor of the building, the 3D printer area, was damaged")

[1795] Step 2: Receiving damage reports and obtaining 3D models

[1796] Server: Based on the received damage report, retrieves 3D model data from the database using the target facility's identification information. Executes a database query using the target facility's ID and name to load the 3D model file. For example, loads the 3D model file for "Factory A" (e.g., "Factory A_3DModel.obj").

[1797] Input: Damage report data, facility identification

[1798] Output: 3D model data of the facility (e.g. "FactoryA_3DModel.obj")

[1799] Step 3: Identify the damage

[1800] Server: Analyzes the damage information entered by the user and identifies the corresponding location on the 3D model. Using image processing algorithms and sensor information analysis technology, the reported damage is mapped onto the digital model. For example, the specific location of the "3D printer area on the second floor" is identified.

[1801] Input: Damage report data, 3D model data

[1802] Output: Damaged area on a 3D model (e.g., "3D printer area on the second floor")

[1803] Step 4: Estimate the impact area

[1804] Server: Analyze the impact area around the identified damaged area. Use structural analysis software (e.g., ANSYS) to simulate the impact of the damage on other areas and structural parts. For example, calculate how damage to the second floor will affect the logistics area on the first floor.

[1805] Input: Damage location, 3D model data

[1806] Output: Analysis results of the impact range (e.g., "Damage on the second floor affects the logistics area on the first floor")

[1807] Step 5: Estimate restoration costs and time

[1808] Server: A prompt is input into the generative AI, which estimates the cost and time required for recovery. The prompt includes information such as the extent of the damage, the extent of the impact, and past similar cases. For example, it might say, "The 3D printer area on the second floor has been damaged, affecting the logistics area on the first floor. Estimate the repair cost and number of days." Based on this, the AI ​​outputs an estimate such as "Repair cost 5 million yen, recovery time 10 days."

[1809] Input: Prompt statement (damage status, affected area, past similar cases)

[1810] Output: Estimated restoration cost and time (e.g., "Repair cost: 5 million yen, restoration time: 10 days")

[1811] Step 6: Integration with other systems

[1812] Server: Links with other systems (e.g., weather forecast systems and traffic information systems) to obtain external environmental data. Uses APIs to obtain necessary data and reflects this information in recovery plans. For example, obtains future weather data from a weather forecast system and uses it to adjust construction schedules.

[1813] Input: Request for external environment data from other systems

[1814] Output: External environmental data (e.g., "Weather forecast for the next week")

[1815] Step 7: Generate and present a recovery plan

[1816] Server: Generates a specific recovery plan based on the estimation results and acquired external data. This plan includes information such as the required materials, personnel, schedule, and construction start and end dates. The created recovery plan is presented on a terminal accessible to the user. The user can use the terminal to review the plan and make any necessary corrections or approvals.

[1817] Input: Estimated recovery costs and time, external environmental data

[1818] Output: A detailed restoration plan (e.g., "Repair cost: 5 million yen, restoration time: 10 days, materials required, personnel schedule, construction start and end dates")

[1819] (Application example 1)

[1820] 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."

[1821] When a disaster occurs and a robot installed in a factory or facility is damaged, it is difficult to quickly and accurately assess the damage and estimate the extent of the impact and the cost and time required for recovery. Conventional methods require a large amount of human resources and time, and there is a risk of delays in recovery plans or incorrect decisions. The purpose of this invention is to solve these problems and provide a system that enables efficient and rapid damage assessment and the generation of recovery plans.

[1822] 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.

[1823] In this invention, the server includes: means for a user to report damage caused by a disaster using a terminal; means for receiving the damage report and retrieving a 3D model of the target facility from a database; means for identifying the damaged area on the 3D model; means for estimating the impact area centered on the damaged area; means for estimating recovery costs and time using generative AI; means for making estimates taking into account external environment predictions in cooperation with other systems; means for generating and presenting a recovery plan to the user; and means for managing a robot that uses the 3D model to identify the damaged area and generate the impact area and recovery plan. This enables faster and more accurate damage assessment of robots and recovery plans in the event of a disaster.

[1824] A "user" is a person who uses this system to report damage caused by a disaster.

[1825] A "terminal" is a device that can be connected to a network, such as a PC, tablet, or smartphone used by a user.

[1826] A "disaster" is an event in which facilities or equipment are damaged due to natural phenomena such as earthquakes, fires, and typhoons, or due to human factors.

[1827] "Damaged area" refers to the specific location where facilities or equipment have been physically damaged by the disaster.

[1828] The "reporting means" refers to an interface that allows a user to input information about the damaged area into the system using a terminal.

