system

Abstract

We provide the system. [Solution] The autonomous device has means for patrolling a structure according to a pre-set route, A means for collecting images of structures and external environmental data during patrols using photographic means, The central device analyzes collected images and external environmental data to identify the location of the malfunction, A means for determining the priority of faulty areas based on the analyzed information and generating a repair plan, A means of presenting the generated repair plan and a three-dimensional model of the faulty area to the user, 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 character of the chatbot, 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] In the inspection and repair plan formulation of conventional structures, there were problems such as a large amount of manual work and difficulty in ensuring safety and efficiency. In addition, in visual inspection by humans, there was a risk of overlooking defective parts or making judgment errors, which might reduce the overall inspection accuracy and speed. Furthermore, it was very difficult to appropriately determine the priority of repairs and formulate an efficient repair plan.

Means for Solving the Problems

[0005] This invention provides a system in which an autonomous device patrols a structure according to a pre-set route and collects images of the structure and external environmental data during the patrol using imaging means. A central device analyzes the collected images and external environmental data to identify faulty areas, determines the priority of faulty areas based on the analyzed information, and generates a repair plan. Furthermore, by presenting the generated repair plan and a three-dimensional model of the faulty areas to the user, it enables safer and more efficient inspection and repair of structures.

[0006] An "autonomous device" is a device that has the function of automatically patrolling a structure according to a pre-set route.

[0007] "Photography means" refers to a camera or sensor equipped with the function of acquiring images of structures and external environmental data.

[0008] The "central device" is a device that has the function of analyzing collected images and external environmental data, and performs information processing to identify the location of defects.

[0009] "Defective areas" refer to parts of a structure that require repair.

[0010] "Priority" is an indicator used to determine the order in which repairs should be carried out, based on the importance and urgency of the repairs needed for each defective area.

[0011] A "repair plan" is a document that specifically outlines the procedures, materials, and timeframe for repairing a defective area.

[0012] A "three-dimensional model" is a digital representation that displays the shape and condition of an object in three dimensions, allowing for visual confirmation of defects.

[0013] A "user" is a person who operates this system and uses the information obtained to carry out inspection and repair plans for structures. [Brief explanation of the drawing]

[0014] [Figure 1] It 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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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.

[0018] 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.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] As an embodiment of the present invention, an infrastructure management system using autonomous devices and a central device will be described. This system includes a process in which the autonomous devices patrol structures along a pre-set route and collect images and external environmental data.

[0036] Specifically, the user inputs information about the target structure from their terminal into the server and sets a patrol route. The autonomous device is equipped with high-precision imaging capabilities and acquires detailed image data and external environmental data during the patrol. The acquired data is transmitted to the server in real time, and the server analyzes this data to identify the location of the problem.

[0037] For example, when inspecting a bridge, the autonomous device photographs the bridge piers and girders to detect corrosion and cracks. The server uses AI algorithms to analyze these defects and determine their priority. Prioritization is evaluated based on safety and the urgency of repairs, and based on this, the server generates an efficient and effective repair plan.

[0038] The generated repair plan is visualized as a three-dimensional model and presented to the user. The device then uses this to help the user decide on specific repair actions. Furthermore, the provision of the three-dimensional model facilitates a detailed visual understanding of the faulty area.

[0039] Furthermore, the server considers real-time external environmental data such as weather and terrain, and optimizes the autonomous device's patrol route accordingly. This ensures the safe and efficient operation of the autonomous device.

[0040] Thus, the present invention aims to improve the efficiency of structural inspection and repair planning through the cooperation of autonomous devices and a central device, enabling the rapid detection and response to defects.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user inputs information about structures from a terminal and sets a patrol route. This information is sent to a server, and an autonomous flight plan for the drone is created.

[0044] Step 2:

[0045] The server transmits the patrol route to the autonomous device and issues a command to begin patrolling. The autonomous device flies around the structure according to the set route.

[0046] Step 3:

[0047] The autonomous device uses high-precision imaging techniques to acquire image data of structures and external environmental data during flight. This data is transmitted to a server in real time.

[0048] Step 4:

[0049] The server analyzes the received data using an AI algorithm and identifies defective areas through image recognition. It detects abnormalities such as cracks and corrosion.

[0050] Step 5:

[0051] The server assesses the severity of the malfunction and prioritizes repairs. Based on this assessment, a specific repair plan is developed.

[0052] Step 6:

[0053] The server generates a three-dimensional model of the faulty area and repair plan, and presents it to the user via the terminal. This allows the user to visually confirm the details of the problem.

[0054] Step 7:

[0055] The device provides users with information about repair plans and priorities, helping them make appropriate decisions.

[0056] Step 8:

[0057] The server optimizes new flight paths for the autonomous devices based on weather and real-time environmental conditions, preparing for future patrols.

[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] Existing infrastructure management systems struggle to quickly and accurately detect structural deterioration and damage, and to effectively plan repairs. Furthermore, conventional methods lack flexible route optimization in response to weather and terrain changes, hindering the safe and efficient operation of autonomous devices. Therefore, there is a need for increased efficiency and reliability in structural inspection work.

[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 analyzing acquired image information and external environmental information to identify faulty locations, means for determining the priority of faulty locations based on the information analyzed using a generated AI model and generating a repair plan, and means for optimizing the patrol route of the autonomous device considering real-time external environmental information. This enables rapid detection of structural defects, effective planning of repairs, and safe operation of the autonomous device.

[0063] An "autonomous device" is a device that patrols structures according to a set route and has the function of collecting image information and external environmental information.

[0064] A "photography device" is a device attached to an autonomous device for acquiring image information of a structure.

[0065] A "central device" is a device that analyzes acquired data, identifies faulty areas, and generates repair plans.

[0066] "Image information" refers to data that visually records the condition of a structure.

[0067] "External environmental information" refers to data about the external environmental conditions during patrols, including, for example, temperature and humidity.

[0068] A "generative AI model" refers to artificial intelligence technology used in data analysis, which contributes to identifying defects and determining priorities.

[0069] A "defective area" refers to a region in a structure where deterioration or damage has been detected.

[0070] "Priority" refers to the order in which identified defects should be repaired, and is set based on safety and urgency.

[0071] A "repair plan" refers to a plan that includes specific procedures and schedules designed to fix a malfunction.

[0072] A "three-dimensional structural model" refers to a three-dimensional digital representation created to visually show defects in a structure.

[0073] "Route optimization" refers to the process of making the patrol routes of autonomous devices safe and efficient by taking real-time external environmental information into consideration.

[0074] As an embodiment of this invention, an infrastructure management system realized through the collaboration of users, servers, and terminals will be described.

[0075] The user uses a terminal to input the necessary information about the target structure. For example, when inspecting a bridge, the user inputs the bridge's name and location into the terminal and sets a patrol route. The autonomous device then begins patrolling according to the specified route, including the bridge's piers and girders.

[0076] The autonomous device is equipped with a high-precision imaging system that acquires detailed image information of structures and external environmental information during patrols. The autonomous device transmits this information to a server in real time. The server receives this data and analyzes it using a generated AI model. The purpose of the analysis is to identify defects in structures at an early stage.

[0077] The server identifies faulty areas based on the acquired data and determines their priority. Prioritization is set based on safety and the urgency of repair. The server also generates a specific repair plan from this information. This repair plan is visualized as a three-dimensional structural model and sent to the terminal. The user can review this information from the terminal and plan detailed repair actions. A concrete example of this would be a prompt message such as, "Detect cracks in the bridge and incorporate them into next week's repair plan."

[0078] Furthermore, the server takes real-time external environmental information such as weather and terrain into consideration and optimizes the autonomous device's patrol route accordingly, thereby ensuring the safe and efficient operation of the autonomous device.

[0079] In this way, collaboration between users, servers, and terminals enables rapid and precise inspection of structures and improves the efficiency of repair work.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The user inputs structure information using a terminal. This information includes the structure's name, location, and patrol route settings. This information is sent to the server, which then receives the basic data needed to identify the structures and routes to be patrolled.

[0083] Step 2:

[0084] The autonomous device begins patrolling according to a route transmitted from the server. It records the condition of the structure using imaging devices and acquires external environmental information using environmental sensors. The acquired images and external environmental information are transmitted to the server in real time. This allows the server to receive a dataset to understand the current state of the structure.

[0085] Step 3:

[0086] The server inputs the acquired images and external environmental information into a generating AI model and analyzes the data. Specifically, it performs a process of detecting deterioration and damage to structures through image analysis. As a result of the analysis, the defective areas are identified, and this information is used for the next processing step.

[0087] Step 4:

[0088] Based on the fault information from the analysis results, the server prioritizes fault locations based on safety and repair urgency. It then uses this priority to generate an efficient repair plan. The generated repair plan is visualized and presented to the user.

[0089] Step 5:

[0090] The terminal presents the user with a three-dimensional structural model based on the repair plan received from the server. The user visually confirms this information and formulates a plan for specific repair actions. This allows for a more systematic execution of repair work.

[0091] Step 6:

[0092] The server optimizes the autonomous robot's patrol route based on real-time external environmental information, such as weather and terrain. This optimization enables efficient and safe operation of the autonomous robot.

[0093] (Application Example 1)

[0094] 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."

[0095] Traditional structural inspection methods often rely heavily on manual labor, resulting in time-consuming and costly inspections, as well as challenges in overlooking defects and ensuring safety. Furthermore, inspections can be difficult depending on external environmental conditions, highlighting the need for efficient inspections and rapid response. Additionally, there has been a lack of methods to present complex defect information in a way that is easily understandable to users.

[0096] 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.

[0097] In this invention, the server includes means for receiving and analyzing data collected by autonomous devices in real time via a communication device, means for creating and providing a detailed report of the faulty area to the user using a generated AI model, and means for optimizing the flight path to realize an optimal flight plan according to external environmental conditions. This enables efficient and advanced inspection work and the presentation of fault information in an easy-to-understand manner.

[0098] An "autonomous device" is a device that has the function of patrolling according to a pre-set route and collecting data.

[0099] "Image and external environment data" refers to information used to capture the condition of a structure and its surrounding environment in detail, and is collected through photographic means.

[0100] A "central device" is a device that analyzes collected images and external environmental data to identify faulty areas and generate repair plans.

[0101] A "repair plan" is a specific repair plan generated based on analyzed information, taking into account the priority of the faulty areas.

[0102] A "three-dimensional model" is a digital model that visualizes defects in a structure in three dimensions, making it easier for users to understand the details.

[0103] A "communication device" is a device that sends and receives data between an autonomous device and a server, enabling real-time information exchange.

[0104] A "generative AI model" is an artificial intelligence system that learns from large amounts of data and automatically performs tasks such as analyzing faulty areas and creating detailed reports.

[0105] A "prompt message" is an explanatory text that organizes and presents information to make it easier for users to understand the AI ​​analysis results.

[0106] The system for implementing this invention mainly consists of an autonomous device, a central device (server), a communication device, and terminals. The autonomous device optimizes the patrol route while considering terrain and weather conditions, and collects detailed image data of structures and external environmental data in real time. This data is transmitted to the server via the communication device.

[0107] The server analyzes the received data using a generating AI model to identify structural defects and their priority. Based on the analysis results, it visualizes a repair plan as a detailed three-dimensional model and generates specific repair instructions. Furthermore, the server sends this information to the user's terminal, presenting the defect information in an easy-to-understand manner using prompt messages.

[0108] The server also completes the analysis based on the generated AI model and provides reports to users in an easy-to-understand format. For example, it presents the results of crack detection on the exterior wall of a commercial building along with images collected by the autonomous device, and helps users understand the current situation with a prompt message such as, "We are conducting an inspection of the exterior wall of a commercial building. Please detect cracks and color changes on the exterior wall and generate a report."

[0109] Specifically, the system utilizes smartphones, drones, and servers as hardware, and employs image recognition (OpenCV) using Python, data analysis (TENSORFLOW® or PyTorch), and real-time communication technology (WebSocket or MQTT) as software. Furthermore, a generative AI model enables detailed information analysis, allowing users to easily identify problems that require attention. This will facilitate efficient and rapid maintenance of structures.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The autonomous device begins patrolling according to a pre-set route. Inputs are the starting point and patrol route information. Outputs are real-time images and external environment data acquired during the patrol. This data is collected by the autonomous device's imaging capabilities.

[0113] Step 2:

[0114] The autonomous device collects images and external environmental data and transmits them to a server via a communication device. The input consists of the acquired images and environmental data. The output is the data awaiting analysis, which has been sent to the server. This data is transmitted in real time.

[0115] Step 3:

[0116] The server analyzes the received data using a generating AI model. The input consists of the transmitted image and environmental data, and the output is the result of identifying and prioritizing the problem areas. In this analysis, the AI ​​uses image data to determine whether or not there are problems.

