system

Abstract

Provide a system. 【Solution means】 Means for obtaining sensing information from the robot in real time, Means for generating and optimizing a work plan based on the sensing information, Means for sending specific work instructions to the robot based on the work plan, Means for collecting and feeding back the work progress and abnormality detection information of the robot, Means for automatically instructing emergency response when an abnormality is detected, Means for automating inspection and safety confirmation work at high places and narrow places at the construction site, Means for scanning abnormal locations at the site and checking the status, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When working in a radiation environment such as a nuclear power plant, it is necessary to prevent workers from being exposed to the risk of radiation and to perform work safely and efficiently. In addition, since the high dependence on conventional protective equipment results in high costs, it is also required to reduce this. In such a situation, a new approach is needed to ensure the safety and health of workers while improving work efficiency.

Means for Solving the Problems

[0005] This invention provides a control system for autonomous robots to perform tasks in a radiation environment. This system utilizes an artificial intelligence agent to acquire sensor data in real time, generate and optimize a work plan based on that data, and send specific work instructions to the robot based on the generated work plan. Furthermore, it includes a function to collect and provide feedback on the robot's work progress and anomaly detection information. In the event of an anomaly detection, the system automatically issues emergency response instructions, thereby ensuring the safety of workers. In this way, work in environments with a risk of radiation exposure can be carried out safely and efficiently.

[0006] A "radiation environment" refers to an environment where radiation is present and, as a result, people or equipment are exposed to the risk of radiation exposure.

[0007] An "autonomous robot" refers to a robot equipped with artificial intelligence that can perform tasks on its own without detailed instructions from an external source.

[0008] An "artificial intelligence agent" refers to a software structure or system that autonomously makes decisions and performs tasks for a specific purpose.

[0009] "Sensor data" refers to information obtained from sensors, specifically data representing certain environment variables (e.g., temperature, radiation level, location information, etc.).

[0010] A "work plan" refers to a plan of operations that optimizes the procedures and sequence in order to efficiently accomplish a specific task.

[0011] "Optimization" refers to the process of adjusting parameters to achieve a goal most effectively under given conditions and constraints.

[0012] "Work instructions" refer to specific action guidelines or commands for performing particular operations or actions.

[0013] "Feedback" refers to the process of collecting information about the work performed and returning it to the system to help with performance evaluation and adjustments.

[0014] "Anomaly detection" refers to the process of identifying and recognizing conditions that deviate from normal operation or expected standards.

[0015] "Emergency response" refers to the swift and appropriate actions that should be taken when faced with unexpected situations or dangers. [Brief explanation of the drawing]

[0016] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] To implement this invention, an autonomous robot equipped with an artificial intelligence agent is used. This robot can perform tasks in a radiation environment. The main components of the overall system include sensors, an AI agent, a server, a robot terminal, and a user interface. Each of these elements is described below.

[0038] First, the server collects data from the robot's sensors to obtain radiation levels and environmental information. This data is continuously collected and analyzed by an AI agent. Based on the collected data, the AI ​​agent generates and optimizes a work plan. This work plan includes safe movement routes and work procedures.

[0039] Next, the robot, acting as a terminal, is equipped with the ability to perform tasks according to work instructions received from the server. Specifically, it moves along a designated route and performs necessary inspections and decontamination work. It also uses sensors to monitor its surroundings in real time and immediately provides feedback to the server if any abnormalities are detected.

[0040] Furthermore, in the event of an emergency, the server immediately analyzes the anomaly and automatically instructs the robot to take appropriate emergency action. This allows the robot to switch actions based on predefined scenarios. For example, if radiation levels suddenly rise, the robot can receive a command to evacuate to a safe zone.

[0041] Users can monitor the entire system through the user interface and intervene as needed. Specifically, they can view the robot's camera feed in real time, manually correct its path, or instruct it to perform more detailed inspections.

[0042] In this way, the present invention provides a specific method for efficiently and safely carrying out work in a radiation environment. This system makes it possible to improve work efficiency and safety while minimizing the risk of radiation exposure to workers.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The server acquires data such as radiation levels, location information, and environmental conditions from the robot's sensors in real time. This information is sent to an AI agent and used to understand the current situation at the work site.

[0046] Step 2:

[0047] The AI ​​agent analyzes sensor data acquired from the server and generates an optimal work plan suitable for the current task. This plan determines safe movement routes and work procedures to minimize radiation risks.

[0048] Step 3:

[0049] The server sends movement paths and specific work instructions to the robot terminal based on the work plan generated by the AI ​​agent. This prepares the terminal to carry out the scheduled task.

[0050] Step 4:

[0051] The terminal receives instructions from the server and moves along a designated route. During this process, it monitors its surroundings in real time using its own sensors and dynamically adjusts its path to avoid obstacles.

[0052] Step 5:

[0053] If an anomaly is detected, the terminal immediately feeds that information back to the server. The server analyzes the data, and if necessary, an AI agent formulates emergency measures and immediately sends new instructions.

[0054] Step 6:

[0055] Users can monitor the robot's real-time movements through a server-based interface. They can also manually control the robot, directing it to correct its path or perform detailed inspections of specific areas.

[0056] This processing flow enables the system to safely and efficiently perform tasks in a radiation environment.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] When humans work directly in hazardous environments containing radiation, the health risks are significant, necessitating the replacement of these workers with autonomous devices. However, existing technologies have faced challenges in ensuring sufficient work efficiency and safety because the devices cannot respond to environmental changes in real time.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes means for continuously acquiring environmental information, means for generating and optimizing work plans based on the environmental information, and means for transmitting specific work instructions to the device based on the work plan. This enables safe and efficient work execution by an autonomous device while adapting to environmental changes in real time.

[0062] "External environment" refers to a work site or situation where direct human intervention poses a risk, necessitating the use of equipment to replace the work.

[0063] An "autonomous device" is a device that can recognize information about its surroundings and perform tasks based on or independently of external instructions.

[0064] An "intelligent system" refers to a program or algorithm installed in an autonomous device that analyzes collected data and determines work plans and countermeasures for abnormal situations.

[0065] "Environmental information" refers to data about the external environment, such as radiation levels, location information, temperature, humidity, and pressure.

[0066] "Means of continuous acquisition" refers to the process of collecting environmental information in real time using sensors and communication technologies.

[0067] A "work plan" is a plan that outlines detailed guidelines and procedures for carrying out work safely and efficiently, based on the environmental information that has been collected.

[0068] A "route optimization method" is an algorithm that calculates an economical and efficient travel route in order to reach a destination while ensuring safety.

[0069] To implement this invention, an autonomous device equipped with artificial intelligence is used. The main components of the system are a server, an autonomous device equipped with sensors (hereinafter referred to as "terminal"), an intelligent system, and an interface for user monitoring.

[0070] The server continuously acquires radiation level and environmental data from the terminal's sensors. This allows the server to understand the surrounding situation in real time and process the necessary information. The intelligent system analyzes the collected data to generate and optimize work plans. In this process, route optimization techniques are used to minimize radiation risk.

[0071] The terminal receives instructions from the server and performs tasks according to the designated route. The terminal is equipped with a motor for movement and an operating arm, which it uses to perform inspection and decontamination work. It also has the ability to autonomously monitor the surrounding environment and immediately provide feedback to the server if it detects any abnormalities.

[0072] Users can monitor the entire system through the user interface. They can grasp the situation on-site in real time and take manual actions as needed. This allows for safe control of the situation without humans directly performing dangerous tasks.

[0073] As a concrete example, in an area of ​​a power plant where a radiation leak is suspected, an autonomous device moves along a safe route to a designated work point, measures radiation levels using sensors, and if an abnormal value is detected, the server immediately issues an emergency response order. In this way, the system can flexibly respond to changes in the external environment and perform work efficiently.

[0074] An example of a prompt message is: "Generate a work plan for the autonomous device to perform safe operations in a radiation environment. Specifically, include safe movement routes, work procedures, and emergency response scenarios."

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The server acquires environmental data such as radiation levels, temperature, and humidity from the terminal's sensors. It receives sensor data as input and registers it in the server's database in real time. Data processing involves filtering out noise and organizing and storing the acquired information. This ensures that the system always maintains the latest environmental information and is ready for analysis.

[0078] Step 2:

[0079] The server analyzes collected environmental data using an intelligent system. The input is processed data acquired from sensors, which the AI ​​algorithm uses to detect behavioral patterns and anomalies. Data processing includes anomaly detection and trend analysis, generating an appropriate work plan and outputting work plan data. This plan includes safe movement routes and work procedures.

[0080] Step 3:

[0081] The server sends the generated work plan as an instruction to the terminal. The input is work plan data, which is converted into a transmission command and sent to the terminal. Upon receiving the instruction, the terminal starts specific actions according to its contents, and an action log is generated as output. The terminal autonomously moves along the designated route and performs inspection and decontamination work.

[0082] Step 4:

[0083] The terminal continuously collects environmental data using sensors during operation and feeds it back to the server. The input is real-time environmental information from the site, which is used for condition monitoring. Data processing includes a process to immediately identify and report any anomalies, especially if they occur. The output becomes feedback data and is used for re-analysis on the server.

[0084] Step 5:

[0085] When the server receives abnormal data, it quickly analyzes it and instructs the terminal on emergency response measures. The input is anomaly detection information, which is analyzed to generate appropriate response commands. The data calculation involves selecting and commanding emergency scenarios according to the situation. The output is an emergency response instruction that is sent to the terminal. The terminal then takes emergency action, such as evacuating to a safe zone.

[0086] Step 6:

[0087] The user monitors the overall system status through the user interface and intervenes as needed. Inputs include real-time system status and camera feeds, which are used to make decisions. Specifically, the user can manually change routes and add work instructions. Outputs include a revised work plan based on user instructions, which is sent to the terminal.

[0088] (Application Example 1)

[0089] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0090] Modern construction sites require inspections and safety checks at high altitudes and in confined spaces, posing significant risks to workers. Traditional methods necessitate direct human intervention in dangerous environments, increasing the risk of workplace accidents. Furthermore, these methods have limitations in terms of efficiency and precision. Therefore, there is a need for automated methods to perform these tasks safely and efficiently.

[0091] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0092] In this invention, the server includes means for acquiring sensing information from a robot in real time, means for generating and optimizing a work plan based on the sensing information, and means for transmitting specific work instructions to the robot based on the work plan. This enables automated inspection and safety verification at construction sites.

[0093] A "robot" is an autonomous mechanical device that operates autonomously and is equipped with the necessary functions to perform a specified task.

[0094] "Sensing information" is a general term for data acquired by robots, including environmental conditions, location information, and structural condition information.

[0095] A "work plan" is a set of specific action guidelines for the robot, generated and optimized based on acquired sensing information.

[0096] A "work instruction" is a command sent from a server to a robot that instructs it to perform specific actions based on a work plan.

[0097] A "construction site" refers to a work environment for building structures, including high places and confined spaces.

[0098] "Inspection" refers to the process of examining the condition of machinery and structures at a construction site to confirm that there are no abnormalities.

[0099] "Scanning" is a series of processes in which a robot uses sensors to acquire detailed information about its environment and structures.

[0100] A "route optimization algorithm" is a computational method used to calculate the most efficient route for a robot's movement while minimizing environmental risks.

[0101] To implement this invention, a system centered around an autonomous robot and a server for its control is required. The server acquires sensing information from the robot in real time, and an AI agent generates and optimizes a work plan based on that data. Specific work instructions are sent to the robot based on the work plan, and the robot automatically performs inspection and safety checks in high places and confined spaces.

[0102] The server includes hardware and software capable of receiving sensing information such as environmental conditions, location information, and structural condition information. For example, an AI agent on the server analyzes the diverse data collected using route optimization algorithms implemented in programming languages ​​such as Python or C++. The important information is then displayed in an interface accessible to the user.

[0103] Users can remotely monitor the system using smartphones or tablets and intervene as needed. This allows for efficient work progress while maintaining safety at the work site.

[0104] As a concrete example, if an abnormality is found in the strength of scaffolding at a construction site, a robot will use sensors to perform a 3D scan of the site. The scan data will be sent to a server, where an AI agent will analyze the data, plan the optimal course of action, and issue instructions to the robot.

[0105] Examples of prompts to input into the generating AI model include: "Design a system that continuously monitors the strength of scaffolding at construction sites, and if an anomaly is detected, a robot goes to the site, performs a detailed scan, and provides a real-time report."

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The server acquires sensing information from the robot in real time. Inputs include on-site environmental conditions, location information, and structural condition information. This allows the server to create a dataset for accurately understanding the surrounding environment.