[1829] "Means for receiving" refers to the function of the server receiving damage reports from users via the network.

[1830] A "3D model" is digital data that reproduces the spatial structure of facilities and equipment.

[1831] A "database" is a storage system that allows the system to store 3D model data, past disaster data, and other data.

[1832] "Means of acquisition" refers to the function by which the server retrieves the necessary 3D model data from the database.

[1833] "Means for identification" refers to technology for identifying user-reported damage locations on a 3D model.

[1834] The "area of ​​impact" refers to the area centered on the reported damage location and indicates the extent to which surrounding facilities and equipment may be affected.

[1835] "Means of estimation" is a function for calculating the extent of the impact based on the location of damage, as well as the cost and time required for recovery.

[1836] "Generative AI" is artificial intelligence that uses machine learning algorithms and generative models to automatically estimate recovery costs and time.

[1837] A "recovery plan" is a plan that includes specific work procedures for repairing damage, necessary materials, work personnel, construction schedule, etc.

[1838] "Means for presenting" refers to a function for displaying the recovery plan generated by the server on the user's terminal.

[1839] "Means of management" refers to the ability to control and monitor the robot using 3D models, and use them to identify damaged areas and generate restoration plans.

[1840] "Other systems" are systems that provide external environmental data, such as weather forecast systems and traffic information systems.

[1841] "External environment forecast" refers to environmental data provided by external systems, such as weather forecasts and traffic conditions, and is a factor taken into account in recovery plans.

[1842] System Overview

[1843] This invention is a system that quickly assesses damage and estimates the extent of the impact and the cost and time required for restoration when a disaster such as an earthquake or fire occurs in a factory or facility. Based on damage information reported by the user via a terminal, this system identifies the damaged area using a 3D model, makes estimates using generative AI, and cooperates with other systems to consider external environment predictions, thereby efficiently and quickly generating restoration plans.

[1844] Key components of the system

[1845] 1. User Interface (Terminal)

[1846] The terminal is a device that can access the system via a network, such as a PC, tablet, or smartphone. Users use the terminal to report damage.

[1847] 2. Server

[1848] The server receives damage reports, obtains 3D models, identifies the damaged areas, estimates the extent of the impact, uses generative AI to estimate recovery costs and time, connects with other systems, generates recovery plans, and presents them to users.

[1849] 3. Database

[1850] The database is a storage system that stores 3D models, data on past disasters, and recovery case studies.

[1851] 4. Other Systems

[1852] Other systems refer to external systems that provide external environmental data, such as weather forecast systems and traffic information systems.

[1853] About Torsion

[1854] Hardware / Software used

[1855] Hardware: Use a smartphone or tablet.

[1856] Software: Python 3.8 or higher, generative AI modules (e.g., TensorFlow or PyTorch), 3D model processing libraries (e.g., trimesh), and external API access libraries (e.g., requests).

[1857] Data processing and calculation

[1858] 1. Damage report

[1859] A user uses a terminal to report damage caused by a disaster to the system, including detailed information about the damage (for example, "damage to the robot arm").

[1860] 2. Obtaining a 3D model

[1861] Based on the damage report received, the server retrieves 3D model data of the target facility from the database. This is digital data that reproduces the spatial structure of the target facility.

[1862] 3. Identifying the damaged area

[1863] The server identifies the reported damage locations on the 3D model using image processing algorithms and sensor information analysis techniques.

[1864] 4. Estimation of the impact range

[1865] The server analyzes the extent of the impact around the damaged area, using structural analysis software to simulate how the damaged area will affect the surrounding area.

[1866] 5. Utilizing generative AI

[1867] The server uses generative AI to estimate recovery costs and time. The AI ​​model references data from past disasters and similar cases to estimate the necessary repair costs and recovery time.

[1868] 6. Integration with other systems

[1869] The server connects with other systems, such as weather forecast systems and traffic information systems, to obtain external environment forecasts, enabling predictions that take future weather and traffic conditions into account.

[1870] 7. Presentation of recovery plan

[1871] The server generates a specific recovery plan based on the results of these analyses, including the necessary materials, a schedule of workers, and a timeline of the start and end dates of the work.The server then presents the final recovery plan to the user.

[1872] Specific examples

[1873] Earthquake damage assessment and restoration planning app for factory robots

[1874] If a robotic arm installed in a factory is damaged in an earthquake, the user can use their terminal to report, "The robotic arm on the second floor is damaged." The server retrieves a 3D model of the factory from the database and identifies the damaged area. It analyzes the extent of the impact and uses generative AI to estimate the cost and time of restoration. It also retrieves data from the weather forecast system, generates an optimal restoration plan, and presents it to the user.