[0117] Step 4:

[0118] Based on the analysis results, the server visualizes the faulty areas as a three-dimensional model and generates a repair plan. The input is data identifying and prioritizing the faulty areas. The output is a visualized three-dimensional model and a detailed repair plan. The server generates the model using 3D modeling software.

[0119] Step 5:

[0120] The server sends the generated 3D model and repair plan to the terminal along with prompts. The inputs are the 3D model, the repair plan, and the prompts. The output is the information presented to the terminal. The prompts make the information easier for the user to understand.

[0121] Step 6:

[0122] The user reviews the information presented through the terminal and decides on repair actions as needed. The input consists of the presented 3D model and repair plan information, while the output is the specific repair decision and its implementation. The user then develops a work plan based on this.

[0123] 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.

[0124] This invention relates to an infrastructure management system combining an autonomous device, a central device, and an emotion engine. The purpose of this system is not only to perform efficient inspection and repair of structures, but also to improve usability by presenting information while considering the user's emotions.

[0125] First, the user uses a terminal to input structural information and set a patrol route. This information is sent to a server, which then sends an appropriate patrol plan to the autonomous device. The autonomous device follows this plan, automatically patrolling the structure and collecting image data and environmental data using various sensors and cameras. This data is sent to the server in real time and analyzed.

[0126] The server identifies faulty areas through data analysis and determines the priority of repairs for those faults. During this process, an emotion engine connected to the server recognizes user reactions and analyzes the user's emotional state. For example, if a user is feeling confused, the emotion engine detects this and decides whether to request a more detailed explanation or change the interface being used.

[0127] For example, when a user receives information about bridge repairs, the terminal displays a 3D model of the damaged area. If the emotion engine determines that the user is feeling anxious, the server provides additional explanations and emphasizes safety-related information.

[0128] Furthermore, the emotion engine continuously learns future information presentation methods based on user feedback. This allows the system to continuously improve, enhancing the user experience.

[0129] Therefore, through the embodiment of the present invention, the addition of an emotion engine makes it possible to realize an infrastructure management system that is easier for users to use and can be used with peace of mind.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The user inputs information about structures from a terminal and sets a patrol route. This information is sent to a server, which then creates a flight plan for the autonomous device.

[0133] Step 2:

[0134] The server transmits instructions for the patrol route to the autonomous device and initiates flight. The autonomous device patrols the structure according to the plan and collects image data and external environmental data using imaging devices.

[0135] Step 3:

[0136] The data collected by the autonomous device is transmitted to the server in real time. The server analyzes this data to identify defects in the structure.

[0137] Step 4:

[0138] The server assesses the severity of the problem, determines its priority, and then generates an appropriate repair plan.

[0139] Step 5:

[0140] The server sends the generated repair plan and a 3D model of the faulty area to the terminal. The terminal then presents this to the user, allowing the user to visually confirm the information.

[0141] Step 6:

[0142] An emotion engine built into the device analyzes the user's emotional state in real time. The emotion engine detects the user's facial expressions and tone of voice to identify their emotions.

[0143] Step 7:

[0144] Based on information from the emotion engine, the server presents information tailored to the user's emotional state. For example, if the user is feeling anxious, it will provide additional explanations or emphasized safety information.

[0145] Step 8:

[0146] The user inputs feedback into the device regarding the information received. The device sends this feedback to the server, where it is stored as training data for the emotion engine. This allows the system to optimize monitoring and information presentation in the future.

[0147] (Example 2)

[0148] 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".

[0149] While efficient inspection and repair are necessary in modern infrastructure management systems, information is not adequately presented in a way that considers the psychological state of users. As a result, users may receive insufficient information or experience anxiety. Furthermore, efficiency in the process of quickly and accurately identifying structural defects and determining their priority remains a challenge. In addition, there is a lack of flexible improvements in information presentation and insufficient mechanisms to prevent user feedback.

[0150] 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.

[0151] In this invention, the server includes means for an emotion analysis device to analyze the user's psychological state and present appropriate information, means for presenting the generated repair plan and a three-dimensional representation of the faulty area to the user, and means for learning user feedback and improving the information presentation method. This makes it possible to present information to the user in a more reassuring and easy-to-understand manner, and enables the efficient operation of the infrastructure management system.

[0152] An "autonomous device" is a device that inspects a target object according to a pre-set inspection route and collects images and external environmental information.

[0153] An "acquisition device" is a device, including sensors and cameras, that is mounted on an autonomous device and used to collect images of the monitored object and external environmental information during inspection.

[0154] The "central device" is a central piece of equipment that analyzes collected images and external environmental information, identifies faulty areas, and generates repair plans.

[0155] "Image and external environmental information" refers to visual data of the monitored object and environmental data such as temperature and humidity around it, collected by the acquisition device.

[0156] An "emotional analysis device" is a device that analyzes the user's psychological state and has analytical functions to present the user with appropriate information.

[0157] "Three-dimensional representation" refers to a three-dimensional model used to visually represent the faulty parts of a monitored system in detail.

[0158] "User feedback" refers to information that includes reactions and opinions from users regarding their use of the system.

[0159] This invention realizes an infrastructure management system that combines autonomous devices, a central device, and an emotion analysis device. The system aims to provide information that takes into account the user's psychological state, in addition to efficient inspection and repair.

[0160] First, the user inputs information about the targets to be monitored via their device and sends it to the server. This information includes an overview of the targets and their monitoring priorities.

[0161] Next, the server uses a generative AI model based on the received information to create a patrol plan suitable for the autonomous device and sends instructions to the autonomous device as prompt messages. An example of a prompt message here would be, "In the next patrol, inspect the bridge structure in detail and look for signs of structural failure."

[0162] The autonomous device inspects designated monitoring targets according to the patrol plan received from the server. The autonomous device is equipped with various sensors and cameras to collect images and external environmental information. This information is transmitted to the server in real time.

[0163] The server uses the collected data to apply image analysis technology and identify the faulty area. Once the faulty area is identified, the server develops a repair plan and generates a three-dimensional representation of the faulty area. Furthermore, the server analyzes the user's psychological state through an emotion analysis device and provides information tailored to the user. This information may include additional safety information and detailed explanations.

[0164] Finally, the server receives feedback from users and continuously improves how information is presented. This feedback is used to optimize the interface and improve the information content for future use.

[0165] In this way, an efficient and reliable infrastructure management system is built.

[0166] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0167] Step 1:

[0168] The user uses a terminal to input information about the items to be monitored and the priorities of patrol tasks. The input data includes the location of the structure and the purpose of the inspection. The terminal sends this data to the server. The output of this process is patrol plan data received by the server.

[0169] Step 2:

[0170] The server uses a generated AI model based on the received data to formulate the optimal patrol plan. The data processing here involves analyzing the input monitoring target information and generating the most efficient patrol route as a prompt message. This output is the patrol instruction sent to the autonomous device.

[0171] Step 3:

[0172] The autonomous device inspects designated monitoring targets based on patrol instructions received from the server. The device collects image data and environmental information using cameras and sensors. This collected data is then transmitted to the server in real time. The output consists of images and environmental data sent to the server.

[0173] Step 4:

[0174] The server analyzes the received image and environmental data to identify the faulty areas. This process uses image recognition technology to process the data and detect problems. The output of the data calculation is the identified faulty areas and their detailed information.

[0175] Step 5:

[0176] The server uses an emotion analysis device to analyze the user's psychological state. It uses reaction data from when the user views information via a terminal as an input component for analysis. The output is personalized information presented to the user.

[0177] Step 6:

[0178] The device displays a three-dimensional representation of the faulty area and information regarding the need for repair to the user. Furthermore, if the emotion analysis device detects user anxiety, additional information is provided from the server, supplementing the explanation with reassuring details. The output of this process is detailed and appropriate content that the user receives on the device.

[0179] Step 7:

[0180] User feedback is sent to the server via the device. The server uses this feedback to learn how to present information and improve it for future use. The output is an improved information presentation protocol designed to enhance the user experience.

[0181] (Application Example 2)

[0182] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0183] The present invention aims to improve the efficient management of urban infrastructure and enhance user comfort. Existing infrastructure management systems suffer from insufficient efficiency in inspection and repair, and lack information presentation that takes user feelings into consideration. As a result, many users feel uneasy about the safety of structures, and there are also issues with the usability of the system.

[0184] 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.

[0185] This invention includes a server that recognizes the user's emotional state using emotion analysis means and optimizes information presentation, a means for learning and improving the information presentation method based on user feedback, and a means for an autonomous device to patrol physical structures according to a pre-set route. This enables efficient inspection and repair of urban infrastructure, and realizes a system that is safe and easy for users to use.

[0186] An "autonomous device" is a device that operates automatically according to a set route, and is a device that patrols structures and collects necessary data.

[0187] "Means of photographing" refers to techniques or devices for acquiring images of physical structures during patrols, and includes sensors and cameras.

[0188] A "central system" is a computer system that analyzes collected data and identifies the location of malfunctions.

[0189] A "defective area" refers to a part of the infrastructure that is abnormal or requires repair.

[0190] A "repair plan" is a specific action plan formulated to fix a problem.

[0191] "Three-dimensional visualization data" refers to digital data used to display defective areas in three dimensions, serving as a means to facilitate visual understanding for users.

[0192] "Emotional analysis means" refers to a technology or system that recognizes and analyzes a user's emotional state.

[0193] "User feedback" refers to opinions and reactions from users regarding the use of the system, and is information that helps improve the system.

[0194] To realize this invention, several key components must function in close coordination. This system combines autonomous devices, a central device, and emotion analysis functions to efficiently manage urban infrastructure and provide users with intuitive and reassuring information.

[0195] The server first controls the autonomous device, guiding it to patrol the physical structure along a pre-configured route. During this process, the autonomous device uses high-precision sensors and imaging devices to collect image data of the structure and external environmental data in real time. This data is instantly transmitted to the central device, where it is analyzed using image processing technology. This analysis utilizes machine learning models, such as TensorFlow, to identify faulty areas and prioritize repair plans.

[0196] Subsequently, the central device generates three-dimensional visualization data and presents it to the user's terminal. This visualization uses 3D graphics technologies such as Unity to create a three-dimensional and intuitive model. Simultaneously, the system analyzes the user's emotional state through emotion analysis means and dynamically changes the content presented. Specifically, it analyzes the user's voice feedback and text input using Google® Cloud Speech-to-Text and OpenAI® generative AI models, highlighting aspects that evoke a sense of security or require detailed information.

[0197] Users review the information obtained through their devices and provide feedback on any questions or concerns. This feedback is analyzed by an emotion analysis system and used to improve future information presentation methods. When a user asks the system a question using a prompt such as, "Please tell me more about the safety of this road," the AI ​​immediately provides detailed information, optimized to alleviate the user's concerns. These steps ensure that urban infrastructure is maintained efficiently and in a user-friendly manner.

[0198] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0199] Step 1:

[0200] The server transmits patrol route information to the autonomous device. The inputs used are geographical data of urban infrastructure and maintenance schedule information. Based on this information, the autonomous device begins moving along a pre-set route and is autonomously operated to patrol the structures.

[0201] Step 2:

[0202] The autonomous device acquires image data of structures and external environmental data using its onboard sensors and cameras during patrols. Here, it senses the surrounding conditions and the physical state of the structure as input, and generates acquired digital images and sensor data as output.

[0203] Step 3:

[0204] The server receives image data and environmental data transmitted from the autonomous device and analyzes this data. It processes the image data using a machine learning model (e.g., TensorFlow) to identify the location of the defect. The input for the analysis is the received raw data, and the output is identification information of the defect location.

[0205] Step 4:

[0206] The server determines the priority of fault locations based on the analysis results. Using the analyzed fault data as input, it generates a priority list for repair plans as output. This list is automatically evaluated according to pre-set criteria and urgency levels.

[0207] Step 5:

[0208] The server generates a repair plan based on priorities, creating it as three-dimensional visualization data and sending it to the user's terminal. Using 3D graphics technology such as Unity, the input is information on priorities and repair locations, and the output is a three-dimensional model.

[0209] Step 6:

[0210] The user reviews the three-dimensional visualization data presented via the terminal. Any feedback or questions provided by the user are entered via voice or text. The output consists of the review results and feedback data.

[0211] Step 7:

[0212] The server analyzes user feedback using sentiment analysis tools. It uses Google Cloud Speech-to-Text and OpenAI's generative AI models to analyze the input feedback data and identify the user's emotions. The output is information about the emotional state.

[0213] Step 8:

[0214] The server dynamically adjusts the information presentation method based on the analyzed emotional state. For example, if the user feels anxious, it will emphasize and present more detailed safety information. The input is the user's emotional information, and the output is the optimized information presentation.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] [Second Embodiment]

[0219] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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).

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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".

