system

Abstract

The system according to this embodiment aims to evaluate the risk of infrastructure deterioration and automatically determine the priority of repairs. [Solution] The system according to the embodiment comprises a data collection unit, a data integration unit, a risk assessment unit, a prioritization unit, a notification / communication unit, and a construction company coordination unit. The data collection unit acquires infrastructure data in real time from drones and ground-penetrating radar. The data integration unit integrates the data acquired by the data collection unit with past maintenance history and environmental data. The risk assessment unit analyzes the data integrated by the data integration unit and evaluates the risk of infrastructure deterioration. The prioritization unit determines the priority of repairs based on the risks assessed by the risk assessment unit. The notification / communication unit notifies relevant parties of the prioritization and repair plans determined by the prioritization unit and arranges materials and workers. The construction company coordination unit autonomously communicates and coordinates with construction companies that will carry out the repairs notified by the notification / communication unit.

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 conventional technology, since the process of evaluating the deterioration risk of the infrastructure and determining the repair priority is performed manually, there is a problem that it is difficult to take efficient countermeasures.

[0005] The system according to the embodiment aims to evaluate the deterioration risk of the infrastructure and automatically determine the repair priority.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a data integration unit, a risk assessment unit, a prioritization unit, a notification / communication unit, and a construction company coordination unit. The data collection unit acquires infrastructure data in real time from drones and ground-penetrating radar. The data integration unit integrates the data acquired by the data collection unit with past maintenance history and environmental data. The risk assessment unit analyzes the data integrated by the data integration unit and evaluates the risk of infrastructure deterioration. The prioritization unit determines the priority of repairs based on the risks assessed by the risk assessment unit. The notification / communication unit notifies relevant parties of the prioritization and repair plans determined by the prioritization unit and arranges materials and workers. The construction company coordination unit autonomously communicates and coordinates with construction companies that will carry out the repairs notified by the notification / communication unit. [Effects of the Invention]

[0007] The system according to this embodiment can assess the risk of infrastructure deterioration and automatically determine the priority of repairs. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 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.

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 (see FIG. 2) acquires data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

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

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) An infrastructure repair management system according to an embodiment of the present invention is a system in which an AI agent evaluates the risks of areas requiring repair of infrastructure and automatically determines the priority of repairs based on resources and budget. In this infrastructure repair management system, a data collection module acquires infrastructure data in real time from drones and ground-penetrating radar. Next, a data integration module integrates the collected data with past maintenance history and environmental data. A risk assessment module analyzes the data using an AI agent and evaluates the risk of infrastructure deterioration. Based on the evaluation results, a prioritization module determines the priority of repairs considering budget and resources. A notification and communication module notifies relevant parties of the determined priority and repair plan and arranges materials and workers. Finally, a construction company coordination module autonomously communicates and coordinates with construction companies that will carry out the repairs. For example, the infrastructure repair management system acquires infrastructure data in real time using drones and ground-penetrating radar. For example, a data collection module collects data from above the infrastructure by flying a drone and acquires underground infrastructure data using ground-penetrating radar. Next, a data integration module integrates the collected data with past maintenance history and environmental data. For example, the data integration module retrieves past maintenance history from a database and environmental data from a weather database. The risk assessment module uses an AI agent to analyze data and assess the risk of infrastructure deterioration. For example, the risk assessment module inputs data into an AI agent and executes an algorithm to assess deterioration risk. The prioritization module determines the priority of repairs based on the assessment results, taking into account budget and resources. For example, the prioritization module executes an algorithm to determine the priority of repairs based on the risk assessment results. The notification and communication module notifies relevant parties of the determined priorities and repair plans and arranges materials and workers. For example, the notification and communication module sends emails and notifications to relevant parties and arranges materials and workers. The contractor coordination module autonomously communicates and coordinates with the contractors that will carry out the repairs. For example, the contractor coordination module contacts the contractors and adjusts the repair schedule.This allows the infrastructure repair management system to efficiently address the highest-risk areas first, reducing maintenance costs and time. It also helps maintain infrastructure health and reduce the risk of emergency repairs. By assessing the risks of areas requiring repair and automatically prioritizing repairs based on resources and budget, the infrastructure repair management system can maintain infrastructure health and reduce the risk of emergency repairs.

[0029] The infrastructure repair management system according to this embodiment includes a data collection unit, a data integration unit, a risk assessment unit, a prioritization unit, a notification / communication unit, and a construction company coordination unit. The data collection unit acquires infrastructure data in real time from drones and ground-penetrating radar. The data collection unit, for example, collects data from above the infrastructure by flying a drone. The data collection unit can also acquire underground infrastructure data using ground-penetrating radar. The data collection unit can also acquire overall infrastructure data by combining drones and ground-penetrating radar. The data integration unit integrates collected data with past maintenance history and environmental data. The data integration unit can, for example, acquire past maintenance history from a database and integrate it with collected data. The data integration unit can also, for example, acquire environmental data from a weather database and integrate it with collected data. The data integration unit can also improve the consistency and reliability of collected data by combining past maintenance history and environmental data. The risk assessment unit analyzes the data using an AI agent and evaluates the risk of infrastructure deterioration. The risk assessment unit, for example, inputs data into an AI agent and executes an algorithm to evaluate the risk of deterioration. The risk assessment unit can, for example, use an AI agent to evaluate deterioration risk based on the data analysis results. The risk assessment unit can, for example, use an AI agent to calculate a risk score based on the data analysis results. The prioritization unit determines the repair priority based on the assessment results, taking into account budget and resources. The prioritization unit can, for example, execute an algorithm to determine the repair priority based on the risk assessment results. The prioritization unit can, for example, determine the repair priority by taking into account budget and resources. The prioritization unit can, for example, determine the repair priority by combining the risk assessment results with budget and resources. The notification and communication unit notifies relevant parties of the determined priorities and repair plan and arranges materials and workers. The notification and communication unit can, for example, send emails or notifications to relevant parties to inform them of the repair plan. The notification and communication unit can also, for example, contact relevant parties to arrange materials and workers.The notification and communication department can, for example, notify relevant parties of the repair plan and arrange for materials and workers. The construction company coordination department autonomously communicates and coordinates with the construction company that will carry out the repairs. The construction company coordination department can, for example, contact the construction company and adjust the repair schedule. The construction company coordination department can, for example, share the progress of the repairs with the construction company and make adjustments. The construction company coordination department can, for example, adjust the repair schedule with the construction company and ensure that the repair work proceeds smoothly. As a result, the infrastructure repair management system according to the embodiment can maintain the soundness of the infrastructure and reduce the risk of emergency repairs by evaluating the risks of areas of the infrastructure that need repair and automatically determining the priority of repairs based on resources and budget.

[0030] The data collection unit acquires infrastructure data in real time from drones and ground-penetrating radar. Specifically, the drones are equipped with high-resolution cameras and LiDAR sensors to collect detailed image data and 3D terrain data from above the infrastructure. The drones have autonomous flight capabilities and can efficiently collect data according to pre-set flight routes. This makes it possible to grasp the condition of infrastructure over a wide area in a short time. Ground-penetrating radar, on the other hand, is a device for non-destructively inspecting the condition of infrastructure buried underground, and it uses electromagnetic waves to detect underground structures and abnormal areas. Ground-penetrating radar can, for example, detect cavities under roads and the deterioration of pipes in real time and collect that data. The data collection unit combines these devices to acquire overall infrastructure data. For example, by integrating the data collected from above by the drones with the data acquired by the ground-penetrating radar, it is possible to comprehensively understand the condition of above-ground and underground infrastructure. This allows the data collection unit to provide foundational data for highly accurate assessment of infrastructure health. Furthermore, the data collection unit transmits the collected data to a cloud server in real time, making it immediately accessible to other departments. This enables centralized data management and rapid analysis, improving the overall efficiency of the system.

[0031] The Data Integration Department integrates collected data with historical maintenance history and environmental data. Specifically, it compares the latest infrastructure data collected from drones and ground-penetrating radar with a database of historical maintenance history to analyze changes in infrastructure condition over time. For example, by referring to past repair history and inspection records and comparing them with current data, it is possible to evaluate the progress of deterioration and the effectiveness of repairs. The Data Integration Department also acquires environmental data from a weather database and integrates it with the collected data. This enables analysis that takes into account the impact of weather conditions on infrastructure. For example, it is possible to analyze how fluctuations in rainfall and temperature affect infrastructure deterioration and identify the causes of deterioration. By integrating this data, the Data Integration Department improves the consistency and reliability of the collected data. Furthermore, the Data Integration Department preprocesses and cleans the data and converts it into a format suitable for analysis. This allows the Risk Assessment Department and Prioritization Department to use the data efficiently. The Data Integration Department automates the data integration process and regularly updates the data to always provide the latest information. This enables continuous monitoring of infrastructure health and prompt response.

[0032] The Risk Assessment Department uses AI agents to analyze data and evaluate the risk of infrastructure deterioration. Specifically, the Risk Assessment Department inputs collected and integrated data into the AI ​​agent and executes an algorithm to evaluate deterioration risk. The AI ​​agent uses machine learning models to automatically detect deterioration patterns and anomalies in the infrastructure. For example, it uses image recognition technology to identify cracks and corrosion from images taken by drones and evaluate the degree of deterioration. It also analyzes ground-penetrating radar data to detect anomalies and cavities in underground structures. Based on these analysis results, the Risk Assessment Department quantitatively evaluates the infrastructure deterioration risk and calculates a risk score. The risk score is an indicator of the soundness of the infrastructure and is important information for determining the need for repairs. Furthermore, the Risk Assessment Department utilizes historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past deterioration data, it predicts deterioration trends for specific regions or infrastructure and evaluates future risks. This allows the Risk Assessment Department to not only grasp the situation in real time but also to handle long-term risk management.

[0033] The prioritization unit determines the priority of repairs based on evaluation results, taking into account budget and resources. Specifically, the prioritization unit evaluates the necessity of repairs based on the risk score provided by the risk assessment unit and executes an algorithm to determine the repair priority. This algorithm considers not only the risk score but also budget and resource constraints to formulate the optimal repair plan. For example, if the budget is limited, repairs will be prioritized starting with the highest-risk areas. In addition, to maximize resource utilization efficiency, the repair work schedule can be optimized, and multiple repair tasks can be carried out simultaneously. The prioritization unit comprehensively evaluates these factors to determine the repair priority. Furthermore, the prioritization unit monitors the progress of the repair plan and modifies the plan as needed. For example, it re-evaluates the repair priority and updates the plan, taking into account the progress of the repair work and any newly arising risks. In this way, the prioritization unit can always provide the optimal repair plan and maintain the health of the infrastructure.

