System and method for ai robot driver licensing and / or permitting and operator tier-based authentication

Abstract

The present invention relates to a method for certifying an artificial intelligence robot driver's license and / or permit and operator grade, comprising the steps of: determining whether a user satisfies qualification requirements based on certification application information regarding the operation of an AI robot received from a user terminal to determine a target certification grade for operating an AI robot; providing an evaluation scenario corresponding to the target certification grade to the user terminal and inputting response data and simulation operation data received from the user terminal into an artificial intelligence-based competency evaluation engine to obtain an evaluation result; determining pass / fail status based on the evaluation result to assign a final certification grade to the user terminal and generating mapping data by mapping the identification information of the user assigned the final certification grade to the unique identification number of the AI ​​robot; and recording a certification history including the mapping data and the fact of assigning the final certification grade on a blockchain network.

Description

Technology Field

[0001] The present disclosure relates to a system and method for driver's licenses and / or permits for artificial intelligence robots and for operator-level certification. More specifically, the AI ​​robots of the present disclosure are physical AIs and include AI robots that have a physical entity and interact directly with the real world, such as humanoid robots, embodied AI, and human AI robots. Background Technology

[0002] Operator certification for Physical AI robots (such as Humanoid Robots, Embodied AI, and Human AI Robots) that possess a physical form and interact directly with the real-world environment is becoming increasingly important as the level of robot autonomy rises. Existing robot-related certification systems primarily focus on design and manufacturing, resulting in a lack of systems to systematically evaluate and certify the capabilities of operators who manage and operate robots in actual operational settings. In particular, AI robots require complex competencies that go beyond simple machine operation skills, including an understanding of AI algorithms, ethical judgment, and the ability to handle emergency situations. However, the absence of a method to systematically evaluate and certify these complex competencies makes it difficult to ensure the safety and reliability of robot operations. Furthermore, despite the fact that the required competency levels for operators vary depending on the robot's application field and autonomy grade, the lack of a differentiated certification system reflecting these factors is increasing the risk of safety accidents. To address these issues, a multi-level operator certification system is required that considers the type, use, risk level, and level of autonomy of Physical AI robots (humanoid robots, embodied AI, human AI robots, etc.). Through this, the operator's theoretical knowledge, practical skills, and ethical judgment abilities are comprehensively evaluated, and the robot's unique identification number The problem to be solved

[0003] The purpose of one embodiment is to provide a system that grades operator qualifications into three levels by considering the type, use, risk level, and level of autonomy of the artificial intelligence robot, and performs operator certification by comprehensively evaluating theoretical knowledge, practical skills, and ethical judgment skills through an AI-based competency evaluation engine. means of solving the problem

[0004] A method for certifying AI robot operators by grade according to one embodiment may include a step of determining whether a user satisfies qualification requirements based on certification application information regarding AI robot operation received from a user terminal, and determining a target certification grade for operating the AI ​​robot. A method according to one embodiment may include a step of providing an evaluation scenario corresponding to the target certification grade to a user terminal, and inputting response data and simulation operation data received from the user terminal into an AI-based competency evaluation engine to obtain an evaluation result. A method according to one embodiment may include a step of determining pass or fail based on the evaluation result to assign a final certification grade to the user terminal, and generating mapping data by mapping the identification information of the user assigned the final certification grade to the unique identification number of the AI ​​robot. A method according to one embodiment may include a step of recording a certification history including the mapping data and the fact of assigning the final certification grade on a blockchain network. Effects of the invention

[0005] The AI ​​robot user grade-based authentication system according to the present invention can systematically grant authentication necessary for AI robot operation by determining a target authentication grade based on authentication application information received from a user terminal and judging the user's qualification requirements. Furthermore, objective and consistent evaluations can be performed by scoring the user's response time, accuracy, and procedural compliance through evaluation scenarios provided by an AI-based competency evaluation engine. Based on these evaluation results, a final authentication grade is assigned, and operational authority is granted differentially by mapping the user's identification information to the AI ​​robot's unique identification number, thereby creating a safe and reliable physical AI robot operating environment. In addition, the transparency and reliability of authentication records can be secured by guaranteeing integrity through the recording of authentication history on a blockchain network. Brief explanation of the drawing

[0006] FIG. 1 shows an example of an AI robot operator certification system by grade according to one embodiment. FIG. 2 is a drawing for explaining the detailed operation of each module according to one embodiment. FIG. 3 is an example of a flowchart of an AI robot operator certification method by grade according to one embodiment. FIG. 4 shows an example of an electronic device according to one embodiment. Specific details for implementing the invention

[0007] Specific structural or functional descriptions of the embodiments are disclosed for illustrative purposes only and may be modified and implemented in various forms. Accordingly, the actual form of implementation is not limited to the specific embodiments disclosed, and the scope of this specification includes modifications, equivalents, or substitutions that fall within the technical concept described by the embodiments. Terms such as "first" or "second" may be used to describe various components, but these terms should be interpreted solely for the purpose of distinguishing one component from another. For example, a first component may be named a second component, and similarly, a second component may be named a first component. When a component is referred to as being "connected" to another component, it should be understood that it may be directly connected to or joined to that other component, or that there may be other components in between. A singular expression includes a plural expression unless the context clearly indicates otherwise. In this specification, terms such as “comprising” or “having” are intended to specify the existence of the described features, numbers, steps, actions, components, parts, or combinations thereof, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof. Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this specification. Hereinafter, embodiments will be described in detail with reference to the accompanying drawings.In the description referring to the attached drawings, identical components are assigned the same reference numeral regardless of drawing symbols, and redundant descriptions thereof are omitted.

[0009] FIG. 1 shows an example of an AI robot operator certification system by grade according to one embodiment.

[0010] According to some embodiments, the system (100) may be configured to include an electronic device (110), a user terminal (130), and a network. The system (100) may implement the electronic device (110), the user terminal (130), and the network as independent hardware or software modules, respectively. The electronic device (110) may include multiple hardware and software components, such as a main server, a data storage device, and an AI computing module. The user terminal (130) may be implemented as various computing devices, such as a PC, a smartphone, a tablet, or a VR HMD. The network may include multiple communication infrastructures, such as a wired LAN, wireless Wi-Fi, or a mobile communication network. Each component may perform mutual data transmission and reception through a standardized communication protocol. This configuration enables the system (100) to ensure scalability and compatibility in various environments.

[0011] According to some embodiments, the electronic device (110) may correspond to a main server or computing device that performs the overall logic of the certification method for AI robot operators by grade. The electronic device (110) can control a series of certification processes, such as receiving certification application information regarding the operation of a Physical AI robot (humanoid robot, embodied artificial intelligence, human AI robot, etc.), data analysis, generating evaluation scenarios, calculating evaluation results, granting authorization, and managing history. The electronic device (110) is equipped with a high-performance processor and large-capacity memory to perform large-scale data processing and AI computation in real time. The electronic device (110) may include multiple internal modules, such as a certification requirement management database, an evaluation module, an authorization management module, and a history management module. The electronic device (110) can ensure the reliability and integrity of data by linking with an external robot unique ID system and a blockchain network. The electronic device (110) can transmit and receive data in real time with a user terminal (130) via a network.

[0012] According to some embodiments, the user terminal (130) is a terminal used by a user who wishes to obtain certification, and can perform data transmission and reception with the electronic device (110) via a network. The user terminal (130) can receive certification application information and transmit it to the electronic device (110). The user terminal (130) can display evaluation scenarios, practical simulations, ethical judgment issues, etc. provided by the electronic device (110) on a screen or in a VR environment. The user terminal (130) can transmit user response data and simulation operation data to the electronic device (110) in real time. The user terminal (130) can receive feedback information, such as the certification progress status, evaluation results, and final certification grade, and display it to the user. The user terminal (130) can be designed to operate in various operating systems and network environments.

[0013] According to some embodiments, the network is a communication network that mediates the transmission and reception of data between an electronic device (110) and a user terminal (130), and may include both wired and wireless communication networks. The network may support standard communication protocols such as TCP / IP, HTTP, and WebSocket to ensure stable transmission of data. The network may enhance the security of transmitted and received data through data encryption and authentication procedures. The network may optimize data paths by including network equipment such as multiple routers, switches, and gateways. The network may maintain service continuity by establishing an automatic bypass path in the event of a failure. The network may support the transmission of large volume data and real-time streaming data.

[0014] In some embodiments of the present invention, the network may include a blockchain network environment in which multiple nodes participate to store data in a distributed manner to prevent tampering with authentication history. The blockchain network can ensure data integrity by having each node store the same authentication history data in a distributed manner. The blockchain network can prevent data tampering through a consensus algorithm (e.g., PoW, PoS, etc.). The blockchain network can create and record a new block whenever a history event occurs, such as new authentication, renewal, suspension, or cancellation. The blockchain network can maintain data reliability even in the event of external attacks or a single failure. The blockchain network can provide transparency and traceability of authentication history.

[0015] According to some embodiments, the certification method for AI robot operators by grade can determine whether the user meets the qualification requirements based on certification application information regarding AI robot operation received from a user terminal, and determine a target certification grade for operating the AI ​​robot. An electronic device (110) can receive certification application information from a user terminal (130). The electronic device (110) can extract data on completion of required training and practical experience data from the certification application information. The electronic device (110) can compare the extracted data with a pre-set qualification requirement database. Based on the comparison result, the electronic device (110) can determine whether to allow entry into the evaluation of the AI ​​competency evaluation engine. If entry into the evaluation is allowed, the electronic device (110) can calculate a target certification grade according to the level of the user's qualification requirements. The target certification grade can be determined as one of Basic (Level 1), Professional (Level 2), or Highest (Level 3). This step-by-step determination enables the assignment of a grade suitable for the operator's competency.

[0016] The configuration can play an essential role in verifying the actual existence of the AI ​​robot in identity authentication and forgery detection systems. Specifically, the AI ​​robot in the present invention refers to a Physical AI robot capable of performing actions such as movement, manipulation, and interaction within a physical environment, having a body structure similar to that of a human. It is characterized by being equipped with physical actuation parts (actuators, joints, walking mechanisms, etc.), multi-sensor modules (visual, tactile, auditory, distance sensors, etc.), and Embodied AI functions that enable physical interaction with the environment. Such Physical AI robots can be implemented in various forms, such as autonomous robots, industrial humanoid robots, medical robots, service robots, and military robots, and the operator authentication system of the present invention can be integrally applied to operators of all these types of Physical AI robots.

[0017] According to some embodiments, the certification method for AI robot operators by grade provides an evaluation scenario corresponding to a target certification grade to a user terminal, and can obtain an evaluation result by inputting response data and simulation operation data received from the user terminal into an AI-based competency evaluation engine. An electronic device (110) can generate an evaluation scenario that matches the robot type, ethics grade, and level of autonomy according to the target certification grade. The electronic device (110) can transmit the generated evaluation scenario to a user terminal (130). The user terminal (130) can output the evaluation scenario to a screen or VR environment. The user can input response data and simulation operation data according to the scenario. The user terminal (130) can transmit the input data to the electronic device (110) in real time. The electronic device (110) can input the received data into an AI-based competency evaluation engine. The competency evaluation engine can score multiple evaluation items, such as response time, accuracy, and procedural compliance. The evaluation results can be used as the basis for determining pass / fail status and assigning a grade.

[0018] In some embodiments of the present invention, the method for certifying AI robot operators by grade determines whether a user passes based on evaluation results and assigns a final certification grade to a user terminal, and can generate mapping data by mapping the identification information of the user assigned the final certification grade to the unique identification number of the AI ​​robot. The electronic device (110) can determine whether the evaluation results meet the pass criteria. When a pass is determined, the electronic device (110) can notify the user terminal (130) of the final certification grade. The electronic device (110) can map the user's identification information to the unique identification number of the AI ​​robot in a 1:N or N:1 ratio. The electronic device (110) can manage operator-robot connection information by generating mapping data. The mapping data may include authorization restriction information such as the maximum allowable quantity per grade, robot type, and autonomy grade. Such mapping enables the systematic management of the relationship between the operator and the robot.

[0019] According to some embodiments, the certification method for AI robot operators by grade can record a certification history, including mapping data and the fact that a final certification grade was granted, on a blockchain network. The electronic device (110) can generate the mapping data and the fact that a certification grade was granted as a new block. The electronic device (110) can distribute and store the generated block across multiple nodes of the blockchain network. The blockchain network can prevent tampering with data through a consensus mechanism. The blockchain recording of the certification history can provide transparency and traceability. Blockchain-based history management can maintain data integrity even in the event of external attacks or system failures.

[0020] According to some embodiments, the system (100) may classify the operator's certification level into three stages: Basic Certification (Level 1), Professional Certification (Level 2), and Advanced Certification (Level 3). The level classification module (210) may assign one of the three levels based on the operator's level of fulfillment of qualification requirements. Basic Certification (Level 1) may be assigned with the requirements of operating 1 to 5 robots and completing basic training. Professional Certification (Level 2) may be assigned with the requirements of operating 6 to 50 robots, having at least one year of experience, and completing professional training. Advanced Certification (Level 3) may be assigned with the requirements of operating more than 50 robots or participating in national projects, having at least three years of experience, and completing a master course. Each level may be defined differentially based on the operator's proficiency and scope of responsibility.

[0021] According to some embodiments, each certification level may have differentially set required training completion requirements, practical experience requirements, and the scale of robots that can be operated. Mandatory training courses, practical experience, and the difficulty and scope of evaluation items may be set differently by level. Basic certification may include basic safety training and an assessment of simple operation skills. Professional certification may include advanced practical training, complex simulation assessments, and an assessment of ethical judgment skills. Highest certification may include completion of a master's course, experience in operating large-scale robots, and an assessment of high-difficulty ethical dilemmas. The maximum allowable number of robots that can be operated may be limited by level. These differential settings can create a safe operating environment for Physical AI robots (humanoid robots, embodied AI robots, human AI robots, etc.) tailored to the operator's capabilities.