[0231] As an embodiment of the present invention, an infrastructure management system using autonomous devices and a central device will be described. This system includes a process in which the autonomous devices patrol structures along a pre-set route and collect images and external environmental data.

[0232] Specifically, the user inputs information about the target structure from their terminal into the server and sets a patrol route. The autonomous device is equipped with high-precision imaging capabilities and acquires detailed image data and external environmental data during the patrol. The acquired data is transmitted to the server in real time, and the server analyzes this data to identify the location of the problem.

[0233] For example, when inspecting a bridge, the autonomous device photographs the bridge piers and girders to detect corrosion and cracks. The server uses AI algorithms to analyze these defects and determine their priority. Prioritization is evaluated based on safety and the urgency of repairs, and based on this, the server generates an efficient and effective repair plan.

[0234] The generated repair plan is visualized as a three-dimensional model and presented to the user. The device then uses this to help the user decide on specific repair actions. Furthermore, the provision of the three-dimensional model facilitates a detailed visual understanding of the faulty area.

[0235] Furthermore, the server considers real-time external environmental data such as weather and terrain, and optimizes the autonomous device's patrol route accordingly. This ensures the safe and efficient operation of the autonomous device.

[0236] Thus, the present invention aims to improve the efficiency of structural inspection and repair planning through the cooperation of autonomous devices and a central device, enabling the rapid detection and response to defects.

[0237] The following describes the processing flow.

[0238] Step 1:

[0239] The user inputs information about structures from a terminal and sets a patrol route. This information is sent to a server, and an autonomous flight plan for the drone is created.

[0240] Step 2:

[0241] The server transmits the patrol route to the autonomous device and issues a command to begin patrolling. The autonomous device flies around the structure according to the set route.

[0242] Step 3:

[0243] The autonomous device uses high-precision imaging techniques to acquire image data of structures and external environmental data during flight. This data is transmitted to a server in real time.

[0244] Step 4:

[0245] The server analyzes the received data using an AI algorithm and identifies defective areas through image recognition. It detects abnormalities such as cracks and corrosion.

[0246] Step 5:

[0247] The server assesses the severity of the malfunction and prioritizes repairs. Based on this assessment, a specific repair plan is developed.

[0248] Step 6:

[0249] The server generates a three-dimensional model of the faulty area and repair plan, and presents it to the user via the terminal. This allows the user to visually confirm the details of the problem.

[0250] Step 7:

[0251] The device provides users with information about repair plans and priorities, helping them make appropriate decisions.

[0252] Step 8:

[0253] The server optimizes new flight paths for the autonomous devices based on weather and real-time environmental conditions, preparing for future patrols.

[0254] (Example 1)

[0255] 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."

[0256] Existing infrastructure management systems struggle to quickly and accurately detect structural deterioration and damage, and to effectively plan repairs. Furthermore, conventional methods lack flexible route optimization in response to weather and terrain changes, hindering the safe and efficient operation of autonomous devices. Therefore, there is a need for increased efficiency and reliability in structural inspection work.

[0257] 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.

[0258] In this invention, the server includes means for analyzing acquired image information and external environmental information to identify faulty locations, means for determining the priority of faulty locations based on the information analyzed using a generated AI model and generating a repair plan, and means for optimizing the patrol route of the autonomous device considering real-time external environmental information. This enables rapid detection of structural defects, effective planning of repairs, and safe operation of the autonomous device.

[0259] An "autonomous device" is a device that patrols structures according to a set route and has the function of collecting image information and external environmental information.

[0260] A "photography device" is a device attached to an autonomous device for acquiring image information of a structure.

[0261] A "central device" is a device that analyzes acquired data, identifies faulty areas, and generates repair plans.

[0262] "Image information" refers to data that visually records the condition of a structure.

[0263] "External environmental information" refers to data about the external environmental conditions during patrols, including, for example, temperature and humidity.

[0264] A "generative AI model" refers to artificial intelligence technology used in data analysis, which contributes to identifying defects and determining priorities.

[0265] A "defective area" refers to a region in a structure where deterioration or damage has been detected.

[0266] "Priority" refers to the order in which identified defects should be repaired, and is set based on safety and urgency.

[0267] A "repair plan" refers to a plan that includes specific procedures and schedules designed to fix a malfunction.

[0268] A "three-dimensional structural model" refers to a three-dimensional digital representation created to visually show defects in a structure.

[0269] "Route optimization" refers to the process of making the patrol routes of autonomous devices safe and efficient by taking real-time external environmental information into consideration.

[0270] As an embodiment of this invention, an infrastructure management system realized through the collaboration of users, servers, and terminals will be described.

[0271] The user uses a terminal to input the necessary information about the target structure. For example, when inspecting a bridge, the user inputs the bridge's name and location into the terminal and sets a patrol route. The autonomous device then begins patrolling according to the specified route, including the bridge's piers and girders.

[0272] The autonomous device is equipped with a high-precision imaging system that acquires detailed image information of structures and external environmental information during patrols. The autonomous device transmits this information to a server in real time. The server receives this data and analyzes it using a generated AI model. The purpose of the analysis is to identify defects in structures at an early stage.

[0273] The server identifies faulty areas based on the acquired data and determines their priority. Prioritization is set based on safety and the urgency of repair. The server also generates a specific repair plan from this information. This repair plan is visualized as a three-dimensional structural model and sent to the terminal. The user can review this information from the terminal and plan detailed repair actions. A concrete example of this would be a prompt message such as, "Detect cracks in the bridge and incorporate them into next week's repair plan."

[0274] Furthermore, the server takes real-time external environmental information such as weather and terrain into consideration and optimizes the autonomous device's patrol route accordingly, thereby ensuring the safe and efficient operation of the autonomous device.

[0275] In this way, collaboration between users, servers, and terminals enables rapid and precise inspection of structures and improves the efficiency of repair work.

[0276] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0277] Step 1:

[0278] The user inputs structure information using a terminal. This information includes the structure's name, location, and patrol route settings. This information is sent to the server, which then receives the basic data needed to identify the structures and routes to be patrolled.

[0279] Step 2:

[0280] The autonomous device begins patrolling according to a route transmitted from the server. It records the condition of the structure using imaging devices and acquires external environmental information using environmental sensors. The acquired images and external environmental information are transmitted to the server in real time. This allows the server to receive a dataset to understand the current state of the structure.

[0281] Step 3:

[0282] The server inputs the acquired image and external environment information into the generative AI model and analyzes the data. Specifically, it performs a process of detecting deterioration and damage of the structure through image analysis. As a result of the analysis, defective parts are identified, and the information is used for the next process.

[0283] Step 4:

[0284] Based on the defect information of the analysis result, the server determines the priority of the defective parts based on safety and repair urgency. Then, using this priority, it generates an efficient repair plan. The generated repair plan is visualized and presented to the user.

[0285] Step 5:

[0286] The terminal presents the three-dimensional structure model to the user based on the repair plan received from the server. The user visually confirms the information and formulates a plan for specific repair actions. This enables the repair work to be carried out more systematically.

[0287] Step 6:

[0288] The server optimizes the patrol route of the autonomous device based on the external environment information acquired in real time, such as weather and terrain. This optimization enables the efficient and safe operation of the autonomous device.

[0289] (Application Example 1)

[0290] 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". <00009***19> Traditional structural inspection methods often rely heavily on manual labor, resulting in time-consuming and costly inspections, as well as challenges in overlooking defects and ensuring safety. Furthermore, inspections can be difficult depending on external environmental conditions, highlighting the need for efficient inspections and rapid response. Additionally, there has been a lack of methods to present complex defect information in a way that is easily understandable to users.

[0292] 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.

[0293] In this invention, the server includes means for receiving and analyzing data collected by autonomous devices in real time via a communication device, means for creating and providing a detailed report of the faulty area to the user using a generated AI model, and means for optimizing the flight path to realize an optimal flight plan according to external environmental conditions. This enables efficient and advanced inspection work and the presentation of fault information in an easy-to-understand manner.

[0294] An "autonomous device" is a device that has the function of patrolling according to a pre-set route and collecting data.

[0295] "Image and external environment data" refers to information used to capture the condition of a structure and its surrounding environment in detail, and is collected through photographic means.

[0296] A "central device" is a device that analyzes collected images and external environmental data to identify faulty areas and generate repair plans.

[0297] A "repair plan" is a specific repair plan generated based on analyzed information, taking into account the priority of the faulty areas.

[0298] A "three-dimensional model" is a digital model that visualizes defects in a structure in three dimensions, making it easier for users to understand the details.

[0299] A "communication device" is a device that sends and receives data between an autonomous device and a server, enabling real-time information exchange.

[0300] A "generative AI model" is an artificial intelligence system that learns from large amounts of data and automatically performs tasks such as analyzing faulty areas and creating detailed reports.

[0301] A "prompt message" is an explanatory text that organizes and presents information to make it easier for users to understand the AI ​​analysis results.

[0302] The system for implementing this invention mainly consists of an autonomous device, a central device (server), a communication device, and terminals. The autonomous device optimizes the patrol route while considering terrain and weather conditions, and collects detailed image data of structures and external environmental data in real time. This data is transmitted to the server via the communication device.

[0303] The server analyzes the received data using a generating AI model to identify structural defects and their priority. Based on the analysis results, it visualizes a repair plan as a detailed three-dimensional model and generates specific repair instructions. Furthermore, the server sends this information to the user's terminal, presenting the defect information in an easy-to-understand manner using prompt messages.

[0304] The server also completes the analysis based on the generated AI model and provides reports to users in an easy-to-understand format. For example, it presents the results of crack detection on the exterior wall of a commercial building along with images collected by the autonomous device, and helps users understand the current situation with a prompt message such as, "We are conducting an inspection of the exterior wall of a commercial building. Please detect cracks and color changes on the exterior wall and generate a report."

[0305] Specifically, a smartphone, a drone, and a server are used as hardware, and image recognition (OpenCV) using Python, data analysis (TensorFlow or PyTorch), and real-time communication technology (WebSocket or MQTT) are utilized as software. In addition, detailed information analysis is performed by a generative AI model so that users can easily grasp the problems to be addressed. This enables efficient and rapid maintenance management of structures.

[0306] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0307] Step 1:

[0308] The autonomous device starts a patrol according to a preset route. The input is the starting point and the patrol route information. The output is the real-time images and external environment data acquired during the patrol. This data is collected by the imaging means of the autonomous device.

[0309] Step 2:

[0310] The autonomous device transmits the images and external environment data collected to the server through a communication device. The input is the acquired images and environment data. The output is the data waiting for analysis transmitted to the server. This data is transmitted in real time.

[0311] Step 3:

[0312] The server analyzes the received data using a generative AI model. The input is the transmitted images and environment data, and the output is the result of identifying the defective parts and prioritizing them. In this analysis, the AI performs an operation to determine the presence or absence of defects using the image data.

[0313] Step 4:

[0314] Based on the analysis results, the server visualizes the faulty areas as a three-dimensional model and generates a repair plan. The input is data identifying and prioritizing the faulty areas. The output is a visualized three-dimensional model and a detailed repair plan. The server generates the model using 3D modeling software.

[0315] Step 5:

[0316] The server sends the generated 3D model and repair plan to the terminal along with prompts. The inputs are the 3D model, the repair plan, and the prompts. The output is the information presented to the terminal. The prompts make the information easier for the user to understand.

[0317] Step 6:

[0318] The user reviews the information presented through the terminal and decides on repair actions as needed. The input consists of the presented 3D model and repair plan information, while the output is the specific repair decision and its implementation. The user then develops a work plan based on this.

[0319] 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.

[0320] This invention relates to an infrastructure management system combining an autonomous device, a central device, and an emotion engine. The purpose of this system is not only to perform efficient inspection and repair of structures, but also to improve usability by presenting information while considering the user's emotions.

[0321] First, the user uses a terminal to input structural information and set a patrol route. This information is sent to a server, which then sends an appropriate patrol plan to the autonomous device. The autonomous device follows this plan, automatically patrolling the structure and collecting image data and environmental data using various sensors and cameras. This data is sent to the server in real time and analyzed.

[0322] The server identifies faulty areas through data analysis and determines the priority of repairs for those faults. During this process, an emotion engine connected to the server recognizes user reactions and analyzes the user's emotional state. For example, if a user is feeling confused, the emotion engine detects this and decides whether to request a more detailed explanation or change the interface being used.

[0323] For example, when a user receives information about bridge repairs, the terminal displays a 3D model of the damaged area. If the emotion engine determines that the user is feeling anxious, the server provides additional explanations and emphasizes safety-related information.

[0324] Furthermore, the emotion engine continuously learns future information presentation methods based on user feedback. This allows the system to continuously improve, enhancing the user experience.

[0325] Therefore, through the embodiment of the present invention, the addition of an emotion engine makes it possible to realize an infrastructure management system that is easier for users to use and can be used with peace of mind.