[0109] Step 2:

[0110] An AI agent on the server generates and optimizes a work plan based on the acquired sensing information. The input is the dataset obtained in step 1, and the output is specific work instructions. In this process, an algorithm that minimizes risk using multiple parameters is in operation to derive the optimal route and procedure.

[0111] Step 3:

[0112] The server sends specific work instructions to the robot based on the work plan. The input is the work instructions generated in step 2, and the output is the control signal for the robot's movement. This allows the robot to automatically begin inspection work in high places and confined spaces.

[0113] Step 4:

[0114] The robot performs the instructed tasks and scans the environment. Here, sensors are used to acquire new sensing information. This information includes, for example, data on the surrounding 3D structure and details of any anomalies. The acquired information is sent to a server and passed on to the next analysis step.

[0115] Step 5:

[0116] The server analyzes the newly acquired data and displays the results in an interface accessible to the user. The results include detailed information on anomaly detection and recommended actions for the future. The input is the data obtained in step 4, and the output is the final report and visualization information. Based on this, the user determines whether additional work or manual intervention is required.

[0117] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0118] This invention provides a control system for autonomous robots operating in radiation environments, combining an artificial intelligence agent and an emotion engine. The system aims to further improve work efficiency and safety by recognizing the user's emotional state.

[0119] The overall system components include sensors, artificial intelligence agents, an emotion engine, servers, robot terminals, and a user interface. Each of these elements is described below.

[0120] First, the server acquires sensor data from the robot in real time. This includes radiation levels, location information, and surrounding environmental conditions. The server uses this data to help the AI ​​agent generate a safe and efficient work plan.

[0121] Next, the emotion engine has the function of monitoring and recognizing the user's emotional state. This is done, for example, by analyzing the user's voice and facial expressions. The emotional state information obtained by the emotion engine is fed to the server and AI agent and reflected in the work instructions.

[0122] The robot, acting as a terminal, receives these instructions and performs the actual tasks. During the task, it monitors its surroundings in real time using its own sensors, and if it detects an anomaly, it immediately provides feedback to the server.

[0123] Furthermore, the server, through its AI agent, integrates information from the emotion engine and performs flexible task adjustments based on the user's stress level and emotions. For example, if it detects that the user is in a high-stress state, it can slow down the robot's movements or re-optimize the route to allow the user to work at their desired pace.

[0124] Finally, the user monitors the robot's movements through an interface and sends manual instructions as needed. This interface helps users make better decisions by indicating their emotional state and providing corresponding operational guidance.

[0125] This system enables the optimization of work processes while taking into account user emotions and psychological burden, achieving high levels of safety and efficiency even in radiation environments.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] The server acquires data such as radiation levels and location information from the robot's sensors in real time. This information is sent to the AI ​​agent and used to evaluate the work environment.

[0129] Step 2:

[0130] The server utilizes an emotion engine to analyze the user's emotional state in real time from their voice and facial expressions. This allows it to determine how the user is reacting to their work environment.

[0131] Step 3:

[0132] The AI ​​agent integrates and analyzes sensor data and emotional state to generate a work plan that takes the user's emotional state into account, while minimizing the risk of radiation exposure. Task priorities and routes are adjusted, especially if the user is experiencing tension or stress.

[0133] Step 4:

[0134] The server sends specific work instructions to the robot, which acts as the terminal, based on the work plan generated by the AI ​​agent. This includes the movement path, speed, and the order in which tasks are executed.

[0135] Step 5:

[0136] As the terminal follows the instructed route and performs its tasks, it uses sensors to monitor its surroundings, dynamically avoiding obstacles and providing timely feedback to the server.

[0137] Step 6:

[0138] Through the interface, users can monitor the robot's movements and progress, and manually control it as needed. Customized operation guides based on the user's emotional state are also provided to help users make better decisions.

[0139] Step 7:

[0140] In the event of an anomaly, the server, under the direction of an AI agent, activates an emergency response protocol and instructs terminals to take immediate action. This includes evacuating robots to a safe zone and suspending operations.

[0141] This process allows the system implementing the invention to safely and efficiently manage work in a radiation environment while taking user emotions into consideration.

[0142] (Example 2)

[0143] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0144] The efficiency and safety of autonomous machinery operating in radiation environments are greatly affected by fluctuations in environmental conditions and the emotional state of the operator. Conventional systems primarily rely on static work plans based on sensor data, making it difficult to flexibly respond to real-time changes in conditions and the emotional state of the operator during actual operation. Therefore, new control technologies are needed to improve work efficiency and safety while minimizing risks.

[0145] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0146] In this invention, the server includes means for acquiring information data from the robot in real time, means for generating and optimizing a work plan based on the information data and emotional state data obtained from an analysis device, and means for adjusting work instructions according to the emotional state. This makes it possible to perform work safely and efficiently while taking into account the emotional state of the operator, while reducing risk.

[0147] "Information data" refers to data that indicates various conditions in the robot's working environment, and specifically includes environmental levels, location information, and surrounding conditions.

[0148] An "analysis device" is a device that analyzes acquired data to understand the user's emotional state, and it identifies emotional states based on voice and facial expressions.

[0149] A "work plan" is a detailed plan that defines the guidelines and processes necessary for a robot to perform a task safely and efficiently.

[0150] "Emotional state data" refers to data that indicates the user's psychological and emotional state, and is shown through changes in voice and facial expressions.

[0151] A "path optimization algorithm" is a mathematical method used to calculate the most efficient and safest path for a robot to reach a specific destination.

[0152] A "control device" is a device that performs the necessary processing to instruct the movements of a robot, and it generates and adjusts instructions in real time.

[0153] The system in this invention is for efficiently and safely controlling autonomous machines in a radiation environment. This system mainly consists of a server, a terminal (robot), a device for analyzing emotional states, and a user interface. The server acquires data in real time from various hardware such as radiation sensors and location information devices, and generates a work plan based on that information.

[0154] The server further processes user emotional data using emotion analysis software and uses this data to flexibly adjust the progress of tasks. For example, if a user is in a highly stressed state, the server adjusts the robot's operating speed based on that emotional data. As a specific example, when a robot is digging a hole at a construction site, the server uses data from sensors to optimize the robot's path so that it can perform the task in the safest and most efficient way possible.

[0155] On the other hand, the robot acting as a terminal has the function of performing tasks based on these instructions, and if an anomaly is detected, it immediately provides feedback to the server.

[0156] The user can monitor the robot's progress and issue manual instructions as needed using the user interface. This interface visually displays the user's emotional state and provides corresponding support operations.

[0157] An example of a prompt message might be: "To improve safety at construction sites, please explain how to adjust the robot's behavior when the user's stress level is high."

[0158] In this way, this system creates a more flexible and safer work environment by combining emotional data and environmental data.

[0159] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0160] Step 1:

[0161] The server acquires environmental data in real time from the robot's sensors. Input data includes information such as radiation levels, location, and surrounding conditions. Based on this data, the server stores it chronologically in a database, using it as material for subsequent processing. Specifically, it acquires data at regular intervals via an API and accumulates that information.

[0162] Step 2:

[0163] The server acquires voice and facial expression data as input data to analyze the user's emotional state, and analyzes it using an emotion engine. This analysis outputs the user's stress level and emotional state. Specifically, the process involves using a speech recognition module to estimate emotion labels from changes in voice tone and speed.

[0164] Step 3:

[0165] The server integrates the environmental data obtained in Step 1 and the emotional state data obtained in Step 2, and generates a work plan using a generative AI model. This work plan includes optimized routes and task schedules. Specifically, it performs data analysis and machine learning algorithms to generate output that balances safety and efficiency.

[0166] Step 4:

[0167] The terminal (robot) begins work based on the work plan sent from the server. The robot uses its own sensors to continuously monitor its progress and surrounding anomalies, and immediately sends feedback to the server if any anomalies are detected. Specifically, it monitors sensor values ​​and sends a warning if they exceed a threshold defined in the program.

[0168] Step 5:

[0169] The user monitors the robot's movements in real time through the user interface and sends manual instructions as needed. Inputs include the user's own judgments and environmental changes, while outputs are specific instructions given to the robot. These specific actions involve manipulating parameters and executing temporary control commands using the GUI.

[0170] The above describes the specific flow of operations in the system's processing steps.

[0171] (Application Example 2)

[0172] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0173] When autonomous machines operate in a radiation-affected environment, minimizing radiation risks and improving work efficiency are essential. Furthermore, considering the emotional state of the people involved in the work, it is crucial to ensure safety while reducing their psychological burden.

[0174] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0175] In this invention, the server includes means for acquiring detection data from machines in real time, means for generating and optimizing a work plan based on the detection data, and means for recognizing the emotional state of humans and adjusting work instructions and progress speed accordingly. This makes it possible to perform work efficiently and safely even in a radiation environment and to reduce the psychological burden on the people involved.

[0176] An "autonomous machine" is a device that operates automatically based on pre-set instructions and can perform specific tasks without direct human intervention.

[0177] An "artificial intelligence processing unit" is a system that uses machine learning and data processing algorithms to make judgments and predictions, and generates instructions necessary for execution.

[0178] "Detection data" refers to environmental and conditional information collected using sensors and measuring devices, and is used for work planning and safety decisions.

[0179] A "work plan" is a plan that defines the specific tasks and procedures to be performed by an autonomous machine, with the aim of ensuring efficient and safe work execution.

[0180] "Emergency response" refers to measures and actions that need to be taken quickly when an anomaly or danger is detected, and is important for ensuring the safety and reliability of the system.

[0181] "Human emotional state" refers to a person's emotional reactions and psychological condition that can be inferred by analyzing their voice, facial expressions, and other factors.

[0182] A "route optimization method" is an algorithm used to calculate the most efficient and safe route to achieve a goal, with the aim of reducing work time and mitigating risks.

[0183] This invention provides a control system using autonomous machines that enables efficient and safe work, particularly in radiation environments. The server generates and optimizes work plans in real time using detection data collected from the machines. Specifically, the server processes information obtained from sensors such as temperature, voice, location, and facial recognition cameras, and makes decisions using an artificial intelligence processing unit. This decision-making process includes recognizing the emotional state of the human being to reduce psychological burden.

[0184] Emotional states are grasped by analyzing voice tone and facial expression data, and this is achieved through emotion recognition technologies such as OpenCV and TENSORFLOW®. The analysis results are reflected in the adjustment of the work plan, and if the person's stress level is high, the work pace is adjusted and tasks are reprioritized.

[0185] As a terminal, the autonomous machine performs actual tasks based on instructions sent from the server. For example, if a facial recognition camera detects that a person is tired, the machine can automatically slow down its work speed or suggest a short break. Furthermore, the user (caregiver or administrator) can monitor the system's status through the interface and give manual instructions as needed. In this process, the system provides specific feedback indicating the user's emotional state and work progress to support their decision-making.

[0186] As a concrete example, a smart care assistant robot continuously monitors the elderly person's complexion and facial expressions, and if signs of anxiety appear, it gently asks, "Are you okay? Is there anything I can help you with?" Simultaneously, it sends a notification to the caregiver, allowing for immediate provision of further support.

[0187] Using a generative AI model, you can use prompt statements like the following:

[0188] In care robots for the elderly, facial recognition cameras are used to analyze facial expressions and engage in conversations that respond to the user's emotions.

[0189] Example: If the user looks tired, suggest they take a break.

[0190] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0191] Step 1:

[0192] The server receives detection data in real time from sensors on the autonomous machine. This data includes temperature, sound, location, and video data from facial recognition cameras. The server inputs this data into an analysis engine, which normalizes and filters the data from each sensor to remove noise and outliers.

[0193] Step 2:

[0194] The server uses an analysis engine to analyze input detection data and recognize human emotional states. Specifically, it uses OpenCV and TensorFlow to estimate stress levels and emotions from facial expression data and voice tone. The results of this analysis are quantified by an emotion recognition module and output as a standard for work adjustments.

[0195] Step 3:

[0196] The server activates the artificial intelligence processing unit and integrates emotional state data into the current work plan. At this point, the AI ​​algorithm performs optimizations to adjust the work priorities and pace. The input is the current work status and emotional recognition results, and the output is the adjusted work plan.

[0197] Step 4:

[0198] Autonomous machines, acting as terminals, begin operation based on a pre-configured work plan received from a server. For example, if fatigue or stress is detected, the machine slows down its pace of operation or temporarily suspends a specific task to provide a break. This allows the machine to perform its work safely and at a more appropriate pace.

[0199] Step 5:

[0200] The user monitors the robot's operation status via an interface. The user can manually send instructions to the robot as needed, including changing task priorities and issuing emergency stop commands. The interface provides the user with feedback that visualizes emotion recognition results and work progress.

