Electronic device, operation method, and system for controlling motor drive using artificial intelligence

Abstract

An electronic device according to various embodiments includes at least one motor, a communication device for transmitting and receiving signals, at least one processor, and a storage device for storing instructions, wherein the instructions are executed individually or collectively by at least one processor, and the electronic device obtains a request message including a target task from a user, obtains a control signal to control the driving of at least one motor through an artificial intelligence agent based on the request message and driving status information for at least one motor, and transmits the control signal to at least one motor so that at least one motor is driven based on the control signal, and the driving status information may be obtained based on a risk prediction model learned to diagnose the driving status of at least one motor. In addition to this, various other embodiments identified through the specification are possible.

Description

Technology Field

[0001] The various embodiments disclosed in this document relate to an electronic device that controls the driving of a motor using artificial intelligence, a method of operation thereof, and a system. Background Technology

[0002] Various types of equipment are used across diverse industrial sectors. Meanwhile, the stable operation of such equipment directly impacts process efficiency and safety; therefore, it is crucial to operate these devices reliably according to target workloads.

[0003] Examples of various equipment used in these industrial fields include pump systems, compressor systems, generator systems, motor-driven valve systems, rotating equipment, and motor systems. In particular, among these, the motor-driven valve, a type of motor-driven valve system, is a device that opens and closes a valve using an electric motor and is primarily used in various fields such as industrial facilities, power plants, chemical processes, and water treatment systems.

[0004] For the various pieces of equipment mentioned above, operating conditions can be varied according to the target workload. To ensure stable operation over a long period, it is crucial that the equipment is operated at an appropriate level (e.g., speed, frequency, etc.) based on the target workload. For instance, in the case of motor-driven valve systems, operating conditions can be adjusted according to the target workload (production volume). To ensure stable long-term use, it is important to ensure that the motor does not over-operate and runs only as much as necessary to meet the target workload. In particular, if the motor is over-operated within a short period based solely on the target workload without considering the current state of the motor, motor failure may occur, leading to unforeseen costs and time losses. For instance, if a motor-driven valve fails to operate properly, significant damage may result, such as process shutdowns, production stoppages, and losses of manpower and materials.

[0005] The information described above may be provided as related art for the purpose of aiding understanding of the present disclosure. No claim or determination is made as to whether any of the foregoing may be applied as prior art in relation to the present disclosure. The problem to be solved

[0006] Various types of equipment are used in industrial environments, and automatically controlling them can enhance user convenience. However, there is a challenge in controlling diverse equipment to meet target workloads, which requires appropriately setting operating conditions based on the equipment's status. In particular, due to the characteristics of industrial environments, it is difficult to operate equipment in an optimal state while considering its condition. For instance, issues such as high temperatures, strong vibrations, and chemical exposure in industrial environments make it difficult to interpret sensor data acquired during equipment operation. Consequently, it becomes challenging to identify equipment failures, making it difficult to operate equipment while considering conditions related to such failures.

[0007] Accordingly, the present disclosure aims to provide an electronic device that automatically controls the operation of equipment (e.g., motor, motor-driven valve) using an artificial intelligence agent in response to a user's request.

[0008] The present disclosure aims to provide an electronic device that identifies the user's intention based on the user's request, sets operating conditions of said equipment, and controls operation to provide appropriate results corresponding to the user's intention.

[0009] The present disclosure aims to provide an electronic device that, in setting and controlling operating conditions of equipment based on user requests, sets operating conditions by considering operating conditions related to equipment failure, thereby preventing equipment failure and enabling stable use of the equipment for a long period, and enabling the achievement of a target workload without equipment failure.

[0010] The present disclosure aims to provide a device that identifies the operating state of equipment (e.g., normal, abnormal, dangerous, faulty) and determines the related cause by utilizing an artificial intelligence model that determines the operating state of equipment based on sensing data acquired during the operation of equipment in order to obtain information on the operating state of equipment.