[0326] The following describes the processing flow.

[0327] Step 1:

[0328] The user inputs information about structures from a terminal and sets a patrol route. This information is sent to a server, which then creates a flight plan for the autonomous device.

[0329] Step 2:

[0330] The server transmits instructions for the patrol route to the autonomous device and initiates flight. The autonomous device patrols the structure according to the plan and collects image data and external environmental data using imaging devices.

[0331] Step 3:

[0332] The data collected by the autonomous device is transmitted to the server in real time. The server analyzes this data to identify defects in the structure.

[0333] Step 4:

[0334] The server assesses the severity of the problem, determines its priority, and then generates an appropriate repair plan.

[0335] Step 5:

[0336] The server sends the generated repair plan and a 3D model of the faulty area to the terminal. The terminal then presents this to the user, allowing the user to visually confirm the information.

[0337] Step 6:

[0338] An emotion engine built into the device analyzes the user's emotional state in real time. The emotion engine detects the user's facial expressions and tone of voice to identify their emotions.

[0339] Step 7:

[0340] Based on information from the emotion engine, the server presents information tailored to the user's emotional state. For example, if the user is feeling anxious, it will provide additional explanations or emphasized safety information.

[0341] Step 8:

[0342] The user inputs feedback into the device regarding the information received. The device sends this feedback to the server, where it is stored as training data for the emotion engine. This allows the system to optimize monitoring and information presentation in the future.

[0343] (Example 2)

[0344] 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".

[0345] While efficient inspection and repair are necessary in modern infrastructure management systems, information is not adequately presented in a way that considers the psychological state of users. As a result, users may receive insufficient information or experience anxiety. Furthermore, efficiency in the process of quickly and accurately identifying structural defects and determining their priority remains a challenge. In addition, there is a lack of flexible improvements in information presentation and insufficient mechanisms to prevent user feedback.

[0346] 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.

[0347] In this invention, the server includes means for an emotion analysis device to analyze the user's psychological state and present appropriate information, means for presenting the generated repair plan and a three-dimensional representation of the faulty area to the user, and means for learning user feedback and improving the information presentation method. This makes it possible to present information to the user in a more reassuring and easy-to-understand manner, and enables the efficient operation of the infrastructure management system.

[0348] An "autonomous device" is a device that inspects a target object according to a pre-set inspection route and collects images and external environmental information.

[0349] An "acquisition device" is a device, including sensors and cameras, that is mounted on an autonomous device and used to collect images of the monitored object and external environmental information during inspection.

[0350] The "central device" is a central piece of equipment that analyzes collected images and external environmental information, identifies faulty areas, and generates repair plans.

[0351] "Image and external environmental information" refers to visual data of the monitored object and environmental data such as temperature and humidity around it, collected by the acquisition device.

[0352] An "emotional analysis device" is a device that analyzes the user's psychological state and has analytical functions to present the user with appropriate information.

[0353] "Three-dimensional representation" refers to a three-dimensional model used to visually represent the faulty parts of a monitored system in detail.

[0354] "User feedback" refers to information that includes reactions and opinions from users regarding their use of the system.

[0355] This invention realizes an infrastructure management system that combines autonomous devices, a central device, and an emotion analysis device. The system aims to provide information that takes into account the user's psychological state, in addition to efficient inspection and repair.

[0356] First, the user inputs information about the targets to be monitored via their device and sends it to the server. This information includes an overview of the targets and their monitoring priorities.

[0357] Next, the server uses a generative AI model based on the received information to create a patrol plan suitable for the autonomous device and sends instructions to the autonomous device as prompt messages. An example of a prompt message here would be, "In the next patrol, inspect the bridge structure in detail and look for signs of structural failure."

[0358] The autonomous device inspects designated monitoring targets according to the patrol plan received from the server. The autonomous device is equipped with various sensors and cameras to collect images and external environmental information. This information is transmitted to the server in real time.

[0359] The server uses the collected data to apply image analysis technology and identify the faulty area. Once the faulty area is identified, the server develops a repair plan and generates a three-dimensional representation of the faulty area. Furthermore, the server analyzes the user's psychological state through an emotion analysis device and provides information tailored to the user. This information may include additional safety information and detailed explanations.

[0360] Finally, the server receives feedback from users and continuously improves how information is presented. This feedback is used to optimize the interface and improve the information content for future use.

[0361] In this way, an efficient and reliable infrastructure management system is built.

[0362] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0363] Step 1:

[0364] The user uses a terminal to input information about the items to be monitored and the priorities of patrol tasks. The input data includes the location of the structure and the purpose of the inspection. The terminal sends this data to the server. The output of this process is patrol plan data received by the server.

[0365] Step 2:

[0366] The server uses a generated AI model based on the received data to formulate the optimal patrol plan. The data processing here involves analyzing the input monitoring target information and generating the most efficient patrol route as a prompt message. This output is the patrol instruction sent to the autonomous device.

[0367] Step 3:

[0368] The autonomous device inspects designated monitoring targets based on patrol instructions received from the server. The device collects image data and environmental information using cameras and sensors. This collected data is then transmitted to the server in real time. The output consists of images and environmental data sent to the server.

[0369] Step 4:

[0370] The server analyzes the received image and environmental data to identify the faulty areas. This process uses image recognition technology to process the data and detect problems. The output of the data calculation is the identified faulty areas and their detailed information.

[0371] Step 5:

[0372] The server uses an emotion analysis device to analyze the user's psychological state. It uses reaction data from when the user views information via a terminal as an input component for analysis. The output is personalized information presented to the user.

[0373] Step 6:

[0374] The device displays a three-dimensional representation of the faulty area and information regarding the need for repair to the user. Furthermore, if the emotion analysis device detects user anxiety, additional information is provided from the server, supplementing the explanation with reassuring details. The output of this process is detailed and appropriate content that the user receives on the device.

[0375] Step 7:

[0376] User feedback is sent to the server via the device. The server uses this feedback to learn how to present information and improve it for future use. The output is an improved information presentation protocol designed to enhance the user experience.

[0377] (Application Example 2)

[0378] 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 as the "terminal".

[0379] The present invention aims to improve the efficient management of urban infrastructure and enhance user comfort. Existing infrastructure management systems suffer from insufficient efficiency in inspection and repair, and lack information presentation that takes user feelings into consideration. As a result, many users feel uneasy about the safety of structures, and there are also issues with the usability of the system.

[0380] 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.

[0381] This invention includes a server that recognizes the user's emotional state using emotion analysis means and optimizes information presentation, a means for learning and improving the information presentation method based on user feedback, and a means for an autonomous device to patrol physical structures according to a pre-set route. This enables efficient inspection and repair of urban infrastructure, and realizes a system that is safe and easy for users to use.

[0382] An "autonomous device" is a device that operates automatically according to a set route, and is a device that patrols structures and collects necessary data.

[0383] "Means of photographing" refers to techniques or devices for acquiring images of physical structures during patrols, and includes sensors and cameras.

[0384] A "central system" is a computer system that analyzes collected data and identifies the location of malfunctions.

[0385] A "defective area" refers to a part of the infrastructure that is abnormal or requires repair.

[0386] A "repair plan" is a specific action plan formulated to fix a problem.

[0387] "Three-dimensional visualization data" refers to digital data used to display defective areas in three dimensions, serving as a means to facilitate visual understanding for users.

[0388] "Emotional analysis means" refers to a technology or system that recognizes and analyzes a user's emotional state.

[0389] "User feedback" refers to opinions and reactions from users regarding the use of the system, and is information that helps improve the system.

[0390] To realize this invention, several key components must function in close coordination. This system combines autonomous devices, a central device, and emotion analysis functions to efficiently manage urban infrastructure and provide users with intuitive and reassuring information.

[0391] The server first controls the autonomous device, guiding it to patrol the physical structure along a pre-configured route. During this process, the autonomous device uses high-precision sensors and imaging devices to collect image data of the structure and external environmental data in real time. This data is instantly transmitted to the central device, where it is analyzed using image processing technology. This analysis utilizes machine learning models, such as TensorFlow, to identify faulty areas and prioritize repair plans.

[0392] Subsequently, the central device generates three-dimensional visualization data and presents it to the user's terminal. This visualization uses 3D graphics technologies such as Unity to create a three-dimensional and intuitive model. Simultaneously, the system analyzes the user's emotional state through emotion analysis tools and dynamically changes the content presented. Specifically, it analyzes the user's voice feedback and text input using Google Cloud Speech-to-Text and OpenAI's generative AI models, highlighting aspects that evoke a sense of security or require detailed information.

[0393] Users review the information obtained through their devices and provide feedback on any questions or concerns. This feedback is analyzed by an emotion analysis system and used to improve future information presentation methods. When a user asks the system a question using a prompt such as, "Please tell me more about the safety of this road," the AI ​​immediately provides detailed information, optimized to alleviate the user's concerns. These steps ensure that urban infrastructure is maintained efficiently and in a user-friendly manner.

[0394] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0395] Step 1:

[0396] The server transmits patrol route information to the autonomous device. The inputs used are geographical data of urban infrastructure and maintenance schedule information. Based on this information, the autonomous device begins moving along a pre-set route and is autonomously operated to patrol the structures.

[0397] Step 2:

[0398] The autonomous device acquires image data of structures and external environmental data using its onboard sensors and cameras during patrols. Here, it senses the surrounding conditions and the physical state of the structure as input, and generates acquired digital images and sensor data as output.

[0399] Step 3:

[0400] The server receives image data and environmental data transmitted from the autonomous device and analyzes this data. It processes the image data using a machine learning model (e.g., TensorFlow) to identify the location of the defect. The input for the analysis is the received raw data, and the output is identification information of the defect location.

[0401] Step 4:

[0402] The server determines the priority of fault locations based on the analysis results. Using the analyzed fault data as input, it generates a priority list for repair plans as output. This list is automatically evaluated according to pre-set criteria and urgency levels.

[0403] Step 5:

[0404] The server generates a repair plan based on priorities, creating it as three-dimensional visualization data and sending it to the user's terminal. Using 3D graphics technology such as Unity, the input is information on priorities and repair locations, and the output is a three-dimensional model.

[0405] Step 6:

[0406] The user reviews the three-dimensional visualization data presented via the terminal. Any feedback or questions provided by the user are entered via voice or text. The output consists of the review results and feedback data.

[0407] Step 7:

[0408] The server analyzes user feedback using sentiment analysis tools. It uses Google Cloud Speech-to-Text and OpenAI's generative AI models to analyze the input feedback data and identify the user's emotions. The output is information about the emotional state.

[0409] Step 8:

[0410] The server dynamically adjusts the information presentation method based on the analyzed emotional state. For example, if the user feels anxious, it will emphasize and present more detailed safety information. The input is the user's emotional information, and the output is the optimized information presentation.

[0411] 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.

[0412] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One 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.

[0413] 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.

[0414] [Third Embodiment]

[0415] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0416] 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.

[0417] 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).

[0418] 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.

[0419] 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.

[0420] 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).

[0421] 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.

[0422] 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.

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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".

[0427] As an embodiment of the present invention, an infrastructure management system using autonomous devices and a central device will be described. This system includes a process in which the autonomous devices patrol structures along a pre-set route and collect images and external environmental data.

[0428] Specifically, the user inputs information about the target structure from their terminal into the server and sets a patrol route. The autonomous device is equipped with high-precision imaging capabilities and acquires detailed image data and external environmental data during the patrol. The acquired data is transmitted to the server in real time, and the server analyzes this data to identify the location of the problem.

[0429] For example, when inspecting a bridge, the autonomous device photographs the bridge piers and girders to detect corrosion and cracks. The server uses AI algorithms to analyze these defects and determine their priority. Prioritization is evaluated based on safety and the urgency of repairs, and based on this, the server generates an efficient and effective repair plan.

[0430] The generated repair plan is visualized as a three-dimensional model and presented to the user. The device then uses this to help the user decide on specific repair actions. Furthermore, the provision of the three-dimensional model facilitates a detailed visual understanding of the faulty area.

[0431] Furthermore, the server considers real-time external environmental data such as weather and terrain, and optimizes the autonomous device's patrol route accordingly. This ensures the safe and efficient operation of the autonomous device.

[0432] Thus, the present invention aims to improve the efficiency of structural inspection and repair planning through the cooperation of autonomous devices and a central device, enabling the rapid detection and response to defects.

[0433] The following describes the processing flow.

[0434] Step 1:

[0435] The user inputs information about structures from a terminal and sets a patrol route. This information is sent to a server, and an autonomous flight plan for the drone is created.

[0436] Step 2:

[0437] The server transmits the patrol route to the autonomous device and issues a command to begin patrolling. The autonomous device flies around the structure according to the set route.

[0438] Step 3:

[0439] The autonomous device uses high-precision imaging techniques to acquire image data of structures and external environmental data during flight. This data is transmitted to a server in real time.