[0022] It can prevent abuse of authority. Such differential authorization can simultaneously ensure the safety and expertise of Physical AI robot operations.

[0023] In some embodiments of the present invention, the electronic device (110) may include hardware and software modules responsible for core computation and control functions of an AI robot user authentication and management system. The electronic device (110) may process each step of the authentication process in real time by having a plurality of hardware components and software modules interact with each other. The hardware may include a high-performance processor, a mass storage device, a network interface, a security module, etc. The software may be composed of an operating system, a database management system, an AI computation engine, an evaluation scenario generator, an authorization management module, a history management module, etc. Each hardware and software component may exchange data and control signals through a standardized interface. This configuration can reliably process large-scale user and robot data and ensure the reliability and scalability of authentication.

[0024] In some embodiments of the present invention, the user terminal (130) may be a terminal used by a user who wishes to obtain authentication for an AI robot. In some embodiments of the present invention, the user terminal (130) may serve as a primary point of contact for verifying the identity and inputting data of a user accessing the authentication system. The user terminal (130) may be responsible for user input and output at each stage of the authentication process, such as authentication application, scenario response, and result verification. The user terminal (130) may provide the effect of improving the accessibility and user experience of the authentication system.

[0025] In some embodiments of the present invention, the user terminal (130) may encompass various computing devices capable of inputting and outputting data by connecting to a communication network, such as a PC, smartphone, tablet PC, or virtual reality (VR) device. The user terminal (130) can transmit and receive data with the electronic device (110) via a wired or wireless network. The user terminal (130) can input authentication application information, scenario responses, and operation data through an input device (keyboard, touchscreen, mouse, VR controller, etc.). The user terminal (130) can provide information such as scenarios, evaluation results, and authentication status to the user through an output device such as a display, speaker, or vibration motor. The inclusion of various computing devices can increase the universality and scalability of the authentication system.

[0026] In some embodiments of the present invention, a user terminal (130) can connect to an electronic device (110) via a network. The user terminal (130) can implement a network connection by supporting various communication protocols such as TCP / IP, Wi-Fi, 5G, and Bluetooth. The user terminal (130) can transmit and receive key data, such as authentication application information, scenario data, and response data, in real time through the network connection. The network connection function can ensure the real-time nature of the authentication process and data synchronization.

[0027] In some embodiments of the present invention, the user terminal (130) can support various data input / output functions, such as the transmission and reception of authentication application information, the input / output of scenario data, and the transmission of response data. The user terminal (130) can transmit personal information, education completion history, career information, etc., entered by the user during the authentication application stage to the electronic device (110). The user terminal (130) can output the simulation scenario received from the electronic device (110) to a screen or VR HMD. The user terminal (130) can transmit the user's scenario response and operation data to the electronic device (110) in real time. These data input / output functions can increase the automation and efficiency of the authentication process.

[0029] According to some embodiments, the network may be a communication network that mediates the transmission and reception of data between an electronic device (110) and a user terminal (130). The network may transmit and receive various data, such as authentication application information, scenario data, and evaluation results, between the electronic device (110) and the user terminal (130). The network may apply standard communication protocols such as TCP / IP, HTTP, WebSocket, and MQTT when transmitting and receiving data. The network may include packet loss, delay, error detection, and retransmission functions during the data transmission and reception process. The network may ensure real-time data synchronization and the continuity of the authentication process.

[0030] In some embodiments of the present invention, the network may include both wired and wireless communication networks. The network may be configured to include a wired communication network, such as Ethernet or optical cables, and a wireless communication network, such as Wi-Fi, 5G, LTE, or Bluetooth. The wired communication network can provide a direct connection between an electronic device (110) and a user terminal (130) in a fixed environment. The wireless communication network can ensure network accessibility for a user terminal (130) while in motion. The parallel configuration of wired and wireless communication networks can increase the flexibility of the system in various operating environments.

[0031] According to some embodiments, the network can support various communication protocols to enable real-time transmission and reception of data. The network may support multiple communication protocols, such as TCP / IP, UDP, HTTP, HTTPS, WebSocket, and MQTT. The network may selectively apply data transmission speed, reliability, and security levels depending on the characteristics of each protocol. For real-time transmission and reception, the network may implement various data processing methods, such as packet-based transmission, streaming, and asynchronous communication. Protocol support by the network can enhance the scalability and interoperability of the authentication system.

[0032] In some embodiments of the present invention, a wired communication network can provide high-capacity data transmission and stable connectivity in a fixed environment. The wired communication network can reliably transmit high-capacity authentication data, simulation images, log files, etc. by utilizing high-speed transmission media such as Ethernet and optical cables. The wired communication network is suitable for real-time evaluation and processing of large amounts of data due to low transmission delay and packet loss rates. The wired communication network is resistant to external interference, which can increase the reliability of data transmission.

[0033] In some embodiments of the present invention, the wireless communication network ensures mobility and accessibility and can support the connection of various user terminals (130). The wireless communication network applies various wireless technologies such as Wi-Fi, 5G, LTE, and Bluetooth, enabling the user terminal (130) to connect to the network not only in a fixed location but also while moving. The wireless communication network allows multiple user terminals (130) to connect simultaneously to perform authentication applications, scenario responses, and data transmission and reception. The wireless communication network can ensure accessibility of the authentication system even in an environment where network infrastructure is limited.

[0034] In some embodiments of the present invention, a parallel structure of wired and wireless communication networks can enhance the scalability and reliability of the authentication system. The parallel structure of wired and wireless communication networks can increase system availability by providing an alternative path in the event of a network failure. The parallel structure enables the continuous operation of the authentication system in various operating environments (fixed, mobile, remote, etc.). The parallel structure is effective for network load balancing and traffic management, and can strengthen the reliability and scalability of the entire system.

[0035] According to some embodiments, the network may include a blockchain network environment in which multiple nodes participate to store data in a distributed manner to prevent tampering with authentication history. The network can store authentication history data distributed across multiple nodes through the blockchain network. The blockchain network can prevent data tampering by having each node maintain the same blockchain ledger. When new authentication history occurs, the blockchain network can generate the corresponding data in blocks and synchronize it with all nodes within the network. The blockchain network can ensure the reliability and transparency of the data through a distributed consensus mechanism.

[0036] In some embodiments of the present invention, a blockchain network can ensure data integrity by having each node store the same data in a distributed manner. Each node of the blockchain network can store a block containing authentication history data in the same way. Each node can verify the validity of the block through a blockchain consensus algorithm (e.g., PoW, PoS, PBFT, etc.). The distributed storage of the same data can maintain the integrity of the entire history even in the event of a single node failure or data loss.

[0037] In some embodiments of the present invention, a multi-node structure can ensure the reliability and transparency of authentication history without a single point of failure. The multi-node structure can maintain the continuity and reliability of the entire blockchain ledger despite failures or attacks on some nodes within the network. The multi-node structure can enhance transparency by requiring verification by multiple nodes against attempts to tamper with blockchain data. The multi-node structure can guarantee the security of the authentication system and data reliability in the long term.

[0038] According to some embodiments, an artificial intelligence robot operator authentication system is characterized by including a history management module (250) that ensures the integrity of the history by recording the authentication history, including the acquisition, renewal, suspension, and cancellation of an operator's authentication, on a blockchain network. The history management module (250) can record the corresponding data as a new block on the blockchain network when each history event, such as the acquisition, renewal, suspension, or cancellation of authentication, occurs. The history management module (250) can prevent tampering with the authentication history data by utilizing the distributed storage structure of the blockchain network. The history management module (250) can verify the integrity of the authentication history through the history data recorded in the blockchain ledger. The history management module (250) can further secure the reliability of the history data through the consensus mechanism of the blockchain network.

[0039] In some embodiments of the present invention, a blockchain network can prevent tampering with authentication history data through a distributed storage structure. By storing identical authentication history data at each node, the blockchain network can maintain data consistency across the entire network against attempts to tamper with data by a single node. The distributed storage structure can guarantee data integrity against various threats, such as external attacks, internal manipulation, and system failures. The blockchain network can detect data tampering in real time by applying technologies such as blockchain hash values, timestamps, and digital signatures.

[0040] According to some embodiments, a distributed storage-based structure can provide high security against external attacks or attempts at internal manipulation. The distributed storage-based structure can enhance defenses against external hacking, data deletion, and tampering attempts through data verification by multiple nodes within the network. The distributed storage structure can prevent unauthorized data manipulation by internal operators or system administrators. The distributed storage structure can strengthen the long-term reliability and data retention of the authentication system.

[0041] In some embodiments of the present invention, a blockchain network can additionally ensure the security and integrity of authentication history by applying data encryption and a consensus mechanism. The blockchain network can store authentication history data and user information by encrypting them using encryption algorithms (AES, RSA, ECC, etc.). The blockchain network can allow multiple nodes within the network to verify the validity of a new block through a consensus mechanism (e.g., PoW, PoS, PBFT, etc.). The application of data encryption and a consensus mechanism can prevent unauthorized access to and tampering with authentication history data. Data encryption and a consensus mechanism can double-strengthen the security and data integrity of the authentication system.

[0042] According to some embodiments, the consensus mechanism can verify the reliability of data through the consent of multiple nodes within the network. The consensus mechanism allows multiple nodes within the network to independently verify the validity of a block when a new block is created. The consensus mechanism allows a block to be added to the blockchain ledger only when there is consent from a majority of nodes or a predefined threshold. The consensus mechanism can effectively block attempts by malicious nodes to tamper with data.

[0043] In some embodiments of the present invention, data encryption can securely protect authentication history and user information. Data encryption can convert key data, such as authentication history data, user identification information, and authorization information, into an encryption key and store it. Data encryption can guarantee the confidentiality of data in both the network transmission section and the storage section. Data encryption can enhance the security of the authentication system against security threats such as unauthorized access, data leakage, and information exposure.

[0044] FIG. 2 is a drawing for explaining the detailed operation of each module according to one embodiment.

[0045] A grade can be calculated. The grade classification module (210) can transmit the grade-specific classification results to the evaluation module (230) and the authorization management module (260). By applying a three-level grade system, the grade classification module (210) enables differential certification and authorization based on the operator's proficiency and scope of responsibility. The three-level grade classification provides flexibility to respond to various operating environments, such as the risk level, autonomy, and operational scale of Physical AI robots (humanoid robots, embodied artificial intelligence, human AI robots, etc.). The step-by-step classification of the grade classification module (210) provides the effect of creating an operating environment for Physical AI robots (humanoid robots, embodied artificial intelligence, human AI robots, etc.) suitable for the operator's capabilities.

[0046] In some embodiments of the present invention, the class classification module (210) can differentially set the required training completion requirements, practical experience requirements, and the number of robots that can be operated for each class. The class classification module (210) can query information on the required training courses, practical experience standards, and the number of robots that can be operated for each class from the qualification requirements database (220). The class classification module (210) can specifically set the training completion requirements for each class. The class classification module (210) can specifically set the practical experience requirements for each class. The class classification module (210) can set the maximum number of robots that can be operated for each class. By differentially applying requirements for each class, the class classification module (210) can limit the scope of robot operation according to the operator's experience and proficiency. Differential setting for each class provides the effect of increasing the safety and efficiency of Physical AI robot operation.

[0047] According to some embodiments, the grade classification module (210) may receive the operator's certification application information and compare it with a pre-set qualification requirement database to determine whether to allow entry into the AI-based competency evaluation engine and the target certification grade. The grade classification module (210) may receive certification application information from a user terminal (130). The grade classification module (210) may extract detailed data from the certification application information, such as training completion history, work experience, and certification status. The grade classification module (210) may compare the extracted data with the qualification requirement management database (220) to verify whether each item is satisfied. If all requirements are satisfied, the grade classification module (210) may allow entry into the evaluation into the AI-based competency evaluation engine. If some requirements are not satisfied, the grade classification module (210) may restrict entry into the evaluation or provide guidance for supplementation. The grade classification module (210) may determine one of the target certification grades (Level 1, Level 2, Level 3) based on the level of satisfied requirements. The grade classification module (210) can transmit the calculated target certification grade information to the evaluation module (230) and the authorization management module (260). The step-by-step comparison and determination process of the grade classification module (210) provides the effect of ensuring objectivity and consistency in the verification of operator qualifications.

[0048] In some embodiments of the present invention, the classification module (210) may allow the operation of one to five robots for basic certification (Level 1) and may set the completion of basic robot operation training and basic safety management qualifications as requirements. The classification module (210) may check whether basic robot operation training has been completed in the training completion history of the applicant for basic certification (Level 1). The classification module (210) may check whether basic safety management qualifications have been acquired in the qualification holding history of the applicant for basic certification (Level 1). The classification module (210) may limit the number of robots that can be operated by the applicant for basic certification (Level 1) to one to five. The classification module (210) may allow entry into the evaluation module (230) upon fulfillment of the basic certification (Level 1) requirements. The application of the basic certification (Level 1) requirements provides the effect of creating a safe robot operation environment for beginner operators.

[0049] In some embodiments of the present invention, the classification module (210) may allow the operation of 6 to 50 robots for professional certification (Level 2) and may set requirements of at least one year of operational experience after obtaining basic certification and completion of a professional training course. The classification module (210) may verify the history and date of obtaining basic certification of the professional certification (Level 2) applicant. The classification module (210) may verify whether the professional certification (Level 2) applicant possesses at least one year of practical experience from the applicant's operational experience data. The classification module (210) may verify whether the professional training course has been completed from the professional certification (Level 2) applicant's training completion history. The classification module (210) may limit the number of robots that can be operated by the professional certification (Level 2) applicant to 6 to 50. The application of the professional certification (Level 2) requirements provides the effect of granting robot operation rights suitable for skilled operators of intermediate level or higher.