[0011] The problems that this disclosure aims to solve are not limited to those mentioned above, and other unmentioned problems will be clearly understood by a person skilled in the art from the description below. means of solving the problem

[0012] An electronic device according to various embodiments includes at least one motor, a communication device for transmitting and receiving signals, at least one processor, and a storage device for storing instructions, wherein the instructions are executed individually or collectively by at least one processor, and the electronic device obtains a request message including a target task from a user, obtains a control signal to control the driving of at least one motor through an artificial intelligence agent based on the request message and driving status information for at least one motor, and transmits the control signal to at least one motor so that at least one motor is driven based on the control signal, and the driving status information may be obtained based on a risk prediction model learned to diagnose the driving status of at least one motor.

[0013] According to various embodiments, a method of operation of an electronic device for controlling the driving of at least one motor comprises the operation of obtaining a request message including a target task from a user, the operation of obtaining a control signal to control the driving of at least one motor through an artificial intelligence agent based on the request message and driving state information for at least one motor, and the operation of transmitting a control signal to at least one motor so that at least one motor is driven based on the control signal, wherein the driving state information may be obtained based on a risk prediction model learned to diagnose the driving state of at least one motor.

[0014] According to various embodiments, a computer-readable recording medium having a program can record a program for executing the following operations: obtaining a request message including a target task from a user; obtaining driving state information through a risk prediction model learned to diagnose the driving state of at least one connected motor; obtaining a control signal to control the driving of at least one motor through an artificial intelligence agent based on the request message and driving state information; and transmitting a control signal to at least one motor so that at least one motor is driven based on the control signal. Effects of the invention

[0015] An electronic device according to the various embodiments disclosed in this document can detect changes in sensing data (e.g., vibration, sound, rotation, temperature, cracks, pressure, etc.) obtained when a specific piece of equipment (e.g., a motor) is driven, diagnose the operating state of the equipment, and control the equipment to operate optimally based on a target workload and the operating state of the equipment in accordance with a user request. Therefore, when a user performs a task using a specific piece of equipment, the task can be performed smoothly by considering the status of the equipment based solely on the task request, thereby preventing failure of the specific piece of equipment, reducing repair costs, and increasing operating stability.

[0016] That is, according to various embodiments, the electronic device can increase user convenience by automatically controlling the operation of equipment (e.g., motor, motor-driven valve) using an artificial intelligence agent in response to a user's request.

[0017] That is, according to various embodiments, the electronic device can continuously optimize driving conditions by reflecting the state of at least one motor in real time based on the use of an artificial intelligence agent that makes specific decisions for efficiently controlling the driving of the motor, thereby enabling the efficiency and stability of at least one motor to be maintained while achieving a target workload.

[0018] According to various embodiments, the electronic device identifies the user's intention based on the user's request, sets operating conditions of the equipment and controls the operation to provide appropriate results corresponding to the user's intention, thereby enabling the achievement of a target workload while preventing equipment failure.

[0019] According to various embodiments, when an electronic device sets and controls the operating conditions of equipment based on a user request, by setting the operating conditions while considering the operating state related to the failure of the equipment, it is possible to prevent equipment failure, enable stable use of the equipment for a long period of time, and achieve the target workload without equipment failure.

[0020] According to various embodiments, an electronic device can provide predictive maintenance of equipment by using an artificial intelligence model that determines the operating state of the equipment based on sensing data acquired during the operation of the equipment to obtain information on the operating state of the equipment, identifying the operating state of the equipment (e.g., normal, abnormal, dangerous, failure), and determining the relevant cause.

[0021] According to various embodiments, the electronic device can diagnose equipment failure in advance and perform appropriate corresponding measures (e.g., control of equipment operation) to reduce damage caused by equipment failure.

[0022] In addition, various effects that can be identified directly or indirectly through this document may be provided. Brief explanation of the drawing

[0023] Figure 1 is a block diagram of a drive control system for controlling the drive of a motor. Figure 2 is a block diagram of an electronic device that controls the driving of a motor using an artificial intelligence agent. Figure 3 is a flowchart illustrating how an electronic device controls the driving of a motor. Figure 4 is a block diagram illustrating the functions provided through an electronic device. FIG. 5 is a diagram illustrating the acquisition of multidimensionally transformed sensing data. Figure 6 is a flowchart illustrating a method for generating a learned risk prediction model to determine the driving state of a motor in an electronic device. Figure 7 is a diagram illustrating a training data set for training a risk prediction model. FIG. 8 is a flowchart showing how the driving of a motor is controlled according to a user's request. In relation to the description of the drawings, the same or similar reference numerals may be used for identical or similar components. Specific details for implementing the invention

[0024] Specific structural or functional descriptions regarding various embodiments are provided merely for the purpose of explaining the various embodiments, and the various embodiments may be implemented in various forms and should not be interpreted as being limited to the embodiments described in this specification or application.