[0034] The Notification and Communication Department notifies relevant parties of the determined priorities and repair plan, and arranges materials and workers. Specifically, the Notification and Communication Department sends the repair plan to relevant parties via email or notification systems, sharing the details of the repairs. For example, it sends notifications containing information such as the repair locations, repair details, and schedule, enabling relevant parties to respond quickly. The Notification and Communication Department also contacts relevant departments and external contractors to arrange materials and workers. For example, it checks the inventory status of necessary materials and places orders as needed. It also adjusts worker schedules to ensure the necessary personnel for the repair work. By carrying out these arrangements quickly and efficiently, the Notification and Communication Department can prevent delays in the repair work and ensure that it proceeds according to plan. Furthermore, the Notification and Communication Department regularly reports the progress of the repair work to relevant parties to share information. This allows relevant parties to understand the status of the repair work and take necessary actions quickly.

[0035] The Construction Company Coordination Department autonomously manages communication and coordination with the construction companies carrying out the repairs. Specifically, the Department coordinates the repair schedule with the construction companies and supports the smooth progress of the repair work. For example, it presents the construction companies with detailed repair schedules and coordinates the start and completion dates of the work. It also regularly checks the progress of the repair work and revises the schedule as needed. The Department also responds quickly to any problems or changes that arise during the repair work and strengthens cooperation with the construction companies. For example, if work is delayed due to unexpected obstacles or changes in weather, it works with the construction companies to quickly take countermeasures. Furthermore, the Department also manages the quality of the repair work and supervises whether the construction companies are working according to appropriate standards. This ensures the quality of the repair work and maintains the soundness of the infrastructure. By coordinating and managing the overall repair work, the Department contributes to the success of the repair project.

[0036] The data collection unit can acquire infrastructure data in real time from drones and ground-penetrating radar. For example, the data collection unit can fly a drone to collect data from above the infrastructure. The data collection unit can also acquire underground infrastructure data using ground-penetrating radar. For example, the data collection unit can combine drones and ground-penetrating radar to acquire overall infrastructure data. This allows for real-time acquisition of infrastructure data using drones and ground-penetrating radar. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from drones and ground-penetrating radar into a generating AI and have the generating AI perform data analysis.

[0037] The data integration unit can integrate collected data with past maintenance history and environmental data. For example, the data integration unit can retrieve past maintenance history from a database and integrate it with collected data. The data integration unit can also retrieve environmental data from a weather database and integrate it with collected data. The data integration unit can also improve the consistency and reliability of collected data by combining past maintenance history and environmental data. This improves the consistency and reliability of the data by integrating past maintenance history and environmental data in addition to collected data. Some or all of the above processing in the data integration unit may be performed using AI, for example, or without AI. For example, the data integration unit can input past maintenance history and environmental data into a generating AI and have the generating AI perform the data integration.

[0038] The risk assessment unit can analyze data using an AI agent and evaluate the risk of infrastructure degradation. For example, the risk assessment unit inputs data into the AI ​​agent and executes an algorithm to evaluate the degradation risk. The risk assessment unit can also evaluate the degradation risk based on the data analysis results using the AI ​​agent. The risk assessment unit can also calculate a risk score based on the data analysis results using the AI ​​agent. This allows for highly accurate evaluation of the infrastructure degradation risk by using the AI ​​agent. Some or all of the above-described processes in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input data into a generating AI and have the generating AI perform the degradation risk evaluation.

[0039] The prioritization unit can determine the priority of repairs based on evaluation results, taking into account budget and resources. The prioritization unit can, for example, execute an algorithm to determine the priority of repairs based on risk assessment results. The prioritization unit can also determine the priority of repairs by, for example, taking budget and resources into account. The prioritization unit can also determine the priority of repairs by, for example, combining risk assessment results, budget, and resources. This allows for the creation of an efficient repair plan by determining the priority of repairs while considering budget and resources. Some or all of the above-described processes in the prioritization unit may be performed using AI, for example, or without AI. For example, the prioritization unit can input risk assessment results, budget, and resources into a generating AI and have the generating AI perform the determination of repair priorities.

[0040] The notification and communication department can notify relevant parties of the determined priorities and repair plan, and arrange for materials and workers. For example, the notification and communication department can send emails or notifications to relevant parties to inform them of the repair plan. The notification and communication department can also contact relevant parties to arrange for materials and workers. For example, the notification and communication department can notify relevant parties of the repair plan and arrange for materials and workers. This ensures that the repair work proceeds smoothly by notifying relevant parties and arranging for materials and workers. Some or all of the above processes in the notification and communication department may be performed using AI, for example, or not using AI. For example, the notification and communication department can input the repair plan and the arrangement of materials and workers into a generating AI, and have the generating AI execute the notifications and arrangements.

[0041] The Construction Company Coordination Department can autonomously communicate and coordinate with the construction company carrying out the repairs. For example, the Construction Company Coordination Department can contact the construction company and adjust the repair schedule. The Construction Company Coordination Department can also share the progress of the repairs with the construction company and make adjustments. The Construction Company Coordination Department can also coordinate the repair schedule with the construction company to ensure the smooth progress of the repair work. By autonomously communicating and coordinating with the construction company, the efficiency of the repair work is improved. Some or all of the above processes in the Construction Company Coordination Department may be performed using AI, for example, or not using AI. For example, the Construction Company Coordination Department can input the repair schedule and progress into a generating AI and have the generating AI perform communication and coordination with the construction company.

[0042] The data collection unit can optimize the drone's flight pattern and efficiently collect data. For example, the data collection unit can set the drone's flight path to the shortest distance to minimize battery consumption. The data collection unit can also adjust the drone's flight altitude to collect data with the optimal field of view. The data collection unit can also adjust the drone's flight speed to improve the accuracy of data collection. In this way, data can be collected efficiently by optimizing the drone's flight pattern. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the optimization of the drone's flight pattern into a generating AI and have the generating AI perform the flight pattern optimization.

[0043] The data collection unit can adjust the sensitivity of the ground-penetrating radar to more accurately detect deteriorated areas of infrastructure. For example, the data collection unit can increase the sensitivity of the ground-penetrating radar to detect minute deteriorated areas. For example, the data collection unit can also decrease the sensitivity of the ground-penetrating radar to detect only important deteriorated areas. For example, the data collection unit can automatically adjust the sensitivity of the ground-penetrating radar to maintain optimal detection accuracy. In this way, by adjusting the sensitivity of the ground-penetrating radar, deteriorated areas of infrastructure can be detected more accurately. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the sensitivity adjustment of the ground-penetrating radar into a generating AI and have the generating AI perform the sensitivity adjustment.

[0044] The data collection unit can select the optimal timing for data collection by considering weather information. For example, the data collection unit can postpone data collection during rainy weather. For example, the data collection unit can immediately collect data during sunny weather. For example, the data collection unit can avoid drone flight during strong winds. By selecting the optimal timing for data collection while considering weather information, the accuracy of data collection is improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input weather information into a generating AI and have the generating AI select the optimal timing for data collection.

[0045] The data collection unit can monitor infrastructure usage in real time during data collection and reflect this in the collected data. For example, the data collection unit can monitor the frequency of infrastructure usage in real time and adjust the frequency of data collection. The data collection unit can also optimize the timing of data collection according to infrastructure usage. The data collection unit can also improve data accuracy by reflecting infrastructure usage in real time. This improves the accuracy of collected data by monitoring infrastructure usage in real time. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input infrastructure usage data into a generating AI and have the generating AI adjust the frequency and timing of data collection.

[0046] The data integration unit can evaluate the quality of the integrated data and automatically remove inaccurate data. For example, the data integration unit can evaluate the reliability of the data and remove inaccurate data. It can also evaluate the consistency of the data and remove inaccurate data. Furthermore, it can evaluate the completeness of the data and remove inaccurate data. This improves the reliability of the data by evaluating the quality of the integrated data and removing inaccurate data. Some or all of the above processes in the data integration unit may be performed using AI, for example, or without AI. For example, the data integration unit can input the integrated data quality evaluation and the removal of inaccurate data into a generating AI, and have the generating AI perform the quality evaluation and removal.

[0047] The data integration unit can improve data reliability by applying an anomaly detection algorithm to the integrated data. The data integration unit can, for example, improve data reliability by applying an anomaly detection algorithm. The data integration unit can also, for example, improve data consistency by applying an anomaly detection algorithm. The data integration unit can also, for example, improve data integrity by applying an anomaly detection algorithm. Thus, applying an anomaly detection algorithm improves data reliability. Some or all of the above processing in the data integration unit may be performed using AI, for example, or without AI. For example, the data integration unit can input the application of an anomaly detection algorithm to a generating AI and have the generating AI perform anomaly detection.

[0048] The data integration unit can visualize the integrated data using a Geographic Information System (GIS). For example, the data integration unit can visualize the integrated data using a GIS and display it on a map. For example, the data integration unit can also visualize the integrated data using a GIS and display the location information of infrastructure. For example, the data integration unit can visualize the integrated data using a GIS and display the deterioration status of infrastructure. This makes it easier to understand the data by visualizing it using a GIS. Some or all of the above processing in the data integration unit may be performed using AI, for example, or without AI. For example, the data integration unit can input the visualization of the integrated data into a generating AI and have the generating AI perform the visualization.

[0049] The data integration unit can improve the accuracy of risk assessment by referencing past disaster data with respect to the integrated data. For example, the data integration unit can improve the accuracy of risk assessment by adding past disaster data to the integrated data. The data integration unit can also improve the consistency of risk assessment by adding past disaster data to the integrated data. For example, the data integration unit can improve the completeness of risk assessment by adding past disaster data to the integrated data. This improves the accuracy of risk assessment by referencing past disaster data. Some or all of the above processing in the data integration unit may be performed using AI, for example, or without AI. For example, the data integration unit can input references to past disaster data into a generating AI and have the generating AI perform the improvement of risk assessment accuracy.

[0050] The risk assessment unit can calculate a risk score by considering the frequency of infrastructure use during the risk assessment. For example, the risk assessment unit calculates a risk score by considering the frequency of infrastructure use. The risk assessment unit can also adjust the risk score according to the frequency of infrastructure use. For example, the risk assessment unit can monitor the frequency of infrastructure use in real time and update the risk score. This improves the accuracy of the risk assessment by calculating the risk score while considering the frequency of infrastructure use. Some or all of the above processes in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input infrastructure usage data into a generating AI and have the generating AI perform the calculation of the risk score.

[0051] The risk assessment unit can improve the accuracy of its risk assessment by considering the materials and structure of the infrastructure during the risk assessment process. For example, the risk assessment unit can improve the accuracy of its risk assessment by considering the materials of the infrastructure. The risk assessment unit can also improve the accuracy of its risk assessment by considering the structure of the infrastructure. For example, the risk assessment unit can improve the accuracy of its risk assessment by comprehensively considering the materials and structure of the infrastructure. As a result, the accuracy of the risk assessment is improved by considering the materials and structure of the infrastructure. Some or all of the above processing in the risk assessment unit may be performed using AI, for example, or without using AI. For example, the risk assessment unit can input infrastructure material and structure data into a generating AI and have the generating AI perform the risk assessment accuracy improvement.