[0050] In some embodiments of the present invention, the classification module (210) may allow the operation of more than 50 robots or participation in national projects for the highest certification (Level 3), and may set requirements of at least 3 years of operational experience and completion of a master course after obtaining professional certification. The classification module (210) may verify the professional certification acquisition history and acquisition date of the applicant for the highest certification (Level 3). The classification module (210) may verify whether the applicant for the highest certification (Level 3) possesses at least 3 years of practical experience from their operational experience data. The classification module (210) may verify whether the applicant for the highest certification (Level 3) has completed a master course from their education completion history. The classification module (210) may limit the number of robots that can be operated by the applicant for the highest certification (Level 3) to more than 50 or participation in national projects. The application of the requirements for the highest certification (Level 3) provides the effect of ensuring safety and expertise in high-risk, high-responsibility environments, such as large-scale robot operation and national projects.

[0051] In some embodiments of the present invention, the certification requirements management database (220) may define mandatory training courses, practical experience requirements, and test items for each level. The certification requirements management database (220) may store a list of mandatory training courses for each of the basic certification (Level 1), professional certification (Level 2), and advanced certification (Level 3). The certification requirements management database (220) may store the required practical experience duration and career type for each level in a table format. The certification requirements management database (220) may define test items such as theoretical tests, practical evaluations, and ethical judgment evaluations for each level. The certification requirements management database (220) may be queried and referenced in real time by the level classification module (210) and the evaluation module (230). The certification requirements management database (220) can increase the consistency and reliability of the certification process due to the systematic management of requirements by level.

[0052] In some embodiments of the present invention, the certification requirements management database (220) may be utilized to determine whether qualification requirements are met and to provide evaluation items by linking with the grade classification module (210) and the evaluation module (230). The certification requirements management database (220) may receive a request to look up data of a certification applicant from the grade classification module (210). The certification requirements management database (220) may receive a request to look up evaluation items and standard values ​​from the evaluation module (230). The certification requirements management database (220) may provide grade-specific requirements and evaluation item data in real time according to the requests of each module. The certification requirements management database (220) may ensure data consistency and up-to-dateness during the process of determining whether qualification requirements are met and providing evaluation items. The linkage structure of the certification requirements management database (220) provides the effect of increasing the automation and efficiency of the certification system.

[0053] In some embodiments of the present invention, the evaluation module (230) is an AI-based competency evaluation engine and can perform theoretical tests (multiple choice / short answer), simulation-based practical evaluations, and ethical dilemma situation judgment evaluations. The evaluation module (230) can receive an evaluation entry permission signal and target certification grade information from the grade classification module (210). The evaluation module (230) can query evaluation items and standard values ​​corresponding to the grade from the certification requirement management database (220). The evaluation module (230) can provide the theoretical test to the operator by configuring it into multiple-choice and short-answer questions. The evaluation module (230) can generate robot operation scenarios in a virtual reality (VR) environment for simulation-based practical evaluations. The evaluation module (230) can generate scenarios requiring ethical judgment for ethical dilemma situation judgment evaluations. The evaluation module (230) can collect response data and operation data from the operator for each evaluation item. The evaluation module (230) can score the collected data by comparing it with standard values. The evaluation module (230) can determine whether to pass or fail based on the scoring result. The evaluation module (230) can increase the objectivity, consistency, and speed of the evaluation by applying an AI-based evaluation engine.

[0054] According to some embodiments, the evaluation module (230) can perform multiple evaluation items, including theoretical tests, simulation-based practical evaluations, and ethical judgment ability evaluations corresponding to each certification level, through an AI-based competency evaluation engine. The evaluation module (230) can query the evaluation items required for each level from the certification requirements management database (220). The evaluation module (230) can sequentially provide theoretical tests, practical evaluations, and ethical judgment evaluations for each certification level. The evaluation module (230) can analyze the operator's response and operation data in real time through an AI-based competency evaluation engine. The evaluation module (230) can determine pass status by scoring the analysis results. The application of multiple evaluation items by level enables the verification of the operator's comprehensive competency.

[0055] According to some embodiments, the evaluation module (230) can evaluate the operator's ability to handle emergency situations and make ethical judgments by generating scenarios for each level of autonomy corresponding to the robot's ethics grades (A to E). The evaluation module (230) can generate scenarios for each level of autonomy by receiving information on the robot's ethics grades (A to E). The evaluation module (230) can provide complex emergency situation and ethical dilemma scenarios for robots with high levels of autonomy. Through the generated scenarios, the evaluation module (230) can evaluate the operator's ability to handle situations such as emergency stop, collision avoidance, and response to malfunctions. The evaluation module (230) can analyze the operator's choices and rationale in situations requiring ethical judgment within the scenarios. The application of scenarios for each level of autonomy enables evaluations similar to actual operating environments, thereby providing the effect of verifying the operator's actual capabilities.

[0056] It can be generated. The evaluation module (230) can provide scenarios that reflect practical and ethical situations required for each type of robot, such as humanoid robots, embodied AI-based service robots, industrial physical AI robots, medical physical AI robots, and military physical AI robots. The evaluation module (230) can evaluate the operator's situational response capabilities and expertise through the type-specific scenarios. The application of differentiated scenarios for each type of physical AI robot provides the effect of enhancing safety and suitability in various industries and environments.

[0057] In some embodiments of the present invention, the evaluation module (230) can provide scenarios including emergency stop, collision avoidance, and malfunction response of a robot in a virtual reality (VR) environment in a simulation-based practical evaluation, and can measure and score the operator's response time, accuracy, and compliance with procedures. The evaluation module (230) can construct a virtual reality (VR) environment to provide real-time simulation scenarios to the operator. The evaluation module (230) can present various situations, such as emergency stop, collision avoidance, and malfunction response, in stages within the scenario. The evaluation module (230) can measure the response time, operation accuracy, and compliance with procedures in real time from the operator's operation data. The evaluation module (230) can score the measured data by comparing it with a reference value. The evaluation module (230) can utilize the scoring results for passing judgments and assigning grades. The application of VR-based practical evaluation provides the effect of objectively verifying the operator's actual capabilities in an environment similar to reality.

[0058] In some embodiments of the present invention, the operator-robot connection module (240) can manage the mapping relationship between an authenticated operator and a registered robot by linking with a robot unique identification number (ID) system. The operator-robot connection module (240) can receive identification information of the authenticated operator and the robot unique identification number (ID). Based on the input information, the operator-robot connection module (240) can create a 1:N mapping relationship between the operator and the robot. The operator-robot connection module (240) can store the generated mapping data in a database. The operator-robot connection module (240) can query, modify, and delete mapping relationships in real time. The operator-robot connection module (240) can link with a blockchain network to ensure the integrity and consistency of the mapping data. The mapping management function of the operator-robot connection module (240) enables the systematization of robot management and authority control for each operator.

[0059] According to some embodiments, the operator-robot connection module (240) maps authenticated operator information to the unique identification number of the robot operated by the operator and can grant operational authority differentially according to the authentication level. The operator-robot connection module (240) receives authentication level information and can query the maximum number of robots that can be operated per level, robot types, and autonomy level limit values. The operator-robot connection module (240) can automatically limit the number and type of robots that can be mapped per operator according to the limit values ​​per level. The operator-robot connection module (240) can reject the mapping request or output a warning message when the limit values ​​are exceeded. The operator-robot connection module (240) provides the effect of increasing the safety and efficiency of operating Physical AI robots (humanoid robots, embodied artificial intelligence, human AI robots, etc.) through differential authority granting by level.

[0060] In some embodiments of the present invention, the history management module (250) can record the history of an operator, such as the acquisition, renewal, suspension, or cancellation of authentication, on a blockchain network. The history management module (250) can generate corresponding event data when various history events, such as the acquisition, renewal, suspension, or cancellation of authentication, occur. The history management module (250) can convert the generated history data into a new block on the blockchain network. The history management module (250) can prevent tampering with data by distributing and storing the new block across multiple nodes. The history management module (250) can provide a function for real-time inquiry and verification of history data. The blockchain recording function of the history management module (250) provides the effect of ensuring the transparency and reliability of the authentication history.

[0061] In some embodiments of the present invention, the history management module (250) can guarantee the integrity of the history by recording the authentication history on a blockchain network. The history management module (250) can verify the integrity of the history data by applying a consensus mechanism of the blockchain network. The history management module (250) can fundamentally block the alteration and deletion of history data recorded on the blockchain. The history management module (250) can provide the results of the integrity verification to an external system or user. Blockchain-based integrity guarantee provides the effect of strengthening the reliability and legal validity of the authentication system.

[0062] The module (260) provides the effect of increasing the safety and efficiency of Physical AI robot operation by granting operation rights only within the limit value.

[0063] According to some embodiments, the qualification maintenance module (270) can manage certification expiration dates and track the status of continuing education completion. The qualification maintenance module (270) can store information on each operator's certification expiration date in a database. The qualification maintenance module (270) can check in real-time whether continuing education has been completed before the certification expiration date arrives. The qualification maintenance module (270) can provide the operator with an expiration date notification or warning message if continuing education is not completed. The qualification maintenance module (270) can automatically update the continuing education completion status by linking with data from external educational institutions. The certification expiration date and continuing education status tracking function provides the effect of systematically managing the operator's qualification maintenance and renewal.

[0064] According to some embodiments, the qualification maintenance module (270) can set the validity period of the certification and check whether refresher training has been completed before the validity period expires, thereby automatically renewing or suspending the certification. The qualification maintenance module (270) can pre-set the validity period of the certification by grade. The qualification maintenance module (270) can automatically check whether refresher training has been completed before the validity period expires. The qualification maintenance module (270) can automatically renew the certification upon completion of refresher training. The qualification maintenance module (270) can automatically suspend the certification upon failure to complete refresher training. The automatic renewal and suspension functions provide the effect of increasing the efficiency and reliability of certification management.

[0065] According to some embodiments, the qualification maintenance module (270) may temporarily suspend or revoke the qualification of the relevant operator in the event of a safety accident. The qualification maintenance module (270) may receive accident occurrence data from an external safety accident history server. The qualification maintenance module (270) may change the qualification status of the relevant operator to temporary suspension or revocation in the event of an accident. The qualification maintenance module (270) may record the history of the change in qualification status to the history management module (250) and the blockchain network. The safety accident-linked suspension / revocation function provides the effect of strengthening the safety and accountability of Physical AI robot operations.

[0066] According to some embodiments, the certification requirements management database (220) may define mandatory training courses, practical experience requirements, and test items for each level. The certification requirements management database (220) may store separate lists of mandatory training courses for each of the Basic Certification (Level 1), Professional Certification (Level 2), and Advanced Certification (Level 3). The certification requirements management database (220) may specify the required period of practical experience and type of experience for each level. The certification requirements management database (220) may define multiple test items, such as theoretical exams, practical simulations, and ethical judgment evaluations, in a table format for each level. The certification requirements management database (220) may include evaluation criteria, points, and passing criteria values ​​as fields for each test item. The certification requirements management database (220) can provide consistent standards when evaluating operator qualifications through the requirements tables by level. Structuring the requirements tables by level can increase the reliability of the automated evaluation and authorization process.

[0067] According to some embodiments, the certification requirements management database (220) may store training completion data and practical work experience data extracted from the operator's certification application information. The certification requirements management database (220) may receive certification application information received from a user terminal (130) or an electronic device (110) as input values. The certification requirements management database (220) may store training completion history and practical work experience data by field from the input certification application information. The certification requirements management database (220) may use the stored training completion data and practical work experience data to determine whether qualifications are met by comparing them with a requirement table by grade. The certification requirements management database (220) may manage the certification application history, training completion history, and practical work experience history in an integrated manner for each operator. Depending on the data storage and management method, the certification requirements management database (220) may ensure traceability and transparency in the qualification evaluation for each operator.

[0068] In some embodiments of the present invention, the certification requirements management database (220) may be linked with the grade classification module (210) and the evaluation module (230) to provide standard information for determining whether each operator meets the qualification requirements. The certification requirements management database (220) may receive information on the operator's certification application from the grade classification module (210). The certification requirements management database (220) may compare the education completion data and practical experience data extracted by the grade classification module (210) with the requirements table by grade. The certification requirements management database (220) may return the comparison results to the grade classification module (210) to be used for determining whether to allow entry into the evaluation and for calculating the target certification grade. The certification requirements management database (220) may provide grade-specific test items, evaluation criteria, and scoring information to the evaluation module (230). The certification requirements management database (220) may include an evaluation result field so that the evaluation module (230) can record evaluation results for each operator. The certification requirements management database (220) can ensure consistency in automated qualification evaluation and pass / fail judgment through linkage with the grade classification module (210) and the evaluation module (230).

[0069] In one embodiment, differential settings by grade can provide the effect of enhancing the safety and efficiency of operating Physical AI robots (humanoid robots, embodied artificial intelligence, etc.) according to the operator's proficiency and scope of responsibility.

[0070] According to some embodiments, the certification requirements management database (220) may include minimum compliance criteria values ​​for each level for basic certification (Level 1), professional certification (Level 2), and top certification (Level 3). The certification requirements management database (220) may store training completion requirements, practical experience requirements, the number of robots that can be operated, and passing criteria values ​​for each evaluation item in fields for each level. The certification requirements management database (220) may provide data to the level classification module (210) and the evaluation module (230) based on the minimum compliance criteria values ​​for each level. The certification requirements management database (220) may link a version management function to maintain consistency with the existing certification history when the minimum compliance criteria values ​​for each level are changed.

[0071] According to some embodiments, the certification requirements management database (220) may store the maximum number of robots that can be operated by grade, the required training courses, and the practical experience criteria as separate fields. The certification requirements management database (220) may store the maximum number of robots that can be operated by grade as an integer field. The certification requirements management database (220) may store the list of required training courses by grade as a string or reference field. The certification requirements management database (220) may store the practical experience criteria by grade separately by detailed items such as career duration and career type. The certification requirements management database (220) can increase the automation and reliability of the grade classification and evaluation process through data structuring for each field.

[0072] In some embodiments of the present invention, the certification requirements management database (220) can perform version management for the requirements tables and standard values ​​for each grade when a change in relevant laws or regulations occurs. The certification requirements management database (220) can detect events of changes in laws or regulations. The certification requirements management database (220) can create new versions of the requirements tables and standard values ​​for each grade according to the changed laws or regulations. The certification requirements management database (220) can maintain consistency with past certification history by storing the requirements tables of the existing version and the new version in parallel. The certification requirements management database (220) can record metadata such as the application date, change history, and applicable target for each version. The certification requirements management database (220) can track the change history of certification standards through the version management function and ensure the reliability of the certification history for each operator.