[0201] Step 6:

[0202] The server periodically collects and records progress and anomaly detection results during the process. This includes generating prompt messages through a generative AI model, enabling appropriate intervention and improvement. The data obtained in this process is useful for improving future work plans and developing new algorithms.

[0203] 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.

[0204] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0205] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0206] [Second Embodiment]

[0207] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] 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.

[0209] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0210] 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.

[0211] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0212] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0213] 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.

[0214] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0215] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0216] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0217] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0218] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0219] To implement this invention, an autonomous robot equipped with an artificial intelligence agent is used. This robot can perform tasks in a radiation environment. The main components of the overall system include sensors, an AI agent, a server, a robot terminal, and a user interface. Each of these elements is described below.

[0220] First, the server collects data from the robot's sensors to obtain radiation levels and environmental information. This data is continuously collected and analyzed by an AI agent. Based on the collected data, the AI ​​agent generates and optimizes a work plan. This work plan includes safe movement routes and work procedures.

[0221] Next, the robot, acting as a terminal, is equipped with the ability to perform tasks according to work instructions received from the server. Specifically, it moves along a designated route and performs necessary inspections and decontamination work. It also uses sensors to monitor its surroundings in real time and immediately provides feedback to the server if any abnormalities are detected.

[0222] Furthermore, in the event of an emergency, the server immediately analyzes the anomaly and automatically instructs the robot to take appropriate emergency action. This allows the robot to switch actions based on predefined scenarios. For example, if radiation levels suddenly rise, the robot can receive a command to evacuate to a safe zone.

[0223] Users can monitor the entire system through the user interface and intervene as needed. Specifically, they can view the robot's camera feed in real time, manually correct its path, or instruct it to perform more detailed inspections.

[0224] In this way, the present invention provides a specific method for efficiently and safely carrying out work in a radiation environment. This system makes it possible to improve work efficiency and safety while minimizing the risk of radiation exposure to workers.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] The server acquires data such as radiation levels, location information, and environmental conditions from the robot's sensors in real time. This information is sent to an AI agent and used to understand the current situation at the work site.

[0228] Step 2:

[0229] The AI ​​agent analyzes sensor data acquired from the server and generates an optimal work plan suitable for the current task. This plan determines safe movement routes and work procedures to minimize radiation risks.

[0230] Step 3:

[0231] The server sends movement paths and specific work instructions to the robot terminal based on the work plan generated by the AI ​​agent. This prepares the terminal to carry out the scheduled task.

[0232] Step 4:

[0233] The terminal receives instructions from the server and moves along a designated route. During this process, it monitors its surroundings in real time using its own sensors and dynamically adjusts its path to avoid obstacles.

[0234] Step 5:

[0235] If an anomaly is detected, the terminal immediately feeds that information back to the server. The server analyzes the data, and if necessary, an AI agent formulates emergency measures and immediately sends new instructions.

[0236] Step 6:

[0237] Users can monitor the robot's real-time movements through a server-based interface. They can also manually control the robot, directing it to correct its path or perform detailed inspections of specific areas.

[0238] This processing flow enables the system to safely and efficiently perform tasks in a radiation environment.

[0239] (Example 1)

[0240] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0241] When humans work directly in hazardous environments containing radiation, the health risks are significant, necessitating the replacement of these workers with autonomous devices. However, existing technologies have faced challenges in ensuring sufficient work efficiency and safety because the devices cannot respond to environmental changes in real time.

[0242] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0243] In this invention, the server includes means for continuously acquiring environmental information, means for generating and optimizing work plans based on the environmental information, and means for transmitting specific work instructions to the device based on the work plan. This enables safe and efficient work execution by an autonomous device while adapting to environmental changes in real time.

[0244] "External environment" refers to a work site or situation where direct human intervention poses a risk, necessitating the use of equipment to replace the work.

[0245] An "autonomous device" is a device that can recognize information about its surroundings and perform tasks based on or independently of external instructions.

[0246] An "intelligent system" refers to a program or algorithm installed in an autonomous device that analyzes collected data and determines work plans and countermeasures for abnormal situations.

[0247] "Environmental information" refers to data about the external environment, such as radiation levels, location information, temperature, humidity, and pressure.

[0248] "Means of continuous acquisition" refers to the process of collecting environmental information in real time using sensors and communication technologies.

[0249] A "work plan" is a plan that outlines detailed guidelines and procedures for carrying out work safely and efficiently, based on the environmental information that has been collected.

[0250] A "route optimization method" is an algorithm that calculates an economical and efficient travel route in order to reach a destination while ensuring safety.

[0251] To implement this invention, an autonomous device equipped with artificial intelligence is used. The main components of the system are a server, an autonomous device equipped with sensors (hereinafter referred to as "terminal"), an intelligent system, and an interface for user monitoring.

[0252] The server continuously acquires radiation level and environmental data from the terminal's sensors. This allows the server to understand the surrounding situation in real time and process the necessary information. The intelligent system analyzes the collected data to generate and optimize work plans. In this process, route optimization techniques are used to minimize radiation risk.

[0253] The terminal receives instructions from the server and performs tasks according to the designated route. The terminal is equipped with a motor for movement and an operating arm, which it uses to perform inspection and decontamination work. It also has the ability to autonomously monitor the surrounding environment and immediately provide feedback to the server if it detects any abnormalities.

[0254] Users can monitor the entire system through the user interface. They can grasp the situation on-site in real time and take manual actions as needed. This allows for safe control of the situation without humans directly performing dangerous tasks.

[0255] As a concrete example, in an area of ​​a power plant where a radiation leak is suspected, an autonomous device moves along a safe route to a designated work point, measures radiation levels using sensors, and if an abnormal value is detected, the server immediately issues an emergency response order. In this way, the system can flexibly respond to changes in the external environment and perform work efficiently.

[0256] An example of a prompt message is: "Generate a work plan for the autonomous device to perform safe operations in a radiation environment. Specifically, include safe movement routes, work procedures, and emergency response scenarios."

[0257] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0258] Step 1:

[0259] The server acquires environmental data such as radiation levels, temperature, and humidity from the terminal's sensors. It receives sensor data as input and registers it in the server's database in real time. Data processing involves filtering out noise and organizing and storing the acquired information. This ensures that the system always maintains the latest environmental information and is ready for analysis.

[0260] Step 2:

[0261] The server analyzes collected environmental data using an intelligent system. The input is processed data acquired from sensors, which the AI ​​algorithm uses to detect behavioral patterns and anomalies. Data processing includes anomaly detection and trend analysis, generating an appropriate work plan and outputting work plan data. This plan includes safe movement routes and work procedures.

[0262] Step 3:

[0263] The server sends the generated work plan as an instruction to the terminal. The input is work plan data, which is converted into a transmission command and sent to the terminal. Upon receiving the instruction, the terminal starts specific actions according to its contents, and an action log is generated as output. The terminal autonomously moves along the designated route and performs inspection and decontamination work.

[0264] Step 4:

[0265] The terminal continuously collects environmental data using sensors during operation and feeds it back to the server. The input is real-time environmental information from the site, which is used for condition monitoring. Data processing includes a process to immediately identify and report any anomalies, especially if they occur. The output becomes feedback data and is used for re-analysis on the server.

[0266] Step 5:

[0267] When the server receives abnormal data, it quickly analyzes it and instructs the terminal on emergency response measures. The input is anomaly detection information, which is analyzed to generate appropriate response commands. The data calculation involves selecting and commanding emergency scenarios according to the situation. The output is an emergency response instruction that is sent to the terminal. The terminal then takes emergency action, such as evacuating to a safe zone.

[0268] Step 6:

[0269] The user monitors the overall system status through the user interface and intervenes as needed. Inputs include real-time system status and camera feeds, which are used to make decisions. Specifically, the user can manually change routes and add work instructions. Outputs include a revised work plan based on user instructions, which is sent to the terminal.

[0270] (Application Example 1)

[0271] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0272] Modern construction sites require inspections and safety checks at high altitudes and in confined spaces, posing significant risks to workers. Traditional methods necessitate direct human intervention in dangerous environments, increasing the risk of workplace accidents. Furthermore, these methods have limitations in terms of efficiency and precision. Therefore, there is a need for automated methods to perform these tasks safely and efficiently.

[0273] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0274] In this invention, the server includes means for acquiring sensing information from a robot in real time, means for generating and optimizing a work plan based on the sensing information, and means for transmitting specific work instructions to the robot based on the work plan. This enables automated inspection and safety verification at construction sites.

[0275] A "robot" is an autonomous mechanical device that operates autonomously and is equipped with the necessary functions to perform a specified task.

[0276] "Sensing information" is a general term for data acquired by robots, including environmental conditions, location information, and structural condition information.

[0277] The "operation plan" is a specific action guideline for the optimized robot generated based on the acquired sensing information.

[0278] The "operation instruction" is an instruction that is sent from the server to the robot and instructs specific actions based on the operation plan.

[0279] The "construction site" refers to the working environment for constructing structures, including high and narrow places.

[0280] "Inspection" refers to the work of investigating the status of mechanical devices and structures at the construction site and checking for abnormalities.

[0281] "Scan" is a series of processes in which a robot uses sensors to obtain detailed information about the environment and structures.

[0282] The "route optimization algorithm" is a calculation method for calculating a route for a robot to move efficiently while minimizing environmental risks when the robot moves.

[0283] To implement this invention, a system centered on an autonomous robot and a server for controlling it is required. The server acquires sensing information from the robot in real time, and based on this data, an AI agent generates and optimizes an operation plan. Specific operation instructions are sent to the robot based on the operation plan, and the robot automatically executes inspection and safety confirmation work in high and narrow places.

[0284] The server includes hardware and software capable of receiving sensing information such as environmental conditions, position information, and structure status information. For example, the AI agent on the server uses a route optimization algorithm implemented in a programming language such as Python or C++ to analyze various types of collected data. Then, important information is displayed on an interface accessible to the user.

[0285] Users can remotely monitor the system using smartphones or tablets and intervene as needed. This can help support the efficient progress of work while maintaining the safety of the work site.

[0286] As a specific example, when an abnormality is found in the strength of the scaffolding at a construction site, the robot uses sensors to perform a 3D scan of the site. The scan data is sent to the server, and the AI agent analyzes the data to plan the optimal course of action and issue instructions to the robot.

[0287] Examples of prompt sentences to input into the generative AI model include "Design a system that continuously monitors the strength of the scaffolding at a construction site and, when an abnormality is detected, sends a robot to the site to perform a detailed scan and provide a real-time report."

[0288] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0289] Step 1:

[0290] The server obtains sensing information from the robot in real time. The inputs include the environmental conditions at the site, location information, and structural condition information. Based on this, the server creates a dataset for accurately understanding the surrounding situation.

[0291] Step 2:

[0292] The AI agent on the server generates and optimizes a work plan based on the acquired sensing information. The input is the dataset obtained in Step 1, and the output is a specific work instruction. In this process, an algorithm that minimizes risks using multiple parameters operates to derive the optimal route and procedure.

[0293] Step 3:

[0294] The server sends specific work instructions to the robot based on the work plan. The input is the work instructions generated in step 2, and the output is the control signal for the robot's movement. This allows the robot to automatically begin inspection work in high places and confined spaces.

[0295] Step 4:

[0296] The robot performs the instructed tasks and scans the environment. Here, sensors are used to acquire new sensing information. This information includes, for example, data on the surrounding 3D structure and details of any anomalies. The acquired information is sent to a server and passed on to the next analysis step.

[0297] Step 5:

[0298] The server analyzes the newly acquired data and displays the results in an interface accessible to the user. The results include detailed information on anomaly detection and recommended actions for the future. The input is the data obtained in step 4, and the output is the final report and visualization information. Based on this, the user determines whether additional work or manual intervention is required.

[0299] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0300] This invention provides a control system for autonomous robots operating in radiation environments, combining an artificial intelligence agent and an emotion engine. The system aims to further improve work efficiency and safety by recognizing the user's emotional state.

[0301] The overall system components include sensors, artificial intelligence agents, an emotion engine, servers, robot terminals, and a user interface. Each of these elements is described below.

[0302] First, the server acquires sensor data from the robot in real time. This includes radiation levels, location information, and the surrounding environmental conditions. The server uses this data as a basis for the AI agent to generate a safe and efficient work plan.

[0303] Next, the emotion engine has the function of monitoring and recognizing the user's emotional state. This is done, for example, by analyzing the user's voice and facial expressions. The information on the emotional state obtained by the emotion engine is fed to the server and the AI agent, and is reflected in the work instructions.

[0304] The robot as a terminal receives these instructions and performs the actual work. During the work, it monitors the surrounding conditions in real time with its own sensors, and if an abnormality is detected, it immediately feeds back to the server.