[0052] The risk assessment unit can calculate a risk score by considering the surrounding environment data of the infrastructure during the risk assessment. For example, the risk assessment unit calculates a risk score by considering the surrounding environment data of the infrastructure. The risk assessment unit can also adjust the risk score based on the surrounding environment data of the infrastructure. For example, the risk assessment unit can monitor the surrounding environment data of the infrastructure in real time and update the risk score. This improves the accuracy of the risk assessment by calculating the risk score while considering the surrounding environment data of the infrastructure. Some or all of the above processing in the risk assessment unit may be performed using AI, for example, or without using AI. For example, the risk assessment unit can input surrounding environment data into a generating AI and have the generating AI perform the calculation of the risk score.

[0053] The risk assessment unit can improve the accuracy of its risk assessment by referring to the infrastructure's past repair history during the risk assessment process. For example, the risk assessment unit can improve the accuracy of its risk assessment by referring to the infrastructure's past repair history. The risk assessment unit can also improve the accuracy of its risk assessment based on the infrastructure's past repair history. The risk assessment unit can also improve the accuracy of its risk assessment by comprehensively considering the infrastructure's past repair history. This improves the accuracy of the risk assessment by referring to the infrastructure's past repair history. Some or all of the above processing in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input past repair history into a generating AI and have the generating AI perform the risk assessment accuracy improvement.

[0054] The prioritization unit can determine the optimal repair plan by considering the cost and effectiveness of the repairs during the prioritization process. For example, the prioritization unit can determine the optimal repair plan by considering the cost of the repairs. The prioritization unit can also determine the optimal repair plan by considering the effectiveness of the repairs. The prioritization unit can also determine the optimal repair plan by comprehensively considering the cost and effectiveness of the repairs. This enables efficient repairs by determining the optimal repair plan by considering the cost and effectiveness of the repairs. Some or all of the above-described processes in the prioritization unit may be performed using AI, for example, or without AI. For example, the prioritization unit can input repair cost and effectiveness data into a generating AI and have the generating AI determine the optimal repair plan.

[0055] The prioritization unit can evaluate the urgency of repairs and determine the most effective repair sequence when prioritizing. For example, the prioritization unit can evaluate the urgency of repairs and determine the most effective repair sequence. The prioritization unit can also adjust the repair sequence based on the urgency of repairs. The prioritization unit can also monitor the urgency of repairs in real time and update the repair sequence. This enables a rapid response by evaluating the urgency of repairs and determining the most effective repair sequence. Some or all of the above processes in the prioritization unit may be performed using AI, or not. For example, the prioritization unit can input repair urgency data into a generating AI and have the generating AI determine the repair sequence.

[0056] The prioritization unit can determine the optimal repair plan by considering the scope of impact of the repairs when prioritizing. For example, the prioritization unit can determine the optimal repair plan by considering the scope of impact of the repairs. The prioritization unit can also adjust the repair plan based on the scope of impact of the repairs. For example, the prioritization unit can monitor the scope of impact of the repairs in real time and update the repair plan. This enables efficient repairs by determining the optimal repair plan by considering the scope of impact of the repairs. Some or all of the above processes in the prioritization unit may be performed using AI, for example, or without AI. For example, the prioritization unit can input repair impact data into a generating AI and have the generating AI determine the optimal repair plan.

[0057] The prioritization unit can evaluate the feasibility of repairs and determine the most realistic repair order when prioritizing. The prioritization unit can, for example, evaluate the feasibility of repairs and determine the most realistic repair order. The prioritization unit can also, for example, adjust the repair order based on the feasibility of repairs. The prioritization unit can also, for example, monitor the feasibility of repairs in real time and update the repair order. This enables efficient repairs by evaluating the feasibility of repairs and determining the most realistic repair order. Some or all of the above processes in the prioritization unit may be performed using AI, for example, or without AI. For example, the prioritization unit can input feasibility data for repairs into a generating AI and have the generating AI perform the determination of the repair order.

[0058] The notification and communication department can send customized notifications according to the roles of the stakeholders. For example, the notification and communication department can customize the content of notifications according to the roles of the stakeholders. For example, the notification and communication department can also adjust the priority of notifications based on the roles of the stakeholders. For example, the notification and communication department can monitor the roles of stakeholders in real time and update the content of notifications. This enables efficient notifications by customizing the content of notifications according to the roles of the stakeholders. Some or all of the above processes in the notification and communication department may be performed using AI, for example, or not using AI. For example, the notification and communication department can input the role data of stakeholders into a generating AI and have the generating AI perform the customization of the notification content.

[0059] The notification and communication department can update the progress of repairs in real time and notify relevant parties when a notification is made. For example, the notification and communication department can update the progress of repairs in real time and notify relevant parties. The notification and communication department can also adjust the content of the notification based on the progress of repairs. The notification and communication department can also monitor the progress of repairs in real time and update the content of the notification. This improves the efficiency of repair work by updating the progress of repairs in real time and notifying relevant parties. Some or all of the above processes in the notification and communication department may be performed using AI, for example, or not using AI. For example, the notification and communication department can input repair progress data into a generating AI and have the generating AI update the content of the notification.

[0060] The notification and communication department can select the optimal notification method when issuing a notification, taking into account the geographical location information of the relevant parties. For example, the notification and communication department can select the optimal notification method by considering the geographical location information of the relevant parties. The notification and communication department can also adjust the priority of notifications based on the geographical location information of the relevant parties. For example, the notification and communication department can monitor the geographical location information of the relevant parties in real time and update the notification method. This enables efficient notification by selecting the optimal notification method by taking into account the geographical location information of the relevant parties. Some or all of the above processes in the notification and communication department may be performed using AI, for example, or without using AI. For example, the notification and communication department can input the geographical location information of the relevant parties into a generating AI and have the generating AI select the notification method.

[0061] The notification and communication department can select the optimal notification timing by considering the schedules of the relevant parties. For example, the notification and communication department can select the optimal notification timing by considering the schedules of the relevant parties. The notification and communication department can also adjust the priority of notifications based on the schedules of the relevant parties. For example, the notification and communication department can monitor the schedules of the relevant parties in real time and update the notification timing. This enables efficient notification by selecting the optimal notification timing by considering the schedules of the relevant parties. Some or all of the above processes in the notification and communication department may be performed using AI, for example, or not using AI. For example, the notification and communication department can input the schedule data of the relevant parties into a generating AI and have the generating AI perform the selection of the notification timing.

[0062] The Construction Company Coordination Department can select the most suitable construction company by considering past cooperation records when coordinating with construction companies. For example, the Construction Company Coordination Department selects the most suitable construction company by considering past cooperation records. The Construction Company Coordination Department can also adjust the selection criteria for construction companies based on past cooperation records. For example, the Construction Company Coordination Department can monitor past cooperation records in real time and update the selection criteria for construction companies. This enables efficient repairs by selecting the most suitable construction company by considering past cooperation records. Some or all of the above processes in the Construction Company Coordination Department may be performed using AI, for example, or without AI. For example, the Construction Company Coordination Department can input past cooperation record data into a generating AI and have the generating AI perform the selection of construction companies.

[0063] The Construction Company Coordination Department can share the progress of repairs in real time and make adjustments when coordinating with construction companies. For example, the Construction Company Coordination Department can share the progress of repairs in real time and make adjustments with construction companies. The Construction Company Coordination Department can also adjust the content of the adjustments with construction companies based on the progress of repairs. The Construction Company Coordination Department can also monitor the progress of repairs in real time and update the content of the adjustments with construction companies. This enables efficient adjustments by sharing the progress of repairs in real time. Some or all of the above processes in the Construction Company Coordination Department may be performed using AI, for example, or not using AI. For example, the Construction Company Coordination Department can input repair progress data into a generating AI and have the generating AI update the adjustment content.

[0064] The Construction Company Coordination Department can select the most suitable construction company by considering geographical factors when coordinating with construction companies. For example, the Construction Company Coordination Department selects the most suitable construction company by considering geographical factors. The Construction Company Coordination Department can also adjust the selection criteria for construction companies based on geographical factors. For example, the Construction Company Coordination Department can monitor geographical factors in real time and update the selection criteria for construction companies. This enables efficient repairs by selecting the most suitable construction company by considering geographical factors. Some or all of the above processes in the Construction Company Coordination Department may be performed using AI, for example, or without AI. For example, the Construction Company Coordination Department can input geographical factor data into a generating AI and have the generating AI perform the selection of construction companies.

[0065] The Construction Company Coordination Department can select the optimal coordination method when coordinating with construction companies, taking into account the scope of impact of the repairs. For example, the Construction Company Coordination Department can select the optimal coordination method by considering the scope of impact of the repairs. The Construction Company Coordination Department can also adjust the coordination method based on the scope of impact of the repairs. For example, the Construction Company Coordination Department can monitor the scope of impact of the repairs in real time and update the coordination method. This enables efficient repairs by selecting the optimal coordination method while considering the scope of impact of the repairs. Some or all of the above processes in the Construction Company Coordination Department may be performed using AI, for example, or without AI. For example, the Construction Company Coordination Department can input repair impact data into a generating AI and have the generating AI select the coordination method.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The data collection unit can acquire infrastructure data in real time from drones and ground-penetrating radar to assess the risk of areas requiring infrastructure repair. For example, the data collection unit can collect data from above the infrastructure by flying a drone. The data collection unit can also acquire underground infrastructure data using ground-penetrating radar. For example, the data collection unit can acquire overall infrastructure data by combining drones and ground-penetrating radar. This allows for real-time acquisition of infrastructure data using drones and ground-penetrating radar. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from drones and ground-penetrating radar into a generating AI and have the generating AI perform data analysis.

[0068] The data integration unit can integrate collected data with past maintenance history and environmental data. For example, the data integration unit can retrieve past maintenance history from a database and integrate it with collected data. The data integration unit can also retrieve environmental data from a weather database and integrate it with collected data. The data integration unit can also improve the consistency and reliability of collected data by combining past maintenance history and environmental data. This improves the consistency and reliability of the data by integrating past maintenance history and environmental data in addition to collected data. Some or all of the above processing in the data integration unit may be performed using AI, for example, or without AI. For example, the data integration unit can input past maintenance history and environmental data into a generating AI and have the generating AI perform the data integration.

[0069] The risk assessment unit can analyze data using an AI agent and evaluate the risk of infrastructure degradation. For example, the risk assessment unit inputs data into the AI ​​agent and executes an algorithm to evaluate the degradation risk. The risk assessment unit can also evaluate the degradation risk based on the data analysis results using the AI ​​agent. The risk assessment unit can also calculate a risk score based on the data analysis results using the AI ​​agent. This allows for highly accurate evaluation of the infrastructure degradation risk by using the AI ​​agent. Some or all of the above-described processes in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input data into a generating AI and have the generating AI perform the degradation risk evaluation.

[0070] The prioritization unit can determine the priority of repairs based on evaluation results, taking into account budget and resources. The prioritization unit can, for example, execute an algorithm to determine the priority of repairs based on risk assessment results. The prioritization unit can also determine the priority of repairs by, for example, taking budget and resources into account. The prioritization unit can also determine the priority of repairs by, for example, combining risk assessment results, budget, and resources. This allows for the creation of an efficient repair plan by determining the priority of repairs while considering budget and resources. Some or all of the above-described processes in the prioritization unit may be performed using AI, for example, or without AI. For example, the prioritization unit can input risk assessment results, budget, and resources into a generating AI and have the generating AI perform the determination of repair priorities.