[0440] Step 4:

[0441] The server analyzes the received data using an AI algorithm and identifies defective areas through image recognition. It detects abnormalities such as cracks and corrosion.

[0442] Step 5:

[0443] The server assesses the severity of the malfunction and prioritizes repairs. Based on this assessment, a specific repair plan is developed.

[0444] Step 6:

[0445] The server generates a three-dimensional model of the faulty area and repair plan, and presents it to the user via the terminal. This allows the user to visually confirm the details of the problem.

[0446] Step 7:

[0447] The device provides users with information about repair plans and priorities, helping them make appropriate decisions.

[0448] Step 8:

[0449] The server optimizes new flight paths for the autonomous devices based on weather and real-time environmental conditions, preparing for future patrols.

[0450] (Example 1)

[0451] 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."

[0452] Existing infrastructure management systems struggle to quickly and accurately detect structural deterioration and damage, and to effectively plan repairs. Furthermore, conventional methods lack flexible route optimization in response to weather and terrain changes, hindering the safe and efficient operation of autonomous devices. Therefore, there is a need for increased efficiency and reliability in structural inspection work.

[0453] 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.

[0454] In this invention, the server includes means for analyzing acquired image information and external environmental information to identify faulty locations, means for determining the priority of faulty locations based on the information analyzed using a generated AI model and generating a repair plan, and means for optimizing the patrol route of the autonomous device considering real-time external environmental information. This enables rapid detection of structural defects, effective planning of repairs, and safe operation of the autonomous device.

[0455] An "autonomous device" is a device that patrols structures according to a set route and has the function of collecting image information and external environmental information.

[0456] A "photography device" is a device attached to an autonomous device for acquiring image information of a structure.

[0457] A "central device" is a device that analyzes acquired data, identifies faulty areas, and generates repair plans.

[0458] "Image information" refers to data that visually records the condition of a structure.

[0459] "External environmental information" refers to data about the external environmental conditions during patrols, including, for example, temperature and humidity.

[0460] A "generative AI model" refers to artificial intelligence technology used in data analysis, which contributes to identifying defects and determining priorities.

[0461] A "defective area" refers to a region in a structure where deterioration or damage has been detected.

[0462] "Priority" refers to the order in which identified defects should be repaired, and is set based on safety and urgency.

[0463] A "repair plan" refers to a plan that includes specific procedures and schedules designed to fix a malfunction.

[0464] A "three-dimensional structural model" refers to a three-dimensional digital representation created to visually show defects in a structure.

[0465] "Route optimization" refers to the process of making the patrol routes of autonomous devices safe and efficient by taking real-time external environmental information into consideration.

[0466] As an embodiment of this invention, an infrastructure management system realized through the collaboration of users, servers, and terminals will be described.

[0467] The user uses a terminal to input the necessary information about the target structure. For example, when inspecting a bridge, the user inputs the bridge's name and location into the terminal and sets a patrol route. The autonomous device then begins patrolling according to the specified route, including the bridge's piers and girders.

[0468] The autonomous device is equipped with a high-precision imaging system that acquires detailed image information of structures and external environmental information during patrols. The autonomous device transmits this information to a server in real time. The server receives this data and analyzes it using a generated AI model. The purpose of the analysis is to identify defects in structures at an early stage.

[0469] The server identifies faulty areas based on the acquired data and determines their priority. Prioritization is set based on safety and the urgency of repair. The server also generates a specific repair plan from this information. This repair plan is visualized as a three-dimensional structural model and sent to the terminal. The user can review this information from the terminal and plan detailed repair actions. A concrete example of this would be a prompt message such as, "Detect cracks in the bridge and incorporate them into next week's repair plan."

[0470] Furthermore, the server takes real-time external environmental information such as weather and terrain into consideration and optimizes the autonomous device's patrol route accordingly, thereby ensuring the safe and efficient operation of the autonomous device.

[0471] In this way, collaboration between users, servers, and terminals enables rapid and precise inspection of structures and improves the efficiency of repair work.

[0472] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0473] Step 1:

[0474] The user inputs structure information using a terminal. This information includes the structure's name, location, and patrol route settings. This information is sent to the server, which then receives the basic data needed to identify the structures and routes to be patrolled.

[0475] Step 2:

[0476] The autonomous device begins patrolling according to a route transmitted from the server. It records the condition of the structure using imaging devices and acquires external environmental information using environmental sensors. The acquired images and external environmental information are transmitted to the server in real time. This allows the server to receive a dataset to understand the current state of the structure.

[0477] Step 3:

[0478] The server inputs the acquired images and external environmental information into a generating AI model and analyzes the data. Specifically, it performs a process of detecting deterioration and damage to structures through image analysis. As a result of the analysis, the defective areas are identified, and this information is used for the next processing step.

[0479] Step 4:

[0480] Based on the fault information from the analysis results, the server prioritizes fault locations based on safety and repair urgency. It then uses this priority to generate an efficient repair plan. The generated repair plan is visualized and presented to the user.

[0481] Step 5:

[0482] The terminal presents the user with a three-dimensional structural model based on the repair plan received from the server. The user visually confirms this information and formulates a plan for specific repair actions. This allows for a more systematic execution of repair work.

[0483] Step 6:

[0484] The server optimizes the autonomous robot's patrol route based on real-time external environmental information, such as weather and terrain. This optimization enables efficient and safe operation of the autonomous robot.

[0485] (Application Example 1)

[0486] 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."

[0487] Traditional structural inspection methods often rely heavily on manual labor, resulting in time-consuming and costly inspections, as well as challenges in overlooking defects and ensuring safety. Furthermore, inspections can be difficult depending on external environmental conditions, highlighting the need for efficient inspections and rapid response. Additionally, there has been a lack of methods to present complex defect information in a way that is easily understandable to users.

[0488] 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.

[0489] In this invention, the server includes means for receiving and analyzing data collected by autonomous devices in real time via a communication device, means for creating and providing a detailed report of the faulty area to the user using a generated AI model, and means for optimizing the flight path to realize an optimal flight plan according to external environmental conditions. This enables efficient and advanced inspection work and the presentation of fault information in an easy-to-understand manner.

[0490] An "autonomous device" is a device that has the function of patrolling according to a pre-set route and collecting data.

[0491] "Image and external environment data" refers to information used to capture the condition of a structure and its surrounding environment in detail, and is collected through photographic means.

[0492] A "central device" is a device that analyzes collected images and external environmental data to identify faulty areas and generate repair plans.

[0493] A "repair plan" is a specific repair plan generated based on analyzed information, taking into account the priority of the faulty areas.

[0494] A "three-dimensional model" is a digital model that visualizes defects in a structure in three dimensions, making it easier for users to understand the details.

[0495] A "communication device" is a device that sends and receives data between an autonomous device and a server, enabling real-time information exchange.

[0496] A "generative AI model" is an artificial intelligence system that learns from large amounts of data and automatically performs tasks such as analyzing faulty areas and creating detailed reports.

[0497] A "prompt message" is an explanatory text that organizes and presents information to make it easier for users to understand the AI ​​analysis results.

[0498] The system for implementing this invention mainly consists of an autonomous device, a central device (server), a communication device, and terminals. The autonomous device optimizes the patrol route while considering terrain and weather conditions, and collects detailed image data of structures and external environmental data in real time. This data is transmitted to the server via the communication device.

[0499] The server analyzes the received data using a generating AI model to identify structural defects and their priority. Based on the analysis results, it visualizes a repair plan as a detailed three-dimensional model and generates specific repair instructions. Furthermore, the server sends this information to the user's terminal, presenting the defect information in an easy-to-understand manner using prompt messages.

[0500] The server also completes the analysis based on the generated AI model and provides reports to users in an easy-to-understand format. For example, it presents the results of crack detection on the exterior wall of a commercial building along with images collected by the autonomous device, and helps users understand the current situation with a prompt message such as, "We are conducting an inspection of the exterior wall of a commercial building. Please detect cracks and color changes on the exterior wall and generate a report."

[0501] Specifically, the system utilizes smartphones, drones, and servers as hardware, and employs Python-based image recognition (OpenCV), data analysis (TensorFlow or PyTorch), and real-time communication technologies (WebSocket or MQTT) as software. Furthermore, a generative AI model enables detailed information analysis, allowing users to easily identify problems that require attention. This will facilitate efficient and rapid maintenance of structures.

[0502] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0503] Step 1:

[0504] The autonomous device begins patrolling according to a pre-set route. Inputs are the starting point and patrol route information. Outputs are real-time images and external environment data acquired during the patrol. This data is collected by the autonomous device's imaging capabilities.

[0505] Step 2:

[0506] The autonomous device collects images and external environmental data and transmits them to a server via a communication device. The input consists of the acquired images and environmental data. The output is the data awaiting analysis, which has been sent to the server. This data is transmitted in real time.

[0507] Step 3:

[0508] The server analyzes the received data using a generating AI model. The input consists of the transmitted image and environmental data, and the output is the result of identifying and prioritizing the problem areas. In this analysis, the AI ​​uses image data to determine whether or not there are problems.

[0509] Step 4:

[0510] Based on the analysis results, the server visualizes the faulty areas as a three-dimensional model and generates a repair plan. The input is data identifying and prioritizing the faulty areas. The output is a visualized three-dimensional model and a detailed repair plan. The server generates the model using 3D modeling software.

[0511] Step 5:

[0512] The server sends the generated 3D model and repair plan to the terminal along with prompts. The inputs are the 3D model, the repair plan, and the prompts. The output is the information presented to the terminal. The prompts make the information easier for the user to understand.

[0513] Step 6:

[0514] The user reviews the information presented through the terminal and decides on repair actions as needed. The input consists of the presented 3D model and repair plan information, while the output is the specific repair decision and its implementation. The user then develops a work plan based on this.

[0515] 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.

[0516] This invention relates to an infrastructure management system combining an autonomous device, a central device, and an emotion engine. The purpose of this system is not only to perform efficient inspection and repair of structures, but also to improve usability by presenting information while considering the user's emotions.

[0517] First, the user uses a terminal to input structural information and set a patrol route. This information is sent to a server, which then sends an appropriate patrol plan to the autonomous device. The autonomous device follows this plan, automatically patrolling the structure and collecting image data and environmental data using various sensors and cameras. This data is sent to the server in real time and analyzed.

[0518] The server identifies faulty areas through data analysis and determines the priority of repairs for those faults. During this process, an emotion engine connected to the server recognizes user reactions and analyzes the user's emotional state. For example, if a user is feeling confused, the emotion engine detects this and decides whether to request a more detailed explanation or change the interface being used.

[0519] For example, when a user receives information about bridge repairs, the terminal displays a 3D model of the damaged area. If the emotion engine determines that the user is feeling anxious, the server provides additional explanations and emphasizes safety-related information.

[0520] Furthermore, the emotion engine continuously learns future information presentation methods based on user feedback. This allows the system to continuously improve, enhancing the user experience.

[0521] Therefore, through the embodiment of the present invention, the addition of an emotion engine makes it possible to realize an infrastructure management system that is easier for users to use and can be used with peace of mind.

[0522] The following describes the processing flow.

[0523] Step 1:

[0524] The user inputs information about structures from a terminal and sets a patrol route. This information is sent to a server, which then creates a flight plan for the autonomous device.

[0525] Step 2:

[0526] The server transmits instructions for the patrol route to the autonomous device and initiates flight. The autonomous device patrols the structure according to the plan and collects image data and external environmental data using imaging devices.

[0527] Step 3:

[0528] The data collected by the autonomous device is transmitted to the server in real time. The server analyzes this data to identify defects in the structure.

[0529] Step 4:

[0530] The server assesses the severity of the problem, determines its priority, and then generates an appropriate repair plan.

[0531] Step 5:

[0532] The server sends the generated repair plan and a 3D model of the faulty area to the terminal. The terminal then presents this to the user, allowing the user to visually confirm the information.

[0533] Step 6:

[0534] An emotion engine built into the device analyzes the user's emotional state in real time. The emotion engine detects the user's facial expressions and tone of voice to identify their emotions.

[0535] Step 7:

[0536] Based on information from the emotion engine, the server presents information tailored to the user's emotional state. For example, if the user is feeling anxious, it will provide additional explanations or emphasized safety information.

[0537] Step 8:

[0538] The user inputs feedback into the device regarding the information received. The device sends this feedback to the server, where it is stored as training data for the emotion engine. This allows the system to optimize monitoring and information presentation in the future.

[0539] (Example 2)

[0540] 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."

[0541] While efficient inspection and repair are necessary in modern infrastructure management systems, information is not adequately presented in a way that considers the psychological state of users. As a result, users may receive insufficient information or experience anxiety. Furthermore, efficiency in the process of quickly and accurately identifying structural defects and determining their priority remains a challenge. In addition, there is a lack of flexible improvements in information presentation and insufficient mechanisms to prevent user feedback.