[0305] In addition, the server integrates the information from the emotion engine through the AI agent and performs flexible task adjustment based on the user's stress level and emotions. For example, when the user is perceived to be in a high-stress state, the movement of the robot can be decelerated, or the route optimization can be performed again so that the work can proceed at the pace desired by the user.

[0306] Finally, the user monitors the operation of the robot via the interface and sends manual instructions as needed. This interface shows the user's emotional state and provides corresponding operation guides to assist in better decision-making.

[0307] This system enables the optimization of work considering the user's emotions and psychological burden, and high safety and efficiency are achieved even in a radiation environment.

[0308] The processing flow will be described below.

[0309] Step 1:

[0310] The server acquires data such as radiation levels and location information from the robot's sensors in real time. This information is sent to the AI ​​agent and used to evaluate the work environment.

[0311] Step 2:

[0312] The server utilizes an emotion engine to analyze the user's emotional state in real time from their voice and facial expressions. This allows it to determine how the user is reacting to their work environment.

[0313] Step 3:

[0314] The AI ​​agent integrates and analyzes sensor data and emotional state to generate a work plan that takes the user's emotional state into account, while minimizing the risk of radiation exposure. Task priorities and routes are adjusted, especially if the user is experiencing tension or stress.

[0315] Step 4:

[0316] The server sends specific work instructions to the robot, which acts as the terminal, based on the work plan generated by the AI ​​agent. This includes the movement path, speed, and the order in which tasks are executed.

[0317] Step 5:

[0318] As the terminal follows the instructed route and performs its tasks, it uses sensors to monitor its surroundings, dynamically avoiding obstacles and providing timely feedback to the server.

[0319] Step 6:

[0320] Through the interface, users can monitor the robot's movements and progress, and manually control it as needed. Customized operation guides based on the user's emotional state are also provided to help users make better decisions.

[0321] Step 7:

[0322] In the event of an anomaly, the server, under the direction of an AI agent, activates an emergency response protocol and instructs terminals to take immediate action. This includes evacuating robots to a safe zone and suspending operations.

[0323] This process allows the system implementing the invention to safely and efficiently manage work in a radiation environment while taking user emotions into consideration.

[0324] (Example 2)

[0325] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0326] The efficiency and safety of autonomous machinery operating in radiation environments are greatly affected by fluctuations in environmental conditions and the emotional state of the operator. Conventional systems primarily rely on static work plans based on sensor data, making it difficult to flexibly respond to real-time changes in conditions and the emotional state of the operator during actual operation. Therefore, new control technologies are needed to improve work efficiency and safety while minimizing risks.

[0327] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0328] In this invention, the server includes means for acquiring information data from the robot in real time, means for generating and optimizing a work plan based on the information data and emotional state data obtained from an analysis device, and means for adjusting work instructions according to the emotional state. This makes it possible to perform work safely and efficiently while taking into account the emotional state of the operator, while reducing risk.

[0329] "Information data" refers to data that indicates various conditions in the robot's working environment, and specifically includes environmental levels, location information, and surrounding conditions.

[0330] An "analysis device" is a device that analyzes acquired data to understand the user's emotional state, and it identifies emotional states based on voice and facial expressions.

[0331] A "work plan" is a detailed plan that defines the guidelines and processes necessary for a robot to perform a task safely and efficiently.

[0332] "Emotional state data" refers to data that indicates the user's psychological and emotional state, and is shown through changes in voice and facial expressions.

[0333] A "path optimization algorithm" is a mathematical method used to calculate the most efficient and safest path for a robot to reach a specific destination.

[0334] A "control device" is a device that performs the necessary processing to instruct the movements of a robot, and it generates and adjusts instructions in real time.

[0335] The system in this invention is for efficiently and safely controlling autonomous machines in a radiation environment. This system mainly consists of a server, a terminal (robot), a device for analyzing emotional states, and a user interface. The server acquires data in real time from various hardware such as radiation sensors and location information devices, and generates a work plan based on that information.

[0336] The server further processes user emotional data using emotion analysis software and uses this data to flexibly adjust the progress of tasks. For example, if a user is in a highly stressed state, the server adjusts the robot's operating speed based on that emotional data. As a specific example, when a robot is digging a hole at a construction site, the server uses data from sensors to optimize the robot's path so that it can perform the task in the safest and most efficient way possible.

[0337] On the other hand, the robot acting as a terminal has the function of performing tasks based on these instructions, and if an anomaly is detected, it immediately provides feedback to the server.

[0338] The user can monitor the robot's progress and issue manual instructions as needed using the user interface. This interface visually displays the user's emotional state and provides corresponding support operations.

[0339] An example of a prompt message might be: "To improve safety at construction sites, please explain how to adjust the robot's behavior when the user's stress level is high."

[0340] In this way, this system creates a more flexible and safer work environment by combining emotional data and environmental data.

[0341] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0342] Step 1:

[0343] The server acquires environmental data in real time from the robot's sensors. Input data includes information such as radiation levels, location, and surrounding conditions. Based on this data, the server stores it chronologically in a database, using it as material for subsequent processing. Specifically, it acquires data at regular intervals via an API and accumulates that information.

[0344] Step 2:

[0345] The server acquires voice and facial expression data as input data to analyze the user's emotional state, and analyzes it using an emotion engine. This analysis outputs the user's stress level and emotional state. Specifically, the process involves using a speech recognition module to estimate emotion labels from changes in voice tone and speed.

[0346] Step 3:

[0347] The server integrates the environmental data obtained in Step 1 and the emotional state data obtained in Step 2, and generates a work plan using a generative AI model. This work plan includes optimized routes and task schedules. Specifically, it performs data analysis and machine learning algorithms to generate output that balances safety and efficiency.

[0348] Step 4:

[0349] The terminal (robot) begins work based on the work plan sent from the server. The robot uses its own sensors to continuously monitor its progress and surrounding anomalies, and immediately sends feedback to the server if any anomalies are detected. Specifically, it monitors sensor values ​​and sends a warning if they exceed a threshold defined in the program.

[0350] Step 5:

[0351] The user monitors the robot's movements in real time through the user interface and sends manual instructions as needed. Inputs include the user's own judgments and environmental changes, while outputs are specific instructions given to the robot. These specific actions involve manipulating parameters and executing temporary control commands using the GUI.

[0352] The above describes the specific flow of operations in the system's processing steps.

[0353] (Application Example 2)

[0354] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0355] When autonomous machines operate in a radiation-affected environment, minimizing radiation risks and improving work efficiency are essential. Furthermore, considering the emotional state of the people involved in the work, it is crucial to ensure safety while reducing their psychological burden.

[0356] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0357] In this invention, the server includes means for acquiring detection data from machines in real time, means for generating and optimizing a work plan based on the detection data, and means for recognizing the emotional state of humans and adjusting work instructions and progress speed accordingly. This makes it possible to perform work efficiently and safely even in a radiation environment and to reduce the psychological burden on the people involved.

[0358] An "autonomous machine" is a device that operates automatically based on pre-set instructions and can perform specific tasks without direct human intervention.

[0359] An "artificial intelligence processing unit" is a system that uses machine learning and data processing algorithms to make judgments and predictions, and generates instructions necessary for execution.

[0360] "Detection data" refers to environmental and conditional information collected using sensors and measuring devices, and is used for work planning and safety decisions.

[0361] A "work plan" is a plan that defines the specific tasks and procedures to be performed by an autonomous machine, with the aim of ensuring efficient and safe work execution.

[0362] "Emergency response" refers to measures and actions that need to be taken quickly when an anomaly or danger is detected, and is important for ensuring the safety and reliability of the system.

[0363] "Human emotional state" refers to a person's emotional reactions and psychological condition that can be inferred by analyzing their voice, facial expressions, and other factors.

[0364] A "route optimization method" is an algorithm used to calculate the most efficient and safe route to achieve a goal, with the aim of reducing work time and mitigating risks.

[0365] This invention provides a control system using autonomous machines that enables efficient and safe work, particularly in radiation environments. The server generates and optimizes work plans in real time using detection data collected from the machines. Specifically, the server processes information obtained from sensors such as temperature, voice, location, and facial recognition cameras, and makes decisions using an artificial intelligence processing unit. This decision-making process includes recognizing the emotional state of the human being to reduce psychological burden.

[0366] Emotional states are grasped by analyzing voice tone and facial expression data, and this is achieved using emotion recognition technologies such as OpenCV and TensorFlow. The analysis results are reflected in the adjustment of the work plan; if a person's stress level is high, the work pace is adjusted and tasks are reprioritized.

[0367] As a terminal, the autonomous machine performs actual tasks based on instructions sent from the server. For example, if a facial recognition camera detects that a person is tired, the machine can automatically slow down its work speed or suggest a short break. Furthermore, the user (caregiver or administrator) can monitor the system's status through the interface and give manual instructions as needed. In this process, the system provides specific feedback indicating the user's emotional state and work progress to support their decision-making.

[0368] As a concrete example, a smart care assistant robot continuously monitors the elderly person's complexion and facial expressions, and if signs of anxiety appear, it gently asks, "Are you okay? Is there anything I can help you with?" Simultaneously, it sends a notification to the caregiver, allowing for immediate provision of further support.

[0369] Using a generative AI model, you can use prompt statements like the following:

[0370] In care robots for the elderly, facial recognition cameras are used to analyze facial expressions and engage in conversations that respond to the user's emotions.

[0371] Example: If the user looks tired, suggest they take a break.

[0372] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0373] Step 1:

[0374] The server receives detection data in real time from sensors on the autonomous machine. This data includes temperature, sound, location, and video data from facial recognition cameras. The server inputs this data into an analysis engine, which normalizes and filters the data from each sensor to remove noise and outliers.

[0375] Step 2:

[0376] The server uses an analysis engine to analyze input detection data and recognize human emotional states. Specifically, it uses OpenCV and TensorFlow to estimate stress levels and emotions from facial expression data and voice tone. The results of this analysis are quantified by an emotion recognition module and output as a standard for work adjustments.

[0377] Step 3:

[0378] The server activates the artificial intelligence processing unit and integrates emotional state data into the current work plan. At this point, the AI ​​algorithm performs optimizations to adjust the work priorities and pace. The input is the current work status and emotional recognition results, and the output is the adjusted work plan.

[0379] Step 4:

[0380] Autonomous machines, acting as terminals, begin operation based on a pre-configured work plan received from a server. For example, if fatigue or stress is detected, the machine slows down its pace of operation or temporarily suspends a specific task to provide a break. This allows the machine to perform its work safely and at a more appropriate pace.

[0381] Step 5:

[0382] The user monitors the robot's operation status via an interface. The user can manually send instructions to the robot as needed, including changing task priorities and issuing emergency stop commands. The interface provides the user with feedback that visualizes emotion recognition results and work progress.

[0383] Step 6:

[0384] The server periodically collects and records progress and anomaly detection results during the process. This includes generating prompt messages through a generative AI model, enabling appropriate intervention and improvement. The data obtained in this process is useful for improving future work plans and developing new algorithms.

[0385] 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.

[0386] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0387] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0388] [Third Embodiment]

[0389] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0390] 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.

[0391] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0392] 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.

[0393] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0394] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0395] 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.

[0396] 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.

[0397] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0398] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0399] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0400] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0401] To implement this invention, an autonomous robot equipped with an artificial intelligence agent is used. This robot can perform tasks in a radiation environment. The main components of the overall system include sensors, an AI agent, a server, a robot terminal, and a user interface. Each of these elements is described below.

[0402] First, the server collects data from the robot's sensors to obtain radiation levels and environmental information. This data is continuously collected and analyzed by an AI agent. Based on the collected data, the AI ​​agent generates and optimizes a work plan. This work plan includes safe movement routes and work procedures.

[0403] Next, the robot, acting as a terminal, is equipped with the ability to perform tasks according to work instructions received from the server. Specifically, it moves along a designated route and performs necessary inspections and decontamination work. It also uses sensors to monitor its surroundings in real time and immediately provides feedback to the server if any abnormalities are detected.

[0404] Furthermore, in the event of an emergency, the server immediately analyzes the anomaly and automatically instructs the robot to take appropriate emergency action. This allows the robot to switch actions based on predefined scenarios. For example, if radiation levels suddenly rise, the robot can receive a command to evacuate to a safe zone.

[0405] Users can monitor the entire system through the user interface and intervene as needed. Specifically, they can view the robot's camera feed in real time, manually correct its path, or instruct it to perform more detailed inspections.

[0406] In this way, the present invention provides a specific method for efficiently and safely carrying out work in a radiation environment. This system makes it possible to improve work efficiency and safety while minimizing the risk of radiation exposure to workers.