[0071] The notification and communication department can notify relevant parties of the determined priorities and repair plan, and arrange for materials and workers. For example, the notification and communication department can send emails or notifications to relevant parties to inform them of the repair plan. The notification and communication department can also contact relevant parties to arrange for materials and workers. For example, the notification and communication department can notify relevant parties of the repair plan and arrange for materials and workers. This ensures that the repair work proceeds smoothly by notifying relevant parties and arranging for materials and workers. Some or all of the above processes in the notification and communication department may be performed using AI, for example, or not using AI. For example, the notification and communication department can input the repair plan and the arrangement of materials and workers into a generating AI, and have the generating AI execute the notifications and arrangements.

[0072] The data collection unit can optimize the drone's flight pattern and efficiently collect data. For example, the data collection unit can set the drone's flight path to the shortest distance to minimize battery consumption. The data collection unit can also adjust the drone's flight altitude to collect data with the optimal field of view. The data collection unit can also adjust the drone's flight speed to improve the accuracy of data collection. In this way, data can be collected efficiently by optimizing the drone's flight pattern. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the optimization of the drone's flight pattern into a generating AI and have the generating AI perform the flight pattern optimization.

[0073] The data collection unit can adjust the sensitivity of the ground-penetrating radar to more accurately detect deteriorated areas of infrastructure. For example, the data collection unit can increase the sensitivity of the ground-penetrating radar to detect minute deteriorated areas. For example, the data collection unit can also decrease the sensitivity of the ground-penetrating radar to detect only important deteriorated areas. For example, the data collection unit can automatically adjust the sensitivity of the ground-penetrating radar to maintain optimal detection accuracy. In this way, by adjusting the sensitivity of the ground-penetrating radar, deteriorated areas of infrastructure can be detected more accurately. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the sensitivity adjustment of the ground-penetrating radar into a generating AI and have the generating AI perform the sensitivity adjustment.

[0074] The data collection unit can select the optimal timing for data collection by considering weather information. For example, the data collection unit can postpone data collection during rainy weather. For example, the data collection unit can immediately collect data during sunny weather. For example, the data collection unit can avoid drone flight during strong winds. By selecting the optimal timing for data collection while considering weather information, the accuracy of data collection is improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input weather information into a generating AI and have the generating AI select the optimal timing for data collection.

[0075] The following briefly describes the processing flow for example form 1.

[0076] Step 1: The data collection unit acquires infrastructure data in real time from drones and ground-penetrating radar. For example, a drone can be flown to collect data from above the infrastructure, and ground-penetrating radar can be used to acquire underground infrastructure data. This allows for the acquisition of overall infrastructure data by combining drones and ground-penetrating radar. Step 2: The data integration unit integrates past maintenance history and environmental data in addition to the data acquired by the data collection unit. For example, past maintenance history can be acquired from a database and integrated with the collected data, and environmental data can be acquired from a weather database and integrated with the collected data. This improves the consistency and reliability of the collected data. Step 3: The risk assessment unit analyzes the data integrated by the data integration unit and evaluates the infrastructure degradation risk. For example, it can use an AI agent to analyze the data, execute an algorithm to evaluate the degradation risk, and calculate a risk score. Step 4: The prioritization unit determines the repair priority based on the risks assessed by the risk assessment unit, taking into account budget and resources. For example, an algorithm can be executed to determine the repair priority based on the risk assessment results, taking into account budget and resources. Step 5: The notification and communication department notifies relevant parties of the priorities and repair plans determined by the prioritization department and arranges for materials and workers. For example, they can send emails or notices to relevant parties to inform them of the repair plan and arrange for materials and workers. Step 6: The Construction Company Coordination Department autonomously communicates and coordinates with the construction company that will carry out the repairs notified by the Notification and Communication Department. For example, it can contact the construction company, adjust the repair schedule, share the progress of the repairs with the construction company, and make adjustments.

[0077] (Example of form 2) An infrastructure repair management system according to an embodiment of the present invention is a system in which an AI agent evaluates the risks of areas requiring repair of infrastructure and automatically determines the priority of repairs based on resources and budget. In this infrastructure repair management system, a data collection module acquires infrastructure data in real time from drones and ground-penetrating radar. Next, a data integration module integrates the collected data with past maintenance history and environmental data. A risk assessment module analyzes the data using an AI agent and evaluates the risk of infrastructure deterioration. Based on the evaluation results, a prioritization module determines the priority of repairs considering budget and resources. A notification and communication module notifies relevant parties of the determined priority and repair plan and arranges materials and workers. Finally, a construction company coordination module autonomously communicates and coordinates with construction companies that will carry out the repairs. For example, the infrastructure repair management system acquires infrastructure data in real time using drones and ground-penetrating radar. For example, a data collection module collects data from above the infrastructure by flying a drone and acquires underground infrastructure data using ground-penetrating radar. Next, a data integration module integrates the collected data with past maintenance history and environmental data. For example, the data integration module retrieves past maintenance history from a database and environmental data from a weather database. The risk assessment module uses an AI agent to analyze data and assess the risk of infrastructure deterioration. For example, the risk assessment module inputs data into an AI agent and executes an algorithm to assess deterioration risk. The prioritization module determines the priority of repairs based on the assessment results, taking into account budget and resources. For example, the prioritization module executes an algorithm to determine the priority of repairs based on the risk assessment results. The notification and communication module notifies relevant parties of the determined priorities and repair plans and arranges materials and workers. For example, the notification and communication module sends emails and notifications to relevant parties and arranges materials and workers. The contractor coordination module autonomously communicates and coordinates with the contractors that will carry out the repairs. For example, the contractor coordination module contacts the contractors and adjusts the repair schedule.This allows the infrastructure repair management system to efficiently address the highest-risk areas first, reducing maintenance costs and time. It also helps maintain infrastructure health and reduce the risk of emergency repairs. By assessing the risks of areas requiring repair and automatically prioritizing repairs based on resources and budget, the infrastructure repair management system can maintain infrastructure health and reduce the risk of emergency repairs.

[0078] The infrastructure repair management system according to this embodiment includes a data collection unit, a data integration unit, a risk assessment unit, a prioritization unit, a notification / communication unit, and a construction company coordination unit. The data collection unit acquires infrastructure data in real time from drones and ground-penetrating radar. The data collection unit, for example, collects data from above the infrastructure by flying a drone. The data collection unit can also acquire underground infrastructure data using ground-penetrating radar. The data collection unit can also acquire overall infrastructure data by combining drones and ground-penetrating radar. The data integration unit integrates collected data with past maintenance history and environmental data. The data integration unit can, for example, acquire past maintenance history from a database and integrate it with collected data. The data integration unit can also, for example, acquire environmental data from a weather database and integrate it with collected data. The data integration unit can also improve the consistency and reliability of collected data by combining past maintenance history and environmental data. The risk assessment unit analyzes the data using an AI agent and evaluates the risk of infrastructure deterioration. The risk assessment unit, for example, inputs data into an AI agent and executes an algorithm to evaluate the risk of deterioration. The risk assessment unit can, for example, use an AI agent to evaluate deterioration risk based on the data analysis results. The risk assessment unit can, for example, use an AI agent to calculate a risk score based on the data analysis results. The prioritization unit determines the repair priority based on the assessment results, taking into account budget and resources. The prioritization unit can, for example, execute an algorithm to determine the repair priority based on the risk assessment results. The prioritization unit can, for example, determine the repair priority by taking into account budget and resources. The prioritization unit can, for example, determine the repair priority by combining the risk assessment results with budget and resources. The notification and communication unit notifies relevant parties of the determined priorities and repair plan and arranges materials and workers. The notification and communication unit can, for example, send emails or notifications to relevant parties to inform them of the repair plan. The notification and communication unit can also, for example, contact relevant parties to arrange materials and workers.The notification and communication department can, for example, notify relevant parties of the repair plan and arrange for materials and workers. The construction company coordination department autonomously communicates and coordinates with the construction company that will carry out the repairs. The construction company coordination department can, for example, contact the construction company and adjust the repair schedule. The construction company coordination department can, for example, share the progress of the repairs with the construction company and make adjustments. The construction company coordination department can, for example, adjust the repair schedule with the construction company and ensure that the repair work proceeds smoothly. As a result, the infrastructure repair management system according to the embodiment can maintain the soundness of the infrastructure and reduce the risk of emergency repairs by evaluating the risks of areas of the infrastructure that need repair and automatically determining the priority of repairs based on resources and budget.

[0079] The data collection unit acquires infrastructure data in real time from drones and ground-penetrating radar. Specifically, the drones are equipped with high-resolution cameras and LiDAR sensors to collect detailed image data and 3D terrain data from above the infrastructure. The drones have autonomous flight capabilities and can efficiently collect data according to pre-set flight routes. This makes it possible to grasp the condition of infrastructure over a wide area in a short time. Ground-penetrating radar, on the other hand, is a device for non-destructively inspecting the condition of infrastructure buried underground, and it uses electromagnetic waves to detect underground structures and abnormal areas. Ground-penetrating radar can, for example, detect cavities under roads and the deterioration of pipes in real time and collect that data. The data collection unit combines these devices to acquire overall infrastructure data. For example, by integrating the data collected from above by the drones with the data acquired by the ground-penetrating radar, it is possible to comprehensively understand the condition of above-ground and underground infrastructure. This allows the data collection unit to provide foundational data for highly accurate assessment of infrastructure health. Furthermore, the data collection unit transmits the collected data to a cloud server in real time, making it immediately accessible to other departments. This enables centralized data management and rapid analysis, improving the overall efficiency of the system.

[0080] The Data Integration Department integrates collected data with historical maintenance history and environmental data. Specifically, it compares the latest infrastructure data collected from drones and ground-penetrating radar with a database of historical maintenance history to analyze changes in infrastructure condition over time. For example, by referring to past repair history and inspection records and comparing them with current data, it is possible to evaluate the progress of deterioration and the effectiveness of repairs. The Data Integration Department also acquires environmental data from a weather database and integrates it with the collected data. This enables analysis that takes into account the impact of weather conditions on infrastructure. For example, it is possible to analyze how fluctuations in rainfall and temperature affect infrastructure deterioration and identify the causes of deterioration. By integrating this data, the Data Integration Department improves the consistency and reliability of the collected data. Furthermore, the Data Integration Department preprocesses and cleans the data and converts it into a format suitable for analysis. This allows the Risk Assessment Department and Prioritization Department to use the data efficiently. The Data Integration Department automates the data integration process and regularly updates the data to always provide the latest information. This enables continuous monitoring of infrastructure health and prompt response.

