system

Abstract

We provide the system. [Solution] A means for acquiring surface information of a structure using information gathering means, The means for analyzing the surface information acquired by the information gathering means and identifying the defective area, A means for setting repair priorities for identified fault areas, A means for generating a repair plan based on priorities, Means for visually presenting the aforementioned repair plan in real time on a portable information terminal or a head-mounted display device, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional infrastructure inspection work largely depends on manual work by humans, and safety risks due to work at heights or dangerous locations, as well as the time and cost required for inspections, have been major problems. Furthermore, in visual inspections, there is a possibility of overlooking damaged areas or delays in judgment. Also, formulating an effective repair plan based on the damage situation requires specialized knowledge and many work processes, and there is also the problem of complex management.

Means for Solving the Problems

[0005] This invention provides a system that automatically collects surface information of infrastructure using an information acquisition device that utilizes an unmanned aerial vehicle. The acquired information is analyzed to identify damaged areas. Furthermore, based on a prioritization algorithm, repair priorities are set and a repair plan is automatically generated. This reduces the effort involved in infrastructure inspection and repair planning, prevents human error, and improves the efficiency of risk management.

[0006] An "information acquisition device" is a device used to automatically collect surface information about infrastructure.

[0007] An "unmanned aerial vehicle" is a device that flies autonomously without human intervention, performing necessary tasks and gathering information.

[0008] "Surface information" refers to visual or measurement data that indicates the external condition of infrastructure and is used to determine the presence and condition of damage.

[0009] "Analysis" is the process of finding specific meanings or patterns in collected data and using that information to aid in decision-making.

[0010] A "damaged area" refers to a part of the infrastructure that has been identified as being deteriorated or damaged in a way that could affect its use or safety.

[0011] "Priority" refers to the system of assigning orders to determine which tasks should be performed first, taking into account resources and time, when carrying out repairs or other maintenance.

[0012] A "repair plan" is a plan that pre-determines the content and sequence of repair work for damaged areas, enabling efficient maintenance and management. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, a numbered communication I / F (Interface) is an interface including a communication processor and an antenna and the like. 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), or Bluetooth (registered trademark), and the like.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the 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.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0027] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention is a system that uses unmanned aerial vehicles to inspect infrastructure, employs AI technology to identify damaged areas, and determines repair priorities. In this system, servers, terminals, and users play crucial roles, each fulfilling a specific function to efficiently manage the infrastructure.

[0035] server

[0036] The server plays a central role in the system, handling the main parts of data processing and analysis. It receives large volumes of image and sensor data transmitted from unmanned aerial vehicles and stores them in a database. Image recognition technology is applied to the received data to identify damaged areas. The server then uses AI algorithms to prioritize repairs based on the severity and impact of the damage. Finally, based on the prioritized damage information, it develops a repair plan, considering historical operational data and budget information to generate an efficient and feasible repair schedule.

[0037] terminal

[0038] The terminal receives processing results from the server and provides them to the user. The terminal visually displays the received analysis results, allowing the user to intuitively understand the damaged areas. Specifically, it uses 3D models and highlighting to indicate areas of damage that require attention. The terminal also displays updated data sent from the server in real time, ensuring that the user always has the latest information.

[0039] User

[0040] The user is responsible for making the final decisions and judgments. The user reviews detailed information about the damage and the proposed repair plan through their device. The user can provide feedback on the repair plan to the server as needed and request a readjustment to accommodate new conditions or additional requests. The user also sets a long-term maintenance schedule and oversees the execution of the plan.

[0041] Specific example

[0042] For example, consider using this system in the regular inspection of a bridge. The server controls an unmanned aerial vehicle (UAV) to acquire surface information of the bridge using a high-resolution camera and infrared sensor. This makes it possible to collect detailed data on the entire bridge without human intervention. Next, the server analyzes the collected data to automatically identify deterioration and cracks in the bridge and assess their severity. The user receives this information via a terminal and is helped to determine the need for repairs. This process can significantly reduce the cost and time required for inspection and repair while ensuring safety.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The server issues inspection commands to the unmanned aerial vehicle (UAV) based on the scheduled date and time. The UAV begins autonomous flight and patrols the infrastructure according to the set flight route.

[0046] Step 2:

[0047] The unmanned aerial vehicle (UAV) uses high-resolution cameras and infrared sensors to acquire surface information about the infrastructure and transmits it to a server in real time. The server stores the received data in storage.

[0048] Step 3:

[0049] The server activates an image recognition algorithm to automatically identify damaged areas from stored image data. This process utilizes edge detection and pattern recognition techniques. The server then records the identified damage information in a database.

[0050] Step 4:

[0051] The server uses an AI agent to assess the severity and impact of damage and perform risk analysis. Based on this, it determines repair priorities and assigns a score to each damaged area.

[0052] Step 5:

[0053] The server develops a repair plan based on priority information. The plan is optimized by considering past operational history, budget, and resource availability. Once the plan is developed, the server notifies the terminal.

[0054] Step 6:

[0055] The terminal receives the repair plan sent from the server and displays detailed information on the user interface. The user can visualize and review damage data and repair suggestions through the terminal.

[0056] Step 7:

[0057] Users can send feedback on the proposed repair plan to the server via their device and request revisions to the plan as needed. Based on the final decision, users can also set up a long-term maintenance schedule.

[0058] (Example 1)

[0059] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0060] Early detection of structural deterioration and damage, and the rapid and efficient development of repair plans, are critical challenges in large-scale infrastructure management. However, conventional methods have problems such as requiring significant time and cost for data collection and analysis, and insufficient prioritization of repairs. This can lead to overlooking high-priority damage or delays in repairs, potentially compromising safety.

[0061] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0062] In this invention, the server includes means for collecting surface data of a structure using data acquisition equipment, means for analyzing the surface data acquired by the data acquisition equipment to identify deterioration locations, means for setting repair priorities for the identified deterioration locations, means for evaluating the severity of deterioration using a generated AI model, and means for generating a repair plan based on the priorities and formulating an actionable schedule. This enables rapid and efficient data analysis and repair planning, resulting in improved infrastructure safety and reduced maintenance costs.

[0063] "Data acquisition equipment" refers to devices or equipment used to collect surface data of structures without human intervention.

[0064] "Structures" refer to infrastructure such as bridges and buildings.

[0065] "Surface data" refers to information that indicates the external condition of a structure, and includes high-resolution images and temperature information.

[0066] "Deterioration location" refers to a part of a structure where its performance or function has deteriorated.

[0067] "Repair priority" refers to a set of criteria used to determine which areas of identified deterioration should be prioritized for repair.

[0068] A "generative AI model" is an artificial intelligence system that uses machine learning algorithms to evaluate the severity of data degradation and support decision-making.

[0069] A "repair plan" is a plan that includes specific procedures and schedules for efficiently repairing deteriorated areas.

[0070] A "feasible schedule" is a realistic schedule for the start and completion of repair work, set considering available resources and conditions.

[0071] This invention is a system for efficiently detecting deterioration and damage to structures and formulating appropriate repair plans. This system is operated through the cooperation of a server, terminals, and users.

[0072] Server Role

[0073] The server controls data acquisition equipment to collect surface data from infrastructure structures. Unmanned aerial vehicles acquire surface information using devices such as high-resolution cameras and infrared sensors. The server receives this data and applies image recognition technology to identify the location of deterioration. Specifically, a generative AI model using machine learning models assesses the severity of the damage. Based on this information, the server sets repair priorities and develops a feasible repair plan considering past maintenance history and budget information.

[0074] Terminal role

[0075] The terminal receives analysis results and repair plans from the server and presents them visually to the user. This uses 3D models and interactive dashboards to allow the user to intuitively understand the data. The terminal also displays real-time updated information, always providing the user with the latest data.

[0076] User roles

[0077] The user reviews the information provided through the device and determines the need to repair the deteriorated areas. Furthermore, they can evaluate the appropriateness of the repair plan, send feedback to the server as needed, and request adjustments to the plan. This also allows the user to oversee the execution schedule.

[0078] Specific example

[0079] For example, when this system is used in bridge inspections, the server can collect bridge surface data via unmanned aerial vehicles, perform advanced AI analysis, and accurately detect damaged areas. Based on the information displayed on the terminal, users can efficiently schedule repair work while ensuring safety. This system reduces the costs and time associated with inspections and repairs.

[0080] Example of a prompt

[0081] "Please build an AI model that analyzes bridge inspection data to identify damaged areas. Furthermore, assess the severity of the damage and prioritize repairs."

[0082] The above is a description of the embodiments for carrying out the invention.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The server controls the unmanned aerial vehicle (UAV) and collects surface data from structures. The input is the location and measurement range of the target structure. Based on this, the server guides the UAV to the desired location and acquires surface images and temperature information using a high-resolution camera and infrared sensors. The output is this sensor dataset.

[0086] Step 2:

[0087] The server receives the collected data and stores it in a database. The input consists of image data and sensor data transmitted from unmanned aerial vehicles (UAVs). The server converts the data format and registers it in the database in a way that allows for efficient storage and management. The output is a set of data converted into an analyzable format.

[0088] Step 3:

[0089] The server uses image recognition technology to identify degradation locations in data stored in a database. The input consists of high-resolution images and temperature distribution data stored in the database. Using a generative AI model, the server automatically detects cracks and degradation within the images, identifying their location and characteristics. The output is the identified degradation locations and their attribute information.

[0090] Step 4:

[0091] The server prioritizes repairs for identified degradation locations. The input is attribute information, including a severity assessment of the degradation location. Utilizing a generative AI model, the server analyzes the impact of the damage and ranks the urgency of repairs. The output is a list of repair priorities.

[0092] Step 5:

[0093] The server develops a repair plan based on a priority list of repairs. Inputs include the prioritized list, maintenance history, and budget information. The server optimizes resource allocation and creates a feasible schedule, referencing historical data. Outputs include a detailed repair plan and its implementation schedule.

[0094] Step 6:

[0095] The terminal receives the repair plan and analysis results sent from the server and presents them to the user. The input consists of repair information and visualization data provided by the server. The terminal generates a 3D model and displays it through a dashboard in a format easily understandable to the user. The output is a display in a format that the user can verify.

[0096] Step 7:

[0097] The user reviews the repair plan and analysis results presented through the terminal and makes corrections or provides feedback as needed. Input is feedback information based on the user's knowledge and judgment. Based on this, the user can send requests to the server to instruct improvements or adjustments to the plan. Output is feedback data, including suggested improvements and additional requests.

[0098] (Application Example 1)

[0099] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0100] In structural inspection work, traditional methods require manual labor, resulting in significant time and expense. Furthermore, identifying damaged areas and prioritizing repairs relies on subjective judgment, necessitating accuracy and efficiency. Additionally, the difficulty of on-site information sharing and immediate decision-making necessitates rapid decision-making.

[0101] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0102] In this invention, the server includes means for acquiring surface information of a structure using information gathering means, means for analyzing the surface information acquired by the information gathering means to identify defective areas, and means for setting repair priorities for the identified defective areas. This makes it possible to identify damaged areas quickly and accurately compared to conventional manual work, and to formulate a repair plan based on priorities. In addition, information can be shared smoothly on-site using a portable information terminal or a head-mounted display device, enabling rapid decision-making.

[0103] "Information gathering means" refers to a device or method for acquiring surface information of a structure.

[0104] A "structure" refers to buildings and facilities used to form infrastructure.

[0105] "Surface information" refers to data about the characteristics and conditions present on the exterior of a structure.

[0106] "Analysis" is the process of evaluating acquired data and extracting specific information.

[0107] A "defective area" refers to a part of a structure that shows damage or abnormality.

[0108] "Priority" refers to the order in which tasks are performed based on the importance or urgency of the repairs.

[0109] A "repair plan" is a plan that outlines the specific procedures and schedule for the repair work that should be carried out to address identified defects.

[0110] A "portable information terminal" is a portable electronic device that enables the display and operation of information.

[0111] A "head-mounted display device" is a device that displays images and information when worn on the user's head.

[0112] "Real-time" means that data acquisition and processing are performed in real time, and the results are reflected immediately.

[0113] This invention provides a system that supports the efficient inspection and repair planning of structures. The system uses an unmanned aerial vehicle as an information gathering means to acquire surface information of infrastructure structures. A server receives this information and uses image processing technology to identify defective areas on the structure. Furthermore, based on the analysis results, the server uses an AI algorithm to determine the repair priorities and generate a repair plan.

[0114] In this system, the server utilizes a high-performance computing environment and AI analysis software (e.g., TENSORFLOW®, OpenCV). Data is stored in a cloud-based database (e.g., AWS®, Google® Cloud), and the server processes it. The processing results are provided to the user via a terminal.

[0115] The terminal is either a portable information terminal or a head-mounted display device, allowing users to intuitively and visually confirm the analysis results. This enables rapid decision-making on-site. Based on this information, users can review the repair work plan and send feedback to the server as needed.

[0116] As a concrete example, in the case of inspecting an old bridge, an unmanned aerial vehicle (UAV) collects surface information of the bridge, and this data is analyzed on a server. Users can then check the details of the damage and the priority of repairs via their mobile devices, and apply this information to developing on-site repair plans.

[0117] An example of a prompt for the generated AI model would be: "In infrastructure inspection work on an old bridge, analyze the damage data acquired by the drone and display the repair priority order in real time on a smart device."