[0542] 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.

[0543] In this invention, the server includes means for an emotion analysis device to analyze the user's psychological state and present appropriate information, means for presenting the generated repair plan and a three-dimensional representation of the faulty area to the user, and means for learning user feedback and improving the information presentation method. This makes it possible to present information to the user in a more reassuring and easy-to-understand manner, and enables the efficient operation of the infrastructure management system.

[0544] An "autonomous device" is a device that inspects a target object according to a pre-set inspection route and collects images and external environmental information.

[0545] An "acquisition device" is a device, including sensors and cameras, that is mounted on an autonomous device and used to collect images of the monitored object and external environmental information during inspection.

[0546] The "central device" is a central piece of equipment that analyzes collected images and external environmental information, identifies faulty areas, and generates repair plans.

[0547] "Image and external environmental information" refers to visual data of the monitored object and environmental data such as temperature and humidity around it, collected by the acquisition device.

[0548] An "emotional analysis device" is a device that analyzes the user's psychological state and has analytical functions to present the user with appropriate information.

[0549] "Three-dimensional representation" refers to a three-dimensional model used to visually represent the faulty parts of a monitored system in detail.

[0550] "User feedback" refers to information that includes reactions and opinions from users regarding their use of the system.

[0551] This invention realizes an infrastructure management system that combines autonomous devices, a central device, and an emotion analysis device. The system aims to provide information that takes into account the user's psychological state, in addition to efficient inspection and repair.

[0552] First, the user inputs information about the targets to be monitored via their device and sends it to the server. This information includes an overview of the targets and their monitoring priorities.

[0553] Next, the server uses a generative AI model based on the received information to create a patrol plan suitable for the autonomous device and sends instructions to the autonomous device as prompt messages. An example of a prompt message here would be, "In the next patrol, inspect the bridge structure in detail and look for signs of structural failure."

[0554] The autonomous device inspects designated monitoring targets according to the patrol plan received from the server. The autonomous device is equipped with various sensors and cameras to collect images and external environmental information. This information is transmitted to the server in real time.

[0555] The server uses the collected data to apply image analysis technology and identify the faulty area. Once the faulty area is identified, the server develops a repair plan and generates a three-dimensional representation of the faulty area. Furthermore, the server analyzes the user's psychological state through an emotion analysis device and provides information tailored to the user. This information may include additional safety information and detailed explanations.

[0556] Finally, the server receives feedback from users and continuously improves how information is presented. This feedback is used to optimize the interface and improve the information content for future use.

[0557] In this way, an efficient and reliable infrastructure management system is built.

[0558] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0559] Step 1:

[0560] The user uses a terminal to input information about the items to be monitored and the priorities of patrol tasks. The input data includes the location of the structure and the purpose of the inspection. The terminal sends this data to the server. The output of this process is patrol plan data received by the server.

[0561] Step 2:

[0562] The server uses a generated AI model based on the received data to formulate the optimal patrol plan. The data processing here involves analyzing the input monitoring target information and generating the most efficient patrol route as a prompt message. This output is the patrol instruction sent to the autonomous device.

[0563] Step 3:

[0564] The autonomous device inspects designated monitoring targets based on patrol instructions received from the server. The device collects image data and environmental information using cameras and sensors. This collected data is then transmitted to the server in real time. The output consists of images and environmental data sent to the server.

[0565] Step 4:

[0566] The server analyzes the received image and environmental data to identify the faulty areas. This process uses image recognition technology to process the data and detect problems. The output of the data calculation is the identified faulty areas and their detailed information.

[0567] Step 5:

[0568] The server uses an emotion analysis device to analyze the user's psychological state. It uses reaction data from when the user views information via a terminal as an input component for analysis. The output is personalized information presented to the user.

[0569] Step 6:

[0570] The device displays a three-dimensional representation of the faulty area and information regarding the need for repair to the user. Furthermore, if the emotion analysis device detects user anxiety, additional information is provided from the server, supplementing the explanation with reassuring details. The output of this process is detailed and appropriate content that the user receives on the device.

[0571] Step 7:

[0572] User feedback is sent to the server via the device. The server uses this feedback to learn how to present information and improve it for future use. The output is an improved information presentation protocol designed to enhance the user experience.

[0573] (Application Example 2)

[0574] 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."

[0575] The present invention aims to improve the efficient management of urban infrastructure and enhance user comfort. Existing infrastructure management systems suffer from insufficient efficiency in inspection and repair, and lack information presentation that takes user feelings into consideration. As a result, many users feel uneasy about the safety of structures, and there are also issues with the usability of the system.

[0576] 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.

[0577] This invention includes a server that recognizes the user's emotional state using emotion analysis means and optimizes information presentation, a means for learning and improving the information presentation method based on user feedback, and a means for an autonomous device to patrol physical structures according to a pre-set route. This enables efficient inspection and repair of urban infrastructure, and realizes a system that is safe and easy for users to use.

[0578] An "autonomous device" is a device that operates automatically according to a set route, and is a device that patrols structures and collects necessary data.

[0579] "Means of photographing" refers to techniques or devices for acquiring images of physical structures during patrols, and includes sensors and cameras.

[0580] A "central system" is a computer system that analyzes collected data and identifies the location of malfunctions.

[0581] A "defective area" refers to a part of the infrastructure that is abnormal or requires repair.

[0582] A "repair plan" is a specific action plan formulated to fix a problem.

[0583] "Three-dimensional visualization data" refers to digital data used to display defective areas in three dimensions, serving as a means to facilitate visual understanding for users.

[0584] "Emotional analysis means" refers to a technology or system that recognizes and analyzes a user's emotional state.

[0585] "User feedback" refers to opinions and reactions from users regarding the use of the system, and is information that helps improve the system.

[0586] To realize this invention, several key components must function in close coordination. This system combines autonomous devices, a central device, and emotion analysis functions to efficiently manage urban infrastructure and provide users with intuitive and reassuring information.

[0587] The server first controls the autonomous device, guiding it to patrol the physical structure along a pre-configured route. During this process, the autonomous device uses high-precision sensors and imaging devices to collect image data of the structure and external environmental data in real time. This data is instantly transmitted to the central device, where it is analyzed using image processing technology. This analysis utilizes machine learning models, such as TensorFlow, to identify faulty areas and prioritize repair plans.

[0588] Subsequently, the central device generates three-dimensional visualization data and presents it to the user's terminal. This visualization uses 3D graphics technologies such as Unity to create a three-dimensional and intuitive model. Simultaneously, the system analyzes the user's emotional state through emotion analysis tools and dynamically changes the content presented. Specifically, it analyzes the user's voice feedback and text input using Google Cloud Speech-to-Text and OpenAI's generative AI models, highlighting aspects that evoke a sense of security or require detailed information.

[0589] Users review the information obtained through their devices and provide feedback on any questions or concerns. This feedback is analyzed by an emotion analysis system and used to improve future information presentation methods. When a user asks the system a question using a prompt such as, "Please tell me more about the safety of this road," the AI ​​immediately provides detailed information, optimized to alleviate the user's concerns. These steps ensure that urban infrastructure is maintained efficiently and in a user-friendly manner.

[0590] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0591] Step 1:

[0592] The server transmits patrol route information to the autonomous device. The inputs used are geographical data of urban infrastructure and maintenance schedule information. Based on this information, the autonomous device begins moving along a pre-set route and is autonomously operated to patrol the structures.

[0593] Step 2:

[0594] The autonomous device acquires image data of structures and external environmental data using its onboard sensors and cameras during patrols. Here, it senses the surrounding conditions and the physical state of the structure as input, and generates acquired digital images and sensor data as output.

[0595] Step 3:

[0596] The server receives image data and environmental data transmitted from the autonomous device and analyzes this data. It processes the image data using a machine learning model (e.g., TensorFlow) to identify the location of the defect. The input for the analysis is the received raw data, and the output is identification information of the defect location.

[0597] Step 4:

[0598] The server determines the priority of fault locations based on the analysis results. Using the analyzed fault data as input, it generates a priority list for repair plans as output. This list is automatically evaluated according to pre-set criteria and urgency levels.

[0599] Step 5:

[0600] The server generates a repair plan based on priorities, creating it as three-dimensional visualization data and sending it to the user's terminal. Using 3D graphics technology such as Unity, the input is information on priorities and repair locations, and the output is a three-dimensional model.

[0601] Step 6:

[0602] The user reviews the three-dimensional visualization data presented via the terminal. Any feedback or questions provided by the user are entered via voice or text. The output consists of the review results and feedback data.

[0603] Step 7:

[0604] The server analyzes user feedback using sentiment analysis tools. It uses Google Cloud Speech-to-Text and OpenAI's generative AI models to analyze the input feedback data and identify the user's emotions. The output is information about the emotional state.

[0605] Step 8:

[0606] The server dynamically adjusts the information presentation method based on the analyzed emotional state. For example, if the user feels anxious, it will emphasize and present more detailed safety information. The input is the user's emotional information, and the output is the optimized information presentation.

[0607] 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.

[0608] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One 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.

[0609] 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.

[0610] [Fourth Embodiment]

[0611] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0612] 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.

[0613] 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).

[0614] 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.

[0615] 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.

[0616] 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).

[0617] 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.

[0618] 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.

[0619] 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.

[0620] 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.

[0621] 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.

[0622] 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.

[0623] 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".

[0624] As an embodiment of the present invention, an infrastructure management system using autonomous devices and a central device will be described. This system includes a process in which the autonomous devices patrol structures along a pre-set route and collect images and external environmental data.

[0625] Specifically, the user inputs information about the target structure from their terminal into the server and sets a patrol route. The autonomous device is equipped with high-precision imaging capabilities and acquires detailed image data and external environmental data during the patrol. The acquired data is transmitted to the server in real time, and the server analyzes this data to identify the location of the problem.

[0626] For example, when inspecting a bridge, the autonomous device photographs the bridge piers and girders to detect corrosion and cracks. The server uses AI algorithms to analyze these defects and determine their priority. Prioritization is evaluated based on safety and the urgency of repairs, and based on this, the server generates an efficient and effective repair plan.

[0627] The generated repair plan is visualized as a three-dimensional model and presented to the user. The device then uses this to help the user decide on specific repair actions. Furthermore, the provision of the three-dimensional model facilitates a detailed visual understanding of the faulty area.

[0628] Furthermore, the server considers real-time external environmental data such as weather and terrain, and optimizes the autonomous device's patrol route accordingly. This ensures the safe and efficient operation of the autonomous device.

[0629] Thus, the present invention aims to improve the efficiency of structural inspection and repair planning through the cooperation of autonomous devices and a central device, enabling the rapid detection and response to defects.

[0630] The following describes the processing flow.

[0631] Step 1:

[0632] The user inputs information about structures from a terminal and sets a patrol route. This information is sent to a server, and an autonomous flight plan for the drone is created.

[0633] Step 2:

[0634] The server transmits the patrol route to the autonomous device and issues a command to begin patrolling. The autonomous device flies around the structure according to the set route.

[0635] Step 3:

[0636] The autonomous device uses high-precision imaging techniques to acquire image data of structures and external environmental data during flight. This data is transmitted to a server in real time.

[0637] Step 4:

[0638] The server analyzes the received data using an AI algorithm and identifies defective areas through image recognition. It detects abnormalities such as cracks and corrosion.

[0639] Step 5:

[0640] The server assesses the severity of the malfunction and prioritizes repairs. Based on this assessment, a specific repair plan is developed.

[0641] Step 6:

[0642] The server generates a three-dimensional model of the faulty area and repair plan, and presents it to the user via the terminal. This allows the user to visually confirm the details of the problem.

[0643] Step 7:

[0644] The device provides users with information about repair plans and priorities, helping them make appropriate decisions.

[0645] Step 8:

[0646] The server optimizes new flight paths for the autonomous devices based on weather and real-time environmental conditions, preparing for future patrols.

[0647] (Example 1)

[0648] 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".

[0649] Existing infrastructure management systems struggle to quickly and accurately detect structural deterioration and damage, and to effectively plan repairs. Furthermore, conventional methods lack flexible route optimization in response to weather and terrain changes, hindering the safe and efficient operation of autonomous devices. Therefore, there is a need for increased efficiency and reliability in structural inspection work.

[0650] 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.

[0651] In this invention, the server includes means for analyzing acquired image information and external environmental information to identify faulty locations, means for determining the priority of faulty locations based on the information analyzed using a generated AI model and generating a repair plan, and means for optimizing the patrol route of the autonomous device considering real-time external environmental information. This enables rapid detection of structural defects, effective planning of repairs, and safe operation of the autonomous device.

[0652] An "autonomous device" is a device that patrols structures according to a set route and has the function of collecting image information and external environmental information.