[0073] According to some embodiments, the certification requirements management database (220) can maintain consistency with past certification history by recording change history and application dates by version. The certification requirements management database (220) can store the changed items, reasons for change, and application dates for each version in separate fields. The certification requirements management database (220) can automatically reference the requirement version at the relevant point in time when querying past certification history. The certification requirements management database (220) can ensure transparency of certification standards and consistency of history management through recording change history by version.

[0074] According to some embodiments, the certification requirements management database (220) can automatically collect the operator's training completion history and practical experience data by linking with external educational institutions and industries. The certification requirements management database (220) can be connected to the databases of external educational institutions or industries via API or a data linkage interface. The certification requirements management database (220) can receive the operator's training completion history and practical experience data from external systems periodically or on an event basis. The certification requirements management database (220) can match the received external data by operator and automatically reflect it in the requirements table by grade. The certification requirements management database (220) can store the data source, date of receipt, and certification body information together to verify the reliability of the externally linked data. The externally linked data collection function can provide the effect of increasing the reliability and timeliness of the operator's qualification evaluation.

[0075] In some embodiments of the present invention, the certification requirements management database (220) can maintain the latest qualification requirements information in real time by reflecting data collected through external integration into a requirement table by grade. The certification requirements management database (220) can automatically update the requirement table by grade whenever external integration data is received. The certification requirements management database (220) can immediately reflect the qualification evaluation results for each operator through real-time data updates. The certification requirements management database (220) can improve the accuracy and reliability of the certification evaluation by maintaining the latest qualification requirements information.

[0076] In some embodiments of the present invention, the grade classification module (210) can perform the role of classifying the certification grade of an operator in an artificial intelligence robot operator certification system. The grade classification module (210) is included in an electronic device (110) and can receive the operator's certification application information as input. The grade classification module (210) can extract data on completion of required training and practical experience data from the input certification application information. The grade classification module (210) can compare the extracted data with the grade-specific standard values ​​stored in the certification requirement management database (220). The grade classification module (210) can determine whether the operator meets the qualification requirements based on the comparison result. The grade classification module (210) can determine the certification grade that can be granted to the operator based on whether the qualification requirements are met. The grade classification module (210) can transmit the determined certification grade information to the evaluation module (230) and the authorization management module (260). The grade classification module (210) can record the certification grade classification results in the history management module (250). The class classification module (210) can generate information on the quantity, type, and autonomy class limit of robots that can be operated according to the class classification results. The class classification module (210) can make a request to a blockchain network for distributed storage to ensure the integrity of the data generated during the class classification process.

[0077] According to some embodiments, the grade classification module (210) may apply a three-level grade system based on whether the operator meets the qualification requirements. The grade classification module (210) may refer to the minimum fulfillment criteria values ​​for each grade stored in the certification requirements management database (220). The grade classification module (210) may apply the grade system based on multiple items, such as the operator's training completion history, practical experience, and history of obtaining previous certification grades. The grade classification module (210) may assign one of Basic Certification (Level 1), Professional Certification (Level 2), or Highest Certification (Level 3) to the operator. Reflecting that the qualification requirements for each grade differ, the grade classification module (210) may link separate evaluation procedures and authority restrictions for each grade. The grade classification module (210) may transmit the results of applying the grade system to the operator-robot connection module (240) and the authority management module (260) to enable the granting of operational authority by grade.

[0078] According to some embodiments, the classification module (210) can classify operator grades into three levels: Basic Certification (Level 1), Professional Certification (Level 2), and Highest Certification (Level 3). The classification module (210) can predefine the requirements for completing training, practical experience, the number of robots that can be operated, and authority restrictions required for each grade of the three-level classification system. The classification module (210) can apply criteria allowing the operation of 1 to 5 robots for Basic Certification (Level 1), 6 to 50 robots for Professional Certification (Level 2), and more than 50 robots or participation in national projects for Highest Certification (Level 3). The classification module (210) can differentially set the training courses and experience periods required of operators for each grade. The classification module (210) can link the types of robots that can be operated and the autonomy grade restrictions for each grade, thereby enabling robot operation with higher levels of autonomy as the grade increases. The classification module (210) can create a safe Physical AI robot operating environment according to the operator's proficiency and scope of responsibility through the application of a three-level classification system.

[0079] According to some embodiments, the class classification module (210) may allow the operation of one to five robots for basic certification (Level 1) and may set the completion of basic robot operation training and basic safety management qualifications as requirements. The class classification module (210) may extract the history of completion of basic robot operation training from the certification application information of the basic certification (Level 1) applicant. The class classification module (210) may verify whether the basic certification (Level 1) applicant has acquired basic safety management qualifications. The class classification module (210) may verify that the number of robots that the basic certification (Level 1) applicant can operate is one to five. The class classification module (210) may grant Level 1 to the operator when the basic certification (Level 1) requirements are met. The class classification module (210) may transmit information regarding the type of robot and autonomy level restriction for the Level 1 operator to the authorization management module (260). The grade classification module (210) can record the certification history of Level 1 grade operators in the history management module (250).

[0080] According to some embodiments, the classification module (210) may allow the operation of 6 to 50 robots for professional certification (Level 2) and may set requirements of at least one year of operational experience after obtaining basic certification and completion of a professional training course. The classification module (210) may extract the history and date of obtaining basic certification (Level 1) from the certification application information of the professional certification (Level 2) applicant. The classification module (210) may confirm that the professional certification (Level 2) applicant has at least one year of operational experience. The classification module (210) may verify the professional certification (Level 2) applicant's history of completing a professional training course. The classification module (210) may verify that the number of robots that the professional certification (Level 2) applicant can operate is at least 6 and no more than 50. The classification module (210) may grant Level 2 to the operator when the Level 2 requirements are met. The class classification module (210) can transmit information regarding the types of robots that can be operated and the autonomy level restrictions for Level 2 operators to the authorization management module (260). The class classification module (210) can record the authentication history of Level 2 operators in the history management module (250).

[0081] According to some embodiments, the classification module (210) may allow the operation of more than 50 robots or participation in national projects for the highest certification (Level 3), and may set requirements of at least 3 years of operational experience after obtaining professional certification and completion of a master course. The classification module (210) may extract the history and date of obtaining professional certification (Level 2) from the certification application information of the applicant for the highest certification (Level 3). The classification module (210) may verify that the applicant for the highest certification (Level 3) has at least 3 years of operational experience. The classification module (210) may verify the completion history of the master course of the applicant for the highest certification (Level 3). The classification module (210) may verify that the applicant for the highest certification (Level 3) has more than 50 robots that can be operated or possesses the qualification to participate in national projects. The classification module (210) may grant Level 3 to the operator when the requirements for Level 3 are met. The class classification module (210) can transmit information regarding the types of robots that can be operated and the autonomy level restrictions for Level 3 operators to the authorization management module (260). The class classification module (210) can record the authentication history of Level 3 operators in the history management module (250).

[0082] According to some embodiments, the grade classification module (210) can perform a qualification requirement evaluation by comparing data extracted from the operator's certification application information with the certification requirement management database (220). The grade classification module (210) can receive the certification application information as input from the user terminal (130). The grade classification module (210) can extract multiple data from the input certification application information, such as training completion history, practical work experience, and history of acquiring previous certification grades. The grade classification module (210) can compare the extracted data with the grade-specific standard values ​​stored in the certification requirement management database (220). The grade classification module (210) can determine whether the operator meets the qualification requirements based on the comparison result. The grade classification module (210) can transmit the qualification requirement evaluation results to the evaluation module (230) and the authority management module (260). The grade classification module (210) can record the qualification requirement evaluation process and results in the history management module (250).

[0083] In some embodiments of the present invention, the grade classification module (210) can extract data on the completion of a user's required training course and work experience data from the certification application information received from the user terminal (130), and compare this with a pre-set qualification requirements database to determine whether to allow entry into the AI ​​competency evaluation engine and the target certification grade. The grade classification module (210) can extract the history of completion of required training courses by field from the certification application information. The grade classification module (210) can extract data on the duration and type of work experience from the certification application information. The grade classification module (210) can compare the extracted training completion data and work experience data with the grade-specific standard values ​​of the qualification requirements database. The grade classification module (210) can determine whether to allow entry into the AI-based competency evaluation engine based on the comparison results. The grade classification module (210) can calculate the target certification grade that can be granted to the operator based on the comparison results. The grade classification module (210) can transmit the calculated target certification grade information to the evaluation module (230). The grade classification module (210) can record the results of whether entry is allowed and the target certification grade calculation in the history management module (250).

[0084] In some embodiments of the present invention, the grade classification module (210) determines whether to allow entry into the evaluation based on the result of comparison with the qualification requirements database and controls entry into the evaluation stage through the AI-based competency evaluation engine only when the qualification requirements are met. The grade classification module (210) may allow entry into the evaluation if the result of comparison with the qualification requirements database is greater than or equal to a threshold value. The grade classification module (210) may restrict entry into the evaluation if the qualification requirements are not met. The grade classification module (210) may transmit the decision on whether to allow entry into the evaluation as a signal to the evaluation module (230). The grade classification module (210) may record the decision on whether to allow entry into the evaluation and related data in the history management module (250). When the grade classification module (210) allows entry into the evaluation, it may send a guidance message to the operator regarding entry into the evaluation stage to the user terminal (130). When the grade classification module (210) restricts entry into the evaluation, it may send a guidance message to the user terminal (130) regarding unmet items and items requiring supplementation. The grade classification module (210) can control the flow of the entire authentication process based on the evaluation entry allowance / restriction result.

[0085] In some embodiments of the present invention, the evaluation module (230) can perform the role of evaluating the capabilities of an operator in an artificial intelligence robot operator certification system. The evaluation module (230) can be included in an electronic device (110) and operate during the operator certification evaluation stage. The evaluation module (230) can receive certification grade information transmitted from the grade classification module (210) as an input value. The evaluation module (230) can automatically set the difficulty level and evaluation range of evaluation items according to the input certification grade. The evaluation module (230) can sequentially perform theoretical tests, simulation-based practical evaluations, and ethical dilemma judgment evaluations for each evaluation item. The evaluation module (230) can collect the operator's response data and operation data in real time for each evaluation item. Based on the collected data, the evaluation module (230) can quantitatively analyze the operator's theoretical knowledge, practical skills, and ethical judgment skills. The evaluation module (230) can convert the analysis results into scores and use them to determine whether the operator passes or fails. The evaluation module (230) can transmit the evaluation results to the authorization management module (260) and the history management module (250). The evaluation module (230) can ensure integrity by distributing and storing the evaluation process and result data on a blockchain network.

[0086] According to some embodiments, the evaluation module (230) may include an evaluation module (230) that performs a theoretical test (multiple choice / short answer), a simulation-based practical evaluation, and an ethical dilemma situation judgment evaluation as an AI-based competency evaluation engine. The evaluation module (230) can analyze the operator's input data in real time through the AI-based competency evaluation engine. The evaluation module (230) can evaluate responses to multiple-choice and short-answer questions in the theoretical test using automatic scoring or AI natural language processing. The evaluation module (230) can record the operator's operation logs, event occurrence times, selection paths, etc., in the simulation-based practical evaluation. The evaluation module (230) can analyze the operator's choices and decision-making grounds in the ethical dilemma situation judgment evaluation. The evaluation module (230) can produce quantitative and qualitative evaluation results by utilizing an AI model for each evaluation item. The evaluation module (230) can determine whether to assign a final score and grade by integrating the results for each evaluation item.

[0087] According to some embodiments, the evaluation module (230) may include an evaluation module (230) that performs multiple evaluation items, including a theoretical test, a simulation-based practical evaluation, and an ethical judgment ability evaluation corresponding to each certification level, through an AI-based competency evaluation engine. The evaluation module (230) may differentially set the difficulty, number of items, and scenario complexity of the evaluation items according to the certification level received from the level classification module (210). The evaluation module (230) may apply basic theory and simple practical scenarios to the basic certification (Level 1), practical and ethical evaluations of intermediate difficulty to the professional certification (Level 2), and high-difficulty scenarios and complex ethical judgment evaluations to the highest certification (Level 3). The evaluation module (230) may automatically assign the evaluation items required for each level and determine the evaluation results according to the criteria for each level.

[0088] In some embodiments of the present invention, the evaluation module (230) can comprehensively evaluate the operator's theoretical knowledge, practical skills, and ethical judgment skills. The evaluation module (230) can evaluate the operator's theoretical knowledge, such as understanding of robot-related regulations, safety management, and AI algorithms, through a theoretical test. The evaluation module (230) can evaluate actual operation skills, problem-solving skills, and emergency response skills through a simulation-based practical evaluation. The evaluation module (230) can evaluate the operator's ethical values, sense of responsibility, and ability to judge autonomy through an evaluation of ethical dilemmas. The evaluation module (230) can quantify the operator's overall capabilities by integrating the scores for each evaluation item.

[0089] In some embodiments of the present invention, the evaluation module (230) may include a plurality of evaluation items to evaluate the qualifications of an operator. The evaluation module (230) may classify the evaluation items into a theoretical test, a simulation-based practical evaluation, and an ethical dilemma judgment evaluation. The evaluation module (230) may set separate evaluation scenarios and scoring criteria for each evaluation item. The evaluation module (230) may independently collect the operator's input data and operation data for each evaluation item. The evaluation module (230) may calculate results for each evaluation item individually and finally integrate them to determine whether the operator passes.

[0090] In some embodiments of the present invention, the evaluation module (230) can perform a theoretical test (multiple choice / short answer). The evaluation module (230) can automatically generate theoretical test questions by grade or select them from a pre-registered question bank. The evaluation module (230) can apply an automatic scoring algorithm to multiple-choice questions. The evaluation module (230) can apply an AI natural language processing-based answer evaluation model to short answer questions. The evaluation module (230) can score the results of the theoretical test and compare them with the grade criteria.