[0407] The following describes the processing flow.

[0408] Step 1:

[0409] The server acquires data such as radiation levels, location information, and environmental conditions from the robot's sensors in real time. This information is sent to an AI agent and used to understand the current situation at the work site.

[0410] Step 2:

[0411] The AI ​​agent analyzes sensor data acquired from the server and generates an optimal work plan suitable for the current task. This plan determines safe movement routes and work procedures to minimize radiation risks.

[0412] Step 3:

[0413] The server sends movement paths and specific work instructions to the robot terminal based on the work plan generated by the AI ​​agent. This prepares the terminal to carry out the scheduled task.

[0414] Step 4:

[0415] The terminal receives instructions from the server and moves along a designated route. During this process, it monitors its surroundings in real time using its own sensors and dynamically adjusts its path to avoid obstacles.

[0416] Step 5:

[0417] If an anomaly is detected, the terminal immediately feeds that information back to the server. The server analyzes the data, and if necessary, an AI agent formulates emergency measures and immediately sends new instructions.

[0418] Step 6:

[0419] Users can monitor the robot's real-time movements through a server-based interface. They can also manually control the robot, directing it to correct its path or perform detailed inspections of specific areas.

[0420] This processing flow enables the system to safely and efficiently perform tasks in a radiation environment.

[0421] (Example 1)

[0422] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0423] When humans work directly in hazardous environments containing radiation, the health risks are significant, necessitating the replacement of these workers with autonomous devices. However, existing technologies have faced challenges in ensuring sufficient work efficiency and safety because the devices cannot respond to environmental changes in real time.

[0424] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0425] In this invention, the server includes means for continuously acquiring environmental information, means for generating and optimizing work plans based on the environmental information, and means for transmitting specific work instructions to the device based on the work plan. This enables safe and efficient work execution by an autonomous device while adapting to environmental changes in real time.

[0426] "External environment" refers to a work site or situation where direct human intervention poses a risk, necessitating the use of equipment to replace the work.

[0427] An "autonomous device" is a device that can recognize information about its surroundings and perform tasks based on or independently of external instructions.

[0428] An "intelligent system" refers to a program or algorithm installed in an autonomous device that analyzes collected data and determines work plans and countermeasures for abnormal situations.

[0429] "Environmental information" refers to data about the external environment, such as radiation levels, location information, temperature, humidity, and pressure.

[0430] "Means of continuous acquisition" refers to the process of collecting environmental information in real time using sensors and communication technologies.

[0431] A "work plan" is a plan that outlines detailed guidelines and procedures for carrying out work safely and efficiently, based on the environmental information that has been collected.

[0432] A "route optimization method" is an algorithm that calculates an economical and efficient travel route in order to reach a destination while ensuring safety.

[0433] To implement this invention, an autonomous device equipped with artificial intelligence is used. The main components of the system are a server, an autonomous device equipped with sensors (hereinafter referred to as "terminal"), an intelligent system, and an interface for user monitoring.

[0434] The server continuously acquires radiation level and environmental data from the terminal's sensors. This allows the server to understand the surrounding situation in real time and process the necessary information. The intelligent system analyzes the collected data to generate and optimize work plans. In this process, route optimization techniques are used to minimize radiation risk.

[0435] The terminal receives instructions from the server and performs tasks according to the designated route. The terminal is equipped with a motor for movement and an operating arm, which it uses to perform inspection and decontamination work. It also has the ability to autonomously monitor the surrounding environment and immediately provide feedback to the server if it detects any abnormalities.

[0436] Users can monitor the entire system through the user interface. They can grasp the situation on-site in real time and take manual actions as needed. This allows for safe control of the situation without humans directly performing dangerous tasks.

[0437] As a concrete example, in an area of ​​a power plant where a radiation leak is suspected, an autonomous device moves along a safe route to a designated work point, measures radiation levels using sensors, and if an abnormal value is detected, the server immediately issues an emergency response order. In this way, the system can flexibly respond to changes in the external environment and perform work efficiently.

[0438] An example of a prompt message is: "Generate a work plan for the autonomous device to perform safe operations in a radiation environment. Specifically, include safe movement routes, work procedures, and emergency response scenarios."

[0439] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0440] Step 1:

[0441] The server acquires environmental data such as radiation levels, temperature, and humidity from the terminal's sensors. It receives sensor data as input and registers it in the server's database in real time. Data processing involves filtering out noise and organizing and storing the acquired information. This ensures that the system always maintains the latest environmental information and is ready for analysis.

[0442] Step 2:

[0443] The server analyzes collected environmental data using an intelligent system. The input is processed data acquired from sensors, which the AI ​​algorithm uses to detect behavioral patterns and anomalies. Data processing includes anomaly detection and trend analysis, generating an appropriate work plan and outputting work plan data. This plan includes safe movement routes and work procedures.

[0444] Step 3:

[0445] The server sends the generated work plan as an instruction to the terminal. The input is work plan data, which is converted into a transmission command and sent to the terminal. Upon receiving the instruction, the terminal starts specific actions according to its contents, and an action log is generated as output. The terminal autonomously moves along the designated route and performs inspection and decontamination work.

[0446] Step 4:

[0447] The terminal continuously collects environmental data using sensors during operation and feeds it back to the server. The input is real-time environmental information from the site, which is used for condition monitoring. Data processing includes a process to immediately identify and report any anomalies, especially if they occur. The output becomes feedback data and is used for re-analysis on the server.

[0448] Step 5:

[0449] When the server receives abnormal data, it quickly analyzes it and instructs the terminal on emergency response measures. The input is anomaly detection information, which is analyzed to generate appropriate response commands. The data calculation involves selecting and commanding emergency scenarios according to the situation. The output is an emergency response instruction that is sent to the terminal. The terminal then takes emergency action, such as evacuating to a safe zone.

[0450] Step 6:

[0451] The user monitors the overall system status through the user interface and intervenes as needed. Inputs include real-time system status and camera feeds, which are used to make decisions. Specifically, the user can manually change routes and add work instructions. Outputs include a revised work plan based on user instructions, which is sent to the terminal.

[0452] (Application Example 1)

[0453] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0454] Modern construction sites require inspections and safety checks at high altitudes and in confined spaces, posing significant risks to workers. Traditional methods necessitate direct human intervention in dangerous environments, increasing the risk of workplace accidents. Furthermore, these methods have limitations in terms of efficiency and precision. Therefore, there is a need for automated methods to perform these tasks safely and efficiently.

[0455] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0456] In this invention, the server includes means for acquiring sensing information from a robot in real time, means for generating and optimizing a work plan based on the sensing information, and means for transmitting specific work instructions to the robot based on the work plan. This enables automated inspection and safety verification at construction sites.

[0457] A "robot" is an autonomous mechanical device that operates autonomously and is equipped with the necessary functions to perform a specified task.

[0458] "Sensing information" is a general term for data acquired by robots, including environmental conditions, location information, and structural condition information.

[0459] A "work plan" is a set of specific action guidelines for the robot, generated and optimized based on acquired sensing information.

[0460] A "work instruction" is a command sent from a server to a robot that instructs it to perform specific actions based on a work plan.

[0461] A "construction site" refers to a work environment for building structures, including high places and confined spaces.

[0462] "Inspection" refers to the process of examining the condition of machinery and structures at a construction site to confirm that there are no abnormalities.

[0463] "Scanning" is a series of processes in which a robot uses sensors to acquire detailed information about its environment and structures.

[0464] A "route optimization algorithm" is a computational method used to calculate the most efficient route for a robot's movement while minimizing environmental risks.

[0465] To implement this invention, a system centered around an autonomous robot and a server for its control is required. The server acquires sensing information from the robot in real time, and an AI agent generates and optimizes a work plan based on that data. Specific work instructions are sent to the robot based on the work plan, and the robot automatically performs inspection and safety checks in high places and confined spaces.

[0466] The server includes hardware and software capable of receiving sensing information such as environmental conditions, location information, and structural condition information. For example, an AI agent on the server analyzes the diverse data collected using route optimization algorithms implemented in programming languages ​​such as Python or C++. The important information is then displayed in an interface accessible to the user.

[0467] Users can remotely monitor the system using smartphones or tablets and intervene as needed. This allows for efficient work progress while maintaining safety at the work site.

[0468] As a concrete example, if an abnormality is found in the strength of scaffolding at a construction site, a robot will use sensors to perform a 3D scan of the site. The scan data will be sent to a server, where an AI agent will analyze the data, plan the optimal course of action, and issue instructions to the robot.

[0469] Examples of prompts to input into the generating AI model include: "Design a system that continuously monitors the strength of scaffolding at construction sites, and if an anomaly is detected, a robot goes to the site, performs a detailed scan, and provides a real-time report."

[0470] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0471] Step 1:

[0472] The server acquires sensing information from the robot in real time. Inputs include on-site environmental conditions, location information, and structural condition information. This allows the server to create a dataset for accurately understanding the surrounding environment.

[0473] Step 2:

[0474] An AI agent on the server generates and optimizes a work plan based on the acquired sensing information. The input is the dataset obtained in step 1, and the output is specific work instructions. In this process, an algorithm that minimizes risk using multiple parameters is in operation to derive the optimal route and procedure.

[0475] Step 3:

[0476] The server sends specific work instructions to the robot based on the work plan. The input is the work instructions generated in step 2, and the output is the control signal for the robot's movement. This allows the robot to automatically begin inspection work in high places and confined spaces.

[0477] Step 4:

[0478] The robot performs the instructed tasks and scans the environment. Here, sensors are used to acquire new sensing information. This information includes, for example, data on the surrounding 3D structure and details of any anomalies. The acquired information is sent to a server and passed on to the next analysis step.

[0479] Step 5:

[0480] The server analyzes the newly acquired data and displays the results in an interface accessible to the user. The results include detailed information on anomaly detection and recommended actions for the future. The input is the data obtained in step 4, and the output is the final report and visualization information. Based on this, the user determines whether additional work or manual intervention is required.

[0481] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0482] This invention provides a control system for autonomous robots operating in radiation environments, combining an artificial intelligence agent and an emotion engine. The system aims to further improve work efficiency and safety by recognizing the user's emotional state.

[0483] The overall system components include sensors, artificial intelligence agents, an emotion engine, servers, robot terminals, and a user interface. Each of these elements is described below.

[0484] First, the server acquires sensor data from the robot in real time. This includes radiation levels, location information, and surrounding environmental conditions. The server uses this data to help the AI ​​agent generate a safe and efficient work plan.

[0485] Next, the emotion engine has the function of monitoring and recognizing the user's emotional state. This is done, for example, by analyzing the user's voice and facial expressions. The emotional state information obtained by the emotion engine is fed to the server and AI agent and reflected in the work instructions.

[0486] The robot, acting as a terminal, receives these instructions and performs the actual tasks. During the task, it monitors its surroundings in real time using its own sensors, and if it detects an anomaly, it immediately provides feedback to the server.

[0487] Furthermore, the server, through its AI agent, integrates information from the emotion engine and performs flexible task adjustments based on the user's stress level and emotions. For example, if it detects that the user is in a high-stress state, it can slow down the robot's movements or re-optimize the route to allow the user to work at their desired pace.

[0488] Finally, the user monitors the robot's movements through an interface and sends manual instructions as needed. This interface helps users make better decisions by indicating their emotional state and providing corresponding operational guidance.

[0489] This system enables the optimization of work processes while taking into account user emotions and psychological burden, achieving high levels of safety and efficiency even in radiation environments.

[0490] The following describes the processing flow.

[0491] Step 1:

[0492] The server acquires data such as radiation levels and location information from the robot's sensors in real time. This information is sent to the AI ​​agent and used to evaluate the work environment.

[0493] Step 2:

[0494] The server utilizes an emotion engine to analyze the user's emotional state in real time from their voice and facial expressions. This allows it to determine how the user is reacting to their work environment.

[0495] Step 3:

[0496] The AI ​​agent integrates and analyzes sensor data and emotional state to generate a work plan that takes the user's emotional state into account, while minimizing the risk of radiation exposure. Task priorities and routes are adjusted, especially if the user is experiencing tension or stress.

[0497] Step 4:

[0498] The server sends specific work instructions to the robot, which acts as the terminal, based on the work plan generated by the AI ​​agent. This includes the movement path, speed, and the order in which tasks are executed.

[0499] Step 5:

[0500] As the terminal follows the instructed route and performs its tasks, it uses sensors to monitor its surroundings, dynamically avoiding obstacles and providing timely feedback to the server.

[0501] Step 6:

[0502] Through the interface, users can monitor the robot's movements and progress, and manually control it as needed. Customized operation guides based on the user's emotional state are also provided to help users make better decisions.