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] The server receives surface information of structures from information gathering devices mounted on unmanned aerial vehicles. The input consists of high-resolution image data and sensor data. The server stores this data in cloud storage and prepares it for subsequent analysis.

[0121] Step 2:

[0122] The server applies image processing techniques to the received image data. To detect faulty areas, AI analysis software (e.g., TensorFlow, OpenCV) is used to process the data, thereby identifying the damaged areas. The output includes information such as the coordinates and size of the identified damaged areas.

[0123] Step 3:

[0124] The server analyzes data on identified damaged areas and uses an AI algorithm to prioritize repairs. Inputs include the type, severity, and location of the damage. The output is a prioritized list, which is used to create a repair plan.

[0125] Step 4:

[0126] The server generates a repair plan. This plan is optimized based on a priority list, taking into account past repair history and cost information. The output is a plan document that includes specific repair procedures and a timeline.

[0127] Step 5:

[0128] The terminal receives repair plan information sent from the server and displays it visually to the user. The input is the generated repair plan. The terminal processes and displays the information using a 3D model or highlighting to make it easy for the user to understand.

[0129] Step 6:

[0130] The user reviews the repair plan displayed on their device and sends feedback to the server to make adjustments or modifications as needed. Inputs include the presented repair plan and on-site findings. Outputs are the revised repair plan, with the updated information reflecting the new conditions, returned to the server.

[0131] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0132] This invention aims to improve the user experience by combining an infrastructure inspection system based on unmanned aerial vehicles and AI technology with an emotion engine that recognizes user emotions. The system components and their operation are described in detail below.

[0133] server

[0134] The server acts as the central control unit of the system, handling data processing and analysis. First, the server acquires surface information of the infrastructure transmitted from the unmanned aerial vehicles and identifies damaged areas using advanced image analysis technology. Subsequently, it generates a repair plan that prioritizes based on the severity of the damage and takes into account past maintenance history and budget information. Furthermore, the server works in conjunction with the emotion engine to analyze emotional data obtained from user feedback and adjust the repair plan as needed.

[0135] terminal

[0136] The terminal receives information from the server and functions as a user interface. To provide users with an intuitive user experience, the terminal uses 3D models and visualized damage information for easy understanding. The terminal is equipped with voice input and camera functions, allowing the emotion engine to accurately analyze the user's emotional state. Emotional data is used to understand user opinions and concerns regarding the repair plan and to provide personalized feedback.

[0137] User

[0138] Users interact with the infrastructure management system via their device. Through the device's UI, users can view damage information and review repair plans. Furthermore, based on their emotional state analyzed by the emotion engine, users can receive more appropriate feedback from the system. Users can send their feedback to the server and request improvements or adjustments to the plan.

[0139] Specific example

[0140] For example, consider the case of conducting a regular inspection of a dam. The server uses an unmanned aerial vehicle to conduct a comprehensive inspection of the dam and identifies damaged areas by analyzing image data. The terminal intuitively displays this damage information to the user and proposes a repair plan. The user uses the terminal to express their emotions and opinions through voice or visual means. An emotion engine analyzes the user's emotions, and the server adjusts the repair plan based on the results, improving user satisfaction. This enables safe and efficient infrastructure management.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] The server issues inspection commands to the unmanned aerial vehicle (UAV), activating a program that autonomously patrols the area around the infrastructure according to a set flight route. The UAV collects surface information of the infrastructure using high-resolution cameras and infrared sensors and transmits it to the server in real time.

[0144] Step 2:

[0145] The server analyzes the received image data and automatically identifies damaged areas using edge detection and pattern matching algorithms. The damage information obtained during this process is stored in a database for later analysis.

[0146] Step 3:

[0147] The server uses an AI agent to assess the severity and impact of damage and prioritize it. Damage that has been prioritized is then compiled into a repair plan by cross-referencing it with past management history and budget information.

[0148] Step 4:

[0149] The terminal receives the repair plan details sent from the server and displays them to the user through the interface. The terminal visualizes the damaged areas using a 3D model, making it easy for the user to understand intuitively.

[0150] Step 5:

[0151] Users can view repair plans and damage information via their device and input feedback using the UI. User feedback is recorded in voice or text. Additionally, a built-in emotion engine analyzes the user's voice and nonverbal facial expressions to identify their emotions.

[0152] Step 6:

[0153] The server processes emotional data collected from the terminal and analyzes the user's emotional state. Based on this information, it readjusts the repair plan as needed, providing a plan that better aligns with the user's wishes.

[0154] Step 7:

[0155] Once the user agrees to the final repair plan, the server will finalize the long-term maintenance schedule and suggest potential dates for the next inspection or repair work. This suggestion will be communicated to the user via the terminal, helping to improve management efficiency.

[0156] (Example 2)

[0157] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0158] In modern infrastructure management, maintaining efficiency and safety requires the rapid and accurate generation of inspection and repair plans. However, conventional technologies, while capable of identifying abnormalities and prioritizing repairs, lack the flexibility to adjust plans while considering human emotions, potentially leading to decreased user satisfaction. In particular, there is a challenge in optimizing repair plans in real time by reflecting user feedback.

[0159] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0160] In this invention, the server includes means for collecting surface information of equipment using an unmanned mobile device, means for analyzing the surface information acquired by the unmanned mobile device using a processing device to identify abnormal areas, and means for evaluating the emotional state of a person. This enables flexible adjustment of repair plans in real time, improving user satisfaction and allowing for efficient infrastructure management.

[0161] An "unmanned mobile device" is a mechanical device that moves autonomously or remotely and performs a specified task, and in particular includes aerospace machines.

[0162] "Surface information of equipment" refers to data about the appearance and condition of structures and equipment that are acquired in a visible or detectable form.

[0163] A "processing device" is a computer or machine used to calculate, analyze, and generate results from data.

[0164] An "abnormal area" refers to a location or part that does not exhibit the intended state or function as designed, and which requires repair or replacement.

[0165] "Priority" refers to the criteria used to determine the order and importance of multiple tasks or options.

[0166] "Maintenance records" refer to data and documents related to past maintenance work and its history.

[0167] "Resource utilization information" refers to information regarding the time, funds, and human resources required to carry out restoration and maintenance activities.

[0168] A "repair plan" is a set of systematic means and procedures for repairing identified anomalies and maintaining the functionality of a system or structure.

[0169] "Emotional state" refers to a state that describes a person's psychological and emotional responses, including subjective evaluations in feedback.

[0170] This invention is a system that utilizes unmanned mobile devices and artificial intelligence technology to perform advanced infrastructure management based on diverse data. Specific embodiments are described below.

[0171] System Configuration

[0172] The system consists of a server, terminals, and users. The server plays a central role in data collection, analysis, and plan generation and adjustment. Terminals provide visualized information to users and function as an interface. Users access the system through terminals and provide data review and feedback.

[0173] Hardware and software

[0174] The server uses a high-performance computer as its processing unit. For data analysis, Python-based libraries such as OpenCV and TensorFlow are used for object detection and identification of anomalies.

[0175] Unmanned mobile devices are designed as aerospace machines and serve as information acquisition devices. Conventional control software is used for autonomous flight and data acquisition.

[0176] The device uses 3D visualization software (e.g., Unity or Blender) to provide information in an easy-to-understand format for the user. Additionally, Google Cloud Speech-to-Text is used for speech recognition, and OpenCV and natural language processing models are employed for sentiment analysis.

[0177] How to use

[0178] The server controls the unmanned mobile device and efficiently collects surface information about the equipment. Through a terminal, the user can check the specific location and extent of damage, and provides emotionally-based feedback through voice and facial expressions. Based on the emotional data obtained from the user, the server uses an AI model to optimize the repair plan and modify the plan as needed to improve user satisfaction.

[0179] Specific example

[0180] For example, when conducting a regular bridge inspection, the server activates an unmanned mobile device to acquire image data of the entire bridge and identify damaged areas. This information is then provided to the user via a terminal, and the user provides feedback using prompt messages such as, "This part of the bridge needs urgent repair." The server then uses this feedback to adjust the repair plan, achieving efficient infrastructure management.

[0181] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0182] Step 1:

[0183] The server controls the unmanned mobile device and collects image data of specified infrastructure. The input consists of flight route information and infrastructure location information, which the unmanned mobile device uses to automatically take images. The resulting output is an image data stream, which is transmitted to the server in real time.

[0184] Step 2:

[0185] The server analyzes the collected image data. The input is image data, and object detection algorithms are executed using OpenCV or TensorFlow. Data calculations identify damaged areas, and related information (e.g., location coordinates and type of damage) is output.

[0186] Step 3:

[0187] The server prioritizes repairs based on the location of the anomaly and generates a repair plan. Inputs include information on the damaged area, past maintenance history, and budget information. A prioritization algorithm is executed to formulate the plan. The output is the details of the repair plan (e.g., repair schedule and required resources).

[0188] Step 4:

[0189] The terminal receives repair plans and damage information from the server and displays them in an easy-to-understand format for the user. Inputs are repair plans and 3D visual data, while output is information displayed on a visualized interface screen. The user can then view detailed information through this screen.

[0190] Step 5:

[0191] Users provide feedback and opinions on repair plans and damage information through their devices. Input consists of the user's voice and facial expressions; emotional states are collected using the device's voice input and camera. The output is user feedback data.

[0192] Step 6:

[0193] The server analyzes user feedback and adjusts the repair plan. The input is user sentiment data, and natural language processing is used to determine whether the sentiment is positive or negative. The output is the adjusted repair plan, which is then sent back to the terminal.

[0194] (Application Example 2)

[0195] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0196] In infrastructure inspection and maintenance, identifying damaged areas and developing repair plans based on priorities is crucial. However, traditional systems have struggled to adjust repair plans to take into account user emotions and feedback. As a result, there was a risk that repair plans might not meet user expectations, leading to decreased satisfaction.

[0197] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0198] In this invention, the server includes means for collecting infrastructure information using information collection means, means for analyzing the information acquired by the information collection means to identify damaged areas, and means for analyzing the user's emotions using emotion analysis means and adjusting the repair plan based on the analysis results. This makes it possible to make adjustments based on the user's emotions and generate a repair plan that is highly satisfactory.

[0199] "Information gathering means" refers to devices and methods for collecting surface information on infrastructure.

[0200] "Information analysis means" refers to the technologies and processes used to analyze collected information and identify damaged areas in infrastructure.

[0201] "Priority setting means" refers to means that have the function of determining the priority of repairs for identified damaged areas.

[0202] "Repair plan generation means" refers to a means for formulating a repair plan based on established priorities.

[0203] "Emotional analysis methods" refer to technologies that analyze users' emotions and reflect them in infrastructure management and repair plans.

[0204] This invention aims to improve the efficiency of infrastructure management and enhance user satisfaction by constructing a system that combines information gathering using unmanned aerial vehicles with emotion analysis technology.

[0205] The server uses unmanned aerial vehicles (UAVs) as a means of information gathering to acquire surface information about the infrastructure. The acquired data is then analyzed by the server to identify damaged areas.

[0206] Next, the server prioritizes repairs based on the identified damaged areas. Then, a repair plan generation system formulates the optimal repair plan, taking into account past maintenance history and budget information.

[0207] Simultaneously, the device is equipped with emotion analysis capabilities that analyze the user's emotions from voice input and video. If the user views images of infrastructure or repair plans and expresses emotions such as anxiety or relief, that emotion data is sent to the server. Based on this data, the server adjusts the repair plan and provides feedback to the user.

[0208] As a concrete example, when inspecting a bridge, a server identifies damage and develops a repair plan based on images taken by an unmanned aerial vehicle. If users express feelings of relief through their smartphones or smart glasses, this information is reflected in the plan through emotion analysis, enabling more effective infrastructure management.

[0209] An example of a prompt sentence is, "How do you feel after seeing the results of this bridge inspection? Do you feel relieved, anxious, or have any other emotions?"

[0210] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0211] Step 1:

[0212] The server uses unmanned aerial vehicles (UAVs) to photograph the surface of infrastructure and collects the information as data. The input is image data from the UAVs, and the output is the collected raw image data. This data is then used for subsequent analysis.

[0213] Step 2:

[0214] The server analyzes the collected image data using an image analysis algorithm (e.g., image recognition software). The input is the image data from step 1, and the output is data identifying the damaged areas. This process identifies which parts are damaged and extracts specific damage information.

[0215] Step 3:

[0216] The server sets repair priorities based on the analyzed damage data. The input is the damage information obtained in step 2, and the output is the damage information with assigned priorities. This process considers the severity and urgency of the damage when determining priorities.

[0217] Step 4:

[0218] The server generates a repair plan using prioritized damage information. The input is the priority information from step 3, and the output is a specific repair plan. This plan incorporates past management history and budget constraints.

[0219] Step 5:

[0220] The device collects audio and video data from users as they check repair plans and infrastructure status, and analyzes it using an emotion analysis engine. The input is data related to the user's emotions, and the output is the analyzed emotion information. This allows for user feedback to be obtained.

[0221] Step 6:

[0222] The server adjusts the repair plan based on the emotional data obtained from the user. The input is the emotional information obtained in step 5 and the repair plan generated in step 4, and the output is the adjusted repair plan. This process provides a repair plan that meets the user's expectations.

[0223] Step 7:

[0224] The device presents the user with a revised repair plan and receives feedback. The input is the repair plan obtained in step 6, and the output is the user's final feedback. This allows for the incorporation of opinions that will ultimately increase user satisfaction.