[0091] In some embodiments of the present invention, the evaluation module (230) can evaluate the operator's theoretical knowledge regarding robots and safety management knowledge, etc. The evaluation module (230) can evaluate various theoretical knowledge items such as the structure of the robot, control principles, AI algorithms, safety regulations, and legal liability. The evaluation module (230) can quantitatively verify the operator's basic knowledge and level of expertise through the results of the theoretical test.

[0092] In some embodiments of the present invention, the evaluation module (230) can perform a simulation-based practical evaluation. The evaluation module (230) can generate a practical scenario in a virtual reality (VR) environment and provide it to an operator in real time. The evaluation module (230) can record the operator's operation input, the time of event occurrence, the selected path, the reaction speed, etc., in the VR environment. The evaluation module (230) can calculate the results of the practical evaluation as quantitative indicators (e.g., success rate, number of errors, reaction time).

[0093] In some embodiments of the present invention, an evaluation module (230) provides simulation scenarios including emergency stop, collision avoidance, and malfunction response of a robot in a virtual reality (VR) environment, and measures and scores the operator's response time, accuracy, and compliance with procedures, thereby providing an artificial intelligence robot operator certification method. The evaluation module (230) can measure the operator's rapid judgment and operation ability in the emergency stop scenario. The evaluation module (230) can evaluate obstacle recognition, selection of avoidance paths, and operation accuracy in the collision avoidance scenario. The evaluation module (230) can evaluate problem diagnosis, compliance with recovery procedures, and implementation of safety measures in the malfunction response scenario. The evaluation module (230) can individually measure and score the response time, accuracy, and compliance with procedures for each scenario. The evaluation module (230) can determine whether practical ability is satisfied by comparing the scored results with grade-specific criteria.

[0094] According to some embodiments, the evaluation module (230) can evaluate operational ability and problem-solving ability in practical situations. The evaluation module (230) can evaluate the operator's ability to handle various problem situations through multiple practical scenarios. The evaluation module (230) can quantitatively analyze detailed items of practical competence, such as operational errors, response to emergency situations, and compliance with procedures.

[0095] In some embodiments of the present invention, the evaluation module (230) can perform an evaluation of an ethical dilemma situation judgment. The evaluation module (230) can automatically generate a scenario reflecting an ethical dilemma situation. The evaluation module (230) can present multiple options and decision-making situations to the operator. The evaluation module (230) can analyze the operator's choice results and the basis for the choice based on AI. The evaluation module (230) can score the results of the ethical judgment evaluation and compare them with grade-specific criteria.

[0096] According to some embodiments, the evaluation module (230) can evaluate the operator's ethical judgment ability and the decision-making capability based on the autonomy of the AI ​​robot. The evaluation module (230) can adjust the difficulty and complexity of ethical judgment scenarios according to the level of autonomy of the robot. The evaluation module (230) can quantitatively evaluate detailed items of ethical capability, such as the operator's sense of responsibility, risk perception, and social values.

[0097] According to some embodiments, the evaluation module (230) can generate various scenarios for each evaluation item. The evaluation module (230) may include a separate scenario generation engine for each of the theoretical test, practical evaluation, and ethical evaluation. When generating scenarios, the evaluation module (230) may use multiple parameters, such as the type, use, risk level, and autonomy grade of the robot, as input values. The evaluation module (230) can transmit the results of scenario generation to the user terminal (130) in real time. The evaluation module (230) can automatically set the difficulty level, situational variables, and evaluation criteria of the generated scenarios.

[0098] In one embodiment, the evaluation module (230) may reflect physical interaction with humans, embodied AI-based environmental perception, and social and ethical situations in the case of a humanoid robot, which is a physical AI robot; situations related to safety accidents and productivity in the case of an industrial robot; and patient safety and emergency response situations in the case of a medical robot. The evaluation module (230) may include specific situations for each field, such as service robots and military robots, in the scenarios. The evaluation module (230) can evaluate the operator's expertise and adaptability in each field through the differentiation of scenarios by robot type.

[0099] According to some embodiments, the evaluation module (230) can differentiate scenarios based on the robot's use, risk level, and field of application. The evaluation module (230) can set different objectives and evaluation criteria for the scenarios depending on the robot's use (e.g., transportation, medical, manufacturing, disaster prevention, etc.). The evaluation module (230) may include high-difficulty evaluation items, such as emergency situations, safety regulation compliance, and rapid response, as the field of high risk increases. The evaluation module (230) can reflect practical skills and ethical judgment criteria required for each field of application into the scenarios.

[0100] In some embodiments of the present invention, the evaluation module (230) can generate scenarios for each level of autonomy corresponding to the ethical grades (A–E) of the robot. The evaluation module (230) can reflect the level of autonomy, the scope of decision-making, and the degree of human intervention for each ethical grade in the scenarios. The evaluation module (230) can include in the scenarios situations where the operator's burden of judgment increases as the level of autonomy increases. The evaluation module (230) can evaluate the operator's risk perception, responsibility sharing, and ethical judgment ability through the scenarios for each level of autonomy.

[0101] According to some embodiments, the evaluation module (230) may adjust the difficulty and judgment criteria of the evaluation scenario according to the autonomy level. The evaluation module (230) may add complex decision-making, multi-variable situations, and unpredictable events to the scenario as the autonomy level increases. The evaluation module (230) may apply evaluation criteria (e.g., speed, accuracy, ethics, etc.) differentially according to the autonomy level.

[0102] According to some embodiments, the evaluation module (230) can calculate and integrate evaluation results. The evaluation module (230) can store the scores and evaluation results calculated for each evaluation item in an integrated database. The evaluation module (230) can determine whether a candidate passes by comparing the evaluation results with the standard values ​​for each grade. The evaluation module (230) can link the evaluation results to the authority management module (260) and the history management module (250) to utilize them for granting operational authority and recording history.

[0103] In some embodiments of the present invention, the evaluation module (230) measures and scores the operator's response time, accuracy, and procedural compliance, thereby providing an artificial intelligence robot operator certification method. The evaluation module (230) can calculate the response time by calculating the difference between the time of event occurrence and the time of operator response in the simulation operation data. The evaluation module (230) can evaluate accuracy by analyzing the accuracy of operation inputs, the number of errors, the goal achievement rate, etc. The evaluation module (230) can evaluate procedural compliance by comparing a predefined procedure flow with the operator's actual operation sequence. The evaluation module (230) can calculate the total score of the practical evaluation by summing the scores for each item.

[0104] According to some embodiments, the evaluation module (230) can extract quantitative evaluation indicators from simulation operation data. The evaluation module (230) can store the quantitative evaluation indicators (e.g., average response time, accuracy ratio, procedure compliance rate, etc.) in a database. The evaluation module (230) can determine the evaluation result by comparing the extracted quantitative indicators with the grade-specific pass criteria.

[0105] In some embodiments of the present invention, the evaluation module (230) determines whether a candidate passes by synthesizing the plurality of evaluation results, and if the candidate passes, grants a corresponding certification grade; the method for certifying an artificial intelligence robot operator is characterized by including the step of: the evaluation module (230) may integrate the scores of a theoretical test, a practical evaluation, and an ethics evaluation according to weights. The evaluation module (230) may determine a candidate passes if the integrated score is equal to or greater than the passing standard for each grade. If the candidate passes, the evaluation module (230) may transmit the corresponding certification grade to the authority management module (260) to link the granting of operational authority. If the candidate fails, the evaluation module (230) may provide guidance on retaking the exam or information on refresher training.

[0106] In some embodiments of the present invention, the evaluation module (230) may determine final acceptance by integrating scores for each evaluation item. The evaluation module (230) may record scores for each evaluation item and detailed evaluation results in the history management module (250). The evaluation module (230) may notify the user terminal (130) of the final acceptance and grade assignment results. The evaluation module (230) may distribute and store result data on a blockchain network to ensure the integrity and transparency of the evaluation results.

[0107] According to some embodiments, the operator-robot connection module (240) can perform the role of managing the mapping relationship between an authenticated operator and a registered robot in an artificial intelligence robot operator authentication system. The operator-robot connection module (240) can receive the identification information of the authenticated operator and the unique identification number of the registered robot as input values. The operator-robot connection module (240) can record the input operator information and the robot unique identification number in a mapping table. The operator-robot connection module (240) can verify the integrity of the data by periodically verifying the mapping table. The operator-robot connection module (240) can view the status of robot management by operator in real time based on the mapped information. The operator-robot connection module (240) can process requests for changes, deletions, and updates to the mapping data. The operator-robot connection module (240) can store mapping history data in a distributed manner by linking it with the history management module (250) and a blockchain network. This mapping management method can systematically manage the relationship between the operator and the robot and improve the transparency and reliability of the authentication system.

[0108] In some embodiments of the present invention, an operator-robot connection module (240) is included in an electronic device (110) and can generate and store connection information between an operator and a robot after the completion of an authentication procedure. The operator-robot connection module (240) can receive the authentication grade information of an operator whose authentication procedure has been completed as an input value. The operator-robot connection module (240) can calculate the maximum number of robots mappable to each operator, the robot type, and the autonomy grade according to the authentication grade. The operator-robot connection module (240) can generate connection information between an operator and a robot according to the calculated constraints. The operator-robot connection module (240) can record the generated connection information in a storage device of the electronic device (110). The operator-robot connection module (240) can transmit the stored connection information to a history management module (250) and an authorization management module (260). This process of generating and storing connection information can contribute to ensuring the safety of operator-specific authorization management and robot operation.

[0109] In some embodiments of the present invention, the operator-robot connection module (240) may include a function for granting operational authority differentially according to an authentication level. The operator-robot connection module (240) may calculate the maximum number of robots allowed for simultaneous mapping for each authentication level. The operator-robot connection module (240) may limit the types of robots and autonomy levels that can be operated for each authentication level. The operator-robot connection module (240) may output an error message for mapping requests that violate the restriction conditions. The operator-robot connection module (240) may update authority restriction information by level in real time by linking with the authority management module (260). This differential authority granting method can create a safe operating environment based on the operator's proficiency and the risk level of the robot.

[0110] In some embodiments of the present invention, the operator-robot connection module (240) can link unique information of the operator and the robot through a mapping procedure. The operator-robot connection module (240) can receive the operator's authentication level, identification information, and the robot's unique identification number as input values. The operator-robot connection module (240) can register operator-robot pairs in a mapping table based on the input values. When registering a mapping, the operator-robot connection module (240) can verify restriction conditions by level and approve the mapping only within the allowed range. The operator-robot connection module (240) can track change history by recording the mapping results in the history management module (250). This mapping procedure can clearly define the relationship between the operator and the robot and increase the transparency of authorization management.

[0111] In some embodiments of the present invention, the operator-robot connection module (240) may include registering and querying a robot unique identification number and setting up a user-robot 1:N mapping in the mapping procedure. The operator-robot connection module (240) may provide a function to register a unique identification number of a new robot. The operator-robot connection module (240) may provide a function to query the unique identification number of a registered robot. The operator-robot connection module (240) may provide a function to map multiple robots in a 1:N manner to a single operator. The operator-robot connection module (240) may query the list of mapped robots by operator. The operator-robot connection module (240) may process requests for changing, deleting, or updating mapping data. Such a mapping procedure can increase the efficiency of robot management by operator and ensure the scalability of the system.

[0112] In some embodiments of the present invention, the operator-robot connection module (240) may include an operator-robot connection module (240) that manages the mapping relationship between an authenticated operator and a registered robot by linking with a robot unique identification number (ID) system. The operator-robot connection module (240) may communicate with the API or database of the robot unique identification number system. The operator-robot connection module (240) may register the identification information of the authenticated operator and the robot unique identification number in a mapping table. The operator-robot connection module (240) may query the registered mapping information in real time. The operator-robot connection module (240) may synchronize requests for changes, deletions, or updates to the mapping information with the robot unique identification number system. This linkage method can ensure the reliability and data consistency of the operator-robot relationship.

[0113] According to some embodiments, the operator-robot connection module (240) can register a robot unique identification number and look up the unique identification number of a registered robot. The operator-robot connection module (240) can receive and register the unique identification number of a new robot. The operator-robot connection module (240) can look up a list of unique identification numbers of registered robots. The operator-robot connection module (240) can check the status, type, and autonomy level information of a robot based on the unique identification number. The operator-robot connection module (240) can provide the registration and lookup results to the operator through a UI or API. These registration and lookup functions can increase the accuracy and speed of robot management.

[0114] In some embodiments of the present invention, the operator-robot connection module (240) includes the step of registering information of an authenticated operator in an artificial intelligence robot unique identification number system and establishing a mapping relationship with a robot operated by the operator; the method for authenticating an artificial intelligence robot operator is characterized by including the step of: the operator-robot connection module (240) can register identification information of an operator whose authentication procedure has been completed in the robot unique identification number system. The operator-robot connection module (240) can record the registered operator information and the robot unique identification number in a mapping table. The operator-robot connection module (240) can query the robot management status by operator in real time based on the mapping table. The operator-robot connection module (240) can process requests for change, deletion, or update of mapping information. This step-by-step registration and mapping process can clearly define the relationship between the operator and the robot and increase the reliability of the authentication system.

[0115] In some embodiments of the present invention, the operator-robot connection module (240) can verify the integrity of operator-robot mapping data based on the unique identification number of a registered robot. The operator-robot connection module (240) can verify the validity of the unique identification number for each item in the mapping table. The operator-robot connection module (240) can verify whether the unique identification number is duplicated, falsified, or deleted. The operator-robot connection module (240) can record the integrity verification results in the history management module (250) and the blockchain network. This integrity verification process can contribute to preventing data falsification and ensuring system reliability.