[0503] Step 7:

[0504] In the event of an anomaly, the server, under the direction of an AI agent, activates an emergency response protocol and instructs terminals to take immediate action. This includes evacuating robots to a safe zone and suspending operations.

[0505] This process allows the system implementing the invention to safely and efficiently manage work in a radiation environment while taking user emotions into consideration.

[0506] (Example 2)

[0507] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0508] The efficiency and safety of autonomous machinery operating in radiation environments are greatly affected by fluctuations in environmental conditions and the emotional state of the operator. Conventional systems primarily rely on static work plans based on sensor data, making it difficult to flexibly respond to real-time changes in conditions and the emotional state of the operator during actual operation. Therefore, new control technologies are needed to improve work efficiency and safety while minimizing risks.

[0509] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0510] In this invention, the server includes means for acquiring information data from the robot in real time, means for generating and optimizing a work plan based on the information data and emotional state data obtained from an analysis device, and means for adjusting work instructions according to the emotional state. This makes it possible to perform work safely and efficiently while taking into account the emotional state of the operator, while reducing risk.

[0511] "Information data" refers to data that indicates various conditions in the robot's working environment, and specifically includes environmental levels, location information, and surrounding conditions.

[0512] An "analysis device" is a device that analyzes acquired data to understand the user's emotional state, and it identifies emotional states based on voice and facial expressions.

[0513] A "work plan" is a detailed plan that defines the guidelines and processes necessary for a robot to perform a task safely and efficiently.

[0514] "Emotional state data" refers to data that indicates the user's psychological and emotional state, and is shown through changes in voice and facial expressions.

[0515] A "path optimization algorithm" is a mathematical method used to calculate the most efficient and safest path for a robot to reach a specific destination.

[0516] A "control device" is a device that performs the necessary processing to instruct the movements of a robot, and it generates and adjusts instructions in real time.

[0517] The system in this invention is for efficiently and safely controlling autonomous machines in a radiation environment. This system mainly consists of a server, a terminal (robot), a device for analyzing emotional states, and a user interface. The server acquires data in real time from various hardware such as radiation sensors and location information devices, and generates a work plan based on that information.

[0518] The server further processes user emotional data using emotion analysis software and uses this data to flexibly adjust the progress of tasks. For example, if a user is in a highly stressed state, the server adjusts the robot's operating speed based on that emotional data. As a specific example, when a robot is digging a hole at a construction site, the server uses data from sensors to optimize the robot's path so that it can perform the task in the safest and most efficient way possible.

[0519] On the other hand, the robot acting as a terminal has the function of performing tasks based on these instructions, and if an anomaly is detected, it immediately provides feedback to the server.

[0520] The user can monitor the robot's progress and issue manual instructions as needed using the user interface. This interface visually displays the user's emotional state and provides corresponding support operations.

[0521] An example of a prompt message might be: "To improve safety at construction sites, please explain how to adjust the robot's behavior when the user's stress level is high."

[0522] In this way, this system creates a more flexible and safer work environment by combining emotional data and environmental data.

[0523] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0524] Step 1:

[0525] The server acquires environmental data in real time from the robot's sensors. Input data includes information such as radiation levels, location, and surrounding conditions. Based on this data, the server stores it chronologically in a database, using it as material for subsequent processing. Specifically, it acquires data at regular intervals via an API and accumulates that information.

[0526] Step 2:

[0527] The server acquires voice and facial expression data as input data to analyze the user's emotional state, and analyzes it using an emotion engine. This analysis outputs the user's stress level and emotional state. Specifically, the process involves using a speech recognition module to estimate emotion labels from changes in voice tone and speed.

[0528] Step 3:

[0529] The server integrates the environmental data obtained in Step 1 and the emotional state data obtained in Step 2, and generates a work plan using a generative AI model. This work plan includes optimized routes and task schedules. Specifically, it performs data analysis and machine learning algorithms to generate output that balances safety and efficiency.

[0530] Step 4:

[0531] The terminal (robot) begins work based on the work plan sent from the server. The robot uses its own sensors to continuously monitor its progress and surrounding anomalies, and immediately sends feedback to the server if any anomalies are detected. Specifically, it monitors sensor values ​​and sends a warning if they exceed a threshold defined in the program.

[0532] Step 5:

[0533] The user monitors the robot's movements in real time through the user interface and sends manual instructions as needed. Inputs include the user's own judgments and environmental changes, while outputs are specific instructions given to the robot. These specific actions involve manipulating parameters and executing temporary control commands using the GUI.

[0534] The above describes the specific flow of operations in the system's processing steps.

[0535] (Application Example 2)

[0536] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0537] When autonomous machines operate in a radiation-affected environment, minimizing radiation risks and improving work efficiency are essential. Furthermore, considering the emotional state of the people involved in the work, it is crucial to ensure safety while reducing their psychological burden.

[0538] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0539] In this invention, the server includes means for acquiring detection data from machines in real time, means for generating and optimizing a work plan based on the detection data, and means for recognizing the emotional state of humans and adjusting work instructions and progress speed accordingly. This makes it possible to perform work efficiently and safely even in a radiation environment and to reduce the psychological burden on the people involved.

[0540] An "autonomous machine" is a device that operates automatically based on pre-set instructions and can perform specific tasks without direct human intervention.

[0541] An "artificial intelligence processing unit" is a system that uses machine learning and data processing algorithms to make judgments and predictions, and generates instructions necessary for execution.

[0542] "Detection data" refers to environmental and conditional information collected using sensors and measuring devices, and is used for work planning and safety decisions.

[0543] A "work plan" is a plan that defines the specific tasks and procedures to be performed by an autonomous machine, with the aim of ensuring efficient and safe work execution.

[0544] "Emergency response" refers to measures and actions that need to be taken quickly when an anomaly or danger is detected, and is important for ensuring the safety and reliability of the system.

[0545] "Human emotional state" refers to a person's emotional reactions and psychological condition that can be inferred by analyzing their voice, facial expressions, and other factors.

[0546] A "route optimization method" is an algorithm used to calculate the most efficient and safe route to achieve a goal, with the aim of reducing work time and mitigating risks.

[0547] This invention provides a control system using autonomous machines that enables efficient and safe work, particularly in radiation environments. The server generates and optimizes work plans in real time using detection data collected from the machines. Specifically, the server processes information obtained from sensors such as temperature, voice, location, and facial recognition cameras, and makes decisions using an artificial intelligence processing unit. This decision-making process includes recognizing the emotional state of the human being to reduce psychological burden.

[0548] Emotional states are grasped by analyzing voice tone and facial expression data, and this is achieved using emotion recognition technologies such as OpenCV and TensorFlow. The analysis results are reflected in the adjustment of the work plan; if a person's stress level is high, the work pace is adjusted and tasks are reprioritized.

[0549] As a terminal, the autonomous machine performs actual tasks based on instructions sent from the server. For example, if a facial recognition camera detects that a person is tired, the machine can automatically slow down its work speed or suggest a short break. Furthermore, the user (caregiver or administrator) can monitor the system's status through the interface and give manual instructions as needed. In this process, the system provides specific feedback indicating the user's emotional state and work progress to support their decision-making.

[0550] As a concrete example, a smart care assistant robot continuously monitors the elderly person's complexion and facial expressions, and if signs of anxiety appear, it gently asks, "Are you okay? Is there anything I can help you with?" Simultaneously, it sends a notification to the caregiver, allowing for immediate provision of further support.

[0551] Using a generative AI model, you can use prompt statements like the following:

[0552] In care robots for the elderly, facial recognition cameras are used to analyze facial expressions and engage in conversations that respond to the user's emotions.

[0553] Example: If the user looks tired, suggest they take a break.

[0554] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0555] Step 1:

[0556] The server receives detection data in real time from sensors on the autonomous machine. This data includes temperature, sound, location, and video data from facial recognition cameras. The server inputs this data into an analysis engine, which normalizes and filters the data from each sensor to remove noise and outliers.

[0557] Step 2:

[0558] The server uses an analysis engine to analyze input detection data and recognize human emotional states. Specifically, it uses OpenCV and TensorFlow to estimate stress levels and emotions from facial expression data and voice tone. The results of this analysis are quantified by an emotion recognition module and output as a standard for work adjustments.

[0559] Step 3:

[0560] The server activates the artificial intelligence processing unit and integrates emotional state data into the current work plan. At this point, the AI ​​algorithm performs optimizations to adjust the work priorities and pace. The input is the current work status and emotional recognition results, and the output is the adjusted work plan.

[0561] Step 4:

[0562] Autonomous machines, acting as terminals, begin operation based on a pre-configured work plan received from a server. For example, if fatigue or stress is detected, the machine slows down its pace of operation or temporarily suspends a specific task to provide a break. This allows the machine to perform its work safely and at a more appropriate pace.

[0563] Step 5:

[0564] The user monitors the robot's operation status via an interface. The user can manually send instructions to the robot as needed, including changing task priorities and issuing emergency stop commands. The interface provides the user with feedback that visualizes emotion recognition results and work progress.

[0565] Step 6:

[0566] The server periodically collects and records progress and anomaly detection results during the process. This includes generating prompt messages through a generative AI model, enabling appropriate intervention and improvement. The data obtained in this process is useful for improving future work plans and developing new algorithms.

[0567] 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.

[0568] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0569] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0570] [Fourth Embodiment]

[0571] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0572] 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.

[0573] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0574] 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.

[0575] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0576] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0577] 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.

[0578] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0579] 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.

[0580] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0581] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0582] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0583] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0584] To implement this invention, an autonomous robot equipped with an artificial intelligence agent is used. This robot can perform tasks in a radiation environment. The main components of the overall system include sensors, an AI agent, a server, a robot terminal, and a user interface. Each of these elements is described below.

[0585] First, the server collects data from the robot's sensors to obtain radiation levels and environmental information. This data is continuously collected and analyzed by an AI agent. Based on the collected data, the AI ​​agent generates and optimizes a work plan. This work plan includes safe movement routes and work procedures.

[0586] Next, the robot, acting as a terminal, is equipped with the ability to perform tasks according to work instructions received from the server. Specifically, it moves along a designated route and performs necessary inspections and decontamination work. It also uses sensors to monitor its surroundings in real time and immediately provides feedback to the server if any abnormalities are detected.

[0587] Furthermore, in the event of an emergency, the server immediately analyzes the anomaly and automatically instructs the robot to take appropriate emergency action. This allows the robot to switch actions based on predefined scenarios. For example, if radiation levels suddenly rise, the robot can receive a command to evacuate to a safe zone.

[0588] Users can monitor the entire system through the user interface and intervene as needed. Specifically, they can view the robot's camera feed in real time, manually correct its path, or instruct it to perform more detailed inspections.

[0589] In this way, the present invention provides a specific method for efficiently and safely carrying out work in a radiation environment. This system makes it possible to improve work efficiency and safety while minimizing the risk of radiation exposure to workers.

[0590] The following describes the processing flow.

[0591] Step 1:

[0592] The server acquires data such as radiation levels, location information, and environmental conditions from the robot's sensors in real time. This information is sent to an AI agent and used to understand the current situation at the work site.

[0593] Step 2:

[0594] The AI ​​agent analyzes sensor data acquired from the server and generates an optimal work plan suitable for the current task. This plan determines safe movement routes and work procedures to minimize radiation risks.

[0595] Step 3:

[0596] The server sends movement paths and specific work instructions to the robot terminal based on the work plan generated by the AI ​​agent. This prepares the terminal to carry out the scheduled task.

[0597] Step 4:

[0598] The terminal receives instructions from the server and moves along a designated route. During this process, it monitors its surroundings in real time using its own sensors and dynamically adjusts its path to avoid obstacles.

[0599] Step 5:

[0600] If an anomaly is detected, the terminal immediately feeds that information back to the server. The server analyzes the data, and if necessary, an AI agent formulates emergency measures and immediately sends new instructions.

[0601] Step 6:

[0602] Users can monitor the robot's real-time movements through a server-based interface. They can also manually control the robot, directing it to correct its path or perform detailed inspections of specific areas.

[0603] This processing flow enables the system to safely and efficiently perform tasks in a radiation environment.

[0604] (Example 1)

[0605] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0606] When humans work directly in hazardous environments containing radiation, the health risks are significant, necessitating the replacement of these workers with autonomous devices. However, existing technologies have faced challenges in ensuring sufficient work efficiency and safety because the devices cannot respond to environmental changes in real time.

[0607] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0608] In this invention, the server includes means for continuously acquiring environmental information, means for generating and optimizing work plans based on the environmental information, and means for transmitting specific work instructions to the device based on the work plan. This enables safe and efficient work execution by an autonomous device while adapting to environmental changes in real time.