[0081] The Risk Assessment Department uses AI agents to analyze data and evaluate the risk of infrastructure deterioration. Specifically, the Risk Assessment Department inputs collected and integrated data into the AI ​​agent and executes an algorithm to evaluate deterioration risk. The AI ​​agent uses machine learning models to automatically detect deterioration patterns and anomalies in the infrastructure. For example, it uses image recognition technology to identify cracks and corrosion from images taken by drones and evaluate the degree of deterioration. It also analyzes ground-penetrating radar data to detect anomalies and cavities in underground structures. Based on these analysis results, the Risk Assessment Department quantitatively evaluates the infrastructure deterioration risk and calculates a risk score. The risk score is an indicator of the soundness of the infrastructure and is important information for determining the need for repairs. Furthermore, the Risk Assessment Department utilizes historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past deterioration data, it predicts deterioration trends for specific regions or infrastructure and evaluates future risks. This allows the Risk Assessment Department to not only grasp the situation in real time but also to handle long-term risk management.

[0082] The prioritization unit determines the priority of repairs based on evaluation results, taking into account budget and resources. Specifically, the prioritization unit evaluates the necessity of repairs based on the risk score provided by the risk assessment unit and executes an algorithm to determine the repair priority. This algorithm considers not only the risk score but also budget and resource constraints to formulate the optimal repair plan. For example, if the budget is limited, repairs will be prioritized starting with the highest-risk areas. In addition, to maximize resource utilization efficiency, the repair work schedule can be optimized, and multiple repair tasks can be carried out simultaneously. The prioritization unit comprehensively evaluates these factors to determine the repair priority. Furthermore, the prioritization unit monitors the progress of the repair plan and modifies the plan as needed. For example, it re-evaluates the repair priority and updates the plan, taking into account the progress of the repair work and any newly arising risks. In this way, the prioritization unit can always provide the optimal repair plan and maintain the health of the infrastructure.

[0083] The Notification and Communication Department notifies relevant parties of the determined priorities and repair plan, and arranges materials and workers. Specifically, the Notification and Communication Department sends the repair plan to relevant parties via email or notification systems, sharing the details of the repairs. For example, it sends notifications containing information such as the repair locations, repair details, and schedule, enabling relevant parties to respond quickly. The Notification and Communication Department also contacts relevant departments and external contractors to arrange materials and workers. For example, it checks the inventory status of necessary materials and places orders as needed. It also adjusts worker schedules to ensure the necessary personnel for the repair work. By carrying out these arrangements quickly and efficiently, the Notification and Communication Department can prevent delays in the repair work and ensure that it proceeds according to plan. Furthermore, the Notification and Communication Department regularly reports the progress of the repair work to relevant parties to share information. This allows relevant parties to understand the status of the repair work and take necessary actions quickly.

[0084] The Construction Company Coordination Department autonomously manages communication and coordination with the construction companies carrying out the repairs. Specifically, the Department coordinates the repair schedule with the construction companies and supports the smooth progress of the repair work. For example, it presents the construction companies with detailed repair schedules and coordinates the start and completion dates of the work. It also regularly checks the progress of the repair work and revises the schedule as needed. The Department also responds quickly to any problems or changes that arise during the repair work and strengthens cooperation with the construction companies. For example, if work is delayed due to unexpected obstacles or changes in weather, it works with the construction companies to quickly take countermeasures. Furthermore, the Department also manages the quality of the repair work and supervises whether the construction companies are working according to appropriate standards. This ensures the quality of the repair work and maintains the soundness of the infrastructure. By coordinating and managing the overall repair work, the Department contributes to the success of the repair project.

[0085] The data collection unit can acquire infrastructure data in real time from drones and ground-penetrating radar. For example, the data collection unit can fly a drone to collect data from above the infrastructure. The data collection unit can also acquire underground infrastructure data using ground-penetrating radar. For example, the data collection unit can combine drones and ground-penetrating radar to acquire overall infrastructure data. This allows for real-time acquisition of infrastructure data using drones and ground-penetrating radar. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from drones and ground-penetrating radar into a generating AI and have the generating AI perform data analysis.

[0086] The data integration unit can integrate collected data with past maintenance history and environmental data. For example, the data integration unit can retrieve past maintenance history from a database and integrate it with collected data. The data integration unit can also retrieve environmental data from a weather database and integrate it with collected data. The data integration unit can also improve the consistency and reliability of collected data by combining past maintenance history and environmental data. This improves the consistency and reliability of the data by integrating past maintenance history and environmental data in addition to collected data. Some or all of the above processing in the data integration unit may be performed using AI, for example, or without AI. For example, the data integration unit can input past maintenance history and environmental data into a generating AI and have the generating AI perform the data integration.

[0087] The risk assessment unit can analyze data using an AI agent and evaluate the risk of infrastructure degradation. For example, the risk assessment unit inputs data into the AI ​​agent and executes an algorithm to evaluate the degradation risk. The risk assessment unit can also evaluate the degradation risk based on the data analysis results using the AI ​​agent. The risk assessment unit can also calculate a risk score based on the data analysis results using the AI ​​agent. This allows for highly accurate evaluation of the infrastructure degradation risk by using the AI ​​agent. Some or all of the above-described processes in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input data into a generating AI and have the generating AI perform the degradation risk evaluation.

[0088] The prioritization unit can determine the priority of repairs based on evaluation results, taking into account budget and resources. The prioritization unit can, for example, execute an algorithm to determine the priority of repairs based on risk assessment results. The prioritization unit can also determine the priority of repairs by, for example, taking budget and resources into account. The prioritization unit can also determine the priority of repairs by, for example, combining risk assessment results, budget, and resources. This allows for the creation of an efficient repair plan by determining the priority of repairs while considering budget and resources. Some or all of the above-described processes in the prioritization unit may be performed using AI, for example, or without AI. For example, the prioritization unit can input risk assessment results, budget, and resources into a generating AI and have the generating AI perform the determination of repair priorities.

[0089] The notification and communication department can notify relevant parties of the determined priorities and repair plan, and arrange for materials and workers. For example, the notification and communication department can send emails or notifications to relevant parties to inform them of the repair plan. The notification and communication department can also contact relevant parties to arrange for materials and workers. For example, the notification and communication department can notify relevant parties of the repair plan and arrange for materials and workers. This ensures that the repair work proceeds smoothly by notifying relevant parties and arranging for materials and workers. Some or all of the above processes in the notification and communication department may be performed using AI, for example, or not using AI. For example, the notification and communication department can input the repair plan and the arrangement of materials and workers into a generating AI, and have the generating AI execute the notifications and arrangements.

[0090] The Construction Company Coordination Department can autonomously communicate and coordinate with the construction company carrying out the repairs. For example, the Construction Company Coordination Department can contact the construction company and adjust the repair schedule. The Construction Company Coordination Department can also share the progress of the repairs with the construction company and make adjustments. The Construction Company Coordination Department can also coordinate the repair schedule with the construction company to ensure the smooth progress of the repair work. By autonomously communicating and coordinating with the construction company, the efficiency of the repair work is improved. Some or all of the above processes in the Construction Company Coordination Department may be performed using AI, for example, or not using AI. For example, the Construction Company Coordination Department can input the repair schedule and progress into a generating AI and have the generating AI perform communication and coordination with the construction company.

[0091] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit may delay data collection. For example, if the user is relaxed, the data collection unit may collect data immediately. For example, if the user is in a hurry, the data collection unit may collect data quickly. This reduces the burden on the user by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI adjust the timing of data collection.

[0092] The data collection unit can optimize the drone's flight pattern and efficiently collect data. For example, the data collection unit can set the drone's flight path to the shortest distance to minimize battery consumption. The data collection unit can also adjust the drone's flight altitude to collect data with the optimal field of view. The data collection unit can also adjust the drone's flight speed to improve the accuracy of data collection. In this way, data can be collected efficiently by optimizing the drone's flight pattern. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the optimization of the drone's flight pattern into a generating AI and have the generating AI perform the flight pattern optimization.

[0093] The data collection unit can adjust the sensitivity of the ground-penetrating radar to more accurately detect deteriorated areas of infrastructure. For example, the data collection unit can increase the sensitivity of the ground-penetrating radar to detect minute deteriorated areas. For example, the data collection unit can also decrease the sensitivity of the ground-penetrating radar to detect only important deteriorated areas. For example, the data collection unit can automatically adjust the sensitivity of the ground-penetrating radar to maintain optimal detection accuracy. In this way, by adjusting the sensitivity of the ground-penetrating radar, deteriorated areas of infrastructure can be detected more accurately. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the sensitivity adjustment of the ground-penetrating radar into a generating AI and have the generating AI perform the sensitivity adjustment.

[0094] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting only the most important data. If the user is relaxed, for example, the data collection unit can collect all data equally. If the user is in a hurry, for example, the data collection unit can quickly collect the most important data. This allows for the priority collection of important data by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of data to collect.

[0095] The data collection unit can select the optimal timing for data collection by considering weather information. For example, the data collection unit can postpone data collection during rainy weather. For example, the data collection unit can immediately collect data during sunny weather. For example, the data collection unit can avoid drone flight during strong winds. By selecting the optimal timing for data collection while considering weather information, the accuracy of data collection is improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input weather information into a generating AI and have the generating AI select the optimal timing for data collection.

[0096] The data collection unit can monitor infrastructure usage in real time during data collection and reflect this in the collected data. For example, the data collection unit can monitor the frequency of infrastructure usage in real time and adjust the frequency of data collection. The data collection unit can also optimize the timing of data collection according to infrastructure usage. The data collection unit can also improve data accuracy by reflecting infrastructure usage in real time. This improves the accuracy of collected data by monitoring infrastructure usage in real time. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input infrastructure usage data into a generating AI and have the generating AI adjust the frequency and timing of data collection.

[0097] The data integration unit can estimate the user's emotions and adjust the data integration method based on the estimated emotions. For example, if the user is stressed, the data integration unit can provide a simple data integration method. For example, if the user is relaxed, the data integration unit can also provide a detailed data integration method. For example, if the user is in a hurry, the data integration unit can also provide a rapid data integration method. This reduces the user's burden by adjusting the data integration method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data integration unit may be performed using AI or not using AI. For example, the data integration unit can input user emotion data into a generative AI and have the generative AI adjust the data integration method.

[0098] The data integration unit can evaluate the quality of the integrated data and automatically remove inaccurate data. For example, the data integration unit can evaluate the reliability of the data and remove inaccurate data. It can also evaluate the consistency of the data and remove inaccurate data. Furthermore, it can evaluate the completeness of the data and remove inaccurate data. This improves the reliability of the data by evaluating the quality of the integrated data and removing inaccurate data. Some or all of the above processes in the data integration unit may be performed using AI, for example, or without AI. For example, the data integration unit can input the integrated data quality evaluation and the removal of inaccurate data into a generating AI, and have the generating AI perform the quality evaluation and removal.

[0099] The data integration unit can improve data reliability by applying an anomaly detection algorithm to the integrated data. The data integration unit can, for example, improve data reliability by applying an anomaly detection algorithm. The data integration unit can also, for example, improve data consistency by applying an anomaly detection algorithm. The data integration unit can also, for example, improve data integrity by applying an anomaly detection algorithm. Thus, applying an anomaly detection algorithm improves data reliability. Some or all of the above processing in the data integration unit may be performed using AI, for example, or without AI. For example, the data integration unit can input the application of an anomaly detection algorithm to a generating AI and have the generating AI perform anomaly detection.