[0116] According to some embodiments, an artificial intelligence robot operator authentication system is characterized by including an operator-robot connection module (240) that links with an artificial intelligence robot unique identification number system to map authenticated operator information to the unique identification number of the robot operated by the operator, and grants differential operation authority according to the authentication level. The operator-robot connection module (240) can receive authenticated operator information and the robot unique identification number as input values. The operator-robot connection module (240) can register them in a mapping table in a 1:N relationship based on the input values. The operator-robot connection module (240) can calculate the maximum quantity, type, and autonomy level of robots that are allowed to be simultaneously mapped according to the authentication level. The operator-robot connection module (240) can approve or reject mapping requests by applying the calculated restrictions. This 1:N mapping and differential authority granting method can create a safe operating environment based on the operator's proficiency and the risk level of the robot.

[0117] According to some embodiments, the operator-robot connection module (240) can enable a single operator to manage multiple robots simultaneously through a user-robot 1:N mapping setting. The operator-robot connection module (240) can register multiple robot unique identification numbers for each operator in a mapping table. The operator-robot connection module (240) can query the list of mapped robots in real time for each operator. The operator-robot connection module (240) can process requests for changes, deletions, and updates to the mapping data. Such a 1:N mapping setting can increase management efficiency in a large-scale robot operation environment.

[0118] In some embodiments of the present invention, the operator-robot connection module (240) can set operational authority by limiting the maximum number of robots allowed for simultaneous mapping, robot types, and autonomy levels according to certification levels. The operator-robot connection module (240) can calculate the maximum number of robots that can be mapped according to certification levels. The operator-robot connection module (240) can limit the types of robots and autonomy levels that can be operated according to certification levels. The operator-robot connection module (240) can output an error message for mapping requests that violate the restriction conditions. The operator-robot connection module (240) can update authority restriction information by level in real time by linking with the authority management module (260). Such authority restriction settings can enhance the safety of Physical AI robot operation and the reliability of the system.

[0119] According to some embodiments, the operator-robot connection module (240) can process requests for changes, deletions, and updates to mapped data. The operator-robot connection module (240) can receive requests for changes, deletions, and updates as input values ​​for each item in the mapping table. The operator-robot connection module (240) can verify the input requests and reflect them in the database. The operator-robot connection module (240) can record the change history in the history management module (250) and the blockchain network. These change, deletion, and update functions can contribute to maintaining the timeliness and integrity of the mapping data.

[0120] According to some embodiments, the authorization management module (260) may perform the role of limiting the quantity, type, and autonomy level of robots that can be operated according to the certification level in the AI ​​robot operator authentication system. The authorization management module (260) may manage the range of authorizations granted to operators by certification level in a data table. The authorization management module (260) may store predefined maximum allowable quantity, allowable robot types, and allowable autonomy levels for each level in memory or a database. The authorization management module (260) may calculate authorization restriction conditions for each operator in real time by referring to the operator-specific certification level information. If an authorization request that violates the restriction conditions occurs, the authorization management module (260) may verify the request and output an error message or refuse authorization. This restriction function may contribute to creating a safe Physical AI robot operating environment by considering the operator's proficiency and the risk level of the Physical AI robot.

[0121] According to some embodiments, the authorization management module (260) may receive authentication level information received from the operator-robot connection module (240) as an input value. The authorization management module (260) may receive operator identification information and authentication level data from the operator-robot connection module (240). The authorization management module (260) may use the received authentication level information as an input value for internal authorization restriction logic. The authorization management module (260) may dynamically calculate authorization restriction conditions by level based on the input value.

[0122] According to some embodiments, the authorization management module (260) can calculate the maximum allowable quantity, robot type, and autonomy level restriction conditions for each level based on the input authentication level. The authorization management module (260) can calculate the maximum allowable quantity, allowable robot type, and allowable autonomy level corresponding to the input authentication level by referring to a predefined restriction condition table for each level. The authorization management module (260) can reflect the calculated restriction conditions in the operator-specific authorization data. When calculating the restriction conditions, the authorization management module (260) can update the restriction condition table according to policy changes or amendments to laws for each level.

[0123] In some embodiments of the present invention, the authority management module (260) can manage the calculated restriction conditions in real time by reflecting them in the authority data for each operator. The authority management module (260) can record the calculated restriction conditions in the authority database for each operator. The authority management module (260) can transmit the change history of the authority data for each operator to the history management module (250). The authority management module (260) can prevent authority abuse or the occurrence of errors by verifying the consistency of the authority data in real time.

[0124] According to some embodiments, the authorization management module (260) may output an error message or refuse authorization for an authorization request that violates a restriction condition. If the authorization request exceeds a restriction condition, the authorization management module (260) may immediately send an error message to the user terminal (130) or the operator-robot connection module (240). When a restriction condition is violated, the authorization management module (260) may stop the authorization process and log the request. The authorization management module (260) may send a notification to the administrator regarding repeated restriction violation requests.

[0125] According to some embodiments, the authorization management module (260) can update authority restriction information by class by linking with the history management module (250) and the qualification maintenance management module (270). The authorization management module (260) can record the change history in the history management module (250) whenever the authority restriction information by class changes. The authorization management module (260) can synchronize authority restriction information by receiving event information, such as certification renewal, suspension, or revocation, from the qualification maintenance management module (270). The authorization management module (260) can maintain the integrity and up-to-dateness of the authority restriction information through linkage with the history management module (250) and the qualification maintenance management module (270).

[0126] According to some embodiments, the authorization management module (260) may include authorization management logic that differentially restricts the number, type, and autonomy level of robots that can be operated according to the authentication level. The authorization management module (260) may implement the level-specific restriction conditions in the form of a logic table or a rule engine. The authorization management module (260) may receive the authentication level for each operator as an input value and automatically calculate the number, type, and autonomy level of robots allowed for each level. The authorization management module (260) may automatically perform verification and denial processing for authorization requests that violate the restriction conditions. The authorization management module (260) may track the change history of the authorization management logic by recording it in the history management module (250).

[0127] In some embodiments of the present invention, the authorization management module (260) may receive operator-specific authentication grades, robot unique identification numbers, robot types, and autonomy grade data as input values. The authorization management module (260) may integrate operator-specific authentication grade data with robot unique identification numbers, robot types, and autonomy grade information to process them as input values. The authorization management module (260) may compare grade-specific restriction conditions with actual request data based on the input values. The authorization management module (260) may verify the validity of the input values ​​to prevent false information or erroneous input in advance.

[0128] In some embodiments of the present invention, the authorization management module (260) may verify grade-specific restriction conditions based on input values ​​and grant authorization only within the allowed range. The authorization management module (260) may compare input values ​​with grade-specific restriction conditions and approve authorization only for requests within the allowed range. When authorization is approved, the authorization management module (260) may record the result in operator-specific authorization data. The authorization management module (260) may notify the user terminal (130) or the operator-robot connection module (240) of the authorization approval or denial result in real time.

[0129] According to some embodiments, the authorization management module (260) can track change history by recording authorization results in the history management module (250). The authorization management module (260) can transmit authorization approval or rejection results to the history management module (250) to store authorization change history in a distributed manner on a blockchain network. The authorization management module (260) can prevent falsification of authorization history through integration with the history management module (250). The authorization management module (260) can analyze the trend of authorization changes by operator based on authorization history data.

[0130] According to some embodiments, the authorization management module (260) may limit the maximum allowable number of robots that can be operated according to the authentication level. The authorization management module (260) may store the maximum allowable number per level in a predefined table. The authorization management module (260) may receive the authentication level for each operator as an input value and calculate the maximum allowable number corresponding to that level. The authorization management module (260) may monitor the number of robots currently mapped for each operator in real time. If an authorization request exceeding the maximum allowable number occurs, the authorization management module (260) may immediately output an error message or refuse authorization.

[0131] According to some embodiments, the authorization management module (260) may allow the operation of one to five robots for basic authentication (Level 1). The authorization management module (260) may set restrictions to grant the right to operate at least one and up to five robots for the basic authentication (Level 1) level. The authorization management module (260) may periodically check the robot mapping status of the basic authentication (Level 1) operator and approve additional mapping only within the allowed range.

[0132] In some embodiments of the present invention, the authorization management module (260) may allow the operation of 6 to 50 robots for professional certification (Level 2). The authorization management module (260) may set restrictions to grant the right to operate at least 6 and up to 50 robots for professional certification (Level 2). The authorization management module (260) may monitor the robot mapping status of professional certification (Level 2) operators in real time and approve authorization requests only within the allowed range.

[0133] In some embodiments of the present invention, the authorization management module (260) may allow the operation of more than 50 robots or participation in national projects for the highest certification (Level 3). The authorization management module (260) may set restrictions on granting the authority to operate more than 50 robots or participate in national projects for the highest certification (Level 3) grade. The authorization management module (260) may manage the robot mapping status of the highest certification (Level 3) operators on a large scale.

[0134] According to some embodiments, the authorization management module (260) may output an error message or refuse authorization for authorization requests that exceed the maximum allowable quantity per grade. When an authorization request exceeding the maximum allowable quantity per grade is input, the authorization management module (260) may immediately send an error message to the user terminal (130) or the operator-robot connection module (240). The authorization management module (260) may stop the authorization process for the excess request and record the request in the history management module (250). The authorization management module (260) may send a warning notification to the administrator when repeated excess requests occur.

[0135] In some embodiments of the present invention, the authorization management module (260) may restrict the types of robots and autonomy levels that can be operated according to the certification level. The authorization management module (260) may store the allowable robot types and autonomy levels for each level in a predefined table. The authorization management module (260) may receive the certification level for each operator as an input value and calculate the allowable robot types and autonomy levels corresponding to that level. The authorization management module (260) may verify in real time whether the input robot types and autonomy levels satisfy the restriction conditions.

[0136] In some embodiments of the present invention, the authorization management module (260) can set grade-specific restrictions according to the type of robot (humanoid, service, industrial, medical, military, etc.) and autonomy grade (A to E). The authorization management module (260) can set restrictions according to the type of Physical AI robot (e.g.,

[0137] In some embodiments of the present invention, the authorization management module (260) can verify whether the input robot type and autonomy level satisfy the level-specific restriction conditions. The authorization management module (260) can compare the input robot type and autonomy level with the level-specific restriction conditions when requesting authorization, and approve authorization only for requests within the allowed range. The authorization management module (260) can output an error message or refuse authorization for requests that do not satisfy the restriction conditions. The authorization management module (260) can record the verification results in the operator-specific authorization data.

[0138] In some embodiments of the present invention, the authorization management module (260) may reject authorization requests for robot types or autonomy levels that violate restrictions. When an authorization request for a robot type or autonomy level that violates restrictions is received, the authorization management module (260) may immediately send an error message to the user terminal (130) or the operator-robot connection module (240). The authorization management module (260) may record rejected requests in the history management module (250) to track them. The authorization management module (260) may send a notification to the administrator regarding repeated requests that violate restrictions.

[0139] According to some embodiments, the authorization management module (260) can manage restriction conditions and authorization results by recording them in the history management module (250). The authorization management module (260) can transmit restriction conditions and authorization results to the history management module (250) and store them in a distributed manner on a blockchain network. The authorization management module (260) can prevent tampering with the authorization restriction history through integration with the history management module (250). The authorization management module (260) can analyze the trend of authorization changes by operator based on restriction conditions and authorization history data.

[0140] According to some embodiments, the history management module (250) can perform the role of managing the operator's authentication history in an artificial intelligence robot operator authentication system. The history management module (250) can record various history events, such as the operator's authentication acquisition, renewal, suspension, and cancellation, in a database. The history management module (250) can store detailed information at the time of occurrence of each history event along with metadata. The history management module (250) can convert the recorded history data into a data format for transmission to a blockchain network. The history management module (250) can receive event signals related to authentication history from an external system or module. The history management module (250) can generate new history data according to the received event signals. The history management module (250) can prevent tampering with data by recording the generated history data on the blockchain network. The history management module (250) can check the status of authentication history for each operator in real time by querying the history data recorded on the blockchain network. The history management module (250) can track the change history of the history data to ensure the transparency and reliability of the authentication history.

[0141] In some embodiments of the present invention, the history management module (250) can record the history of an operator, such as the acquisition, renewal, suspension, or cancellation of authentication, on a blockchain network. The history management module (250) can generate a data packet containing detailed information of the event whenever an event of authentication acquisition, renewal, suspension, or cancellation occurs. The history management module (250) can convert the generated data packet into a transaction on the blockchain network. The history management module (250) can transmit the converted transaction to the blockchain network and include it in a new block. The history management module (250) can verify the integrity of the history data through the consensus mechanism of the blockchain network. The history management module (250) can classify and manage the history data recorded on the blockchain by operator.

[0142] In some embodiments of the present invention, the history management module (250) can prevent tampering with authentication records and ensure transparency through blockchain-based history management. The history management module (250) can maintain the reliability of the entire history data despite attempts to tamper with data by a single node by utilizing the distributed storage structure of the blockchain network. The history management module (250) can verify in real time whether the data has been changed by using the hash value of the history data recorded on the blockchain. The history management module (250) can guarantee the transparency of the authentication history by making all history data recorded on the blockchain publicly verifiable. The history management module (250) can improve the reliability of the authentication system by preventing tampering and ensuring transparency.

[0143] In some embodiments of the present invention, the history management module (250) can ensure the integrity of the history by recording the authentication history, including the acquisition, renewal, suspension, and cancellation of an operator's authentication, on a blockchain network. When each authentication event occurs, the history management module (250) can generate a new block containing detailed data such as the event type, time of occurrence, operator identification information, related robot identification information, and the cause of the event. The history management module (250) can distribute and store the generated new block across multiple nodes of the blockchain network. The history management module (250) can verify the consistency of the new block through a consensus algorithm of the blockchain network. The history management module (250) can efficiently manage each event-specific block recorded on the blockchain by indexing it by operator and by event. The history management module (250) can technically ensure the integrity of the authentication history through the creation and distributed storage of event-specific blocks.