[0609] "External environment" refers to a work site or situation where direct human intervention poses a risk, necessitating the use of equipment to replace the work.

[0610] An "autonomous device" is a device that can recognize information about its surroundings and perform tasks based on or independently of external instructions.

[0611] An "intelligent system" refers to a program or algorithm installed in an autonomous device that analyzes collected data and determines work plans and countermeasures for abnormal situations.

[0612] "Environmental information" refers to data about the external environment, such as radiation levels, location information, temperature, humidity, and pressure.

[0613] "Means of continuous acquisition" refers to the process of collecting environmental information in real time using sensors and communication technologies.

[0614] A "work plan" is a plan that outlines detailed guidelines and procedures for carrying out work safely and efficiently, based on the environmental information that has been collected.

[0615] A "route optimization method" is an algorithm that calculates an economical and efficient travel route in order to reach a destination while ensuring safety.

[0616] To implement this invention, an autonomous device equipped with artificial intelligence is used. The main components of the system are a server, an autonomous device equipped with sensors (hereinafter referred to as "terminal"), an intelligent system, and an interface for user monitoring.

[0617] The server continuously acquires radiation level and environmental data from the terminal's sensors. This allows the server to understand the surrounding situation in real time and process the necessary information. The intelligent system analyzes the collected data to generate and optimize work plans. In this process, route optimization techniques are used to minimize radiation risk.

[0618] The terminal receives instructions from the server and performs tasks according to the designated route. The terminal is equipped with a motor for movement and an operating arm, which it uses to perform inspection and decontamination work. It also has the ability to autonomously monitor the surrounding environment and immediately provide feedback to the server if it detects any abnormalities.

[0619] Users can monitor the entire system through the user interface. They can grasp the situation on-site in real time and take manual actions as needed. This allows for safe control of the situation without humans directly performing dangerous tasks.

[0620] As a concrete example, in an area of ​​a power plant where a radiation leak is suspected, an autonomous device moves along a safe route to a designated work point, measures radiation levels using sensors, and if an abnormal value is detected, the server immediately issues an emergency response order. In this way, the system can flexibly respond to changes in the external environment and perform work efficiently.

[0621] An example of a prompt message is: "Generate a work plan for the autonomous device to perform safe operations in a radiation environment. Specifically, include safe movement routes, work procedures, and emergency response scenarios."

[0622] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0623] Step 1:

[0624] The server acquires environmental data such as radiation levels, temperature, and humidity from the terminal's sensors. It receives sensor data as input and registers it in the server's database in real time. Data processing involves filtering out noise and organizing and storing the acquired information. This ensures that the system always maintains the latest environmental information and is ready for analysis.

[0625] Step 2:

[0626] The server analyzes collected environmental data using an intelligent system. The input is processed data acquired from sensors, which the AI ​​algorithm uses to detect behavioral patterns and anomalies. Data processing includes anomaly detection and trend analysis, generating an appropriate work plan and outputting work plan data. This plan includes safe movement routes and work procedures.

[0627] Step 3:

[0628] The server sends the generated work plan as an instruction to the terminal. The input is work plan data, which is converted into a transmission command and sent to the terminal. Upon receiving the instruction, the terminal starts specific actions according to its contents, and an action log is generated as output. The terminal autonomously moves along the designated route and performs inspection and decontamination work.

[0629] Step 4:

[0630] The terminal continuously collects environmental data using sensors during operation and feeds it back to the server. The input is real-time environmental information from the site, which is used for condition monitoring. Data processing includes a process to immediately identify and report any anomalies, especially if they occur. The output becomes feedback data and is used for re-analysis on the server.

[0631] Step 5:

[0632] When the server receives abnormal data, it quickly analyzes it and instructs the terminal on emergency response measures. The input is anomaly detection information, which is analyzed to generate appropriate response commands. The data calculation involves selecting and commanding emergency scenarios according to the situation. The output is an emergency response instruction that is sent to the terminal. The terminal then takes emergency action, such as evacuating to a safe zone.

[0633] Step 6:

[0634] The user monitors the overall system status through the user interface and intervenes as needed. Inputs include real-time system status and camera feeds, which are used to make decisions. Specifically, the user can manually change routes and add work instructions. Outputs include a revised work plan based on user instructions, which is sent to the terminal.

[0635] (Application Example 1)

[0636] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0637] Modern construction sites require inspections and safety checks at high altitudes and in confined spaces, posing significant risks to workers. Traditional methods necessitate direct human intervention in dangerous environments, increasing the risk of workplace accidents. Furthermore, these methods have limitations in terms of efficiency and precision. Therefore, there is a need for automated methods to perform these tasks safely and efficiently.

[0638] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0639] In this invention, the server includes means for acquiring sensing information from a robot in real time, means for generating and optimizing a work plan based on the sensing information, and means for transmitting specific work instructions to the robot based on the work plan. This enables automated inspection and safety verification at construction sites.

[0640] A "robot" is an autonomous mechanical device that operates autonomously and is equipped with the necessary functions to perform a specified task.

[0641] "Sensing information" is a general term for data acquired by robots, including environmental conditions, location information, and structural condition information.

[0642] A "work plan" is a set of specific action guidelines for the robot, generated and optimized based on acquired sensing information.

[0643] A "work instruction" is a command sent from a server to a robot that instructs it to perform specific actions based on a work plan.

[0644] A "construction site" refers to a work environment for building structures, including high places and confined spaces.

[0645] "Inspection" refers to the process of examining the condition of machinery and structures at a construction site to confirm that there are no abnormalities.

[0646] "Scanning" is a series of processes in which a robot uses sensors to acquire detailed information about its environment and structures.

[0647] A "route optimization algorithm" is a computational method used to calculate the most efficient route for a robot's movement while minimizing environmental risks.

[0648] To implement this invention, a system centered around an autonomous robot and a server for its control is required. The server acquires sensing information from the robot in real time, and an AI agent generates and optimizes a work plan based on that data. Specific work instructions are sent to the robot based on the work plan, and the robot automatically performs inspection and safety checks in high places and confined spaces.

[0649] The server includes hardware and software capable of receiving sensing information such as environmental conditions, location information, and structural condition information. For example, an AI agent on the server analyzes the diverse data collected using route optimization algorithms implemented in programming languages ​​such as Python or C++. The important information is then displayed in an interface accessible to the user.

[0650] Users can remotely monitor the system using smartphones or tablets and intervene as needed. This allows for efficient work progress while maintaining safety at the work site.

[0651] As a concrete example, if an abnormality is found in the strength of scaffolding at a construction site, a robot will use sensors to perform a 3D scan of the site. The scan data will be sent to a server, where an AI agent will analyze the data, plan the optimal course of action, and issue instructions to the robot.

[0652] Examples of prompts to input into the generating AI model include: "Design a system that continuously monitors the strength of scaffolding at construction sites, and if an anomaly is detected, a robot goes to the site, performs a detailed scan, and provides a real-time report."

[0653] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0654] Step 1:

[0655] The server acquires sensing information from the robot in real time. Inputs include on-site environmental conditions, location information, and structural condition information. This allows the server to create a dataset for accurately understanding the surrounding environment.

[0656] Step 2:

[0657] An AI agent on the server generates and optimizes a work plan based on the acquired sensing information. The input is the dataset obtained in step 1, and the output is specific work instructions. In this process, an algorithm that minimizes risk using multiple parameters is in operation to derive the optimal route and procedure.

[0658] Step 3:

[0659] The server sends specific work instructions to the robot based on the work plan. The input is the work instructions generated in step 2, and the output is the control signal for the robot's movement. This allows the robot to automatically begin inspection work in high places and confined spaces.

[0660] Step 4:

[0661] The robot performs the instructed tasks and scans the environment. Here, sensors are used to acquire new sensing information. This information includes, for example, data on the surrounding 3D structure and details of any anomalies. The acquired information is sent to a server and passed on to the next analysis step.

[0662] Step 5:

[0663] The server analyzes the newly acquired data and displays the results in an interface accessible to the user. The results include detailed information on anomaly detection and recommended actions for the future. The input is the data obtained in step 4, and the output is the final report and visualization information. Based on this, the user determines whether additional work or manual intervention is required.

[0664] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0665] This invention provides a control system for autonomous robots operating in radiation environments, combining an artificial intelligence agent and an emotion engine. The system aims to further improve work efficiency and safety by recognizing the user's emotional state.

[0666] The overall system components include sensors, artificial intelligence agents, an emotion engine, servers, robot terminals, and a user interface. Each of these elements is described below.

[0667] First, the server acquires sensor data from the robot in real time. This includes radiation levels, location information, and surrounding environmental conditions. The server uses this data to help the AI ​​agent generate a safe and efficient work plan.

[0668] Next, the emotion engine has the function of monitoring and recognizing the user's emotional state. This is done, for example, by analyzing the user's voice and facial expressions. The emotional state information obtained by the emotion engine is fed to the server and AI agent and reflected in the work instructions.

[0669] The robot, acting as a terminal, receives these instructions and performs the actual tasks. During the task, it monitors its surroundings in real time using its own sensors, and if it detects an anomaly, it immediately provides feedback to the server.

[0670] Furthermore, the server, through its AI agent, integrates information from the emotion engine and performs flexible task adjustments based on the user's stress level and emotions. For example, if it detects that the user is in a high-stress state, it can slow down the robot's movements or re-optimize the route to allow the user to work at their desired pace.

[0671] Finally, the user monitors the robot's movements through an interface and sends manual instructions as needed. This interface helps users make better decisions by indicating their emotional state and providing corresponding operational guidance.

[0672] This system enables the optimization of work processes while taking into account user emotions and psychological burden, achieving high levels of safety and efficiency even in radiation environments.

[0673] The following describes the processing flow.

[0674] Step 1:

[0675] The server acquires data such as radiation levels and location information from the robot's sensors in real time. This information is sent to the AI ​​agent and used to evaluate the work environment.

[0676] Step 2:

[0677] The server utilizes an emotion engine to analyze the user's emotional state in real time from their voice and facial expressions. This allows it to determine how the user is reacting to their work environment.

[0678] Step 3:

[0679] The AI ​​agent integrates and analyzes sensor data and emotional state to generate a work plan that takes the user's emotional state into account, while minimizing the risk of radiation exposure. Task priorities and routes are adjusted, especially if the user is experiencing tension or stress.

[0680] Step 4:

[0681] The server sends specific work instructions to the robot, which acts as the terminal, based on the work plan generated by the AI ​​agent. This includes the movement path, speed, and the order in which tasks are executed.

[0682] Step 5:

[0683] As the terminal follows the instructed route and performs its tasks, it uses sensors to monitor its surroundings, dynamically avoiding obstacles and providing timely feedback to the server.

[0684] Step 6:

[0685] Through the interface, users can monitor the robot's movements and progress, and manually control it as needed. Customized operation guides based on the user's emotional state are also provided to help users make better decisions.

[0686] Step 7:

[0687] In the event of an anomaly, the server, under the direction of an AI agent, activates an emergency response protocol and instructs terminals to take immediate action. This includes evacuating robots to a safe zone and suspending operations.

[0688] This process allows the system implementing the invention to safely and efficiently manage work in a radiation environment while taking user emotions into consideration.

[0689] (Example 2)

[0690] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0691] The efficiency and safety of autonomous machinery operating in radiation environments are greatly affected by fluctuations in environmental conditions and the emotional state of the operator. Conventional systems primarily rely on static work plans based on sensor data, making it difficult to flexibly respond to real-time changes in conditions and the emotional state of the operator during actual operation. Therefore, new control technologies are needed to improve work efficiency and safety while minimizing risks.

[0692] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0693] In this invention, the server includes means for acquiring information data from the robot in real time, means for generating and optimizing a work plan based on the information data and emotional state data obtained from an analysis device, and means for adjusting work instructions according to the emotional state. This makes it possible to perform work safely and efficiently while taking into account the emotional state of the operator, while reducing risk.

[0694] "Information data" refers to data that indicates various conditions in the robot's working environment, and specifically includes environmental levels, location information, and surrounding conditions.

[0695] An "analysis device" is a device that analyzes acquired data to understand the user's emotional state, and it identifies emotional states based on voice and facial expressions.

[0696] A "work plan" is a detailed plan that defines the guidelines and processes necessary for a robot to perform a task safely and efficiently.

[0697] "Emotional state data" refers to data that indicates the user's psychological and emotional state, and is shown through changes in voice and facial expressions.

[0698] A "path optimization algorithm" is a mathematical method used to calculate the most efficient and safest path for a robot to reach a specific destination.

[0699] A "control device" is a device that performs the necessary processing to instruct the movements of a robot, and it generates and adjusts instructions in real time.