[0100] The data integration unit can estimate the user's emotions and adjust how the integrated data is displayed based on the estimated emotions. For example, if the user is stressed, the data integration unit can provide a simple display method. For example, if the user is relaxed, the data integration unit can also provide a detailed display method. For example, if the user is in a hurry, the data integration unit can also provide a fast display method. This reduces the user's burden by adjusting how the integrated data is displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data integration unit may be performed using AI, for example, or not using AI. For example, the data integration unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0101] The data integration unit can visualize the integrated data using a Geographic Information System (GIS). For example, the data integration unit can visualize the integrated data using a GIS and display it on a map. For example, the data integration unit can also visualize the integrated data using a GIS and display the location information of infrastructure. For example, the data integration unit can visualize the integrated data using a GIS and display the deterioration status of infrastructure. This makes it easier to understand the data by visualizing it using a GIS. Some or all of the above processing in the data integration unit may be performed using AI, for example, or without AI. For example, the data integration unit can input the visualization of the integrated data into a generating AI and have the generating AI perform the visualization.

[0102] The data integration unit can improve the accuracy of risk assessment by referencing past disaster data with respect to the integrated data. For example, the data integration unit can improve the accuracy of risk assessment by adding past disaster data to the integrated data. The data integration unit can also improve the consistency of risk assessment by adding past disaster data to the integrated data. For example, the data integration unit can improve the completeness of risk assessment by adding past disaster data to the integrated data. This improves the accuracy of risk assessment by referencing past disaster data. Some or all of the above processing in the data integration unit may be performed using AI, for example, or without AI. For example, the data integration unit can input references to past disaster data into a generating AI and have the generating AI perform the improvement of risk assessment accuracy.

[0103] The risk assessment unit can estimate the user's emotions and adjust the risk assessment criteria based on the estimated user emotions. For example, if the user is stressed, the risk assessment unit may relax the risk assessment criteria. For example, if the user is relaxed, the risk assessment unit may tighten the risk assessment criteria. For example, if the user is in a hurry, the risk assessment unit may quickly adjust the risk assessment criteria. This reduces the user's burden by adjusting the risk assessment criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the risk assessment unit may be performed using AI or not using AI. For example, the risk assessment unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the risk assessment criteria.

[0104] The risk assessment unit can calculate a risk score by considering the frequency of infrastructure use during the risk assessment. For example, the risk assessment unit calculates a risk score by considering the frequency of infrastructure use. The risk assessment unit can also adjust the risk score according to the frequency of infrastructure use. For example, the risk assessment unit can monitor the frequency of infrastructure use in real time and update the risk score. This improves the accuracy of the risk assessment by calculating the risk score while considering the frequency of infrastructure use. Some or all of the above processes in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input infrastructure usage data into a generating AI and have the generating AI perform the calculation of the risk score.

[0105] The risk assessment unit can improve the accuracy of its risk assessment by considering the materials and structure of the infrastructure during the risk assessment process. For example, the risk assessment unit can improve the accuracy of its risk assessment by considering the materials of the infrastructure. The risk assessment unit can also improve the accuracy of its risk assessment by considering the structure of the infrastructure. For example, the risk assessment unit can improve the accuracy of its risk assessment by comprehensively considering the materials and structure of the infrastructure. As a result, the accuracy of the risk assessment is improved by considering the materials and structure of the infrastructure. Some or all of the above processing in the risk assessment unit may be performed using AI, for example, or without using AI. For example, the risk assessment unit can input infrastructure material and structure data into a generating AI and have the generating AI perform the risk assessment accuracy improvement.

[0106] The risk assessment unit can estimate the user's emotions and adjust the display method of the risk assessment results based on the estimated user emotions. For example, if the user is stressed, the risk assessment unit can provide a simple display method. For example, if the user is relaxed, the risk assessment unit can also provide a detailed display method. For example, if the user is in a hurry, the risk assessment unit can also provide a rapid display method. This reduces the burden on the user by adjusting the display method of the risk assessment results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0107] The risk assessment unit can calculate a risk score by considering the surrounding environment data of the infrastructure during the risk assessment. For example, the risk assessment unit calculates a risk score by considering the surrounding environment data of the infrastructure. The risk assessment unit can also adjust the risk score based on the surrounding environment data of the infrastructure. For example, the risk assessment unit can monitor the surrounding environment data of the infrastructure in real time and update the risk score. This improves the accuracy of the risk assessment by calculating the risk score while considering the surrounding environment data of the infrastructure. Some or all of the above processing in the risk assessment unit may be performed using AI, for example, or without using AI. For example, the risk assessment unit can input surrounding environment data into a generating AI and have the generating AI perform the calculation of the risk score.

[0108] The risk assessment unit can improve the accuracy of its risk assessment by referring to the infrastructure's past repair history during the risk assessment process. For example, the risk assessment unit can improve the accuracy of its risk assessment by referring to the infrastructure's past repair history. The risk assessment unit can also improve the accuracy of its risk assessment based on the infrastructure's past repair history. The risk assessment unit can also improve the accuracy of its risk assessment by comprehensively considering the infrastructure's past repair history. This improves the accuracy of the risk assessment by referring to the infrastructure's past repair history. Some or all of the above processing in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input past repair history into a generating AI and have the generating AI perform the risk assessment accuracy improvement.

[0109] The prioritization unit can estimate the user's emotions and adjust the prioritization criteria based on the estimated emotions. For example, if the user is stressed, the prioritization unit may relax the prioritization criteria. For example, if the user is relaxed, the prioritization unit may tighten the prioritization criteria. For example, if the user is in a hurry, the prioritization unit may quickly adjust the prioritization criteria. This reduces the user's burden by adjusting the prioritization criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prioritization unit may be performed using AI, for example, or not using AI. For example, the prioritization unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the prioritization criteria.

[0110] The prioritization unit can determine the optimal repair plan by considering the cost and effectiveness of the repairs during the prioritization process. For example, the prioritization unit can determine the optimal repair plan by considering the cost of the repairs. The prioritization unit can also determine the optimal repair plan by considering the effectiveness of the repairs. The prioritization unit can also determine the optimal repair plan by comprehensively considering the cost and effectiveness of the repairs. This enables efficient repairs by determining the optimal repair plan by considering the cost and effectiveness of the repairs. Some or all of the above-described processes in the prioritization unit may be performed using AI, for example, or without AI. For example, the prioritization unit can input repair cost and effectiveness data into a generating AI and have the generating AI determine the optimal repair plan.

[0111] The prioritization unit can evaluate the urgency of repairs and determine the most effective repair sequence when prioritizing. For example, the prioritization unit can evaluate the urgency of repairs and determine the most effective repair sequence. The prioritization unit can also adjust the repair sequence based on the urgency of repairs. The prioritization unit can also monitor the urgency of repairs in real time and update the repair sequence. This enables a rapid response by evaluating the urgency of repairs and determining the most effective repair sequence. Some or all of the above processes in the prioritization unit may be performed using AI, or not. For example, the prioritization unit can input repair urgency data into a generating AI and have the generating AI determine the repair sequence.

[0112] The prioritization unit can estimate the user's emotions and adjust the display method of the prioritization results based on the estimated user emotions. For example, if the user is stressed, the prioritization unit can provide a simple display method. For example, if the user is relaxed, the prioritization unit can also provide a detailed display method. For example, if the user is in a hurry, the prioritization unit can also provide a fast display method. This reduces the burden on the user by adjusting the display method of the prioritization results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prioritization unit may be performed using AI, for example, or without AI. For example, the prioritization unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0113] The prioritization unit can determine the optimal repair plan by considering the scope of impact of the repairs when prioritizing. For example, the prioritization unit can determine the optimal repair plan by considering the scope of impact of the repairs. The prioritization unit can also adjust the repair plan based on the scope of impact of the repairs. For example, the prioritization unit can monitor the scope of impact of the repairs in real time and update the repair plan. This enables efficient repairs by determining the optimal repair plan by considering the scope of impact of the repairs. Some or all of the above processes in the prioritization unit may be performed using AI, for example, or without AI. For example, the prioritization unit can input repair impact data into a generating AI and have the generating AI determine the optimal repair plan.

[0114] The prioritization unit can evaluate the feasibility of repairs and determine the most realistic repair order when prioritizing. The prioritization unit can, for example, evaluate the feasibility of repairs and determine the most realistic repair order. The prioritization unit can also, for example, adjust the repair order based on the feasibility of repairs. The prioritization unit can also, for example, monitor the feasibility of repairs in real time and update the repair order. This enables efficient repairs by evaluating the feasibility of repairs and determining the most realistic repair order. Some or all of the above processes in the prioritization unit may be performed using AI, for example, or without AI. For example, the prioritization unit can input feasibility data for repairs into a generating AI and have the generating AI perform the determination of the repair order.

[0115] The notification and communication unit can estimate the user's emotions and adjust the notification content based on the estimated emotions. For example, if the user is stressed, the notification and communication unit can provide a concise notification. For example, if the user is relaxed, the notification and communication unit can provide a detailed notification. For example, if the user is in a hurry, the notification and communication unit can provide a quick notification. This reduces the user's burden by adjusting the notification content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification and communication unit may be performed using AI or not using AI. For example, the notification and communication unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the notification content.

[0116] The notification and communication department can send customized notifications according to the roles of the stakeholders. For example, the notification and communication department can customize the content of notifications according to the roles of the stakeholders. For example, the notification and communication department can also adjust the priority of notifications based on the roles of the stakeholders. For example, the notification and communication department can monitor the roles of stakeholders in real time and update the content of notifications. This enables efficient notifications by customizing the content of notifications according to the roles of the stakeholders. Some or all of the above processes in the notification and communication department may be performed using AI, for example, or not using AI. For example, the notification and communication department can input the role data of stakeholders into a generating AI and have the generating AI perform the customization of the notification content.

[0117] The notification and communication department can update the progress of repairs in real time and notify relevant parties when a notification is made. For example, the notification and communication department can update the progress of repairs in real time and notify relevant parties. The notification and communication department can also adjust the content of the notification based on the progress of repairs. The notification and communication department can also monitor the progress of repairs in real time and update the content of the notification. This improves the efficiency of repair work by updating the progress of repairs in real time and notifying relevant parties. Some or all of the above processes in the notification and communication department may be performed using AI, for example, or not using AI. For example, the notification and communication department can input repair progress data into a generating AI and have the generating AI update the content of the notification.

[0118] The notification and communication unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is stressed, the notification and communication unit can prioritize sending only important notifications. For example, if the user is relaxed, the notification and communication unit can send all notifications equally. For example, if the user is in a hurry, the notification and communication unit can quickly send the most important notifications. In this way, important notifications can be sent preferentially by determining the priority of notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification and communication unit may be performed using AI or not using AI. For example, the notification and communication unit can input user emotion data into a generative AI and have the generative AI perform the determination of notification priorities.