[0225] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0226] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0227] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0228] [Second Embodiment]

[0229] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0230] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0231] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0232] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0233] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0234] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0235] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0236] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0237] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0238] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0239] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0240] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0241] This invention is a system that uses unmanned aerial vehicles to inspect infrastructure, employs AI technology to identify damaged areas, and determines repair priorities. In this system, servers, terminals, and users play crucial roles, each fulfilling a specific function to efficiently manage the infrastructure.

[0242] server

[0243] The server plays a central role in the system, handling the main parts of data processing and analysis. It receives large volumes of image and sensor data transmitted from unmanned aerial vehicles and stores them in a database. Image recognition technology is applied to the received data to identify damaged areas. The server then uses AI algorithms to prioritize repairs based on the severity and impact of the damage. Finally, based on the prioritized damage information, it develops a repair plan, considering historical operational data and budget information to generate an efficient and feasible repair schedule.

[0244] terminal

[0245] The terminal receives processing results from the server and provides them to the user. The terminal visually displays the received analysis results, allowing the user to intuitively understand the damaged areas. Specifically, it uses 3D models and highlighting to indicate areas of damage that require attention. The terminal also displays updated data sent from the server in real time, ensuring that the user always has the latest information.

[0246] User

[0247] The user is responsible for making the final decisions and judgments. The user reviews detailed information about the damage and the proposed repair plan through their device. The user can provide feedback on the repair plan to the server as needed and request a readjustment to accommodate new conditions or additional requests. The user also sets a long-term maintenance schedule and oversees the execution of the plan.

[0248] Specific example

[0249] For example, consider using this system in the regular inspection of a bridge. The server controls an unmanned aerial vehicle (UAV) to acquire surface information of the bridge using a high-resolution camera and infrared sensor. This makes it possible to collect detailed data on the entire bridge without human intervention. Next, the server analyzes the collected data to automatically identify deterioration and cracks in the bridge and assess their severity. The user receives this information via a terminal and is helped to determine the need for repairs. This process can significantly reduce the cost and time required for inspection and repair while ensuring safety.

[0250] The following describes the processing flow.

[0251] Step 1:

[0252] The server issues inspection commands to the unmanned aerial vehicle (UAV) based on the scheduled date and time. The UAV begins autonomous flight and patrols the infrastructure according to the set flight route.

[0253] Step 2:

[0254] The unmanned aerial vehicle (UAV) uses high-resolution cameras and infrared sensors to acquire surface information about the infrastructure and transmits it to a server in real time. The server stores the received data in storage.

[0255] Step 3:

[0256] The server activates an image recognition algorithm to automatically identify damaged areas from stored image data. This process utilizes edge detection and pattern recognition techniques. The server then records the identified damage information in a database.

[0257] Step 4:

[0258] The server uses an AI agent to assess the severity and impact of damage and perform risk analysis. Based on this, it determines repair priorities and assigns a score to each damaged area.

[0259] Step 5:

[0260] The server develops a repair plan based on priority information. The plan is optimized by considering past operational history, budget, and resource availability. Once the plan is developed, the server notifies the terminal.

[0261] Step 6:

[0262] The terminal receives the repair plan sent from the server and displays detailed information on the user interface. The user can visualize and review damage data and repair suggestions through the terminal.

[0263] Step 7:

[0264] Users can send feedback on the proposed repair plan to the server via their device and request revisions to the plan as needed. Based on the final decision, users can also set up a long-term maintenance schedule.

[0265] (Example 1)

[0266] Next, we will describe Example 1. 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".

[0267] Early detection of structural deterioration and damage, and the rapid and efficient development of repair plans, are critical challenges in large-scale infrastructure management. However, conventional methods have problems such as requiring significant time and cost for data collection and analysis, and insufficient prioritization of repairs. This can lead to overlooking high-priority damage or delays in repairs, potentially compromising safety.

[0268] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0269] In this invention, the server includes means for collecting surface data of a structure using data acquisition equipment, means for analyzing the surface data acquired by the data acquisition equipment to identify deterioration locations, means for setting repair priorities for the identified deterioration locations, means for evaluating the severity of deterioration using a generated AI model, and means for generating a repair plan based on the priorities and formulating an actionable schedule. This enables rapid and efficient data analysis and repair planning, resulting in improved infrastructure safety and reduced maintenance costs.

[0270] "Data acquisition equipment" refers to devices or equipment used to collect surface data of structures without human intervention.

[0271] "Structures" refer to infrastructure such as bridges and buildings.

[0272] "Surface data" refers to information that indicates the external condition of a structure, and includes high-resolution images and temperature information.

[0273] "Deterioration location" refers to a part of a structure where its performance or function has deteriorated.

[0274] "Repair priority" refers to a set of criteria used to determine which areas of identified deterioration should be prioritized for repair.

[0275] A "generative AI model" is an artificial intelligence system that uses machine learning algorithms to evaluate the severity of data degradation and support decision-making.

[0276] A "repair plan" is a plan that includes specific procedures and schedules for efficiently repairing deteriorated areas.

[0277] A "feasible schedule" is a realistic schedule for the start and completion of repair work, set considering available resources and conditions.

[0278] The present invention is a system for efficiently detecting deterioration and damage of structures and formulating appropriate repair plans. This system is executed through the cooperation of a server, a terminal, and a user.

[0279] Role of the server

[0280] The server controls data acquisition equipment to collect surface data of infrastructure. Using devices such as high-resolution cameras and infrared sensors, an unmanned aircraft device acquires surface information. The server receives these data and applies image recognition technology to identify deterioration locations. Specifically, a generative AI model using a machine learning model evaluates the severity of damage. Based on this information, the server sets the priority of repair and formulates an executable repair plan considering past maintenance history and budget information.

[0281] Role of the terminal

[0282] The terminal receives the analysis results and repair plan transmitted from the server and visually presents them to the user. This uses 3D models and interactive dashboards to enable the user to intuitively understand the data. Also, the terminal displays real-time updated information and always provides the user with the latest data.

[0283] Role of the user

[0284] The user checks the information provided through the terminal and determines the necessity of repairing the deteriorated areas. Furthermore, the user can evaluate the appropriateness of the repair plan, send feedback to the server if necessary, and request adjustment of the plan. Thus, the user also supervises the execution schedule.

[0285] Specific example

[0286] For example, when this system is utilized in bridge inspections, the server can collect surface data of the bridge through an unmanned aircraft, perform advanced AI analysis, and accurately detect damaged areas. Based on the information displayed on the terminal, the user can efficiently schedule repair work while ensuring safety. This system realizes cost and time reduction related to inspections and repairs.

[0287] Example of prompt text

[0288] "Please build an AI model that analyzes bridge inspection data and identifies damaged areas. Additionally, evaluate the severity of the damage and prioritize the repairs."

[0289] The above is the description of the form for implementing the invention.

[0290] The flow of the specific process in Example 1 will be described using Figure 11.

[0291] Step 1:

[0292] The server controls the unmanned aircraft and collects surface data of the structure. The input is the position information and measurement range of the target structure. Based on this, the server guides the unmanned aircraft to the target position and acquires surface images and temperature information using a high-resolution camera and an infrared sensor. The output is these sensor data sets.

[0293] Step 2:

[0294] The server receives the collected data and stores them in the database. The input is the image data and sensor data transmitted from the unmanned aircraft. The server performs data format conversion and registers the data in the database in a form that can be efficiently stored and managed. The output is a group of data converted into an analyzable format.

[0295] Step 3:

[0296] The server uses image recognition technology to identify degradation locations in data stored in a database. The input consists of high-resolution images and temperature distribution data stored in the database. Using a generative AI model, the server automatically detects cracks and degradation within the images, identifying their location and characteristics. The output is the identified degradation locations and their attribute information.

[0297] Step 4:

[0298] The server prioritizes repairs for identified degradation locations. The input is attribute information, including a severity assessment of the degradation location. Utilizing a generative AI model, the server analyzes the impact of the damage and ranks the urgency of repairs. The output is a list of repair priorities.

[0299] Step 5:

[0300] The server develops a repair plan based on a priority list of repairs. Inputs include the prioritized list, maintenance history, and budget information. The server optimizes resource allocation and creates a feasible schedule, referencing historical data. Outputs include a detailed repair plan and its implementation schedule.

[0301] Step 6:

[0302] The terminal receives the repair plan and analysis results sent from the server and presents them to the user. The input consists of repair information and visualization data provided by the server. The terminal generates a 3D model and displays it through a dashboard in a format easily understandable to the user. The output is a display in a format that the user can verify.

[0303] Step 7:

[0304] The user checks the repair plan and analysis results presented through the terminal, and makes corrections and provides feedback as necessary. The input is feedback information based on the user's knowledge and judgment. Based on this, the user can send a request to the server and instruct the improvement or adjustment of the plan. The output is feedback data including improvement proposals and additional requests.

[0305] (Application Example 1)

[0306] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0307] In the inspection work of structures, the conventional method requires manual work, which has the problem of taking a lot of time and cost. Also, in identifying damaged locations and determining the priority of repairs, there is a problem that since it depends on subjectivity, the accuracy and efficiency of judgment are required. Furthermore, because it is difficult to share information and make immediate judgments on-site, quick decision-making is required.

[0308] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0309] In this invention, the server includes means for acquiring surface information of a structure by information collection means, means for analyzing the surface information acquired by the information collection means to identify a defective area, and means for setting a repair priority for the identified defective area. Thereby, compared with the conventional manual work, the damaged location can be identified quickly and accurately, and a repair plan based on the priority can be formulated. Also, since information sharing on-site can be smoothly performed by a portable information terminal or a head-mounted display device, quick decision-making can be realized.

[0310] The "information collection means" is a device or method for acquiring surface information of a structure.

[0311] The "structure" refers to buildings and facilities used to form infrastructure.

[0312] "Surface information" refers to data about the characteristics and conditions present on the exterior of a structure.

[0313] "Analysis" is the process of evaluating acquired data and extracting specific information.

[0314] A "defective area" refers to a part of a structure that shows damage or abnormality.

[0315] "Priority" refers to the order in which tasks are performed based on the importance or urgency of the repairs.

[0316] A "repair plan" is a plan that outlines the specific procedures and schedule for the repair work that should be carried out to address identified defects.

[0317] A "portable information terminal" is a portable electronic device that enables the display and operation of information.

[0318] A "head-mounted display device" is a device that displays images and information when worn on the user's head.

[0319] "Real-time" means that data acquisition and processing are performed in real time, and the results are reflected immediately.

[0320] This invention provides a system that supports the efficient inspection and repair planning of structures. The system uses an unmanned aerial vehicle as an information gathering means to acquire surface information of infrastructure structures. A server receives this information and uses image processing technology to identify defective areas on the structure. Furthermore, based on the analysis results, the server uses an AI algorithm to determine the repair priorities and generate a repair plan.

[0321] In this system, the server utilizes a high-performance computing environment and AI analysis software (e.g., TensorFlow, OpenCV). Data is stored in a cloud-based database (e.g., AWS, Google Cloud), and the server processes it. The processing results are provided to the user via a terminal.

[0322] The terminal is either a portable information terminal or a head-mounted display device, allowing users to intuitively and visually confirm the analysis results. This enables rapid decision-making on-site. Based on this information, users can review the repair work plan and send feedback to the server as needed.

[0323] As a concrete example, in the case of inspecting an old bridge, an unmanned aerial vehicle (UAV) collects surface information of the bridge, and this data is analyzed on a server. Users can then check the details of the damage and the priority of repairs via their mobile devices, and apply this information to developing on-site repair plans.

[0324] An example of a prompt for the generated AI model would be: "In infrastructure inspection work on an old bridge, analyze the damage data acquired by the drone and display the repair priority order in real time on a smart device."

[0325] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0326] Step 1:

[0327] The server receives surface information of structures from information gathering devices mounted on unmanned aerial vehicles. The input consists of high-resolution image data and sensor data. The server stores this data in cloud storage and prepares it for subsequent analysis.

[0328] Step 2:

[0329] The server applies image processing techniques to the received image data. To detect faulty areas, AI analysis software (e.g., TensorFlow, OpenCV) is used to process the data, thereby identifying the damaged areas. The output includes information such as the coordinates and size of the identified damaged areas.

[0330] Step 3:

[0331] The server analyzes data on identified damaged areas and uses an AI algorithm to prioritize repairs. Inputs include the type, severity, and location of the damage. The output is a prioritized list, which is used to create a repair plan.

[0332] Step 4:

[0333] The server generates a repair plan. This plan is optimized based on a priority list, taking into account past repair history and cost information. The output is a plan document that includes specific repair procedures and a timeline.

[0334] Step 5:

[0335] The terminal receives repair plan information sent from the server and displays it visually to the user. The input is the generated repair plan. The terminal processes and displays the information using a 3D model or highlighting to make it easy for the user to understand.

[0336] Step 6:

[0337] The user reviews the repair plan displayed on their device and sends feedback to the server to make adjustments or modifications as needed. Inputs include the presented repair plan and on-site findings. Outputs are the revised repair plan, with the updated information reflecting the new conditions, returned to the server.

[0338] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0339] This invention aims to improve the user experience by combining an infrastructure inspection system based on unmanned aerial vehicles and AI technology with an emotion engine that recognizes user emotions. The system components and their operation are described in detail below.