[1875] Example prompt statement

[1876] "An earthquake has occurred in Factory A. The robot arm on the second floor has been damaged. Please make a recovery plan."

[1877] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1878] Step 1:

[1879] The user uses a terminal to report damage caused by the disaster. The user enters specific damage information, such as "The robot arm on the second floor is damaged," into the form displayed on the terminal. The entered damage report is sent to the server via the network.

[1880] Step 2:

[1881] The server receives the damage report, analyzes the received data, and extracts information about the damaged location (for example, which part of the facility was damaged). It then retrieves a 3D model of the target facility from the database. If the target facility is "Factory A," the server loads the 3D model data of Factory A from the database.

[1882] Step 3:

[1883] The server identifies the damaged area on the 3D model. The server maps the received information on the damaged area (e.g., "second floor" or "robot arm area") onto the 3D model and identifies the specific damaged area using image processing algorithms and sensor information analysis technology. During this process, the server generates coordinate data for the damaged area.

[1884] Step 4:

[1885] The server estimates the extent of the impact around the damaged area. Using structural analysis software, the server simulates the impact of the damaged area on surrounding structures and equipment. For example, it analyzes how damage to the second floor will affect the first floor area and other rooms, and outputs the specific extent of the impact.

[1886] Step 5:

[1887] The server uses generative AI to estimate recovery costs and time. The server runs a generative AI model that uses data from past disasters and similar cases to estimate repair costs and the number of days required for recovery based on the location of damage and the extent of the impact. The input in this step is coordinate data of the damaged area and data on the extent of the impact, and the output is the estimated recovery cost and time.

[1888] Step 6:

[1889] The server connects with other systems to obtain external environment forecasts. The server then calls the APIs of weather forecast systems and traffic information systems to obtain future weather and traffic data. This data is used to schedule restoration work.

[1890] Step 7:

[1891] The server generates a recovery plan and presents it to the user. The server creates a specific recovery plan based on estimated recovery costs, time, impact area, and external environmental data. This plan includes the necessary materials, a schedule of work personnel, and a timeline of construction start and end dates. The generated recovery plan is displayed on the user's terminal via a user interface.

[1892] 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.

[1893] System Overview

[1894] This invention is a system that assesses damage to building facilities caused by disasters such as earthquakes and estimates the extent of the impact and the cost and time required for restoration as a BCP (Business Continuity Plan) measure for large factories and facilities. This system identifies damaged areas using 3D models, makes estimates using generative AI, and provides efficient restoration plans by taking into account external environment predictions. In addition, by combining it with an emotion engine that recognizes user emotions, more effective responses are possible.

[1895] Key components of the system

[1896] 1. User Interface (Terminal)

[1897] The terminal is a device that can access the system via a network, such as a PC, tablet, or smartphone. Users use the terminal to report damage.

[1898] 2. Server

[1899] The server receives damage reports, obtains 3D models, identifies damaged areas, estimates the extent of the impact, uses generative AI to estimate recovery costs and time, connects with other systems, generates recovery plans, and presents them to users. It also uses an emotion engine to analyze user emotions and adjust response measures based on that information.

[1900] 3. Database

[1901] The database is a storage system that stores 3D models, data on past disasters, and recovery case studies.

[1902] 4. Other Systems

[1903] The other systems are external systems that provide external environmental data, such as a weather forecast system or a traffic information system.

[1904] 5. Emotion Engine

[1905] The emotion engine extracts emotional information from user reports and operational logs during use, which is used for recovery planning and customer support resource allocation.

[1906] Program processing flow

[1907] 1. User reports damage

[1908] User: After a disaster occurs, the user enters the damaged areas of the factory or facility into the system. Using a terminal, the user accesses the system's damage report form, enters the location information and photos of the damaged areas, and specific details of the damage (e.g., cracks in the wall, broken machinery), and sends the form to the system.

[1909] 2. The server receives the damage report

[1910] Server: Receives damage reports sent by users. Based on the received information, it analyzes the necessary data, such as the location of the damage and the timestamp of the report, and starts the damage assessment process.

[1911] 3. The server retrieves the 3D model

[1912] Server: Retrieves 3D model data of the target facility from the database. Specifically, it loads the 3D model data corresponding to the building or area where the damage was reported via the database access API.

[1913] 4. The server identifies the damage

[1914] Server: Identifies the damage areas reported by users on the 3D model. Image processing algorithms are used to match the reported photo data with the 3D model. Sensor information analysis technology is also used to ensure that the damage areas are accurately identified.