[0119] The notification and communication department can select the optimal notification method when issuing a notification, taking into account the geographical location information of the relevant parties. For example, the notification and communication department can select the optimal notification method by considering the geographical location information of the relevant parties. The notification and communication department can also adjust the priority of notifications based on the geographical location information of the relevant parties. For example, the notification and communication department can monitor the geographical location information of the relevant parties in real time and update the notification method. This enables efficient notification by selecting the optimal notification method by taking into account the geographical location information of the relevant parties. Some or all of the above processes in the notification and communication department may be performed using AI, for example, or without using AI. For example, the notification and communication department can input the geographical location information of the relevant parties into a generating AI and have the generating AI select the notification method.

[0120] The notification and communication department can select the optimal notification timing by considering the schedules of the relevant parties. For example, the notification and communication department can select the optimal notification timing by considering the schedules of the relevant parties. The notification and communication department can also adjust the priority of notifications based on the schedules of the relevant parties. For example, the notification and communication department can monitor the schedules of the relevant parties in real time and update the notification timing. This enables efficient notification by selecting the optimal notification timing by considering the schedules of the relevant parties. Some or all of the above processes in the notification and communication department may be performed using AI, for example, or not using AI. For example, the notification and communication department can input the schedule data of the relevant parties into a generating AI and have the generating AI perform the selection of the notification timing.

[0121] The Construction Company Coordination Department can estimate the user's emotions and adjust the coordination method with the construction company based on the estimated emotions. For example, if the user is stressed, the Construction Company Coordination Department can provide a simple coordination method. For example, if the user is relaxed, the Construction Company Coordination Department can also provide a detailed coordination method. For example, if the user is in a hurry, the Construction Company Coordination Department can also provide a quick coordination method. This reduces the burden on the user by adjusting the coordination method with the construction company according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Construction Company Coordination Department may be performed using AI or not using AI. For example, the Construction Company Coordination Department can input user emotion data into a generative AI and have the generative AI perform the adjustment of the coordination method.

[0122] The Construction Company Coordination Department can select the most suitable construction company by considering past cooperation records when coordinating with construction companies. For example, the Construction Company Coordination Department selects the most suitable construction company by considering past cooperation records. The Construction Company Coordination Department can also adjust the selection criteria for construction companies based on past cooperation records. For example, the Construction Company Coordination Department can monitor past cooperation records in real time and update the selection criteria for construction companies. This enables efficient repairs by selecting the most suitable construction company by considering past cooperation records. Some or all of the above processes in the Construction Company Coordination Department may be performed using AI, for example, or without AI. For example, the Construction Company Coordination Department can input past cooperation record data into a generating AI and have the generating AI perform the selection of construction companies.

[0123] The Construction Company Coordination Department can share the progress of repairs in real time and make adjustments when coordinating with construction companies. For example, the Construction Company Coordination Department can share the progress of repairs in real time and make adjustments with construction companies. The Construction Company Coordination Department can also adjust the content of the adjustments with construction companies based on the progress of repairs. The Construction Company Coordination Department can also monitor the progress of repairs in real time and update the content of the adjustments with construction companies. This enables efficient adjustments by sharing the progress of repairs in real time. Some or all of the above processes in the Construction Company Coordination Department may be performed using AI, for example, or not using AI. For example, the Construction Company Coordination Department can input repair progress data into a generating AI and have the generating AI update the adjustment content.

[0124] The construction company coordination department can estimate the user's emotions and determine the priority of coordination with construction companies based on the estimated emotions. For example, if the user is stressed, the construction company coordination department can prioritize only the most important coordinations. For example, if the user is relaxed, the construction company coordination department can also distribute all coordinations evenly. For example, if the user is in a hurry, the construction company coordination department can also quickly perform the most important coordinations. This allows important coordinations to be prioritized by determining the priority of coordination with construction companies according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the construction company coordination department may be performed using AI or not using AI. For example, the construction company coordination department can input user emotion data into a generative AI and have the generative AI determine the priority of coordinations.

[0125] The Construction Company Coordination Department can select the most suitable construction company by considering geographical factors when coordinating with construction companies. For example, the Construction Company Coordination Department selects the most suitable construction company by considering geographical factors. The Construction Company Coordination Department can also adjust the selection criteria for construction companies based on geographical factors. For example, the Construction Company Coordination Department can monitor geographical factors in real time and update the selection criteria for construction companies. This enables efficient repairs by selecting the most suitable construction company by considering geographical factors. Some or all of the above processes in the Construction Company Coordination Department may be performed using AI, for example, or without AI. For example, the Construction Company Coordination Department can input geographical factor data into a generating AI and have the generating AI perform the selection of construction companies.

[0126] The Construction Company Coordination Department can select the optimal coordination method when coordinating with construction companies, taking into account the scope of impact of the repairs. For example, the Construction Company Coordination Department can select the optimal coordination method by considering the scope of impact of the repairs. The Construction Company Coordination Department can also adjust the coordination method based on the scope of impact of the repairs. For example, the Construction Company Coordination Department can monitor the scope of impact of the repairs in real time and update the coordination method. This enables efficient repairs by selecting the optimal coordination method while considering the scope of impact of the repairs. Some or all of the above processes in the Construction Company Coordination Department may be performed using AI, for example, or without AI. For example, the Construction Company Coordination Department can input repair impact data into a generating AI and have the generating AI select the coordination method.

[0127] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0128] The data collection unit can acquire infrastructure data in real time from drones and ground-penetrating radar to assess the risk of areas requiring infrastructure repair. For example, the data collection unit can collect data from above the infrastructure by flying a drone. The data collection unit can also acquire underground infrastructure data using ground-penetrating radar. For example, the data collection unit can acquire overall infrastructure data by combining drones and ground-penetrating radar. This allows for real-time acquisition of infrastructure data using drones and ground-penetrating radar. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from drones and ground-penetrating radar into a generating AI and have the generating AI perform data analysis.

[0129] The data integration unit can integrate collected data with past maintenance history and environmental data. For example, the data integration unit can retrieve past maintenance history from a database and integrate it with collected data. The data integration unit can also retrieve environmental data from a weather database and integrate it with collected data. The data integration unit can also improve the consistency and reliability of collected data by combining past maintenance history and environmental data. This improves the consistency and reliability of the data by integrating past maintenance history and environmental data in addition to collected data. Some or all of the above processing in the data integration unit may be performed using AI, for example, or without AI. For example, the data integration unit can input past maintenance history and environmental data into a generating AI and have the generating AI perform the data integration.

[0130] The risk assessment unit can analyze data using an AI agent and evaluate the risk of infrastructure degradation. For example, the risk assessment unit inputs data into the AI ​​agent and executes an algorithm to evaluate the degradation risk. The risk assessment unit can also evaluate the degradation risk based on the data analysis results using the AI ​​agent. The risk assessment unit can also calculate a risk score based on the data analysis results using the AI ​​agent. This allows for highly accurate evaluation of the infrastructure degradation risk by using the AI ​​agent. Some or all of the above-described processes in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input data into a generating AI and have the generating AI perform the degradation risk evaluation.

[0131] The prioritization unit can determine the priority of repairs based on evaluation results, taking into account budget and resources. The prioritization unit can, for example, execute an algorithm to determine the priority of repairs based on risk assessment results. The prioritization unit can also determine the priority of repairs by, for example, taking budget and resources into account. The prioritization unit can also determine the priority of repairs by, for example, combining risk assessment results, budget, and resources. This allows for the creation of an efficient repair plan by determining the priority of repairs while considering budget and resources. Some or all of the above-described processes in the prioritization unit may be performed using AI, for example, or without AI. For example, the prioritization unit can input risk assessment results, budget, and resources into a generating AI and have the generating AI perform the determination of repair priorities.

[0132] The notification and communication department can notify relevant parties of the determined priorities and repair plan, and arrange for materials and workers. For example, the notification and communication department can send emails or notifications to relevant parties to inform them of the repair plan. The notification and communication department can also contact relevant parties to arrange for materials and workers. For example, the notification and communication department can notify relevant parties of the repair plan and arrange for materials and workers. This ensures that the repair work proceeds smoothly by notifying relevant parties and arranging for materials and workers. Some or all of the above processes in the notification and communication department may be performed using AI, for example, or not using AI. For example, the notification and communication department can input the repair plan and the arrangement of materials and workers into a generating AI, and have the generating AI execute the notifications and arrangements.

[0133] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit may delay data collection. For example, if the user is relaxed, the data collection unit may collect data immediately. For example, if the user is in a hurry, the data collection unit may collect data quickly. This reduces the burden on the user by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI adjust the timing of data collection.

[0134] The data collection unit can optimize the drone's flight pattern and efficiently collect data. For example, the data collection unit can set the drone's flight path to the shortest distance to minimize battery consumption. The data collection unit can also adjust the drone's flight altitude to collect data with the optimal field of view. The data collection unit can also adjust the drone's flight speed to improve the accuracy of data collection. In this way, data can be collected efficiently by optimizing the drone's flight pattern. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the optimization of the drone's flight pattern into a generating AI and have the generating AI perform the flight pattern optimization.

[0135] The data collection unit can adjust the sensitivity of the ground-penetrating radar to more accurately detect deteriorated areas of infrastructure. For example, the data collection unit can increase the sensitivity of the ground-penetrating radar to detect minute deteriorated areas. For example, the data collection unit can also decrease the sensitivity of the ground-penetrating radar to detect only important deteriorated areas. For example, the data collection unit can automatically adjust the sensitivity of the ground-penetrating radar to maintain optimal detection accuracy. In this way, by adjusting the sensitivity of the ground-penetrating radar, deteriorated areas of infrastructure can be detected more accurately. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the sensitivity adjustment of the ground-penetrating radar into a generating AI and have the generating AI perform the sensitivity adjustment.

[0136] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting only the most important data. If the user is relaxed, for example, the data collection unit can collect all data equally. If the user is in a hurry, for example, the data collection unit can quickly collect the most important data. This allows for the priority collection of important data by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of data to collect.

[0137] The data collection unit can select the optimal timing for data collection by considering weather information. For example, the data collection unit can postpone data collection during rainy weather. For example, the data collection unit can immediately collect data during sunny weather. For example, the data collection unit can avoid drone flight during strong winds. By selecting the optimal timing for data collection while considering weather information, the accuracy of data collection is improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input weather information into a generating AI and have the generating AI select the optimal timing for data collection.

[0138] The following briefly describes the processing flow for example form 2.