[0340] server

[0341] The server acts as the central control unit of the system, handling data processing and analysis. First, the server acquires surface information of the infrastructure transmitted from the unmanned aerial vehicles and identifies damaged areas using advanced image analysis technology. Subsequently, it generates a repair plan that prioritizes based on the severity of the damage and takes into account past maintenance history and budget information. Furthermore, the server works in conjunction with the emotion engine to analyze emotional data obtained from user feedback and adjust the repair plan as needed.

[0342] terminal

[0343] The terminal receives information from the server and functions as a user interface. To provide users with an intuitive user experience, the terminal uses 3D models and visualized damage information for easy understanding. The terminal is equipped with voice input and camera functions, allowing the emotion engine to accurately analyze the user's emotional state. Emotional data is used to understand user opinions and concerns regarding the repair plan and to provide personalized feedback.

[0344] User

[0345] Users interact with the infrastructure management system via their device. Through the device's UI, users can view damage information and review repair plans. Furthermore, based on their emotional state analyzed by the emotion engine, users can receive more appropriate feedback from the system. Users can send their feedback to the server and request improvements or adjustments to the plan.

[0346] Specific example

[0347] For example, consider the case of conducting a regular inspection of a dam. The server uses an unmanned aerial vehicle to conduct a comprehensive inspection of the dam and identifies damaged areas by analyzing image data. The terminal intuitively displays this damage information to the user and proposes a repair plan. The user uses the terminal to express their emotions and opinions through voice or visual means. An emotion engine analyzes the user's emotions, and the server adjusts the repair plan based on the results, improving user satisfaction. This enables safe and efficient infrastructure management.

[0348] The following describes the processing flow.

[0349] Step 1:

[0350] The server issues inspection commands to the unmanned aerial vehicle (UAV), activating a program that autonomously patrols the area around the infrastructure according to a set flight route. The UAV collects surface information of the infrastructure using high-resolution cameras and infrared sensors and transmits it to the server in real time.

[0351] Step 2:

[0352] The server analyzes the received image data and automatically identifies damaged areas using edge detection and pattern matching algorithms. The damage information obtained during this process is stored in a database for later analysis.

[0353] Step 3:

[0354] The server uses an AI agent to assess the severity and impact of damage and prioritize it. Damage that has been prioritized is then compiled into a repair plan by cross-referencing it with past management history and budget information.

[0355] Step 4:

[0356] The terminal receives the repair plan details sent from the server and displays them to the user through the interface. The terminal visualizes the damaged areas using a 3D model, making it easy for the user to understand intuitively.

[0357] Step 5:

[0358] Users can view repair plans and damage information via their device and input feedback using the UI. User feedback is recorded in voice or text. Additionally, a built-in emotion engine analyzes the user's voice and nonverbal facial expressions to identify their emotions.

[0359] Step 6:

[0360] The server processes emotional data collected from the terminal and analyzes the user's emotional state. Based on this information, it readjusts the repair plan as needed, providing a plan that better aligns with the user's wishes.

[0361] Step 7:

[0362] Once the user agrees to the final repair plan, the server will finalize the long-term maintenance schedule and suggest potential dates for the next inspection or repair work. This suggestion will be communicated to the user via the terminal, helping to improve management efficiency.

[0363] (Example 2)

[0364] Next, we will describe Example 2. 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".

[0365] In modern infrastructure management, maintaining efficiency and safety requires the rapid and accurate generation of inspection and repair plans. However, conventional technologies, while capable of identifying abnormalities and prioritizing repairs, lack the flexibility to adjust plans while considering human emotions, potentially leading to decreased user satisfaction. In particular, there is a challenge in optimizing repair plans in real time by reflecting user feedback.

[0366] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0367] In this invention, the server includes means for collecting surface information of equipment using an unmanned mobile device, means for analyzing the surface information acquired by the unmanned mobile device using a processing device to identify abnormal areas, and means for evaluating the emotional state of a person. This enables flexible adjustment of repair plans in real time, improving user satisfaction and allowing for efficient infrastructure management.

[0368] An "unmanned mobile device" is a mechanical device that moves autonomously or remotely and performs a specified task, and in particular includes aerospace machines.

[0369] "Surface information of equipment" refers to data about the appearance and condition of structures and equipment that are acquired in a visible or detectable form.

[0370] A "processing device" is a computer or machine used to calculate, analyze, and generate results from data.

[0371] An "abnormal area" refers to a location or part that does not exhibit the intended state or function as designed, and which requires repair or replacement.

[0372] "Priority" refers to the criteria used to determine the order and importance of multiple tasks or options.

[0373] "Maintenance records" refer to data and documents related to past maintenance work and its history.

[0374] "Resource utilization information" refers to information regarding the time, funds, and human resources required to carry out restoration and maintenance activities.

[0375] A "repair plan" is a set of systematic means and procedures for repairing identified anomalies and maintaining the functionality of a system or structure.

[0376] "Emotional state" refers to a state that describes a person's psychological and emotional responses, including subjective evaluations in feedback.

[0377] This invention is a system that utilizes unmanned mobile devices and artificial intelligence technology to perform advanced infrastructure management based on diverse data. Specific embodiments are described below.

[0378] System Configuration

[0379] The system consists of a server, terminals, and users. The server plays a central role in data collection, analysis, and plan generation and adjustment. Terminals provide visualized information to users and function as an interface. Users access the system through terminals and provide data review and feedback.

[0380] Hardware and software

[0381] The server uses a high-performance computer as its processing unit. For data analysis, Python-based libraries such as OpenCV and TensorFlow are used for object detection and identification of anomalies.

[0382] Unmanned mobile devices are designed as aerospace machines and serve as information acquisition devices. Conventional control software is used for autonomous flight and data acquisition.

[0383] The device uses 3D visualization software (e.g., Unity or Blender) to provide information in an easy-to-understand format for the user. Additionally, Google Cloud Speech-to-Text is used for speech recognition, and OpenCV and natural language processing models are employed for sentiment analysis.

[0384] How to use

[0385] The server controls the unmanned mobile device and efficiently collects surface information about the equipment. Through a terminal, the user can check the specific location and extent of damage, and provides emotionally-based feedback through voice and facial expressions. Based on the emotional data obtained from the user, the server uses an AI model to optimize the repair plan and modify the plan as needed to improve user satisfaction.

[0386] Specific example

[0387] For example, when conducting a regular bridge inspection, the server activates an unmanned mobile device to acquire image data of the entire bridge and identify damaged areas. This information is then provided to the user via a terminal, and the user provides feedback using prompt messages such as, "This part of the bridge needs urgent repair." The server then uses this feedback to adjust the repair plan, achieving efficient infrastructure management.

[0388] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0389] Step 1:

[0390] The server controls the unmanned mobile device and collects image data of specified infrastructure. The input consists of flight route information and infrastructure location information, which the unmanned mobile device uses to automatically take images. The resulting output is an image data stream, which is transmitted to the server in real time.

[0391] Step 2:

[0392] The server analyzes the collected image data. The input is image data, and object detection algorithms are executed using OpenCV or TensorFlow. Data calculations identify damaged areas, and related information (e.g., location coordinates and type of damage) is output.

[0393] Step 3:

[0394] The server prioritizes repairs based on the location of the anomaly and generates a repair plan. Inputs include information on the damaged area, past maintenance history, and budget information. A prioritization algorithm is executed to formulate the plan. The output is the details of the repair plan (e.g., repair schedule and required resources).

[0395] Step 4:

[0396] The terminal receives repair plans and damage information from the server and displays them in an easy-to-understand format for the user. Inputs are repair plans and 3D visual data, while output is information displayed on a visualized interface screen. The user can then view detailed information through this screen.

[0397] Step 5:

[0398] Users provide feedback and opinions on repair plans and damage information through their devices. Input consists of the user's voice and facial expressions; emotional states are collected using the device's voice input and camera. The output is user feedback data.

[0399] Step 6:

[0400] The server analyzes user feedback and adjusts the repair plan. The input is user sentiment data, and natural language processing is used to determine whether the sentiment is positive or negative. The output is the adjusted repair plan, which is then sent back to the terminal.

[0401] (Application Example 2)

[0402] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0403] In infrastructure inspection and maintenance, identifying damaged areas and developing repair plans based on priorities is crucial. However, traditional systems have struggled to adjust repair plans to take into account user emotions and feedback. As a result, there was a risk that repair plans might not meet user expectations, leading to decreased satisfaction.

[0404] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0405] In this invention, the server includes means for collecting infrastructure information using information collection means, means for analyzing the information acquired by the information collection means to identify damaged areas, and means for analyzing the user's emotions using emotion analysis means and adjusting the repair plan based on the analysis results. This makes it possible to make adjustments based on the user's emotions and generate a repair plan that is highly satisfactory.

[0406] "Information gathering means" refers to devices and methods for collecting surface information on infrastructure.

[0407] "Information analysis means" refers to the technologies and processes used to analyze collected information and identify damaged areas in infrastructure.

[0408] "Priority setting means" refers to means that have the function of determining the priority of repairs for identified damaged areas.

[0409] "Repair plan generation means" refers to a means for formulating a repair plan based on established priorities.

[0410] "Emotional analysis methods" refer to technologies that analyze users' emotions and reflect them in infrastructure management and repair plans.

[0411] This invention aims to improve the efficiency of infrastructure management and enhance user satisfaction by constructing a system that combines information gathering using unmanned aerial vehicles with emotion analysis technology.

[0412] The server uses unmanned aerial vehicles (UAVs) as a means of information gathering to acquire surface information about the infrastructure. The acquired data is then analyzed by the server to identify damaged areas.

[0413] Next, the server prioritizes repairs based on the identified damaged areas. Then, a repair plan generation system formulates the optimal repair plan, taking into account past maintenance history and budget information.

[0414] Simultaneously, the device is equipped with emotion analysis capabilities that analyze the user's emotions from voice input and video. If the user views images of infrastructure or repair plans and expresses emotions such as anxiety or relief, that emotion data is sent to the server. Based on this data, the server adjusts the repair plan and provides feedback to the user.

[0415] As a concrete example, when inspecting a bridge, a server identifies damage and develops a repair plan based on images taken by an unmanned aerial vehicle. If users express feelings of relief through their smartphones or smart glasses, this information is reflected in the plan through emotion analysis, enabling more effective infrastructure management.

[0416] An example of a prompt sentence is, "How do you feel after seeing the results of this bridge inspection? Do you feel relieved, anxious, or have any other emotions?"

[0417] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0418] Step 1:

[0419] The server uses unmanned aerial vehicles (UAVs) to photograph the surface of infrastructure and collects the information as data. The input is image data from the UAVs, and the output is the collected raw image data. This data is then used for subsequent analysis.

[0420] Step 2:

[0421] The server analyzes the collected image data using an image analysis algorithm (e.g., image recognition software). The input is the image data from step 1, and the output is data identifying the damaged areas. This process identifies which parts are damaged and extracts specific damage information.

[0422] Step 3:

[0423] The server sets repair priorities based on the analyzed damage data. The input is the damage information obtained in step 2, and the output is the damage information with assigned priorities. This process considers the severity and urgency of the damage when determining priorities.

[0424] Step 4:

[0425] The server generates a repair plan using prioritized damage information. The input is the priority information from step 3, and the output is a specific repair plan. This plan incorporates past management history and budget constraints.

[0426] Step 5:

[0427] The device collects audio and video data from users as they check repair plans and infrastructure status, and analyzes it using an emotion analysis engine. The input is data related to the user's emotions, and the output is the analyzed emotion information. This allows for user feedback to be obtained.

[0428] Step 6:

[0429] The server adjusts the repair plan based on the emotional data obtained from the user. The input is the emotional information obtained in step 5 and the repair plan generated in step 4, and the output is the adjusted repair plan. This process provides a repair plan that meets the user's expectations.

[0430] Step 7:

[0431] The device presents the user with a revised repair plan and receives feedback. The input is the repair plan obtained in step 6, and the output is the user's final feedback. This allows for the incorporation of opinions that will ultimately increase user satisfaction.

[0432] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0433] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0434] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0435] [Third Embodiment]

[0436] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0437] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0438] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0439] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0440] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0441] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0442] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0443] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0444] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0445] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0446] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0447] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0448] This invention is a system that uses unmanned aerial vehicles to inspect infrastructure, employs AI technology to identify damaged areas, and determines repair priorities. In this system, servers, terminals, and users play crucial roles, each fulfilling a specific function to efficiently manage the infrastructure.

[0449] server

[0450] The server plays a central role in the system, handling the main parts of data processing and analysis. It receives large volumes of image and sensor data transmitted from unmanned aerial vehicles and stores them in a database. Image recognition technology is applied to the received data to identify damaged areas. The server then uses AI algorithms to prioritize repairs based on the severity and impact of the damage. Finally, based on the prioritized damage information, it develops a repair plan, considering historical operational data and budget information to generate an efficient and feasible repair schedule.

[0451] terminal

[0452] The terminal receives processing results from the server and provides them to the user. The terminal visually displays the received analysis results, allowing the user to intuitively understand the damaged areas. Specifically, it uses 3D models and highlighting to indicate areas of damage that require attention. The terminal also displays updated data sent from the server in real time, ensuring that the user always has the latest information.