[0653] A "photography device" is a device attached to an autonomous device for acquiring image information of a structure.

[0654] A "central device" is a device that analyzes acquired data, identifies faulty areas, and generates repair plans.

[0655] "Image information" refers to data that visually records the condition of a structure.

[0656] "External environmental information" refers to data about the external environmental conditions during patrols, including, for example, temperature and humidity.

[0657] A "generative AI model" refers to artificial intelligence technology used in data analysis, which contributes to identifying defects and determining priorities.

[0658] A "defective area" refers to a region in a structure where deterioration or damage has been detected.

[0659] "Priority" refers to the order in which identified defects should be repaired, and is set based on safety and urgency.

[0660] A "repair plan" refers to a plan that includes specific procedures and schedules designed to fix a malfunction.

[0661] A "three-dimensional structural model" refers to a three-dimensional digital representation created to visually show defects in a structure.

[0662] "Route optimization" refers to the process of making the patrol routes of autonomous devices safe and efficient by taking real-time external environmental information into consideration.

[0663] As an embodiment of this invention, an infrastructure management system realized through the collaboration of users, servers, and terminals will be described.

[0664] The user uses a terminal to input the necessary information about the target structure. For example, when inspecting a bridge, the user inputs the bridge's name and location into the terminal and sets a patrol route. The autonomous device then begins patrolling according to the specified route, including the bridge's piers and girders.

[0665] The autonomous device is equipped with a high-precision imaging system that acquires detailed image information of structures and external environmental information during patrols. The autonomous device transmits this information to a server in real time. The server receives this data and analyzes it using a generated AI model. The purpose of the analysis is to identify defects in structures at an early stage.

[0666] The server identifies faulty areas based on the acquired data and determines their priority. Prioritization is set based on safety and the urgency of repair. The server also generates a specific repair plan from this information. This repair plan is visualized as a three-dimensional structural model and sent to the terminal. The user can review this information from the terminal and plan detailed repair actions. A concrete example of this would be a prompt message such as, "Detect cracks in the bridge and incorporate them into next week's repair plan."

[0667] Furthermore, the server takes real-time external environmental information such as weather and terrain into consideration and optimizes the autonomous device's patrol route accordingly, thereby ensuring the safe and efficient operation of the autonomous device.

[0668] In this way, collaboration between users, servers, and terminals enables rapid and precise inspection of structures and improves the efficiency of repair work.

[0669] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0670] Step 1:

[0671] The user inputs structure information using a terminal. This information includes the structure's name, location, and patrol route settings. This information is sent to the server, which then receives the basic data needed to identify the structures and routes to be patrolled.

[0672] Step 2:

[0673] The autonomous device begins patrolling according to a route transmitted from the server. It records the condition of the structure using imaging devices and acquires external environmental information using environmental sensors. The acquired images and external environmental information are transmitted to the server in real time. This allows the server to receive a dataset to understand the current state of the structure.

[0674] Step 3:

[0675] The server inputs the acquired images and external environmental information into a generating AI model and analyzes the data. Specifically, it performs a process of detecting deterioration and damage to structures through image analysis. As a result of the analysis, the defective areas are identified, and this information is used for the next processing step.

[0676] Step 4:

[0677] Based on the fault information from the analysis results, the server prioritizes fault locations based on safety and repair urgency. It then uses this priority to generate an efficient repair plan. The generated repair plan is visualized and presented to the user.

[0678] Step 5:

[0679] The terminal presents the user with a three-dimensional structural model based on the repair plan received from the server. The user visually confirms this information and formulates a plan for specific repair actions. This allows for a more systematic execution of repair work.

[0680] Step 6:

[0681] The server optimizes the autonomous robot's patrol route based on real-time external environmental information, such as weather and terrain. This optimization enables efficient and safe operation of the autonomous robot.

[0682] (Application Example 1)

[0683] 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".

[0684] Traditional structural inspection methods often rely heavily on manual labor, resulting in time-consuming and costly inspections, as well as challenges in overlooking defects and ensuring safety. Furthermore, inspections can be difficult depending on external environmental conditions, highlighting the need for efficient inspections and rapid response. Additionally, there has been a lack of methods to present complex defect information in a way that is easily understandable to users.

[0685] 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.

[0686] In this invention, the server includes means for receiving and analyzing data collected by autonomous devices in real time via a communication device, means for creating and providing a detailed report of the faulty area to the user using a generated AI model, and means for optimizing the flight path to realize an optimal flight plan according to external environmental conditions. This enables efficient and advanced inspection work and the presentation of fault information in an easy-to-understand manner.

[0687] An "autonomous device" is a device that has the function of patrolling according to a pre-set route and collecting data.

[0688] "Image and external environment data" refers to information used to capture the condition of a structure and its surrounding environment in detail, and is collected through photographic means.

[0689] A "central device" is a device that analyzes collected images and external environmental data to identify faulty areas and generate repair plans.

[0690] A "repair plan" is a specific repair plan generated based on analyzed information, taking into account the priority of the faulty areas.

[0691] A "three-dimensional model" is a digital model that visualizes defects in a structure in three dimensions, making it easier for users to understand the details.

[0692] A "communication device" is a device that sends and receives data between an autonomous device and a server, enabling real-time information exchange.

[0693] A "generative AI model" is an artificial intelligence system that learns from large amounts of data and automatically performs tasks such as analyzing faulty areas and creating detailed reports.

[0694] A "prompt message" is an explanatory text that organizes and presents information to make it easier for users to understand the AI ​​analysis results.

[0695] The system for implementing this invention mainly consists of an autonomous device, a central device (server), a communication device, and terminals. The autonomous device optimizes the patrol route while considering terrain and weather conditions, and collects detailed image data of structures and external environmental data in real time. This data is transmitted to the server via the communication device.

[0696] The server analyzes the received data using a generating AI model to identify structural defects and their priority. Based on the analysis results, it visualizes a repair plan as a detailed three-dimensional model and generates specific repair instructions. Furthermore, the server sends this information to the user's terminal, presenting the defect information in an easy-to-understand manner using prompt messages.

[0697] The server also completes the analysis based on the generated AI model and provides reports to users in an easy-to-understand format. For example, it presents the results of crack detection on the exterior wall of a commercial building along with images collected by the autonomous device, and helps users understand the current situation with a prompt message such as, "We are conducting an inspection of the exterior wall of a commercial building. Please detect cracks and color changes on the exterior wall and generate a report."

[0698] Specifically, the system utilizes smartphones, drones, and servers as hardware, and employs Python-based image recognition (OpenCV), data analysis (TensorFlow or PyTorch), and real-time communication technologies (WebSocket or MQTT) as software. Furthermore, a generative AI model enables detailed information analysis, allowing users to easily identify problems that require attention. This will facilitate efficient and rapid maintenance of structures.

[0699] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0700] Step 1:

[0701] The autonomous device begins patrolling according to a pre-set route. Inputs are the starting point and patrol route information. Outputs are real-time images and external environment data acquired during the patrol. This data is collected by the autonomous device's imaging capabilities.

[0702] Step 2:

[0703] The autonomous device collects images and external environmental data and transmits them to a server via a communication device. The input consists of the acquired images and environmental data. The output is the data awaiting analysis, which has been sent to the server. This data is transmitted in real time.

[0704] Step 3:

[0705] The server analyzes the received data using a generating AI model. The input consists of the transmitted image and environmental data, and the output is the result of identifying and prioritizing the problem areas. In this analysis, the AI ​​uses image data to determine whether or not there are problems.

[0706] Step 4:

[0707] Based on the analysis results, the server visualizes the faulty areas as a three-dimensional model and generates a repair plan. The input is data identifying and prioritizing the faulty areas. The output is a visualized three-dimensional model and a detailed repair plan. The server generates the model using 3D modeling software.

[0708] Step 5:

[0709] The server sends the generated 3D model and repair plan to the terminal along with prompts. The inputs are the 3D model, the repair plan, and the prompts. The output is the information presented to the terminal. The prompts make the information easier for the user to understand.

[0710] Step 6:

[0711] The user reviews the information presented through the terminal and decides on repair actions as needed. The input consists of the presented 3D model and repair plan information, while the output is the specific repair decision and its implementation. The user then develops a work plan based on this.

[0712] 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.

[0713] This invention relates to an infrastructure management system combining an autonomous device, a central device, and an emotion engine. The purpose of this system is not only to perform efficient inspection and repair of structures, but also to improve usability by presenting information while considering the user's emotions.

[0714] First, the user uses a terminal to input structural information and set a patrol route. This information is sent to a server, which then sends an appropriate patrol plan to the autonomous device. The autonomous device follows this plan, automatically patrolling the structure and collecting image data and environmental data using various sensors and cameras. This data is sent to the server in real time and analyzed.

[0715] The server identifies faulty areas through data analysis and determines the priority of repairs for those faults. During this process, an emotion engine connected to the server recognizes user reactions and analyzes the user's emotional state. For example, if a user is feeling confused, the emotion engine detects this and decides whether to request a more detailed explanation or change the interface being used.

[0716] For example, when a user receives information about bridge repairs, the terminal displays a 3D model of the damaged area. If the emotion engine determines that the user is feeling anxious, the server provides additional explanations and emphasizes safety-related information.

[0717] Furthermore, the emotion engine continuously learns future information presentation methods based on user feedback. This allows the system to continuously improve, enhancing the user experience.

[0718] Therefore, through the embodiment of the present invention, the addition of an emotion engine makes it possible to realize an infrastructure management system that is easier for users to use and can be used with peace of mind.

[0719] The following describes the processing flow.

[0720] Step 1:

[0721] The user inputs information about structures from a terminal and sets a patrol route. This information is sent to a server, which then creates a flight plan for the autonomous device.

[0722] Step 2:

[0723] The server transmits instructions for the patrol route to the autonomous device and initiates flight. The autonomous device patrols the structure according to the plan and collects image data and external environmental data using imaging devices.

[0724] Step 3:

[0725] The data collected by the autonomous device is transmitted to the server in real time. The server analyzes this data to identify defects in the structure.

[0726] Step 4:

[0727] The server assesses the severity of the problem, determines its priority, and then generates an appropriate repair plan.

[0728] Step 5:

[0729] The server sends the generated repair plan and a 3D model of the faulty area to the terminal. The terminal then presents this to the user, allowing the user to visually confirm the information.

[0730] Step 6:

[0731] An emotion engine built into the device analyzes the user's emotional state in real time. The emotion engine detects the user's facial expressions and tone of voice to identify their emotions.

[0732] Step 7:

[0733] Based on information from the emotion engine, the server presents information tailored to the user's emotional state. For example, if the user is feeling anxious, it will provide additional explanations or emphasized safety information.

[0734] Step 8:

[0735] The user inputs feedback into the device regarding the information received. The device sends this feedback to the server, where it is stored as training data for the emotion engine. This allows the system to optimize monitoring and information presentation in the future.

[0736] (Example 2)

[0737] 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".

[0738] While efficient inspection and repair are necessary in modern infrastructure management systems, information is not adequately presented in a way that considers the psychological state of users. As a result, users may receive insufficient information or experience anxiety. Furthermore, efficiency in the process of quickly and accurately identifying structural defects and determining their priority remains a challenge. In addition, there is a lack of flexible improvements in information presentation and insufficient mechanisms to prevent user feedback.

[0739] 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.

[0740] In this invention, the server includes means for an emotion analysis device to analyze the user's psychological state and present appropriate information, means for presenting the generated repair plan and a three-dimensional representation of the faulty area to the user, and means for learning user feedback and improving the information presentation method. This makes it possible to present information to the user in a more reassuring and easy-to-understand manner, and enables the efficient operation of the infrastructure management system.

[0741] An "autonomous device" is a device that inspects a target object according to a pre-set inspection route and collects images and external environmental information.

[0742] An "acquisition device" is a device, including sensors and cameras, that is mounted on an autonomous device and used to collect images of the monitored object and external environmental information during inspection.

[0743] The "central device" is a central piece of equipment that analyzes collected images and external environmental information, identifies faulty areas, and generates repair plans.

[0744] "Image and external environmental information" refers to visual data of the monitored object and environmental data such as temperature and humidity around it, collected by the acquisition device.

[0745] An "emotional analysis device" is a device that analyzes the user's psychological state and has analytical functions to present the user with appropriate information.

[0746] "Three-dimensional representation" refers to a three-dimensional model used to visually represent the faulty parts of a monitored system in detail.

[0747] "User feedback" refers to information that includes reactions and opinions from users regarding their use of the system.

[0748] This invention realizes an infrastructure management system that combines autonomous devices, a central device, and an emotion analysis device. The system aims to provide information that takes into account the user's psychological state, in addition to efficient inspection and repair.

[0749] First, the user inputs information about the targets to be monitored via their device and sends it to the server. This information includes an overview of the targets and their monitoring priorities.