[0700] The system in this invention is for efficiently and safely controlling autonomous machines in a radiation environment. This system mainly consists of a server, a terminal (robot), a device for analyzing emotional states, and a user interface. The server acquires data in real time from various hardware such as radiation sensors and location information devices, and generates a work plan based on that information.

[0701] The server further processes user emotional data using emotion analysis software and uses this data to flexibly adjust the progress of tasks. For example, if a user is in a highly stressed state, the server adjusts the robot's operating speed based on that emotional data. As a specific example, when a robot is digging a hole at a construction site, the server uses data from sensors to optimize the robot's path so that it can perform the task in the safest and most efficient way possible.

[0702] On the other hand, the robot acting as a terminal has the function of performing tasks based on these instructions, and if an anomaly is detected, it immediately provides feedback to the server.

[0703] The user can monitor the robot's progress and issue manual instructions as needed using the user interface. This interface visually displays the user's emotional state and provides corresponding support operations.

[0704] An example of a prompt message might be: "To improve safety at construction sites, please explain how to adjust the robot's behavior when the user's stress level is high."

[0705] In this way, this system creates a more flexible and safer work environment by combining emotional data and environmental data.

[0706] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0707] Step 1:

[0708] The server acquires environmental data in real time from the robot's sensors. Input data includes information such as radiation levels, location, and surrounding conditions. Based on this data, the server stores it chronologically in a database, using it as material for subsequent processing. Specifically, it acquires data at regular intervals via an API and accumulates that information.

[0709] Step 2:

[0710] The server acquires voice and facial expression data as input data to analyze the user's emotional state, and analyzes it using an emotion engine. This analysis outputs the user's stress level and emotional state. Specifically, the process involves using a speech recognition module to estimate emotion labels from changes in voice tone and speed.

[0711] Step 3:

[0712] The server integrates the environmental data obtained in Step 1 and the emotional state data obtained in Step 2, and generates a work plan using a generative AI model. This work plan includes optimized routes and task schedules. Specifically, it performs data analysis and machine learning algorithms to generate output that balances safety and efficiency.

[0713] Step 4:

[0714] The terminal (robot) begins work based on the work plan sent from the server. The robot uses its own sensors to continuously monitor its progress and surrounding anomalies, and immediately sends feedback to the server if any anomalies are detected. Specifically, it monitors sensor values ​​and sends a warning if they exceed a threshold defined in the program.

[0715] Step 5:

[0716] The user monitors the robot's movements in real time through the user interface and sends manual instructions as needed. Inputs include the user's own judgments and environmental changes, while outputs are specific instructions given to the robot. These specific actions involve manipulating parameters and executing temporary control commands using the GUI.

[0717] The above describes the specific flow of operations in the system's processing steps.

[0718] (Application Example 2)

[0719] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0720] When autonomous machines operate in a radiation-affected environment, minimizing radiation risks and improving work efficiency are essential. Furthermore, considering the emotional state of the people involved in the work, it is crucial to ensure safety while reducing their psychological burden.

[0721] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0722] In this invention, the server includes means for acquiring detection data from machines in real time, means for generating and optimizing a work plan based on the detection data, and means for recognizing the emotional state of humans and adjusting work instructions and progress speed accordingly. This makes it possible to perform work efficiently and safely even in a radiation environment and to reduce the psychological burden on the people involved.

[0723] An "autonomous machine" is a device that operates automatically based on pre-set instructions and can perform specific tasks without direct human intervention.

[0724] An "artificial intelligence processing unit" is a system that uses machine learning and data processing algorithms to make judgments and predictions, and generates instructions necessary for execution.

[0725] "Detection data" refers to environmental and conditional information collected using sensors and measuring devices, and is used for work planning and safety decisions.

[0726] A "work plan" is a plan that defines the specific tasks and procedures to be performed by an autonomous machine, with the aim of ensuring efficient and safe work execution.

[0727] "Emergency response" refers to measures and actions that need to be taken quickly when an anomaly or danger is detected, and is important for ensuring the safety and reliability of the system.

[0728] "Human emotional state" refers to a person's emotional reactions and psychological condition that can be inferred by analyzing their voice, facial expressions, and other factors.

[0729] A "route optimization method" is an algorithm used to calculate the most efficient and safe route to achieve a goal, with the aim of reducing work time and mitigating risks.

[0730] This invention provides a control system using autonomous machines that enables efficient and safe work, particularly in radiation environments. The server generates and optimizes work plans in real time using detection data collected from the machines. Specifically, the server processes information obtained from sensors such as temperature, voice, location, and facial recognition cameras, and makes decisions using an artificial intelligence processing unit. This decision-making process includes recognizing the emotional state of the human being to reduce psychological burden.

[0731] Emotional states are grasped by analyzing voice tone and facial expression data, and this is achieved using emotion recognition technologies such as OpenCV and TensorFlow. The analysis results are reflected in the adjustment of the work plan; if a person's stress level is high, the work pace is adjusted and tasks are reprioritized.

[0732] As a terminal, the autonomous machine performs actual tasks based on instructions sent from the server. For example, if a facial recognition camera detects that a person is tired, the machine can automatically slow down its work speed or suggest a short break. Furthermore, the user (caregiver or administrator) can monitor the system's status through the interface and give manual instructions as needed. In this process, the system provides specific feedback indicating the user's emotional state and work progress to support their decision-making.

[0733] As a concrete example, a smart care assistant robot continuously monitors the elderly person's complexion and facial expressions, and if signs of anxiety appear, it gently asks, "Are you okay? Is there anything I can help you with?" Simultaneously, it sends a notification to the caregiver, allowing for immediate provision of further support.

[0734] Using a generative AI model, you can use prompt statements like the following:

[0735] In care robots for the elderly, facial recognition cameras are used to analyze facial expressions and engage in conversations that respond to the user's emotions.

[0736] Example: If the user looks tired, suggest they take a break.

[0737] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0738] Step 1:

[0739] The server receives detection data in real time from sensors on the autonomous machine. This data includes temperature, sound, location, and video data from facial recognition cameras. The server inputs this data into an analysis engine, which normalizes and filters the data from each sensor to remove noise and outliers.

[0740] Step 2:

[0741] The server uses an analysis engine to analyze input detection data and recognize human emotional states. Specifically, it uses OpenCV and TensorFlow to estimate stress levels and emotions from facial expression data and voice tone. The results of this analysis are quantified by an emotion recognition module and output as a standard for work adjustments.

[0742] Step 3:

[0743] The server activates the artificial intelligence processing unit and integrates emotional state data into the current work plan. At this point, the AI ​​algorithm performs optimizations to adjust the work priorities and pace. The input is the current work status and emotional recognition results, and the output is the adjusted work plan.

[0744] Step 4:

[0745] Autonomous machines, acting as terminals, begin operation based on a pre-configured work plan received from a server. For example, if fatigue or stress is detected, the machine slows down its pace of operation or temporarily suspends a specific task to provide a break. This allows the machine to perform its work safely and at a more appropriate pace.

[0746] Step 5:

[0747] The user monitors the robot's operation status via an interface. The user can manually send instructions to the robot as needed, including changing task priorities and issuing emergency stop commands. The interface provides the user with feedback that visualizes emotion recognition results and work progress.

[0748] Step 6:

[0749] The server periodically collects and records progress and anomaly detection results during the process. This includes generating prompt messages through a generative AI model, enabling appropriate intervention and improvement. The data obtained in this process is useful for improving future work plans and developing new algorithms.

[0750] 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.

[0751] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0752] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0753] 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.

[0754] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0755] 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.

[0756] 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.

[0757] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0758] 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."

[0759] 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.

[0760] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0761] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0770] 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.

[0771] The following is further disclosed regarding the embodiments described above.

[0772] (Claim 1)

[0773] A control system using an artificial intelligence agent mounted on an autonomous robot for working in a radiation environment,

[0774] A means of acquiring sensor data from a robot in real time,

[0775] A means for generating and optimizing a work plan based on the aforementioned sensor data,

[0776] A means for transmitting specific work instructions to the robot based on the aforementioned work plan,

[0777] A means for collecting and feeding back robot work progress and anomaly detection information,

[0778] A means of automatically issuing emergency response instructions when an anomaly is detected,

[0779] A system that includes this.

[0780] (Claim 2)

[0781] The system according to claim 1, characterized in that the sensor data includes radiation level, location information, and surrounding environment data.

[0782] (Claim 3)

[0783] The system according to claim 1, characterized in that the artificial intelligence agent incorporates a route optimization algorithm for minimizing radiation risk.

[0784] "Example 1"

[0785] (Claim 1)

[0786] A control device using an intelligent system mounted on an autonomous device for performing tasks in an external environment,

[0787] Means for continuously acquiring environmental information from the device,

[0788] A means for generating and streamlining a work plan based on the aforementioned environmental information,

[0789] A means for transmitting specific work instructions to the device based on the aforementioned work plan,

[0790] A means for collecting and notifying information on the work progress and anomaly detection of the device,

[0791] A means of automatically issuing emergency response instructions when an anomaly is detected,

[0792] A system that includes this.

[0793] (Claim 2)

[0794] The system according to claim 1, characterized in that the aforementioned environmental information includes radiation levels, location information, and surrounding environmental data.

[0795] (Claim 3)

[0796] The system according to claim 1, characterized in that the intelligent system incorporates a path optimization method for minimizing radiation risk.

[0797] "Application Example 1"

[0798] (Claim 1)

[0799] A means of acquiring sensing information from a robot in real time,

[0800] A means for generating and optimizing a work plan based on the aforementioned sensing information,

[0801] A means for transmitting specific work instructions to the robot based on the aforementioned work plan,

[0802] A means for collecting and feeding back robot work progress and anomaly detection information,

[0803] A means of automatically issuing emergency response instructions when an anomaly is detected,

[0804] A means to automate inspection and safety check work at high places and confined spaces in construction sites,

[0805] A method for scanning abnormal areas on site and checking their condition,

[0806] A system that includes this.

[0807] (Claim 2)

[0808] The system according to claim 1, characterized in that the sensing information includes environmental conditions, location information, and structural condition information.

[0809] (Claim 3)

[0810] The system according to claim 1, characterized in that the artificial intelligence agent incorporates a route optimization algorithm for minimizing environmental risks.

[0811] "Example 2 of combining an emotion engine"

[0812] (Claim 1)

[0813] A means of acquiring information data from robots in real time,

[0814] A means for generating and optimizing a work plan based on the aforementioned information data and emotional state data obtained from the analysis device,

[0815] A means for transmitting specific work instructions to the robot based on the aforementioned work plan,

[0816] A means of adjusting work instructions according to emotional state,

[0817] A means for collecting and feeding back robot work progress and anomaly detection information,

[0818] A means of automatically issuing emergency response instructions when an anomaly is detected,

[0819] A system that includes this.

[0820] (Claim 2)

[0821] The system according to claim 1, characterized in that the aforementioned information data includes environmental level, location information, and surrounding condition data.

[0822] (Claim 3)

[0823] The system according to claim 1, characterized in that the control device incorporates a route optimization algorithm for minimizing risk.

[0824] "Application example 2 when combining with an emotional engine"

[0825] (Claim 1)

[0826] A control technology using an artificial intelligence processing unit mounted on an autonomous machine for performing work in a radiation environment,

[0827] A means of acquiring detection data from machines in real time,

[0828] A means for generating and optimizing a work plan based on the aforementioned detection data,

[0829] A means for transmitting specific work instructions to a machine based on the aforementioned work plan,

[0830] A means for collecting and transmitting information on the progress of machine operations and anomaly detection,

[0831] A means of automatically issuing emergency response instructions when an anomaly is detected,

[0832] A means of recognizing a person's emotional state and adjusting work instructions and progress speed based on that,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, characterized in that the detection data includes radiation level, location information, and surrounding environment data.

[0836] (Claim 3)

[0837] The system according to claim 1, characterized in that the artificial intelligence processing device incorporates a path optimization method for minimizing radiation risk. [Explanation of Symbols]

[0838] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of acquiring sensing information from a robot in real time, A means for generating and optimizing a work plan based on the aforementioned sensing information, A means for transmitting specific work instructions to the robot based on the aforementioned work plan, A means for collecting and feeding back robot work progress and anomaly detection information, A means of automatically issuing emergency response instructions when an anomaly is detected, A means to automate inspection and safety check work at high places and confined spaces in construction sites, A method for scanning abnormal areas on site and checking their condition, A system that includes this.

2. The system according to claim 1, characterized in that the sensing information includes environmental conditions, location information, and structural condition information.

3. The system according to claim 1, characterized in that the artificial intelligence agent incorporates a route optimization algorithm for minimizing environmental risks.

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