[0453] User

[0454] The user is responsible for making the final decisions and judgments. The user reviews detailed information about the damage and the proposed repair plan through their device. The user can provide feedback on the repair plan to the server as needed and request a readjustment to accommodate new conditions or additional requests. The user also sets a long-term maintenance schedule and oversees the execution of the plan.

[0455] Specific example

[0456] For example, consider using this system in the regular inspection of a bridge. The server controls an unmanned aerial vehicle (UAV) to acquire surface information of the bridge using a high-resolution camera and infrared sensor. This makes it possible to collect detailed data on the entire bridge without human intervention. Next, the server analyzes the collected data to automatically identify deterioration and cracks in the bridge and assess their severity. The user receives this information via a terminal and is helped to determine the need for repairs. This process can significantly reduce the cost and time required for inspection and repair while ensuring safety.

[0457] The following describes the processing flow.

[0458] Step 1:

[0459] The server issues inspection commands to the unmanned aerial vehicle (UAV) based on the scheduled date and time. The UAV begins autonomous flight and patrols the infrastructure according to the set flight route.

[0460] Step 2:

[0461] The unmanned aerial vehicle (UAV) uses high-resolution cameras and infrared sensors to acquire surface information about the infrastructure and transmits it to a server in real time. The server stores the received data in storage.

[0462] Step 3:

[0463] The server activates an image recognition algorithm to automatically identify damaged areas from stored image data. This process utilizes edge detection and pattern recognition techniques. The server then records the identified damage information in a database.

[0464] Step 4:

[0465] The server uses an AI agent to assess the severity and impact of damage and perform risk analysis. Based on this, it determines repair priorities and assigns a score to each damaged area.

[0466] Step 5:

[0467] The server develops a repair plan based on priority information. The plan is optimized by considering past operational history, budget, and resource availability. Once the plan is developed, the server notifies the terminal.

[0468] Step 6:

[0469] The terminal receives the repair plan sent from the server and displays detailed information on the user interface. The user can visualize and review damage data and repair suggestions through the terminal.

[0470] Step 7:

[0471] Users can send feedback on the proposed repair plan to the server via their device and request revisions to the plan as needed. Based on the final decision, users can also set up a long-term maintenance schedule.

[0472] (Example 1)

[0473] Next, we will describe Example 1. 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."

[0474] Early detection of structural deterioration and damage, and the rapid and efficient development of repair plans, are critical challenges in large-scale infrastructure management. However, conventional methods have problems such as requiring significant time and cost for data collection and analysis, and insufficient prioritization of repairs. This can lead to overlooking high-priority damage or delays in repairs, potentially compromising safety.

[0475] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0476] In this invention, the server includes means for collecting surface data of a structure using data acquisition equipment, means for analyzing the surface data acquired by the data acquisition equipment to identify deterioration locations, means for setting repair priorities for the identified deterioration locations, means for evaluating the severity of deterioration using a generated AI model, and means for generating a repair plan based on the priorities and formulating an actionable schedule. This enables rapid and efficient data analysis and repair planning, resulting in improved infrastructure safety and reduced maintenance costs.

[0477] "Data acquisition equipment" refers to devices or equipment used to collect surface data of structures without human intervention.

[0478] "Structures" refer to infrastructure such as bridges and buildings.

[0479] "Surface data" refers to information that indicates the external condition of a structure, and includes high-resolution images and temperature information.

[0480] "Deterioration location" refers to a part of a structure where its performance or function has deteriorated.

[0481] "Repair priority" refers to a set of criteria used to determine which areas of identified deterioration should be prioritized for repair.

[0482] A "generative AI model" is an artificial intelligence system that uses machine learning algorithms to evaluate the severity of data degradation and support decision-making.

[0483] A "repair plan" is a plan that includes specific procedures and schedules for efficiently repairing deteriorated areas.

[0484] A "feasible schedule" is a realistic schedule for the start and completion of repair work, set considering available resources and conditions.

[0485] This invention is a system for efficiently detecting deterioration and damage to structures and formulating appropriate repair plans. This system is operated through the cooperation of a server, terminals, and users.

[0486] Server Role

[0487] The server controls data acquisition equipment to collect surface data from infrastructure structures. Unmanned aerial vehicles acquire surface information using devices such as high-resolution cameras and infrared sensors. The server receives this data and applies image recognition technology to identify the location of deterioration. Specifically, a generative AI model using machine learning models assesses the severity of the damage. Based on this information, the server sets repair priorities and develops a feasible repair plan considering past maintenance history and budget information.

[0488] Terminal role

[0489] The terminal receives analysis results and repair plans from the server and presents them visually to the user. This uses 3D models and interactive dashboards to allow the user to intuitively understand the data. The terminal also displays real-time updated information, always providing the user with the latest data.

[0490] User roles

[0491] The user reviews the information provided through the device and determines the need to repair the deteriorated areas. Furthermore, they can evaluate the appropriateness of the repair plan, send feedback to the server as needed, and request adjustments to the plan. This also allows the user to oversee the execution schedule.

[0492] Specific example

[0493] For example, when this system is used in bridge inspections, the server can collect bridge surface data via unmanned aerial vehicles, perform advanced AI analysis, and accurately detect damaged areas. Based on the information displayed on the terminal, users can efficiently schedule repair work while ensuring safety. This system reduces the costs and time associated with inspections and repairs.

[0494] Example of a prompt

[0495] "Please build an AI model that analyzes bridge inspection data to identify damaged areas. Furthermore, assess the severity of the damage and prioritize repairs."

[0496] The above is a description of the embodiments for carrying out the invention.

[0497] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0498] Step 1:

[0499] The server controls the unmanned aerial vehicle (UAV) and collects surface data from structures. The input is the location and measurement range of the target structure. Based on this, the server guides the UAV to the desired location and acquires surface images and temperature information using a high-resolution camera and infrared sensors. The output is this sensor dataset.

[0500] Step 2:

[0501] The server receives the collected data and stores it in a database. The input consists of image data and sensor data transmitted from unmanned aerial vehicles (UAVs). The server converts the data format and registers it in the database in a way that allows for efficient storage and management. The output is a set of data converted into an analyzable format.

[0502] Step 3:

[0503] The server uses image recognition technology to identify degradation locations in data stored in a database. The input consists of high-resolution images and temperature distribution data stored in the database. Using a generative AI model, the server automatically detects cracks and degradation within the images, identifying their location and characteristics. The output is the identified degradation locations and their attribute information.

[0504] Step 4:

[0505] The server prioritizes repairs for identified degradation locations. The input is attribute information, including a severity assessment of the degradation location. Utilizing a generative AI model, the server analyzes the impact of the damage and ranks the urgency of repairs. The output is a list of repair priorities.

[0506] Step 5:

[0507] The server develops a repair plan based on a priority list of repairs. Inputs include the prioritized list, maintenance history, and budget information. The server optimizes resource allocation and creates a feasible schedule, referencing historical data. Outputs include a detailed repair plan and its implementation schedule.

[0508] Step 6:

[0509] The terminal receives the repair plan and analysis results sent from the server and presents them to the user. The input consists of repair information and visualization data provided by the server. The terminal generates a 3D model and displays it through a dashboard in a format easily understandable to the user. The output is a display in a format that the user can verify.

[0510] Step 7:

[0511] The user reviews the repair plan and analysis results presented through the terminal and makes corrections or provides feedback as needed. Input is feedback information based on the user's knowledge and judgment. Based on this, the user can send requests to the server to instruct improvements or adjustments to the plan. Output is feedback data, including suggested improvements and additional requests.

[0512] (Application Example 1)

[0513] Next, we will explain Application Example 1. In the following explanation, 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."

[0514] In structural inspection work, traditional methods require manual labor, resulting in significant time and expense. Furthermore, identifying damaged areas and prioritizing repairs relies on subjective judgment, necessitating accuracy and efficiency. Additionally, the difficulty of on-site information sharing and immediate decision-making necessitates rapid decision-making.

[0515] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0516] In this invention, the server includes means for acquiring surface information of a structure using information gathering means, means for analyzing the surface information acquired by the information gathering means to identify defective areas, and means for setting repair priorities for the identified defective areas. This makes it possible to identify damaged areas quickly and accurately compared to conventional manual work, and to formulate a repair plan based on priorities. In addition, information can be shared smoothly on-site using a portable information terminal or a head-mounted display device, enabling rapid decision-making.

[0517] "Information gathering means" refers to a device or method for acquiring surface information of a structure.

[0518] A "structure" refers to buildings and facilities used to form infrastructure.

[0519] "Surface information" refers to data about the characteristics and conditions present on the exterior of a structure.

[0520] "Analysis" is the process of evaluating acquired data and extracting specific information.

[0521] A "defective area" refers to a part of a structure that shows damage or abnormality.

[0522] "Priority" refers to the order in which tasks are performed based on the importance or urgency of the repairs.

[0523] A "repair plan" is a plan that outlines the specific procedures and schedule for the repair work that should be carried out to address identified defects.

[0524] A "portable information terminal" is a portable electronic device that enables the display and operation of information.

[0525] A "head-mounted display device" is a device that displays images and information when worn on the user's head.

[0526] "Real-time" means that data acquisition and processing are performed in real time, and the results are reflected immediately.

[0527] This invention provides a system that supports the efficient inspection and repair planning of structures. The system uses an unmanned aerial vehicle as an information gathering means to acquire surface information of infrastructure structures. A server receives this information and uses image processing technology to identify defective areas on the structure. Furthermore, based on the analysis results, the server uses an AI algorithm to determine the repair priorities and generate a repair plan.

[0528] In this system, the server utilizes a high-performance computing environment and AI analysis software (e.g., TensorFlow, OpenCV). Data is stored in a cloud-based database (e.g., AWS, Google Cloud), and the server processes it. The processing results are provided to the user via a terminal.

[0529] The terminal is either a portable information terminal or a head-mounted display device, allowing users to intuitively and visually confirm the analysis results. This enables rapid decision-making on-site. Based on this information, users can review the repair work plan and send feedback to the server as needed.

[0530] As a concrete example, in the case of inspecting an old bridge, an unmanned aerial vehicle (UAV) collects surface information of the bridge, and this data is analyzed on a server. Users can then check the details of the damage and the priority of repairs via their mobile devices, and apply this information to developing on-site repair plans.

[0531] An example of a prompt for the generated AI model would be: "In infrastructure inspection work on an old bridge, analyze the damage data acquired by the drone and display the repair priority order in real time on a smart device."

[0532] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0533] Step 1:

[0534] The server receives surface information of structures from information gathering devices mounted on unmanned aerial vehicles. The input consists of high-resolution image data and sensor data. The server stores this data in cloud storage and prepares it for subsequent analysis.

[0535] Step 2:

[0536] The server applies image processing techniques to the received image data. To detect faulty areas, AI analysis software (e.g., TensorFlow, OpenCV) is used to process the data, thereby identifying the damaged areas. The output includes information such as the coordinates and size of the identified damaged areas.

[0537] Step 3:

[0538] The server analyzes data on identified damaged areas and uses an AI algorithm to prioritize repairs. Inputs include the type, severity, and location of the damage. The output is a prioritized list, which is used to create a repair plan.

[0539] Step 4:

[0540] The server generates a repair plan. This plan is optimized based on a priority list, taking into account past repair history and cost information. The output is a plan document that includes specific repair procedures and a timeline.

[0541] Step 5:

[0542] The terminal receives repair plan information sent from the server and displays it visually to the user. The input is the generated repair plan. The terminal processes and displays the information using a 3D model or highlighting to make it easy for the user to understand.

[0543] Step 6:

[0544] The user reviews the repair plan displayed on their device and sends feedback to the server to make adjustments or modifications as needed. Inputs include the presented repair plan and on-site findings. Outputs are the revised repair plan, with the updated information reflecting the new conditions, returned to the server.

[0545] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0546] This invention aims to improve the user experience by combining an infrastructure inspection system based on unmanned aerial vehicles and AI technology with an emotion engine that recognizes user emotions. The system components and their operation are described in detail below.

[0547] server

[0548] The server acts as the central control unit of the system, handling data processing and analysis. First, the server acquires surface information of the infrastructure transmitted from the unmanned aerial vehicles and identifies damaged areas using advanced image analysis technology. Subsequently, it generates a repair plan that prioritizes based on the severity of the damage and takes into account past maintenance history and budget information. Furthermore, the server works in conjunction with the emotion engine to analyze emotional data obtained from user feedback and adjust the repair plan as needed.

[0549] terminal

[0550] The terminal receives information from the server and functions as a user interface. To provide users with an intuitive user experience, the terminal uses 3D models and visualized damage information for easy understanding. The terminal is equipped with voice input and camera functions, allowing the emotion engine to accurately analyze the user's emotional state. Emotional data is used to understand user opinions and concerns regarding the repair plan and to provide personalized feedback.

[0551] User

[0552] Users interact with the infrastructure management system via their device. Through the device's UI, users can view damage information and review repair plans. Furthermore, based on their emotional state analyzed by the emotion engine, users can receive more appropriate feedback from the system. Users can send their feedback to the server and request improvements or adjustments to the plan.

[0553] Specific example

[0554] For example, consider the case of conducting a regular inspection of a dam. The server uses an unmanned aerial vehicle to conduct a comprehensive inspection of the dam and identifies damaged areas by analyzing image data. The terminal intuitively displays this damage information to the user and proposes a repair plan. The user uses the terminal to express their emotions and opinions through voice or visual means. An emotion engine analyzes the user's emotions, and the server adjusts the repair plan based on the results, improving user satisfaction. This enables safe and efficient infrastructure management.