[0139] Step 1: The data collection unit acquires infrastructure data in real time from drones and ground-penetrating radar. For example, a drone can be flown to collect data from above the infrastructure, and ground-penetrating radar can be used to acquire underground infrastructure data. This allows for the acquisition of overall infrastructure data by combining drones and ground-penetrating radar. Step 2: The data integration unit integrates past maintenance history and environmental data in addition to the data acquired by the data collection unit. For example, past maintenance history can be acquired from a database and integrated with the collected data, and environmental data can be acquired from a weather database and integrated with the collected data. This improves the consistency and reliability of the collected data. Step 3: The risk assessment unit analyzes the data integrated by the data integration unit and evaluates the infrastructure degradation risk. For example, it can use an AI agent to analyze the data, execute an algorithm to evaluate the degradation risk, and calculate a risk score. Step 4: The prioritization unit determines the repair priority based on the risks assessed by the risk assessment unit, taking into account budget and resources. For example, an algorithm can be executed to determine the repair priority based on the risk assessment results, taking into account budget and resources. Step 5: The notification and communication department notifies relevant parties of the priorities and repair plans determined by the prioritization department and arranges for materials and workers. For example, they can send emails or notices to relevant parties to inform them of the repair plan and arrange for materials and workers. Step 6: The Construction Company Coordination Department autonomously communicates and coordinates with the construction company that will carry out the repairs notified by the Notification and Communication Department. For example, it can contact the construction company, adjust the repair schedule, share the progress of the repairs with the construction company, and make adjustments.

[0140] 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.

[0141] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0142] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the data collection unit, data integration unit, risk assessment unit, prioritization unit, notification / communication unit, and construction company coordination unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit acquires infrastructure data in real time using the camera 42 or ground-penetrating radar of the smart device 14. The data integration unit integrates the collected data, past maintenance history, and environmental data by, for example, the specific processing unit 290 of the data processing unit 12. The risk assessment unit analyzes the data using an AI agent by, for example, the specific processing unit 290 of the data processing unit 12 to assess the risk of infrastructure deterioration. The prioritization unit determines the priority of repairs based on the evaluation results, taking into account budget and resources, by, for example, the specific processing unit 290 of the data processing unit 12. The notification / communication unit notifies relevant parties of the priority and repair plan determined by the control unit 46A of the smart device 14 and arranges materials and workers. The construction company coordination unit autonomously communicates and coordinates with the construction company carrying out the repairs, for example, using the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0144] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0145] 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.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0147] 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.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0149] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] 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.

[0151] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] 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.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the data collection unit, data integration unit, risk assessment unit, prioritization unit, notification / communication unit, and construction company coordination unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit acquires infrastructure data in real time using the camera 42 or ground-penetrating radar of the smart glasses 214. The data integration unit integrates the collected data, past maintenance history, and environmental data, for example, by the specific processing unit 290 of the data processing unit 12. The risk assessment unit analyzes the data using an AI agent, for example, by the specific processing unit 290 of the data processing unit 12, to assess the risk of infrastructure deterioration. The prioritization unit determines the priority of repairs based on the evaluation results, taking into account budget and resources, for example, by the specific processing unit 290 of the data processing unit 12. The notification / communication unit notifies relevant parties of the priority and repair plan determined by the control unit 46A of the smart glasses 214, and arranges materials and workers. The construction company coordination unit autonomously communicates and coordinates with the construction company carrying out the repairs, for example, using the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0160] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0161] 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.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0163] 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.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0165] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] 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.

[0167] 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.

[0168] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] 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.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the data collection unit, data integration unit, risk assessment unit, prioritization unit, notification / communication unit, and construction company coordination unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit acquires infrastructure data in real time using the camera 42 or ground-penetrating radar of the headset terminal 314. The data integration unit integrates the collected data, past maintenance history, and environmental data by, for example, the specific processing unit 290 of the data processing unit 12. The risk assessment unit analyzes the data using an AI agent by, for example, the specific processing unit 290 of the data processing unit 12 to assess the risk of infrastructure deterioration. The prioritization unit determines the priority of repairs based on the evaluation results, taking into account budget and resources, by, for example, the specific processing unit 290 of the data processing unit 12. The notification / communication unit notifies relevant parties of the priority and repair plan determined by the control unit 46A of the headset terminal 314 and arranges materials and workers. The construction company coordination unit autonomously communicates and coordinates with the construction company carrying out the repairs, for example, using the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0176] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0177] 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.

[0178] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0179] 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.

[0180] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0181] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0182] 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.

[0183] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0184] 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.

[0185] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0186] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0187] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0188] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0189] 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.

[0190] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0191] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0192] Each of the multiple elements described above, including the data collection unit, data integration unit, risk assessment unit, prioritization unit, notification / communication unit, and construction company coordination unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit acquires infrastructure data in real time using the camera 42 or ground-penetrating radar of the robot 414. The data integration unit integrates the collected data, past maintenance history, and environmental data by, for example, the specific processing unit 290 of the data processing unit 12. The risk assessment unit analyzes the data using an AI agent by, for example, the specific processing unit 290 of the data processing unit 12 to assess the risk of infrastructure deterioration. The prioritization unit determines the priority of repairs based on the evaluation results, taking into account budget and resources, by, for example, the specific processing unit 290 of the data processing unit 12. The notification / communication unit notifies relevant parties of the prioritization and repair plan determined by the control unit 46A of the robot 414 and arranges materials and workers. The construction company coordination unit autonomously communicates and coordinates with the construction company carrying out the repairs, for example, using the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0193] 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.

[0194] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0195] 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.

[0196] 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.

[0197] 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, and motorcycles, 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 based, for example, 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.

[0198] 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."

[0199] 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.

[0200] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0209] 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 other things 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.

[0210] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0211] (Note 1) A data collection unit that acquires infrastructure data in real time from drones and ground-penetrating radar, In addition to the data acquired by the aforementioned data collection unit, a data integration unit integrates past maintenance history and environmental data. A risk assessment unit analyzes the data integrated by the aforementioned data integration unit and evaluates the risk of infrastructure deterioration, A prioritization unit that determines the priority of repairs based on the risks evaluated by the risk assessment unit, The notification and communication department notifies relevant parties of the priorities and repair plans determined by the prioritization department and arranges materials and workers. The system includes a Construction Company Coordination Department that autonomously communicates and coordinates with construction companies to carry out repairs notified by the aforementioned Notification and Communication Department. A system characterized by the following features. (Note 2) The aforementioned data acquisition unit is Acquire infrastructure data in real time using drones and ground-penetrating radar. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned data integration unit, In addition to collected data, past maintenance history and environmental data will be integrated. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned risk assessment unit, AI agents analyze data and assess the risk of infrastructure degradation. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned prioritization unit, Based on the evaluation results, repair priorities will be determined considering budget and resources. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned notification and communication department, Notify relevant parties of the determined priorities and repair plan, and arrange for materials and workers. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned construction company coordination department, Autonomously communicate and coordinate with the construction company carrying out the repairs. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned data acquisition unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned data acquisition unit is Optimize drone flight patterns to efficiently collect data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned data acquisition unit is By adjusting the sensitivity of the ground-penetrating radar, we can more accurately detect areas of infrastructure deterioration. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned data acquisition unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned data acquisition unit is When collecting data, the optimal collection timing is selected by taking weather information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned data acquisition unit is During data collection, infrastructure usage is monitored in real time and reflected in the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned data integration unit, We estimate user sentiment and adjust the data integration method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned data integration unit, Evaluate the quality of the integrated data and automatically remove inaccurate data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned data integration unit, Apply anomaly detection algorithms to integrated data to improve data reliability. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned data integration unit, It estimates the user's emotions and adjusts how the integrated data is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned data integration unit, Visualize integrated data using geographic information systems. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned data integration unit, By referencing historical disaster data with integrated data, the accuracy of risk assessment can be improved. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned risk assessment unit, We estimate user sentiment and adjust risk assessment criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned risk assessment unit, When assessing risk, the frequency of infrastructure use is taken into consideration when calculating the risk score. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned risk assessment unit, When conducting risk assessments, consider the materials and structure of the infrastructure to improve the accuracy of the risk assessment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned risk assessment unit, The system estimates the user's emotions and adjusts how the risk assessment results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned risk assessment unit, When assessing risk, the risk score is calculated by considering data on the surrounding environment of the infrastructure. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned risk assessment unit, When conducting risk assessments, referencing past infrastructure repair history improves the accuracy of the risk assessment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned prioritization unit, It estimates the user's emotions and adjusts the prioritization criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned prioritization unit, When prioritizing, determine the optimal repair plan by considering the cost and effectiveness of the repairs. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned prioritization unit, When prioritizing, assess the urgency of the repairs and determine the most effective repair sequence. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned prioritization unit, It estimates the user's emotions and adjusts how the prioritization results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned prioritization unit, When prioritizing, consider the scope of impact of the repairs to determine the optimal repair plan. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned prioritization unit, When prioritizing, assess the feasibility of the repairs and determine the most realistic repair sequence. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned notification and communication department, It estimates the user's emotions and adjusts the notification content based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned notification and communication department, When a notification is sent, it will send a customized notification based on the roles of the stakeholders. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned notification and communication department, When a notification is sent, the progress of the repairs will be updated in real time and the relevant parties will be notified. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned notification and communication department, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned notification and communication department, When sending notifications, the most suitable notification method will be selected, taking into account the geographical location information of the relevant parties. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned notification and communication department, When sending notifications, we will select the optimal timing considering the schedules of all parties involved. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned construction company coordination department, The system estimates the user's emotions and adjusts the method of communication with the construction company based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned construction company coordination department, When coordinating with construction companies, we select the most suitable company by considering our past collaboration track record. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned construction company coordination department, When coordinating with the construction company, we share the progress of the repairs in real time and make adjustments accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned construction company coordination department, The system estimates the user's emotions and prioritizes coordination with construction companies based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned construction company coordination department, When coordinating with construction companies, we select the most suitable company by taking geographical factors into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned construction company coordination department, When coordinating with the construction company, select the most appropriate coordination method considering the scope of impact of the repairs. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0212] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that acquires infrastructure data in real time from drones and ground-penetrating radar, In addition to the data acquired by the aforementioned data collection unit, a data integration unit integrates past maintenance history and environmental data. A risk assessment unit analyzes the data integrated by the aforementioned data integration unit and evaluates the risk of infrastructure deterioration, A prioritization unit that determines the priority of repairs based on the risks evaluated by the risk assessment unit, The notification and communication department notifies relevant parties of the priorities and repair plans determined by the prioritization department and arranges materials and workers. The system includes a Construction Company Coordination Department that autonomously communicates and coordinates with construction companies to carry out repairs notified by the aforementioned Notification and Communication Department. A system characterized by the following features.

2. The aforementioned data acquisition unit is Acquire infrastructure data in real time using drones and ground-penetrating radar. The system according to feature 1.

3. The aforementioned data integration unit, In addition to collected data, past maintenance history and environmental data will be integrated. The system according to feature 1.

4. The aforementioned risk assessment unit, AI agents analyze data and assess the risk of infrastructure degradation. The system according to feature 1.

5. The aforementioned prioritization unit, Based on the evaluation results, repair priorities will be determined considering budget and resources. The system according to feature 1.

6. The aforementioned notification and communication department, Notify relevant parties of the determined priorities and repair plan, and arrange for materials and workers. The system according to feature 1.

7. The aforementioned construction company coordination department, Autonomously communicate and coordinate with the construction company carrying out the repairs. The system according to feature 1.

8. The aforementioned data acquisition unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

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