[0144] In some embodiments of the present invention, the history management module (250) can ensure integrity by generating and distributing authentication history data as a new block on a blockchain network. The history management module (250) can simultaneously store the new block containing the authentication history data on multiple nodes of the blockchain network. The history management module (250) can verify the consistency of the data by periodically comparing the hash values ​​of the distributedly stored block data. The history management module (250) can detect attempts to tamper with the data in real time through the distributed consensus mechanism of the blockchain network. The history management module (250) ensures that the integrity of the entire history data is maintained even in the event of a single node failure or attack due to the distributed storage structure. The history management module (250) can guarantee the reliability and stability of the authentication history data by ensuring integrity.

[0145] In some embodiments of the present invention, the history management module (250) can prevent tampering with the authentication history by distributing and storing data in a blockchain network environment in which multiple nodes participate. The history management module (250) can maintain the reliability of the entire data even in the event of attempts to tamper with data by some nodes by storing the same history data at each node of the blockchain network. The history management module (250) can continuously verify the consistency of the data through the consensus protocol of the blockchain network. The history management module (250) can technically block tampering with the authentication history data through a distributed storage and consensus mechanism. The history management module (250) can improve the transparency and reliability of the authentication system through the anti-tampering function.

[0146] According to some embodiments, the qualification maintenance module (270) can perform various functions for maintaining the qualification of an operator in an artificial intelligence robot operator certification system. The qualification maintenance module (270) can periodically check the validity period of the operator's certification. The qualification maintenance module (270) can receive the status of the operator's refresher training completion from a database or an external training institution. The qualification maintenance module (270) can receive the operator's accident history data in real time by linking with an external safety accident history server. The qualification maintenance module (270) can analyze the received data to determine conditions for changing the certification status, such as certification renewal, suspension, or cancellation. The qualification maintenance module (270) can transmit the results of the certification status change to the history management module (250) to record them on a blockchain network. The qualification maintenance module (270) can notify the authority management module (260) of changes in operational authority resulting from the certification status change. The qualification maintenance management module (270) can automatically manage the maintenance of an operator's qualification by comprehensively evaluating multiple conditions such as the validity period of the certification, completion of refresher training, and history of safety accidents.

[0147] According to some embodiments, the qualification maintenance module (270) manages the validity period of the certification and can automatically renew, suspend, or revoke the certification status by linking external data such as the status of completion of refresher training and safety accident history. The qualification maintenance module (270) can check whether refresher training has been completed based on the expiration date of the certification validity period. The qualification maintenance module (270) can periodically receive refresher training completion data from an external educational institution or an internal database. The qualification maintenance module (270) can determine whether the received refresher training completion data satisfies the certification renewal conditions. If the certification renewal conditions are satisfied, the qualification maintenance module (270) can automatically extend the certification validity period. If the certification renewal conditions are not satisfied, the qualification maintenance module (270) can automatically suspend or revoke the certification. The qualification maintenance module (270) can receive accident occurrence data from an external safety accident history server. The qualification maintenance module (270) can analyze the received accident history data to determine whether it meets the conditions for suspension or revocation of certification. The qualification maintenance module (270) can transmit the results of the authentication status change to the history management module (250) to record them on the blockchain network. The qualification maintenance module (270) can transmit information regarding operational authority restrictions resulting from the authentication status change to the authority management module (260).

[0148] In some embodiments of the present invention, the qualification maintenance module (270) can monitor the certification validity period. The qualification maintenance module (270) can store certification validity period information per operator in a database. The qualification maintenance module (270) can calculate the remaining period based on the certification validity period expiration date. The qualification maintenance module (270) can generate an expiration notification if the remaining period decreases below a preset threshold. The qualification maintenance module (270) can automatically evaluate the certification status change conditions at the time the certification validity period expiration date arrives.

[0149] According to some embodiments, the qualification maintenance module (270) can check whether refresher training has been completed before the expiration of the certification validity period. The qualification maintenance module (270) can check whether refresher training completion data has been received prior to the certification validity period expiration date. The qualification maintenance module (270) can periodically query the operator's refresher training completion data from an external training institution or an internal database. The qualification maintenance module (270) can determine whether the refresher training completion data satisfies the certification renewal conditions.

[0150] According to some embodiments, the qualification maintenance module (270) may provide an operator with an expiration notification before the certification expiration date arrives. The qualification maintenance module (270) may generate an expiration notification message before a preset period (e.g., 30 days, 7 days, etc.) based on the certification validity period expiration date. The qualification maintenance module (270) may transmit the generated expiration notification message to a user terminal (130). The qualification maintenance module (270) may include information on whether refresher training is required and how to complete it in the notification message.

[0151] According to some embodiments, the qualification maintenance module (270) may receive data on the completion of continuing education by an operator from an external educational institution or an internal database to determine whether the conditions for certification renewal are met. The qualification maintenance module (270) may receive the continuing education completion data in real time through an API or data linkage interface of an external educational institution. The qualification maintenance module (270) may verify the validity and timeliness of the received continuing education completion data. If the continuing education completion data meets the conditions for certification renewal, the qualification maintenance module (270) may automatically extend the validity period of the certification. If the continuing education completion data is not met, the qualification maintenance module (270) may switch the certification status to suspension or revocation.

[0152] According to some embodiments, the qualification maintenance module (270) can automatically renew or suspend the certification by checking whether refresher training has been completed before the expiration of the validity period. The qualification maintenance module (270) can automatically extend the validity period of the certification if the refresher training completion data is satisfied before the expiration date of the certification validity period. The qualification maintenance module (270) can automatically switch the certification to a suspended state if the refresher training completion data is not satisfied before the expiration date of the certification validity period. When suspending the certification, the qualification maintenance module (270) can send a message to the operator regarding the reason for suspension and instructions for re-completion.

[0153] In some embodiments of the present invention, the qualification maintenance module (270) may automatically switch the certification to a suspended state if the certification renewal conditions are not met. The qualification maintenance module (270) may generate a trigger signal to change the certification status to 'suspended' when the certification renewal conditions are not met. The qualification maintenance module (270) may transmit the authority information of the operator switched to the certification suspension state to the authority management module (260). The qualification maintenance module (270) may automatically restore the certification when the conditions for lifting the certification suspension state (e.g., re-completion of refresher training) are met.

[0154] In some embodiments of the present invention, the qualification maintenance module (270) can transmit the result of the authentication renewal or suspension processing to the history management module (250) to record it on a blockchain network. The qualification maintenance module (270) can generate a data packet containing detailed information of the event when an authentication renewal or suspension event occurs. The qualification maintenance module (270) can transmit the generated data packet to the history management module (250). The history management module (250) can record the received data packet as a new block on the blockchain network. The qualification maintenance module (270) can query the history of authentication status changes recorded on the blockchain network in real time.

[0155] In some embodiments of the present invention, the qualification maintenance management module (270) can manage the operator's authentication status by linking with an external safety accident history server. The qualification maintenance management module (270) can receive accident history data periodically or in real time through a data linkage interface with the external safety accident history server. The qualification maintenance management module (270) can analyze detailed information such as the type, time of occurrence, and accident grade of the received accident history data. The qualification maintenance management module (270) can determine whether the accident history data corresponds to conditions for suspension or revocation of authentication. The qualification maintenance management module (270) can automatically switch the authentication status to temporary suspension or revocation based on the accident history data. The qualification maintenance management module (270) can transmit the result of the authentication status change to the history management module (250) to record it on a blockchain network.

[0156] According to some embodiments, the qualification maintenance module (270) may receive safety accident history data from an external server connected thereto. The qualification maintenance module (270) may receive accident history data in real-time or in batch mode through an API or data connection protocol with an external safety accident history server. The qualification maintenance module (270) may verify the integrity and reliability of the received accident history data. The qualification maintenance module (270) may extract the time of occurrence, type of accident, and identification information of related robots and operators from the accident history data.

[0157] In some embodiments of the present invention, the qualification maintenance module (270) can determine the conditions for changing the certification status by analyzing received accident history data. The qualification maintenance module (270) can evaluate multiple conditions of the accident history data, such as the accident grade, the cause of the accident, and whether it occurs repeatedly. The qualification maintenance module (270) can determine whether the accident history data corresponds to a pre-set condition for suspending or revoking certification. When the conditions for changing the certification status are met, the qualification maintenance module (270) can automatically switch the certification to temporary suspension or revocation.

[0158] In some embodiments of the present invention, the qualification maintenance module (270) may temporarily suspend or revoke the certification of the operator in the event of a safety accident. The qualification maintenance module (270) may switch the certification status to 'temporarily suspended' if the accident history data corresponds to the certification suspension condition. The qualification maintenance module (270) may switch the certification status to 'revoked' if the accident history data corresponds to the certification revocation condition. The qualification maintenance module (270) may send a notification message to the operator informing them of the fact when processing the certification suspension or revocation. The qualification maintenance module (270) may transmit information regarding the restriction of operational authority resulting from the certification suspension or revocation to the authority management module (260).

[0159] In some embodiments of the present invention, the qualification maintenance module (270) can transmit the results of a suspension or deletion processing to the history management module (250) to be recorded in a blockchain network. The qualification maintenance module (270) can generate a data packet containing detailed information of the event when an authentication suspension or deletion event occurs. The qualification maintenance module (270) can transmit the generated data packet to the history management module (250). The history management module (250) can record the received data packet as a new block in the blockchain network. The qualification maintenance module (270) can query the history of authentication status changes recorded in the blockchain network in real time.

[0161] FIG. 3 is an example of a flowchart of an AI robot operator certification method by grade according to one embodiment.

[0162] In step 310, the electronic device (110) can determine whether the user meets the qualification requirements based on the certification application information regarding AI robot operation received from the user terminal (130) and determine the target certification grade for operating the AI ​​robot.

[0163] In step 320, the electronic device (110) provides an evaluation scenario corresponding to a target certification grade to a user terminal (130), and can obtain an evaluation result by inputting the response data and simulation operation data received from the user terminal (130) into an artificial intelligence-based capability evaluation engine.

[0164] In step 330, the electronic device (110) determines whether to pass based on the evaluation results and assigns a final certification grade to the user terminal (130), and can generate mapping data by mapping the identification information of the user assigned the final certification grade to the unique identification number of the AI ​​robot.

[0165] In step 340, the electronic device (110) can record an authentication history, including mapping data and the fact that a final authentication grade was granted, on a blockchain network.

[0166] According to some embodiments, the artificial intelligence robot operator certification method may receive certification application information from an operator via an electronic device (110). The electronic device (110) may extract data on completion of required training and practical work experience from the certification application information. The electronic device (110) may compare the extracted data with a qualification requirements database (220) to determine whether the qualification requirements corresponding to the application grade are met. If the qualification requirements are met, the electronic device (110) may sequentially provide a theoretical test, a simulation-based practical evaluation, and an ethical judgment ability evaluation through an AI-based competency evaluation engine.

[0167] In one embodiment, the electronic device (110) can perform practical evaluation and ethical judgment evaluation by transmitting a virtual reality (VR)-based scenario to a user terminal (130). The electronic device (110) can score response time, accuracy, and procedural compliance by analyzing response data and operation data received from the user terminal (130). The electronic device (110) can determine pass status by synthesizing multiple evaluation results. Upon passing, the electronic device (110) can grant the corresponding certification grade to the operator.

[0168] In one embodiment, the electronic device (110) can register information of an authenticated operator in an artificial intelligence robot unique identification number system. The electronic device (110) can establish a mapping relationship between the operator and the operating robot. The electronic device (110) can create and distribute the authentication grant facts and mapping information as new blocks on a blockchain network. The blockchain network can prevent tampering with the authentication history and ensure its integrity. Each step can be performed independently, and the order or detailed implementation method of some steps may be changed according to the embodiment. This process can provide various effects, such as differential authentication by grade, objective competency evaluation, and transparent history management.

[0169] In some embodiments of the present invention, according to an embodiment of the method for certifying an artificial intelligence robot operator, the certification process may include the operation of receiving a certification application from an operator, the operation of determining whether qualification requirements corresponding to the application grade are met, the operation of sequentially performing a theoretical test, a practical evaluation, and an ethical judgment evaluation through an AI-based competency evaluation engine, the operation of determining pass status by synthesizing evaluation results and granting a certification grade upon passing, the operation of registering certified operator information in an artificial intelligence robot unique identification number system and establishing an operator-robot mapping relationship, and the operation of recording the fact of granting certification on a blockchain network. The certification process may begin with the step of receiving certification application information from an operator terminal (130). An electronic device (110) may receive the input application information through a network. An electronic device (110) may look up qualification requirements corresponding to the application grade in a database (220). An electronic device (110) may determine whether the applicant's education completion and practical experience data are met by comparing them with the qualification requirements. If the qualification requirements are met, the electronic device (110) may activate an AI-based competency evaluation engine. The AI-based competency evaluation engine can sequentially provide theoretical tests, practical evaluations, and ethical judgment evaluations to the user terminal (130). The user terminal (130) can transmit response and operation data for each evaluation item to the electronic device (110) in real time. The electronic device (110) can calculate an evaluation score by analyzing the received data. The electronic device (110) can determine whether the user passes by combining the evaluation score and the degree of compliance with procedures. The electronic device (110) can grant a certification grade to the operator upon passing. The electronic device (110) can register the certified operator information in the robot unique identification number system. The electronic device (110) can establish a mapping relationship between the operator and the robot. The electronic device (110) can record the fact of certification granting and mapping information on a blockchain network.A blockchain network can prevent tampering with authentication history. Each operation can be performed independently, and the order or detailed implementation method of some operations may be changed depending on the embodiment.

[0170] In some embodiments of the present invention, the certification process sequence may be implemented through data flow between multiple modules, such as a grade classification module (210), an evaluation module (230), an operator-robot connection module (240), and a history management module (250). The grade classification module (210) may receive certification application information and determine the application grade. The grade classification module (210) may verify whether the requirements for each grade are met by linking with the qualification requirements database (220). The evaluation module (230) may receive the evaluation subject from the grade classification module (210) and perform AI-based theoretical tests, practical evaluations, and ethical judgment evaluations. The evaluation module (230) may generate evaluation result data and transmit it to the history management module (250). The operator-robot connection module (240) may map the unique identification numbers of the certified operator and the robot. The operator-robot connection module (240) may apply information on the maximum allowable quantity per grade, robot type, and autonomy grade restriction. The history management module (250) can record history events, such as certification acquisition, renewal, suspension, and cancellation, on the blockchain network. Each module can operate independently or in conjunction with the data flow. Data flow between modules can be implemented in various ways depending on the embodiment.