[0555] The following describes the processing flow.

[0556] Step 1:

[0557] The server issues inspection commands to the unmanned aerial vehicle (UAV), activating a program that autonomously patrols the area around the infrastructure according to a set flight route. The UAV collects surface information of the infrastructure using high-resolution cameras and infrared sensors and transmits it to the server in real time.

[0558] Step 2:

[0559] The server analyzes the received image data and automatically identifies damaged areas using edge detection and pattern matching algorithms. The damage information obtained during this process is stored in a database for later analysis.

[0560] Step 3:

[0561] The server uses an AI agent to assess the severity and impact of damage and prioritize it. Damage that has been prioritized is then compiled into a repair plan by cross-referencing it with past management history and budget information.

[0562] Step 4:

[0563] The terminal receives the repair plan details sent from the server and displays them to the user through the interface. The terminal visualizes the damaged areas using a 3D model, making it easy for the user to understand intuitively.

[0564] Step 5:

[0565] Users can view repair plans and damage information via their device and input feedback using the UI. User feedback is recorded in voice or text. Additionally, a built-in emotion engine analyzes the user's voice and nonverbal facial expressions to identify their emotions.

[0566] Step 6:

[0567] The server processes emotional data collected from the terminal and analyzes the user's emotional state. Based on this information, it readjusts the repair plan as needed, providing a plan that better aligns with the user's wishes.

[0568] Step 7:

[0569] Once the user agrees to the final repair plan, the server will finalize the long-term maintenance schedule and suggest potential dates for the next inspection or repair work. This suggestion will be communicated to the user via the terminal, helping to improve management efficiency.

[0570] (Example 2)

[0571] Next, we will describe Example 2. 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."

[0572] In modern infrastructure management, maintaining efficiency and safety requires the rapid and accurate generation of inspection and repair plans. However, conventional technologies, while capable of identifying abnormalities and prioritizing repairs, lack the flexibility to adjust plans while considering human emotions, potentially leading to decreased user satisfaction. In particular, there is a challenge in optimizing repair plans in real time by reflecting user feedback.

[0573] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0574] In this invention, the server includes means for collecting surface information of equipment using an unmanned mobile device, means for analyzing the surface information acquired by the unmanned mobile device using a processing device to identify abnormal areas, and means for evaluating the emotional state of a person. This enables flexible adjustment of repair plans in real time, improving user satisfaction and allowing for efficient infrastructure management.

[0575] An "unmanned mobile device" is a mechanical device that moves autonomously or remotely and performs a specified task, and in particular includes aerospace machines.

[0576] "Surface information of equipment" refers to data about the appearance and condition of structures and equipment that are acquired in a visible or detectable form.

[0577] A "processing device" is a computer or machine used to calculate, analyze, and generate results from data.

[0578] An "abnormal area" refers to a location or part that does not exhibit the intended state or function as designed, and which requires repair or replacement.

[0579] "Priority" refers to the criteria used to determine the order and importance of multiple tasks or options.

[0580] "Maintenance records" refer to data and documents related to past maintenance work and its history.

[0581] "Resource utilization information" refers to information regarding the time, funds, and human resources required to carry out restoration and maintenance activities.

[0582] A "repair plan" is a set of systematic means and procedures for repairing identified anomalies and maintaining the functionality of a system or structure.

[0583] "Emotional state" refers to a state that describes a person's psychological and emotional responses, including subjective evaluations in feedback.

[0584] This invention is a system that utilizes unmanned mobile devices and artificial intelligence technology to perform advanced infrastructure management based on diverse data. Specific embodiments are described below.

[0585] System Configuration

[0586] The system consists of a server, terminals, and users. The server plays a central role in data collection, analysis, and plan generation and adjustment. Terminals provide visualized information to users and function as an interface. Users access the system through terminals and provide data review and feedback.

[0587] Hardware and software

[0588] The server uses a high-performance computer as its processing unit. For data analysis, Python-based libraries such as OpenCV and TensorFlow are used for object detection and identification of anomalies.

[0589] Unmanned mobile devices are designed as aerospace machines and serve as information acquisition devices. Conventional control software is used for autonomous flight and data acquisition.

[0590] The device uses 3D visualization software (e.g., Unity or Blender) to provide information in an easy-to-understand format for the user. Additionally, Google Cloud Speech-to-Text is used for speech recognition, and OpenCV and natural language processing models are employed for sentiment analysis.

[0591] How to use

[0592] The server controls the unmanned mobile device and efficiently collects surface information about the equipment. Through a terminal, the user can check the specific location and extent of damage, and provides emotionally-based feedback through voice and facial expressions. Based on the emotional data obtained from the user, the server uses an AI model to optimize the repair plan and modify the plan as needed to improve user satisfaction.

[0593] Specific example

[0594] For example, when conducting a regular bridge inspection, the server activates an unmanned mobile device to acquire image data of the entire bridge and identify damaged areas. This information is then provided to the user via a terminal, and the user provides feedback using prompt messages such as, "This part of the bridge needs urgent repair." The server then uses this feedback to adjust the repair plan, achieving efficient infrastructure management.

[0595] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0596] Step 1:

[0597] The server controls the unmanned mobile device and collects image data of specified infrastructure. The input consists of flight route information and infrastructure location information, which the unmanned mobile device uses to automatically take images. The resulting output is an image data stream, which is transmitted to the server in real time.

[0598] Step 2:

[0599] The server analyzes the collected image data. The input is image data, and object detection algorithms are executed using OpenCV or TensorFlow. Data calculations identify damaged areas, and related information (e.g., location coordinates and type of damage) is output.

[0600] Step 3:

[0601] The server prioritizes repairs based on the location of the anomaly and generates a repair plan. Inputs include information on the damaged area, past maintenance history, and budget information. A prioritization algorithm is executed to formulate the plan. The output is the details of the repair plan (e.g., repair schedule and required resources).

[0602] Step 4:

[0603] The terminal receives repair plans and damage information from the server and displays them in an easy-to-understand format for the user. Inputs are repair plans and 3D visual data, while output is information displayed on a visualized interface screen. The user can then view detailed information through this screen.

[0604] Step 5:

[0605] Users provide feedback and opinions on repair plans and damage information through their devices. Input consists of the user's voice and facial expressions; emotional states are collected using the device's voice input and camera. The output is user feedback data.

[0606] Step 6:

[0607] The server analyzes user feedback and adjusts the repair plan. The input is user sentiment data, and natural language processing is used to determine whether the sentiment is positive or negative. The output is the adjusted repair plan, which is then sent back to the terminal.

[0608] (Application Example 2)

[0609] Next, we will explain application example 2. In the following explanation, 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."

[0610] In infrastructure inspection and maintenance, identifying damaged areas and developing repair plans based on priorities is crucial. However, traditional systems have struggled to adjust repair plans to take into account user emotions and feedback. As a result, there was a risk that repair plans might not meet user expectations, leading to decreased satisfaction.

[0611] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0612] In this invention, the server includes means for collecting infrastructure information using information collection means, means for analyzing the information acquired by the information collection means to identify damaged areas, and means for analyzing the user's emotions using emotion analysis means and adjusting the repair plan based on the analysis results. This makes it possible to make adjustments based on the user's emotions and generate a repair plan that is highly satisfactory.

[0613] "Information gathering means" refers to devices and methods for collecting surface information on infrastructure.

[0614] "Information analysis means" refers to the technologies and processes used to analyze collected information and identify damaged areas in infrastructure.

[0615] "Priority setting means" refers to means that have the function of determining the priority of repairs for identified damaged areas.

[0616] "Repair plan generation means" refers to a means for formulating a repair plan based on established priorities.

[0617] "Emotional analysis methods" refer to technologies that analyze users' emotions and reflect them in infrastructure management and repair plans.

[0618] This invention aims to improve the efficiency of infrastructure management and enhance user satisfaction by constructing a system that combines information gathering using unmanned aerial vehicles with emotion analysis technology.

[0619] The server uses unmanned aerial vehicles (UAVs) as a means of information gathering to acquire surface information about the infrastructure. The acquired data is then analyzed by the server to identify damaged areas.

[0620] Next, the server prioritizes repairs based on the identified damaged areas. Then, a repair plan generation system formulates the optimal repair plan, taking into account past maintenance history and budget information.

[0621] Simultaneously, the device is equipped with emotion analysis capabilities that analyze the user's emotions from voice input and video. If the user views images of infrastructure or repair plans and expresses emotions such as anxiety or relief, that emotion data is sent to the server. Based on this data, the server adjusts the repair plan and provides feedback to the user.

[0622] As a concrete example, when inspecting a bridge, a server identifies damage and develops a repair plan based on images taken by an unmanned aerial vehicle. If users express feelings of relief through their smartphones or smart glasses, this information is reflected in the plan through emotion analysis, enabling more effective infrastructure management.

[0623] An example of a prompt sentence is, "How do you feel after seeing the results of this bridge inspection? Do you feel relieved, anxious, or have any other emotions?"

[0624] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0625] Step 1:

[0626] The server uses unmanned aerial vehicles (UAVs) to photograph the surface of infrastructure and collects the information as data. The input is image data from the UAVs, and the output is the collected raw image data. This data is then used for subsequent analysis.

[0627] Step 2:

[0628] The server analyzes the collected image data using an image analysis algorithm (e.g., image recognition software). The input is the image data from step 1, and the output is data identifying the damaged areas. This process identifies which parts are damaged and extracts specific damage information.

[0629] Step 3:

[0630] The server sets repair priorities based on the analyzed damage data. The input is the damage information obtained in step 2, and the output is the damage information with assigned priorities. This process considers the severity and urgency of the damage when determining priorities.

[0631] Step 4:

[0632] The server generates a repair plan using prioritized damage information. The input is the priority information from step 3, and the output is a specific repair plan. This plan incorporates past management history and budget constraints.

[0633] Step 5:

[0634] The device collects audio and video data from users as they check repair plans and infrastructure status, and analyzes it using an emotion analysis engine. The input is data related to the user's emotions, and the output is the analyzed emotion information. This allows for user feedback to be obtained.

[0635] Step 6:

[0636] The server adjusts the repair plan based on the emotional data obtained from the user. The input is the emotional information obtained in step 5 and the repair plan generated in step 4, and the output is the adjusted repair plan. This process provides a repair plan that meets the user's expectations.

[0637] Step 7:

[0638] The device presents the user with a revised repair plan and receives feedback. The input is the repair plan obtained in step 6, and the output is the user's final feedback. This allows for the incorporation of opinions that will ultimately increase user satisfaction.

[0639] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0640] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0641] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0642] [Fourth Embodiment]

[0643] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0644] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0645] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0646] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0647] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0648] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0649] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0650] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0651] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0652] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0653] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0654] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0655] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0656] This invention is a system that uses unmanned aerial vehicles to inspect infrastructure, employs AI technology to identify damaged areas, and determines repair priorities. In this system, servers, terminals, and users play crucial roles, each fulfilling a specific function to efficiently manage the infrastructure.

[0657] server

[0658] The server plays a central role in the system, handling the main parts of data processing and analysis. It receives large volumes of image and sensor data transmitted from unmanned aerial vehicles and stores them in a database. Image recognition technology is applied to the received data to identify damaged areas. The server then uses AI algorithms to prioritize repairs based on the severity and impact of the damage. Finally, based on the prioritized damage information, it develops a repair plan, considering historical operational data and budget information to generate an efficient and feasible repair schedule.

[0659] terminal

[0660] The terminal receives processing results from the server and provides them to the user. The terminal visually displays the received analysis results, allowing the user to intuitively understand the damaged areas. Specifically, it uses 3D models and highlighting to indicate areas of damage that require attention. The terminal also displays updated data sent from the server in real time, ensuring that the user always has the latest information.

[0661] User

[0662] The user is responsible for making the final decisions and judgments. The user reviews detailed information about the damage and the proposed repair plan through their device. The user can provide feedback on the repair plan to the server as needed and request a readjustment to accommodate new conditions or additional requests. The user also sets a long-term maintenance schedule and oversees the execution of the plan.

[0663] Specific example

[0664] For example, consider using this system in the regular inspection of a bridge. The server controls an unmanned aerial vehicle (UAV) to acquire surface information of the bridge using a high-resolution camera and infrared sensor. This makes it possible to collect detailed data on the entire bridge without human intervention. Next, the server analyzes the collected data to automatically identify deterioration and cracks in the bridge and assess their severity. The user receives this information via a terminal and is helped to determine the need for repairs. This process can significantly reduce the cost and time required for inspection and repair while ensuring safety.

[0665] The following describes the processing flow.

[0666] Step 1:

[0667] The server issues inspection commands to the unmanned aerial vehicle (UAV) based on the scheduled date and time. The UAV begins autonomous flight and patrols the infrastructure according to the set flight route.

[0668] Step 2:

[0669] The unmanned aerial vehicle (UAV) uses high-resolution cameras and infrared sensors to acquire surface information about the infrastructure and transmits it to a server in real time. The server stores the received data in storage.

[0670] Step 3:

[0671] The server activates an image recognition algorithm to automatically identify damaged areas from stored image data. This process utilizes edge detection and pattern recognition techniques. The server then records the identified damage information in a database.