[0750] Next, the server uses a generative AI model based on the received information to create a patrol plan suitable for the autonomous device and sends instructions to the autonomous device as prompt messages. An example of a prompt message here would be, "In the next patrol, inspect the bridge structure in detail and look for signs of structural failure."

[0751] The autonomous device inspects designated monitoring targets according to the patrol plan received from the server. The autonomous device is equipped with various sensors and cameras to collect images and external environmental information. This information is transmitted to the server in real time.

[0752] The server uses the collected data to apply image analysis technology and identify the faulty area. Once the faulty area is identified, the server develops a repair plan and generates a three-dimensional representation of the faulty area. Furthermore, the server analyzes the user's psychological state through an emotion analysis device and provides information tailored to the user. This information may include additional safety information and detailed explanations.

[0753] Finally, the server receives feedback from users and continuously improves how information is presented. This feedback is used to optimize the interface and improve the information content for future use.

[0754] In this way, an efficient and reliable infrastructure management system is built.

[0755] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0756] Step 1:

[0757] The user uses a terminal to input information about the items to be monitored and the priorities of patrol tasks. The input data includes the location of the structure and the purpose of the inspection. The terminal sends this data to the server. The output of this process is patrol plan data received by the server.

[0758] Step 2:

[0759] The server uses a generated AI model based on the received data to formulate the optimal patrol plan. The data processing here involves analyzing the input monitoring target information and generating the most efficient patrol route as a prompt message. This output is the patrol instruction sent to the autonomous device.

[0760] Step 3:

[0761] The autonomous device inspects designated monitoring targets based on patrol instructions received from the server. The device collects image data and environmental information using cameras and sensors. This collected data is then transmitted to the server in real time. The output consists of images and environmental data sent to the server.

[0762] Step 4:

[0763] The server analyzes the received image and environmental data to identify the faulty areas. This process uses image recognition technology to process the data and detect problems. The output of the data calculation is the identified faulty areas and their detailed information.

[0764] Step 5:

[0765] The server uses an emotion analysis device to analyze the user's psychological state. It uses reaction data from when the user views information via a terminal as an input component for analysis. The output is personalized information presented to the user.

[0766] Step 6:

[0767] The device displays a three-dimensional representation of the faulty area and information regarding the need for repair to the user. Furthermore, if the emotion analysis device detects user anxiety, additional information is provided from the server, supplementing the explanation with reassuring details. The output of this process is detailed and appropriate content that the user receives on the device.

[0768] Step 7:

[0769] User feedback is sent to the server via the device. The server uses this feedback to learn how to present information and improve it for future use. The output is an improved information presentation protocol designed to enhance the user experience.

[0770] (Application Example 2)

[0771] 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".

[0772] The present invention aims to improve the efficient management of urban infrastructure and enhance user comfort. Existing infrastructure management systems suffer from insufficient efficiency in inspection and repair, and lack information presentation that takes user feelings into consideration. As a result, many users feel uneasy about the safety of structures, and there are also issues with the usability of the system.

[0773] 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.

[0774] This invention includes a server that recognizes the user's emotional state using emotion analysis means and optimizes information presentation, a means for learning and improving the information presentation method based on user feedback, and a means for an autonomous device to patrol physical structures according to a pre-set route. This enables efficient inspection and repair of urban infrastructure, and realizes a system that is safe and easy for users to use.

[0775] An "autonomous device" is a device that operates automatically according to a set route, and is a device that patrols structures and collects necessary data.

[0776] "Means of photographing" refers to techniques or devices for acquiring images of physical structures during patrols, and includes sensors and cameras.

[0777] A "central system" is a computer system that analyzes collected data and identifies the location of malfunctions.

[0778] A "defective area" refers to a part of the infrastructure that is abnormal or requires repair.

[0779] A "repair plan" is a specific action plan formulated to fix a problem.

[0780] "Three-dimensional visualization data" refers to digital data used to display defective areas in three dimensions, serving as a means to facilitate visual understanding for users.

[0781] "Emotional analysis means" refers to a technology or system that recognizes and analyzes a user's emotional state.

[0782] "User feedback" refers to opinions and reactions from users regarding the use of the system, and is information that helps improve the system.

[0783] To realize this invention, several key components must function in close coordination. This system combines autonomous devices, a central device, and emotion analysis functions to efficiently manage urban infrastructure and provide users with intuitive and reassuring information.

[0784] The server first controls the autonomous device, guiding it to patrol the physical structure along a pre-configured route. During this process, the autonomous device uses high-precision sensors and imaging devices to collect image data of the structure and external environmental data in real time. This data is instantly transmitted to the central device, where it is analyzed using image processing technology. This analysis utilizes machine learning models, such as TensorFlow, to identify faulty areas and prioritize repair plans.

[0785] Subsequently, the central device generates three-dimensional visualization data and presents it to the user's terminal. This visualization uses 3D graphics technologies such as Unity to create a three-dimensional and intuitive model. Simultaneously, the system analyzes the user's emotional state through emotion analysis tools and dynamically changes the content presented. Specifically, it analyzes the user's voice feedback and text input using Google Cloud Speech-to-Text and OpenAI's generative AI models, highlighting aspects that evoke a sense of security or require detailed information.

[0786] Users review the information obtained through their devices and provide feedback on any questions or concerns. This feedback is analyzed by an emotion analysis system and used to improve future information presentation methods. When a user asks the system a question using a prompt such as, "Please tell me more about the safety of this road," the AI ​​immediately provides detailed information, optimized to alleviate the user's concerns. These steps ensure that urban infrastructure is maintained efficiently and in a user-friendly manner.

[0787] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0788] Step 1:

[0789] The server transmits patrol route information to the autonomous device. The inputs used are geographical data of urban infrastructure and maintenance schedule information. Based on this information, the autonomous device begins moving along a pre-set route and is autonomously operated to patrol the structures.

[0790] Step 2:

[0791] The autonomous device acquires image data of structures and external environmental data using its onboard sensors and cameras during patrols. Here, it senses the surrounding conditions and the physical state of the structure as input, and generates acquired digital images and sensor data as output.

[0792] Step 3:

[0793] The server receives image data and environmental data transmitted from the autonomous device and analyzes this data. It processes the image data using a machine learning model (e.g., TensorFlow) to identify the location of the defect. The input for the analysis is the received raw data, and the output is identification information of the defect location.

[0794] Step 4:

[0795] The server determines the priority of fault locations based on the analysis results. Using the analyzed fault data as input, it generates a priority list for repair plans as output. This list is automatically evaluated according to pre-set criteria and urgency levels.

[0796] Step 5:

[0797] The server generates a repair plan based on priorities, creating it as three-dimensional visualization data and sending it to the user's terminal. Using 3D graphics technology such as Unity, the input is information on priorities and repair locations, and the output is a three-dimensional model.

[0798] Step 6:

[0799] The user reviews the three-dimensional visualization data presented via the terminal. Any feedback or questions provided by the user are entered via voice or text. The output consists of the review results and feedback data.

[0800] Step 7:

[0801] The server analyzes user feedback using sentiment analysis tools. It uses Google Cloud Speech-to-Text and OpenAI's generative AI models to analyze the input feedback data and identify the user's emotions. The output is information about the emotional state.

[0802] Step 8:

[0803] The server dynamically adjusts the information presentation method based on the analyzed emotional state. For example, if the user feels anxious, it will emphasize and present more detailed safety information. The input is the user's emotional information, and the output is the optimized information presentation.

[0804] 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.

[0805] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One 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.

[0806] 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 robot 414.

[0807] 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.

[0808] 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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."

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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 as being incorporated by reference.

[0825] The following is further disclosed regarding the embodiments described above.

[0826] (Claim 1)

[0827] The autonomous device has means for patrolling a structure according to a pre-set route,

[0828] A means for collecting images of structures and external environmental data during patrols using photographic means,

[0829] The central device analyzes collected images and external environmental data to identify the location of the malfunction,

[0830] A means for determining the priority of faulty areas based on the analyzed information and generating a repair plan,

[0831] A means of presenting the generated repair plan and a three-dimensional model of the faulty area to the user,

[0832] A system that includes this.

[0833] (Claim 2)

[0834] The system according to claim 1, wherein the autonomous device optimizes the flight path based on data acquired during patrol.

[0835] (Claim 3)

[0836] The system according to claim 1, wherein a central device generates a three-dimensional model and provides the user with a visual representation of the details of the defective area.

[0837] "Example 1"

[0838] (Claim 1)

[0839] The autonomous device has means for patrolling a structure according to a pre-set route,

[0840] A means for acquiring image information of structures and external environmental information during patrols using a camera,

[0841] The central device analyzes acquired image information and external environmental information to identify the location of the malfunction,

[0842] A means for determining the priority of faulty areas and generating a repair plan based on information analyzed using a generative AI model,

[0843] A means of presenting the generated repair plan and a three-dimensional structural model of the faulty area to the user,

[0844] The autonomous device has means for optimizing its patrol route by taking real-time external environmental information into consideration,

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, wherein the autonomous device optimizes the patrol route based on information acquired during the patrol.

[0848] (Claim 3)

[0849] The system according to claim 1, wherein a central device generates a three-dimensional structural model and provides the user with a visual representation of the details of the defective area.

[0850] "Application Example 1"

[0851] (Claim 1)

[0852] The autonomous device has means for patrolling a structure according to a pre-set route,

[0853] A means for collecting images of structures and external environmental data during patrols using photographic means,

[0854] The central device analyzes collected images and external environmental data to identify the location of the malfunction,

[0855] A means of determining the priority of faulty areas based on the analyzed information, generating a repair plan, and visualizing it as a three-dimensional model to present to the user,

[0856] A means of transmitting data collected by an autonomous device to a server in real time via a communication device,

[0857] A method for creating and providing users with detailed reports of defects using a generative AI model,

[0858] A system that includes this.

[0859] (Claim 2)

[0860] The system according to claim 1, wherein the autonomous device optimizes the flight path based on data acquired during patrol and realizes an optimal flight plan according to external environmental conditions.

[0861] (Claim 3)

[0862] The system according to claim 1, in which a central device uses a generative AI model to explain the analysis results in natural language and provides the information in a format that is easily understandable to the user using prompt sentences.

[0863] "Example 2 of combining an emotion engine"

[0864] (Claim 1)

[0865] The autonomous device includes means for inspecting the object to be monitored according to a pre-set inspection route,

[0866] A means for collecting images of the monitored object and external environmental information during inspection using an acquisition device,

[0867] The central device analyzes collected images and external environmental information to identify the location of the malfunction,

[0868] A means for determining the priority of faulty areas based on the analyzed information and generating a repair plan,

[0869] The emotion analysis device is a means of analyzing the user's psychological state and presenting the user with appropriate information.

[0870] A means of presenting the generated repair plan and a three-dimensional representation of the faulty area to the user,

[0871] A means of improving information presentation methods by learning from user feedback,

[0872] A system that includes this.

[0873] (Claim 2)

[0874] The system according to claim 1, wherein the autonomous device optimizes the operating path based on information acquired during inspection.

[0875] (Claim 3)

[0876] The system according to claim 1, wherein a central device generates a three-dimensional representation and provides the user with an intuitive view of the details of the faulty area.

[0877] "Application example 2 when combining with an emotional engine"

[0878] (Claim 1)

[0879] The autonomous device has means for patrolling physical structures according to a pre-set route,

[0880] A means for collecting images of physical structures and external environmental data during a patrol using photographic means,

[0881] The central device analyzes collected images and external environmental data to identify the location of the malfunction,

[0882] A means for determining the priority of faulty areas based on the analyzed information and generating a repair plan,

[0883] A means of presenting the generated repair plan and three-dimensional visualization data of the faulty areas to the user,

[0884] A means for recognizing the user's emotional state using emotion analysis methods and optimizing information presentation,

[0885] A means of learning and improving information presentation methods based on user feedback,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, wherein the autonomous device optimizes the travel route based on information acquired during patrol.

[0889] (Claim 3)

[0890] The system according to claim 1, wherein a central device generates three-dimensional visualization data and provides the user with a visual representation of the details of the defective area, and also provides additional explanations based on the sentiment analysis results. [Explanation of Symbols]

[0891] 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. The autonomous device has means for patrolling a structure according to a pre-set route, A means for collecting images of structures and external environmental data during patrols using photographic means, The central device analyzes collected images and external environmental data to identify the location of the malfunction, A means for determining the priority of faulty areas based on the analyzed information and generating a repair plan, A means of presenting the generated repair plan and a three-dimensional model of the faulty area to the user, A system that includes this.

2. The system according to claim 1, wherein the autonomous device optimizes the flight path based on data acquired during patrol.

3. The system according to claim 1, wherein a central device generates a three-dimensional model and provides the user with a visual representation of the details of the defective area.

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