[0171] In some embodiments of the present invention, each operation may be performed independently, and the order or detailed implementation method of some operations may be changed depending on the embodiment. Each step of the authentication process may be separated by module and executed independently. In some embodiments, the evaluation step may be performed in parallel. In some embodiments, the establishment of mapping relationships may be performed simultaneously with the assignment of authentication grades. In some embodiments, blockchain recording may be performed in real-time or at regular intervals. The detailed implementation method of each operation may vary depending on the system environment, network structure, method of integration with external systems, etc. Such a structure can provide the effect of flexibly responding to various operating environments.

[0172] According to some embodiments, according to one embodiment of the present invention, an operator-robot connection module (240) may be included to manage a mapping relationship between an authenticated operator and a registered robot in conjunction with a robot unique identification number (ID) system. The operator-robot connection module (240) may provide a data interface with an external robot unique identification number system. The operator-robot connection module (240) may generate mapping data by linking the identification information of an authenticated operator with the robot's unique identification number. The operator-robot connection module (240) may register the mapping data with the robot unique identification number system. The operator-robot connection module (240) may query or modify the registered mapping information. The operator-robot connection module (240) may transmit the change history of the mapping information to a history management module (250). The operator-robot connection module (240) may grant operational authority differentially by applying information on the maximum allowable quantity per grade, robot type, and autonomy grade restriction. The operator-robot connection module (240) can request a record on the blockchain network when establishing or changing the mapping relationship. This interconnection structure can systematically manage the relationship between the operator and the robot and ensure transparency of operational authority.

[0173] In some embodiments of the present invention, the operator-robot connection module (240) can link operator information and the robot's unique identification number through an interface with an external robot unique identification number system. The operator-robot connection module (240) can transmit and receive mapping information in real time by linking with an API or database of an external system. The operator-robot connection module (240) can view a list of robots registered by operator. The operator-robot connection module (240) can support various linking operations such as registering a new robot, deleting an existing robot, and changing a mapping. The operator-robot connection module (240) can perform data integrity verification and duplicate registration prevention functions during the linking process. The operator-robot connection module (240) can transmit the linked mapping information to the history management module (250) to record it on a blockchain network.

[0174] According to some embodiments, the operator-robot connection module (240) may include a function that grants operational authority differentially according to the certification level. The operator-robot connection module (240) may limit the maximum number of robots that can be operated by each certification level. The operator-robot connection module (240) may subdivide operational authority by robot type and autonomy level. The operator-robot connection module (240) may apply authority restriction information by level in conjunction with the authority management module (260). The operator-robot connection module (240) may automatically update mapping information when authority restriction conditions change. These functions may contribute to creating a safe Physical AI robot operation environment.

[0175] In some embodiments of the present invention, the operator-robot connection module (240) may receive robot unique identification number data from an external system or transmit mapping information to an external system. The operator-robot connection module (240) may convert data to match the data format of the external system and transmit / receive it. The operator-robot connection module (240) may periodically check the data synchronization status with the external system. The operator-robot connection module (240) may perform retransmission or exception handling in the event of an error during the data transmission / reception process. The operator-robot connection module (240) may record the history of integration with the external system in the history management module (250).

[0176] In some embodiments of the present invention, according to one embodiment of the present invention, a qualification maintenance management module (270) for managing certification expiration dates and tracking the status of completion of continuing education may be included. The qualification maintenance management module (270) may store certification validity period information in a database. The qualification maintenance management module (270) may check whether continuing education has been completed at the time the certification expiration date arrives. The qualification maintenance management module (270) may determine whether the continuing education completion data satisfies the certification renewal conditions. If the certification renewal conditions are satisfied, the qualification maintenance management module (270) may automatically extend the certification validity period. If the certification renewal conditions are not satisfied, the qualification maintenance management module (270) may automatically suspend or cancel the certification. The qualification maintenance management module (270) may transmit the results of the certification status change to the history management module (250) to record them on a blockchain network. The qualification maintenance management module (270) may transmit information on operational authority restrictions based on the certification status change to the authority management module (260).

[0177] In some embodiments of the present invention, the qualification maintenance management module (270) may receive accident occurrence data from an external safety accident history server. The qualification maintenance management module (270) may provide a communication interface for data linkage with the external safety accident history server. The qualification maintenance management module (270) may verify the format of the accident occurrence data and store it in a database. The qualification maintenance management module (270) may analyze the received accident history data in real time. The qualification maintenance management module (270) may determine whether the accident history data corresponds to conditions for certification suspension or revocation. If the conditions for certification suspension or revocation are met, the qualification maintenance management module (270) may automatically change the certification status. The qualification maintenance management module (270) may transmit the result of the certification status change to the history management module (250) to record it on a blockchain network. The qualification maintenance management module (270) may transmit information on operational authority restriction due to the certification status change to the authority management module (260).

[0178] According to some embodiments, the qualification maintenance module (270) can analyze received accident history data to determine whether it meets the conditions for suspension or revocation of certification. The qualification maintenance module (270) can extract accident type, date and time of occurrence, and related robot and operator information for each item of the accident history data. The qualification maintenance module (270) can compare the extracted information with predefined conditions for suspension or revocation of certification. The qualification maintenance module (270) can automatically suspend or revoke the certification status when the conditions are met. The qualification maintenance module (270) can record the history of changes in the certification status in the history management module (250).

[0179] In some embodiments of the present invention, the qualification maintenance module (270) can transmit the result of an authentication status change to the history management module (250) to record it on a blockchain network. The qualification maintenance module (270) can transmit change data to the history management module (250) when an authentication status change event occurs. The history management module (250) can create a new block of the received change data on the blockchain network and store it in a distributed manner. The blockchain network can prevent tampering with the authentication history and guarantee its integrity.

[0180] According to some embodiments, the qualification maintenance management module (270) can automatically manage the operator's qualification maintenance and certification status through data exchange with external systems. The qualification maintenance management module (270) can be linked with multiple external systems, such as an external safety accident history server, an educational institution, and an internal database. The qualification maintenance management module (270) can integrate and analyze data received from external systems in real time. The qualification maintenance management module (270) can automatically change the certification status, such as renewal, suspension, or revocation, based on the results of the integrated analysis. The qualification maintenance management module (270) can transmit the results of the certification status change to the history management module (250) and the authority management module (260). This linkage structure can provide the effect of managing the operator's qualification maintenance and certification status quickly and accurately.

[0181] FIG. 4 shows an example of an electronic device according to one embodiment.

[0182] Referring to FIG. 4, the electronic device (110) may include memory (410) and a processor (430).

[0183] The memory (410) can store instructions (e.g., programs) executable by the processor (430). For example, the instructions may include instructions for executing the operation of the processor (430) and / or the operation of each component of the processor (430).

[0184] The memory (410) can be implemented as a volatile memory device or a non-volatile memory device.

[0185] Volatile memory devices can be implemented as DRAM (dynamic random access memory), SRAM (static random access memory), T-RAM (thyristor RAM), Z-RAM (zero capacitor RAM), or TTRAM (Twin Transistor RAM).

[0186] Non-volatile memory devices can be implemented as EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory, MRAM (Magnetic RAM), Spin-Transfer Torque (STT)-MRAM, Conductive Bridging RAM (CBRAM), FeRAM (Ferroelectric RAM), PRAM (Phase change RAM), Resistive RAM (RRAM), Nanotube RRAM, Polymer RAM (PoRAM), Nano Floating Gate Memory (NFGM), holographic memory, Molecular Electronic Memory Device, or Insulator Resistance Change Memory.

[0187] The processor (430) can process data stored in memory (410). The processor (430) can execute computer-readable code (e.g., software) stored in memory (410) and instructions triggered by the processor (430).

[0188] The processor (430) may be a data processing device implemented in hardware having a circuit having a physical structure for executing desired operations. For example, the desired operations may include code or instructions included in a program.

[0189] For example, a data processing device implemented in hardware may include a microprocessor, a central processing unit, a processor core, a multi-core processor, a multiprocessor, an Application-Specific Integrated Circuit (ASIC), and a Field Programmable Gate Array (FPGA).

[0190] The processor (430) can cause the electronic device (110) to perform one or more operations by executing code and / or instructions stored in memory (410). The operations performed by the electronic device (110) have been described in detail with reference to FIGS. 1 to 3, so redundant descriptions will be omitted below.

[0192] Although the embodiments have been described above with reference to the limited drawings, those skilled in the art can apply various technical modifications and variations based thereon. For example, appropriate results may be achieved even if the described techniques are performed in a different order than described, and / or if the components of the described system, structure, device, circuit, etc. are combined or assembled in a form different from described, or replaced or substituted by other components or equivalents. Therefore, other implementations, other embodiments, and equivalents to the claims below also fall within the scope of the claims.

Claims

Claim 1 A method for certifying AI robot operators by grade, comprising: a step of determining a target certification grade for operating the AI ​​robot by comparing certification application information regarding AI robot operation received from a user terminal with minimum satisfaction criteria values ​​for each grade pre-stored in a certification requirement management database to determine whether the user meets the qualification requirements; a step of receiving ethics grade data and autonomy grade data pre-assigned to the AI ​​robot, and generating an evaluation scenario corresponding to the target certification grade such that higher autonomy grades include emergency situations and ethical dilemmas of higher difficulty - wherein the evaluation scenario includes practical simulation scenarios including emergency stop, collision avoidance, and malfunction response provided in a virtual reality (VR) environment, and an ethical dilemma judgment scenario -; a step of providing the generated evaluation scenario to the user terminal and inputting response data and simulation operation data received from the user terminal into an AI-based competency evaluation engine, wherein the user's response time, operation accuracy, and procedural compliance are extracted from the simulation operation data and scored to obtain an evaluation result; a step of determining pass or fail based on the evaluation result and assigning a final certification grade to the user terminal, and the identification information of the user assigned the final certification grade and the AI A step of generating mapping data by mapping the unique identification number of a robot - the generation of the mapping data is achieved by querying limit values ​​for the maximum number of robots allowed for simultaneous mapping, robot type, and autonomy level, which are pre-set in correspondence with the final certification level, and rejecting mapping requests that exceed the limit values ​​-; a step of recording an authentication history including the mapping data and the fact of granting the final certification level on a blockchain network;A method comprising the step of, when safety accident occurrence data regarding the user is received from an external safety accident history server linked thereto, changing the operating authority of the AI ​​robot according to the final authentication grade to temporary suspension or cancellation based on the mapping data, and creating and distributing the state of the changed operating authority as a new block on the blockchain network. Claim 2 The method according to claim 1, wherein the step of generating the evaluation scenario comprises: a step of querying a mapping table in which the scenario complexity corresponding to each grade of the ethics grade data is predefined—wherein the ethics grade data represents any one of grades A through E—; and a step of generating the evaluation scenario differentially according to the queryed scenario complexity, such that different practical and ethical situations are reflected for each type of AI robot, which is any one of a humanoid robot, an embodied AI-based service robot, an industrial robot, a medical robot, and a military robot. Claim 3 A method according to claim 1, further comprising the step of, after the final certification level is granted, monitoring the user's certification validity period and renewing the final certification level when pre-set continuing education completion data is received—wherein the certification validity period is pre-set differently for each certification level, and if the continuing education completion data is not received before the expiration of the certification validity period, the final certification level is automatically suspended. Claim 4 A method comprising: the step of recording the certification history on a blockchain network, wherein, in a state where multiple versions of the minimum satisfaction criteria value for each grade are stored in parallel in the certification requirement management database along with the application date and change history of each version, a requirement version identifier representing the version applied at the time of granting the final certification grade among the multiple versions is recorded together in the certification history; and the step of ensuring the integrity of the certification history by creating and distributing the corresponding event data as a new block on the blockchain network whenever any one of the history events of new acquisition, renewal, suspension, and cancellation of the final certification grade occurs, wherein the new block is distributedly stored after undergoing integrity verification by the consensus mechanism of the blockchain network, and the event data includes the requirement version identifier applied at the time of occurrence of the corresponding history event. Claim 5 A method according to claim 4, further comprising: a step of, when the certification requirement management database detects a change event of a statute or regulation related to the minimum satisfaction standard value by grade, creating a new version according to the changed statute or regulation and storing it in parallel with the existing version; and, when a request for inquiry regarding past certification history recorded on the blockchain network is received, a step of verifying the validity of the certification based on the minimum satisfaction standard value by grade of the version applied at the time the certification history was created by referring to the requirement version identifier included in the past certification history. Claim 6 A method according to claim 1, wherein the step of generating the mapping data comprises the step of granting operational authority by differentially restricting the maximum allowable number, robot type, and autonomy grade of the AI ​​robots that are allowed to be simultaneously mapped with the identification information based on the user’s final certification grade—wherein the final certification grade is any one of the basic certification grade, professional certification grade, and highest certification grade, wherein the basic certification grade allows the operation of 1 to 5 robots upon completion of basic robot operation training and possession of basic safety management qualifications, the professional certification grade allows the operation of 6 to 50 robots upon completion of a professional training course and operational experience of 1 year or more after obtaining the basic certification grade, and the highest certification grade allows the operation of more than 50 robots or participation in national projects upon completion of a master course and operational experience of 3 years or more after obtaining the professional certification grade. Claim 7 A method according to claim 1, wherein the step of changing the operating authority to temporary suspension or cancellation comprises: a step of extracting a unique identification number of the AI ​​robot that caused the accident from the safety accident occurrence data; a step of specifying the identification information of a user that was mapped to the extracted unique identification number at the time the accident occurred by referring to the mapping data recorded in the blockchain network; and a step of changing the operating authority of the final authentication grade corresponding to the specified user identification information to temporary suspension or cancellation.

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