[0672] Step 4:

[0673] The server uses an AI agent to assess the severity and impact of damage and perform risk analysis. Based on this, it determines repair priorities and assigns a score to each damaged area.

[0674] Step 5:

[0675] The server develops a repair plan based on priority information. The plan is optimized by considering past operational history, budget, and resource availability. Once the plan is developed, the server notifies the terminal.

[0676] Step 6:

[0677] The terminal receives the repair plan sent from the server and displays detailed information on the user interface. The user can visualize and review damage data and repair suggestions through the terminal.

[0678] Step 7:

[0679] Users can send feedback on the proposed repair plan to the server via their device and request revisions to the plan as needed. Based on the final decision, users can also set up a long-term maintenance schedule.

[0680] (Example 1)

[0681] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0682] Early detection of structural deterioration and damage, and the rapid and efficient development of repair plans, are critical challenges in large-scale infrastructure management. However, conventional methods have problems such as requiring significant time and cost for data collection and analysis, and insufficient prioritization of repairs. This can lead to overlooking high-priority damage or delays in repairs, potentially compromising safety.

[0683] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0684] In this invention, the server includes means for collecting surface data of a structure using data acquisition equipment, means for analyzing the surface data acquired by the data acquisition equipment to identify deterioration locations, means for setting repair priorities for the identified deterioration locations, means for evaluating the severity of deterioration using a generated AI model, and means for generating a repair plan based on the priorities and formulating an actionable schedule. This enables rapid and efficient data analysis and repair planning, resulting in improved infrastructure safety and reduced maintenance costs.

[0685] "Data acquisition equipment" refers to devices or equipment used to collect surface data of structures without human intervention.

[0686] "Structures" refer to infrastructure such as bridges and buildings.

[0687] "Surface data" refers to information that indicates the external condition of a structure, and includes high-resolution images and temperature information.

[0688] "Deterioration location" refers to a part of a structure where its performance or function has deteriorated.

[0689] "Repair priority" refers to a set of criteria used to determine which areas of identified deterioration should be prioritized for repair.

[0690] A "generative AI model" is an artificial intelligence system that uses machine learning algorithms to evaluate the severity of data degradation and support decision-making.

[0691] A "repair plan" is a plan that includes specific procedures and schedules for efficiently repairing deteriorated areas.

[0692] A "feasible schedule" is a realistic schedule for the start and completion of repair work, set considering available resources and conditions.

[0693] This invention is a system for efficiently detecting deterioration and damage to structures and formulating appropriate repair plans. This system is operated through the cooperation of a server, terminals, and users.

[0694] Server Role

[0695] The server controls data acquisition equipment to collect surface data from infrastructure structures. Unmanned aerial vehicles acquire surface information using devices such as high-resolution cameras and infrared sensors. The server receives this data and applies image recognition technology to identify the location of deterioration. Specifically, a generative AI model using machine learning models assesses the severity of the damage. Based on this information, the server sets repair priorities and develops a feasible repair plan considering past maintenance history and budget information.

[0696] Terminal role

[0697] The terminal receives analysis results and repair plans from the server and presents them visually to the user. This uses 3D models and interactive dashboards to allow the user to intuitively understand the data. The terminal also displays real-time updated information, always providing the user with the latest data.

[0698] User roles

[0699] The user reviews the information provided through the device and determines the need to repair the deteriorated areas. Furthermore, they can evaluate the appropriateness of the repair plan, send feedback to the server as needed, and request adjustments to the plan. This also allows the user to oversee the execution schedule.

[0700] Specific example

[0701] For example, when this system is used in bridge inspections, the server can collect bridge surface data via unmanned aerial vehicles, perform advanced AI analysis, and accurately detect damaged areas. Based on the information displayed on the terminal, users can efficiently schedule repair work while ensuring safety. This system reduces the costs and time associated with inspections and repairs.

[0702] Example of a prompt

[0703] "Please build an AI model that analyzes bridge inspection data to identify damaged areas. Furthermore, assess the severity of the damage and prioritize repairs."

[0704] The above is a description of the embodiments for carrying out the invention.

[0705] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0706] Step 1:

[0707] The server controls the unmanned aerial vehicle (UAV) and collects surface data from structures. The input is the location and measurement range of the target structure. Based on this, the server guides the UAV to the desired location and acquires surface images and temperature information using a high-resolution camera and infrared sensors. The output is this sensor dataset.

[0708] Step 2:

[0709] The server receives the collected data and stores it in a database. The input consists of image data and sensor data transmitted from unmanned aerial vehicles (UAVs). The server converts the data format and registers it in the database in a way that allows for efficient storage and management. The output is a set of data converted into an analyzable format.

[0710] Step 3:

[0711] The server uses image recognition technology to identify degradation locations in data stored in a database. The input consists of high-resolution images and temperature distribution data stored in the database. Using a generative AI model, the server automatically detects cracks and degradation within the images, identifying their location and characteristics. The output is the identified degradation locations and their attribute information.

[0712] Step 4:

[0713] The server prioritizes repairs for identified degradation locations. The input is attribute information, including a severity assessment of the degradation location. Utilizing a generative AI model, the server analyzes the impact of the damage and ranks the urgency of repairs. The output is a list of repair priorities.

[0714] Step 5:

[0715] The server develops a repair plan based on a priority list of repairs. Inputs include the prioritized list, maintenance history, and budget information. The server optimizes resource allocation and creates a feasible schedule, referencing historical data. Outputs include a detailed repair plan and its implementation schedule.

[0716] Step 6:

[0717] The terminal receives the repair plan and analysis results sent from the server and presents them to the user. The input consists of repair information and visualization data provided by the server. The terminal generates a 3D model and displays it through a dashboard in a format easily understandable to the user. The output is a display in a format that the user can verify.

[0718] Step 7:

[0719] The user reviews the repair plan and analysis results presented through the terminal and makes corrections or provides feedback as needed. Input is feedback information based on the user's knowledge and judgment. Based on this, the user can send requests to the server to instruct improvements or adjustments to the plan. Output is feedback data, including suggested improvements and additional requests.

[0720] (Application Example 1)

[0721] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0722] In structural inspection work, traditional methods require manual labor, resulting in significant time and expense. Furthermore, identifying damaged areas and prioritizing repairs relies on subjective judgment, necessitating accuracy and efficiency. Additionally, the difficulty of on-site information sharing and immediate decision-making necessitates rapid decision-making.

[0723] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0724] In this invention, the server includes means for acquiring surface information of a structure using information gathering means, means for analyzing the surface information acquired by the information gathering means to identify defective areas, and means for setting repair priorities for the identified defective areas. This makes it possible to identify damaged areas quickly and accurately compared to conventional manual work, and to formulate a repair plan based on priorities. In addition, information can be shared smoothly on-site using a portable information terminal or a head-mounted display device, enabling rapid decision-making.

[0725] "Information gathering means" refers to a device or method for acquiring surface information of a structure.

[0726] A "structure" refers to buildings and facilities used to form infrastructure.

[0727] "Surface information" refers to data about the characteristics and conditions present on the exterior of a structure.

[0728] "Analysis" is the process of evaluating acquired data and extracting specific information.

[0729] A "defective area" refers to a part of a structure that shows damage or abnormality.

[0730] "Priority" refers to the order in which tasks are performed based on the importance or urgency of the repairs.

[0731] A "repair plan" is a plan that outlines the specific procedures and schedule for the repair work that should be carried out to address identified defects.

[0732] A "portable information terminal" is a portable electronic device that enables the display and operation of information.

[0733] A "head-mounted display device" is a device that displays images and information when worn on the user's head.

[0734] "Real-time" means that data acquisition and processing are performed in real time, and the results are reflected immediately.

[0735] This invention provides a system that supports the efficient inspection and repair planning of structures. The system uses an unmanned aerial vehicle as an information gathering means to acquire surface information of infrastructure structures. A server receives this information and uses image processing technology to identify defective areas on the structure. Furthermore, based on the analysis results, the server uses an AI algorithm to determine the repair priorities and generate a repair plan.

[0736] In this system, the server utilizes a high-performance computing environment and AI analysis software (e.g., TensorFlow, OpenCV). Data is stored in a cloud-based database (e.g., AWS, Google Cloud), and the server processes it. The processing results are provided to the user via a terminal.

[0737] The terminal is either a portable information terminal or a head-mounted display device, allowing users to intuitively and visually confirm the analysis results. This enables rapid decision-making on-site. Based on this information, users can review the repair work plan and send feedback to the server as needed.

[0738] As a concrete example, in the case of inspecting an old bridge, an unmanned aerial vehicle (UAV) collects surface information of the bridge, and this data is analyzed on a server. Users can then check the details of the damage and the priority of repairs via their mobile devices, and apply this information to developing on-site repair plans.

[0739] An example of a prompt for the generated AI model would be: "In infrastructure inspection work on an old bridge, analyze the damage data acquired by the drone and display the repair priority order in real time on a smart device."

[0740] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0741] Step 1:

[0742] The server receives surface information of structures from information gathering devices mounted on unmanned aerial vehicles. The input consists of high-resolution image data and sensor data. The server stores this data in cloud storage and prepares it for subsequent analysis.

[0743] Step 2:

[0744] The server applies image processing techniques to the received image data. To detect faulty areas, AI analysis software (e.g., TensorFlow, OpenCV) is used to process the data, thereby identifying the damaged areas. The output includes information such as the coordinates and size of the identified damaged areas.

[0745] Step 3:

[0746] The server analyzes data on identified damaged areas and uses an AI algorithm to prioritize repairs. Inputs include the type, severity, and location of the damage. The output is a prioritized list, which is used to create a repair plan.

[0747] Step 4:

[0748] The server generates a repair plan. This plan is optimized based on a priority list, taking into account past repair history and cost information. The output is a plan document that includes specific repair procedures and a timeline.

[0749] Step 5:

[0750] The terminal receives repair plan information sent from the server and displays it visually to the user. The input is the generated repair plan. The terminal processes and displays the information using a 3D model or highlighting to make it easy for the user to understand.

[0751] Step 6:

[0752] The user reviews the repair plan displayed on their device and sends feedback to the server to make adjustments or modifications as needed. Inputs include the presented repair plan and on-site findings. Outputs are the revised repair plan, with the updated information reflecting the new conditions, returned to the server.

[0753] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0754] This invention aims to improve the user experience by combining an infrastructure inspection system based on unmanned aerial vehicles and AI technology with an emotion engine that recognizes user emotions. The system components and their operation are described in detail below.

[0755] server

[0756] The server acts as the central control unit of the system, handling data processing and analysis. First, the server acquires surface information of the infrastructure transmitted from the unmanned aerial vehicles and identifies damaged areas using advanced image analysis technology. Subsequently, it generates a repair plan that prioritizes based on the severity of the damage and takes into account past maintenance history and budget information. Furthermore, the server works in conjunction with the emotion engine to analyze emotional data obtained from user feedback and adjust the repair plan as needed.

[0757] terminal

[0758] The terminal receives information from the server and functions as a user interface. To provide users with an intuitive user experience, the terminal uses 3D models and visualized damage information for easy understanding. The terminal is equipped with voice input and camera functions, allowing the emotion engine to accurately analyze the user's emotional state. Emotional data is used to understand user opinions and concerns regarding the repair plan and to provide personalized feedback.

[0759] User

[0760] Users interact with the infrastructure management system via their device. Through the device's UI, users can view damage information and review repair plans. Furthermore, based on their emotional state analyzed by the emotion engine, users can receive more appropriate feedback from the system. Users can send their feedback to the server and request improvements or adjustments to the plan.

[0761] Specific example

[0762] For example, consider the case of conducting a regular inspection of a dam. The server uses an unmanned aerial vehicle to conduct a comprehensive inspection of the dam and identifies damaged areas by analyzing image data. The terminal intuitively displays this damage information to the user and proposes a repair plan. The user uses the terminal to express their emotions and opinions through voice or visual means. An emotion engine analyzes the user's emotions, and the server adjusts the repair plan based on the results, improving user satisfaction. This enables safe and efficient infrastructure management.

[0763] The following describes the processing flow.

[0764] Step 1:

[0765] The server issues inspection commands to the unmanned aerial vehicle (UAV), activating a program that autonomously patrols the area around the infrastructure according to a set flight route. The UAV collects surface information of the infrastructure using high-resolution cameras and infrared sensors and transmits it to the server in real time.

[0766] Step 2:

[0767] The server analyzes the received image data and automatically identifies damaged areas using edge detection and pattern matching algorithms. The damage information obtained during this process is stored in a database for later analysis.

[0768] Step 3:

[0769] The server uses an AI agent to assess the severity and impact of damage and prioritize it. Damage that has been prioritized is then compiled into a repair plan by cross-referencing it with past management history and budget information.

[0770] Step 4:

[0771] The terminal receives the repair plan details sent from the server and displays them to the user through the interface. The terminal visualizes the damaged areas using a 3D model, making it easy for the user to understand intuitively.

[0772] Step 5:

[0773] Users can view repair plans and damage information via their device and input feedback using the UI. User feedback is recorded in voice or text. Additionally, a built-in emotion engine analyzes the user's voice and nonverbal facial expressions to identify their emotions.

[0774] Step 6:

[0775] The server processes emotional data collected from the terminal and analyzes the user's emotional state. Based on this information, it readjusts the repair plan as needed, providing a plan that better aligns with the user's wishes.