[1915] 5. The server estimates the affected area

[1916] Server: Analyze the impact area around the damaged area. Use structural analysis software to simulate how the damage will affect other parts and identify potential areas of impact.

[1917] 6. The server uses generative AI to estimate recovery costs and time.

[1918] Server: Uses generative AI to estimate recovery costs and time. It references data from past disasters and similar cases and uses AI models to predict costs and time. This estimate also includes the required personnel, materials, and construction procedures.

[1919] 7. The server connects with other systems

[1920] Server: Calls the API to obtain necessary data from other forecasting systems. For example, it obtains future weather data from a weather forecast system and adds factors that may affect construction schedules. It also obtains data from a traffic information system to perform risk assessments for material transportation.

[1921] 8. The server uses the emotion engine

[1922] Server: Analyzes the user's report and extracts emotional information using an emotion engine. Identifies the stress or anxiety the user is feeling when reporting and adjusts the response accordingly.

[1923] 9. The server generates a recovery plan and presents it to the user.

[1924] Server: Generates and presents the final recovery plan to the user. The recovery plan includes a list of needed materials, a schedule of repair personnel, and estimated start and completion dates for work. Once the plan is complete, the user is notified via push notification or email so they can review the details. The server also takes into account the user's emotional state and presents appropriate language and support options.

[1925] Specific examples

[1926] Example 1: Earthquake damage at a parts manufacturing plant

[1927] 1. User: After the earthquake, report damage to the production line area on the second floor of the factory.

[1928] 2. Server: Obtain a 3D model of the factory and identify damaged areas in the production line area on the second floor.

[1929] 3. Server: Analyze the impact area around the damaged area and determine the potential impact on the logistics area on the first floor.

[1930] 4. Server: Using generative AI, we estimate that repair costs will be 5 million yen and recovery will take 10 days.

[1931] 5. Server: Works with the weather forecast system to make predictions that take into account the weather for the next week.

[1932] 6. Server: Analyzes the stress level from the user's report using an emotion engine. If high stress is detected, allocate additional resources and include them in the recovery plan.

[1933] 7. Server: Provide users with a concrete recovery plan.

[1934] Example 2: Data center fire damage

[1935] 1. User: Reports that a fire has broken out in the data center and some server racks have been damaged.

[1936] 2. Server: Obtain a 3D model of the data center and identify areas of damage.

[1937] 3. Server: Analyze the extent of the impact around the damaged area and evaluate the impact on network equipment.

[1938] 4. Server: Using generative AI, we estimate that repair costs will be 2 million yen and recovery will take 5 days.

[1939] 5. Server: Obtains information on air conditioning and power supply status after a fire from the linked system and reflects it in the recovery plan.

[1940] 6. Server: Analyzes the user's report using an emotion engine to determine whether there is any anxiety. If a serious anxiety is detected, a special support plan is presented.

[1941] 7. Server: Provide users with a concrete recovery plan.

[1942] The above is an embodiment of the present invention. This system makes it possible to quickly and efficiently assess damage and formulate recovery plans in the event of a disaster, and also takes into account the emotions of users.

[1943] The processing flow will be explained below.

[1944] Step 1:

[1945] After a disaster occurs, users report damage using a terminal. Specifically, users access the system's damage report form, enter location information and photos of the damaged area, and specific details of the damage (e.g., cracks in the wall, broken machinery), and send the information to the system.

[1946] Step 2:

[1947] The server receives the damage report sent by the user, which includes the location of the damage, details of the damage, and a timestamp of the report.

[1948] Step 3:

[1949] The server retrieves the 3D model data of the target facility from the database, and based on the damage report, loads the 3D model file of the target building or facility via the database access API.

[1950] Step 4:

[1951] The server identifies damaged areas on the 3D model. It uses image processing algorithms to analyze the photo data sent by the user and match it with the damaged areas in the 3D model. It also uses sensor information analysis technology to accurately identify damaged areas based on IoT sensor and camera data.

[1952] Step 5:

[1953] The server estimates the extent of the damage from the center, and uses structural analysis software to simulate the impact of the damage on other parts, thereby identifying areas that may be affected by the damage.

[1954] Step 6:

[1955] The server uses generative AI to estimate recovery costs and time. Specifically, data from past disasters and similar recovery cases is input into the AI ​​model to predict costs and time, including the required personnel, materials, and construction procedures. The AI ​​uses neural networks to generate highly accurate estimates.

[1956] Step 7:

[1957] The server works in conjunction with other systems to make estimates that take into account external environmental forecasts. It calls the weather forecast system's API to obtain future weather data and calculates factors that will affect construction schedules. It also obtains data from the traffic information system and performs risk assessments for material transportation.

[1958] Step 8:

[1959] The server uses an emotion engine to recognize the user's emotions. It analyzes the user's report content and the operation log at the time of sending, and extracts emotional information such as stress, anxiety, and satisfaction. This information is analyzed based on the wording in the report content and the timing of sending.

[1960] Step 9:

[1961] The server tailors the recovery plan it presents based on emotional information, for example automatically assigning additional customer support if high stress levels are detected, or generating a plan with detailed, easy-to-understand explanations if the user is feeling anxious.

[1962] Step 10:

[1963] The server generates a final recovery plan and presents it to the user, which includes a list of required materials, a schedule of repair personnel, and estimated start and completion dates for work. Once the plan is complete, the user is notified via push notification or email, allowing them to review the details.

[1964] The above are the specific processing steps.

[1965] Example 2

[1966] 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."

[1967] It has been difficult to effectively carry out rapid damage assessment and recovery plan formulation for factories and facilities in the event of a disaster using conventional methods. In addition, it is necessary to take into account the psychological stress of users, but there has been a lack of concrete methods for doing so.

[1968] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1969] In this invention, the server includes means for a user to report damage caused by a disaster using a terminal, means for receiving the damage report and retrieving a 3D model of the target facility from a database, means for identifying the damaged area on the 3D model, means for estimating the extent of the impact centered on the damaged area, means for estimating recovery costs and time using generative AI, means for making estimates taking into account external environment predictions in cooperation with other systems, means for analyzing the user's emotional information and using an emotion engine to adjust the response based on that information, and means for generating a recovery plan and presenting it to the user. This enables quick and effective damage assessment and formulation of a recovery plan, as well as responses that take into account the user's psychological stress.

[1970] "User" refers to the person or organization that operates the system to report damage caused by a disaster.

[1971] "Terminal" refers to a device (PC, tablet, smartphone, etc.) that can access the system via a network.

[1972] "Damage report" refers to a report from a user that includes location information, photos, and specific details of damage to building facilities caused by a disaster.

[1973] A "3D model" refers to digital data that shows the three-dimensional structure of the target facility.

[1974] "Database" refers to a storage system for storing information required by the system.

[1975] "Image processing algorithm" refers to a computational method for analyzing digital images to extract useful information.

[1976] "Sensor information analysis technology" refers to the technology for analyzing data obtained from sensors and converting it into meaningful information.

[1977] "Affected area" refers to the extent of the impact on other areas and facilities centered on the damaged area.

[1978] "Generative AI" refers to artificial intelligence models that perform inference and generative tasks.

[1979] "External environment forecast" refers to forecast data obtained from external sources, such as weather forecasts and traffic information.

[1980] An "emotion engine" refers to a system that analyzes a user's emotional information and adjusts the response based on that information.

[1981] "Restoration plan" refers to a plan that includes repair procedures for damaged areas, necessary materials, work schedules, etc.

[1982] System Overview

[1983] This invention is a system that assesses damage to building facilities caused by disasters such as earthquakes and estimates the extent of the impact and the cost and time required for restoration as a BCP (Business Continuity Plan) measure for large factories and facilities. This system identifies damaged areas using a 3D model, makes estimates using a generative AI model, and provides an efficient restoration plan by taking into account external environment predictions. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, more effective responses are possible.

[1984] Hardware and software used

[1985] Hardware

[1986] 1. Devices (PC, tablet, smartphone)

[1987] A device that can be accessed by the user to report damage.

[1988] 2. Server

[1989] Responsible for data processing for the entire system, receiving damage reports, acquiring 3D models, identifying damaged areas, estimating the extent of the impact, estimating recovery costs and time using generative AI, linking with other systems, and generating and presenting recovery plans to users.

[1990] 3. Database

[1991] ...

Claims

1. A means for a user to report damage caused by a disaster using a terminal; means for receiving the damage report and retrieving a three-dimensional model of the target facility from a database; a means for identifying a damaged area on the three-dimensional model; A means for estimating the extent of the impact centered on the damaged area; A means of estimating recovery costs and time using generative AI; A means of making estimates that take into account external environment forecasts in cooperation with other systems; means for generating and presenting a recovery plan to a user; A system including:

2. 2. The system according to claim 1, wherein the means for identifying the damaged area on the three-dimensional model uses an image processing algorithm and a sensor information analysis technique.

3. The system according to claim 1, characterized in that the means for estimating recovery costs and time using generative AI refers to past disaster data and similar case data.

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