[0776] Step 7:

[0777] Once the user agrees to the final repair plan, the server will finalize the long-term maintenance schedule and suggest potential dates for the next inspection or repair work. This suggestion will be communicated to the user via the terminal, helping to improve management efficiency.

[0778] (Example 2)

[0779] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0780] In modern infrastructure management, maintaining efficiency and safety requires the rapid and accurate generation of inspection and repair plans. However, conventional technologies, while capable of identifying abnormalities and prioritizing repairs, lack the flexibility to adjust plans while considering human emotions, potentially leading to decreased user satisfaction. In particular, there is a challenge in optimizing repair plans in real time by reflecting user feedback.

[0781] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0782] In this invention, the server includes means for collecting surface information of equipment using an unmanned mobile device, means for analyzing the surface information acquired by the unmanned mobile device using a processing device to identify abnormal areas, and means for evaluating the emotional state of a person. This enables flexible adjustment of repair plans in real time, improving user satisfaction and allowing for efficient infrastructure management.

[0783] An "unmanned mobile device" is a mechanical device that moves autonomously or remotely and performs a specified task, and in particular includes aerospace machines.

[0784] "Surface information of equipment" refers to data about the appearance and condition of structures and equipment that are acquired in a visible or detectable form.

[0785] A "processing device" is a computer or machine used to calculate, analyze, and generate results from data.

[0786] An "abnormal area" refers to a location or part that does not exhibit the intended state or function as designed, and which requires repair or replacement.

[0787] "Priority" refers to the criteria used to determine the order and importance of multiple tasks or options.

[0788] "Maintenance records" refer to data and documents related to past maintenance work and its history.

[0789] "Resource utilization information" refers to information regarding the time, funds, and human resources required to carry out restoration and maintenance activities.

[0790] A "repair plan" is a set of systematic means and procedures for repairing identified anomalies and maintaining the functionality of a system or structure.

[0791] "Emotional state" refers to a state that describes a person's psychological and emotional responses, including subjective evaluations in feedback.

[0792] This invention is a system that utilizes unmanned mobile devices and artificial intelligence technology to perform advanced infrastructure management based on diverse data. Specific embodiments are described below.

[0793] System Configuration

[0794] The system consists of a server, terminals, and users. The server plays a central role in data collection, analysis, and plan generation and adjustment. Terminals provide visualized information to users and function as an interface. Users access the system through terminals and provide data review and feedback.

[0795] Hardware and software

[0796] The server uses a high-performance computer as its processing unit. For data analysis, Python-based libraries such as OpenCV and TensorFlow are used for object detection and identification of anomalies.

[0797] Unmanned mobile devices are designed as aerospace machines and serve as information acquisition devices. Conventional control software is used for autonomous flight and data acquisition.

[0798] The device uses 3D visualization software (e.g., Unity or Blender) to provide information in an easy-to-understand format for the user. Additionally, Google Cloud Speech-to-Text is used for speech recognition, and OpenCV and natural language processing models are employed for sentiment analysis.

[0799] How to use

[0800] The server controls the unmanned mobile device and efficiently collects surface information about the equipment. Through a terminal, the user can check the specific location and extent of damage, and provides emotionally-based feedback through voice and facial expressions. Based on the emotional data obtained from the user, the server uses an AI model to optimize the repair plan and modify the plan as needed to improve user satisfaction.

[0801] Specific example

[0802] For example, when conducting a regular bridge inspection, the server activates an unmanned mobile device to acquire image data of the entire bridge and identify damaged areas. This information is then provided to the user via a terminal, and the user provides feedback using prompt messages such as, "This part of the bridge needs urgent repair." The server then uses this feedback to adjust the repair plan, achieving efficient infrastructure management.

[0803] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0804] Step 1:

[0805] The server controls the unmanned mobile device and collects image data of specified infrastructure. The input consists of flight route information and infrastructure location information, which the unmanned mobile device uses to automatically take images. The resulting output is an image data stream, which is transmitted to the server in real time.

[0806] Step 2:

[0807] The server analyzes the collected image data. The input is image data, and object detection algorithms are executed using OpenCV or TensorFlow. Data calculations identify damaged areas, and related information (e.g., location coordinates and type of damage) is output.

[0808] Step 3:

[0809] The server prioritizes repairs based on the location of the anomaly and generates a repair plan. Inputs include information on the damaged area, past maintenance history, and budget information. A prioritization algorithm is executed to formulate the plan. The output is the details of the repair plan (e.g., repair schedule and required resources).

[0810] Step 4:

[0811] The terminal receives repair plans and damage information from the server and displays them in an easy-to-understand format for the user. Inputs are repair plans and 3D visual data, while output is information displayed on a visualized interface screen. The user can then view detailed information through this screen.

[0812] Step 5:

[0813] Users provide feedback and opinions on repair plans and damage information through their devices. Input consists of the user's voice and facial expressions; emotional states are collected using the device's voice input and camera. The output is user feedback data.

[0814] Step 6:

[0815] The server analyzes user feedback and adjusts the repair plan. The input is user sentiment data, and natural language processing is used to determine whether the sentiment is positive or negative. The output is the adjusted repair plan, which is then sent back to the terminal.

[0816] (Application Example 2)

[0817] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0818] In infrastructure inspection and maintenance, identifying damaged areas and developing repair plans based on priorities is crucial. However, traditional systems have struggled to adjust repair plans to take into account user emotions and feedback. As a result, there was a risk that repair plans might not meet user expectations, leading to decreased satisfaction.

[0819] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0820] In this invention, the server includes means for collecting infrastructure information using information collection means, means for analyzing the information acquired by the information collection means to identify damaged areas, and means for analyzing the user's emotions using emotion analysis means and adjusting the repair plan based on the analysis results. This makes it possible to make adjustments based on the user's emotions and generate a repair plan that is highly satisfactory.

[0821] "Information gathering means" refers to devices and methods for collecting surface information on infrastructure.

[0822] "Information analysis means" refers to the technologies and processes used to analyze collected information and identify damaged areas in infrastructure.

[0823] "Priority setting means" refers to means that have the function of determining the priority of repairs for identified damaged areas.

[0824] "Repair plan generation means" refers to a means for formulating a repair plan based on established priorities.

[0825] "Emotional analysis methods" refer to technologies that analyze users' emotions and reflect them in infrastructure management and repair plans.

[0826] This invention aims to improve the efficiency of infrastructure management and enhance user satisfaction by constructing a system that combines information gathering using unmanned aerial vehicles with emotion analysis technology.

[0827] The server uses unmanned aerial vehicles (UAVs) as a means of information gathering to acquire surface information about the infrastructure. The acquired data is then analyzed by the server to identify damaged areas.

[0828] Next, the server prioritizes repairs based on the identified damaged areas. Then, a repair plan generation system formulates the optimal repair plan, taking into account past maintenance history and budget information.

[0829] Simultaneously, the device is equipped with emotion analysis capabilities that analyze the user's emotions from voice input and video. If the user views images of infrastructure or repair plans and expresses emotions such as anxiety or relief, that emotion data is sent to the server. Based on this data, the server adjusts the repair plan and provides feedback to the user.

[0830] As a concrete example, when inspecting a bridge, a server identifies damage and develops a repair plan based on images taken by an unmanned aerial vehicle. If users express feelings of relief through their smartphones or smart glasses, this information is reflected in the plan through emotion analysis, enabling more effective infrastructure management.

[0831] An example of a prompt sentence is, "How do you feel after seeing the results of this bridge inspection? Do you feel relieved, anxious, or have any other emotions?"

[0832] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0833] Step 1:

[0834] The server uses unmanned aerial vehicles (UAVs) to photograph the surface of infrastructure and collects the information as data. The input is image data from the UAVs, and the output is the collected raw image data. This data is then used for subsequent analysis.

[0835] Step 2:

[0836] The server analyzes the collected image data using an image analysis algorithm (e.g., image recognition software). The input is the image data from step 1, and the output is data identifying the damaged areas. This process identifies which parts are damaged and extracts specific damage information.

[0837] Step 3:

[0838] The server sets repair priorities based on the analyzed damage data. The input is the damage information obtained in step 2, and the output is the damage information with assigned priorities. This process considers the severity and urgency of the damage when determining priorities.

[0839] Step 4:

[0840] The server generates a repair plan using prioritized damage information. The input is the priority information from step 3, and the output is a specific repair plan. This plan incorporates past management history and budget constraints.

[0841] Step 5:

[0842] The device collects audio and video data from users as they check repair plans and infrastructure status, and analyzes it using an emotion analysis engine. The input is data related to the user's emotions, and the output is the analyzed emotion information. This allows for user feedback to be obtained.

[0843] Step 6:

[0844] The server adjusts the repair plan based on the emotional data obtained from the user. The input is the emotional information obtained in step 5 and the repair plan generated in step 4, and the output is the adjusted repair plan. This process provides a repair plan that meets the user's expectations.

[0845] Step 7:

[0846] The device presents the user with a revised repair plan and receives feedback. The input is the repair plan obtained in step 6, and the output is the user's final feedback. This allows for the incorporation of opinions that will ultimately increase user satisfaction.

[0847] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0848] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0849] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0850] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0851] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0852] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0853] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0854] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0855] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0856] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0857] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0858] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0859] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0860] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0861] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0862] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0863] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0864] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0865] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0866] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0867] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0868] The following is further disclosed regarding the embodiments described above.

[0869] (Claim 1)

[0870] A means for collecting surface information of infrastructure using an information acquisition device,

[0871] The information acquisition device analyzes the surface information it has acquired and identifies the location of the damage.

[0872] A means for setting repair priorities for identified damaged areas,

[0873] A means for generating a repair plan based on priority,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, wherein the information acquisition device is an unmanned aerial vehicle.

[0877] (Claim 3)

[0878] The system according to claim 1, further comprising means for optimizing the repair plan based on past maintenance history and budget information.

[0879] "Example 1"

[0880] (Claim 1)

[0881] A means for collecting surface data of structures using data acquisition equipment,

[0882] The aforementioned data acquisition equipment analyzes the surface data acquired and provides means for identifying the location of deterioration,

[0883] A means for setting the priority of repairs for identified deterioration locations,

[0884] A method for evaluating the severity of degradation using a generative AI model,

[0885] A means of generating a repair plan based on priorities and formulating an actionable schedule,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, wherein the data acquisition equipment is an unmanned aerial vehicle.

[0889] (Claim 3)

[0890] The system according to claim 1, further comprising means for optimizing the repair plan based on past operational history and financial information.

[0891] "Application Example 1"

[0892] (Claim 1)

[0893] A means for acquiring surface information of a structure using information gathering means,

[0894] The means for analyzing the surface information acquired by the information gathering means and identifying the defective area,

[0895] A means for setting repair priorities for identified fault areas,

[0896] A means for generating a repair plan based on priorities,

[0897] Means for visually presenting the aforementioned repair plan in real time on a portable information terminal or a head-mounted display device,

[0898] A system that includes this.

[0899] (Claim 2)

[0900] The system according to claim 1, wherein the information gathering means is an unmanned aerial vehicle.

[0901] (Claim 3)

[0902] The system according to claim 1, further comprising means for optimizing the repair plan based on past operational history and cost information.

[0903] "Example 2 of combining an emotion engine"

[0904] (Claim 1)

[0905] A means for collecting surface information of an instrument using an unmanned mobile device,

[0906] A means for analyzing surface information acquired by the aforementioned unmanned mobile device using a processing device to identify abnormal areas,

[0907] A means for setting the priority of repairs for identified abnormal areas,

[0908] A means for generating a repair plan based on priorities and previous maintenance records and resource utilization information,

[0909] A means of evaluating a person's emotional state,

[0910] Means for adjusting the repair plan in consideration of the aforementioned emotional state,

[0911] A system that includes this.

[0912] (Claim 2)

[0913] The system according to claim 1, wherein the unmanned mobile device is an aerospace machine.

[0914] (Claim 3)

[0915] The system according to claim 1, further comprising means for collecting emotional data through dialogue with a person and adjusting a repair plan based on the emotional data.

[0916] "Application example 2 when combining with an emotional engine"

[0917] (Claim 1)

[0918] Information gathering means for collecting infrastructure information,

[0919] The information gathering means analyzes the information acquired by the aforementioned information gathering means to identify the location of the damage,

[0920] A means for setting repair priorities for identified damaged areas,

[0921] A means for generating a repair plan based on priority,

[0922] A means for analyzing the user's emotions using emotion analysis tools and adjusting the repair plan based on the analysis results,

[0923] A system that includes this.

[0924] (Claim 2)

[0925] The system according to claim 1, wherein the information gathering means is an unmanned aerial vehicle.

[0926] (Claim 3)

[0927] The system according to claim 1, further comprising means for optimizing the repair plan based on past management history and budget information. [Explanation of symbols]

[0928] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for acquiring surface information of a structure using information gathering means, The means for analyzing the surface information acquired by the information gathering means and identifying the defective area, A means for setting repair priorities for identified fault areas, A means for generating a repair plan based on priorities, Means for visually presenting the aforementioned repair plan in real time on a portable information terminal or a head-mounted display device, A system that includes this.

2. The system according to claim 1, wherein the information gathering means is an unmanned aerial vehicle.

3. The system according to claim 1, further comprising means for optimizing the repair plan based on past operational history and cost information.

Images