[0025] Since various embodiments may be subject to various modifications and may take various forms, various embodiments are illustrated in the drawings and described in detail in this specification or application. However, the details disclosed in the drawings are not intended to specify or limit the various embodiments, and should be understood to include all modifications, equivalents, and substitutions that fall within the spirit and technical scope of the various embodiments.

[0026] Terms such as "first" and / or "second" may be used to describe various components, but said components shall not be limited by said terms. For the sole purpose of distinguishing one component from another, for example, without departing from the scope of rights according to the concept of the present disclosure, the first component may be named the second component, and similarly, the second component may be named the first component.

[0027] When it is stated that one component is "connected" or "connected" to another component, it should be understood that while it may be directly connected or connected to that other component, there may also be other components in between. Conversely, when it is stated that one component is "directly connected" or "directly connected" to another component, it should be understood that there are no other components in between. Other expressions describing the relationship between components, such as "between" and "exactly between," or "adjacent to" and "directly adjacent to," should be interpreted in the same way.

[0028] The terms used herein are used merely to describe specific embodiments and are not intended to limit various embodiments. Singular expressions include plural expressions 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.

[0029] 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 to which this disclosure pertains. 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.

[0030] The present disclosure will be described in detail below by explaining preferred embodiments of the present disclosure with reference to the attached drawings. Identical reference numerals in each drawing indicate identical components.

[0032] Figure 1 is a block diagram of a drive control system for controlling the drive of a motor.

[0033] Referring to FIG. 1, a drive control system for controlling the drive of a motor is illustrated. According to various embodiments, the drive control system may include a motor (110), an electronic device (100), and a sensor device (120). However, the drive control system may be implemented to include more devices and / or fewer devices, not limited to the described and / or illustrated examples. Additionally, at least two of the devices illustrated in the drive control system may be implemented as a single device.

[0034] Meanwhile, the drive control system disclosed in this document is described as controlling the drive of a motor. However, this is merely for convenience of explanation, and the system may be applied to control the drive of various equipment (e.g., motor-driven valves) that are driven in the same or similar manner as the motor. Additionally, for convenience of explanation, the motor (110) is described as a single motor (110) in the text, but the embodiment disclosed in this document may be applied equally to at least one motor (110).

[0035] According to various embodiments, the electronic device (100), the motor (110), and the sensor device (120) may be connected electrically, mechanically, or via wired or wireless communication. According to various embodiments, the electronic device (100), the motor (110), and the sensor device (120) may be connected indirectly. For example, the electronic device (100) may control the operation of the motor by transmitting a control signal related to controlling the operation of the motor to the motor (110). For example, the electronic device (100) may obtain sensing data obtained through the sensor device (120) through a data linkage device (e.g., a data linkage module, a hub device).

[0036] According to various embodiments, the electronic device (100) can control the operation of the motor (110) based on a request message obtained from a user. For example, the electronic device (100) can obtain a request message including a target task from a user, determine conditions related to the operation of the motor (110) (e.g., motor operation time, motor operation speed (frequency), etc.) based on the request message, and control the motor (110) to operate according to the conditions.

[0037] According to various embodiments, when the electronic device (100) generates a control signal for driving the motor (110), it may use a request message obtained from a user and driving status information of the motor (110). For example, the electronic device (100) may use an artificial intelligence agent (e.g., an artificial intelligence agent (403) described later with reference to FIG. 4) to generate a driving control signal for the motor (110) that takes into account the driving status information of the motor (110) in order to achieve a target task according to a request message obtained from a user.

[0038] According to various embodiments, the electronic device (100) can obtain driving state information of the motor (110) based on sensing data obtained through the sensor device (120) in relation to the driving of the motor (110). For example, the electronic device (100) can determine whether the motor (110) is in a normal state, an abnormal driving state, or a dangerous driving state based on sensing data related to the driving of the motor (110) obtained through the sensor device (120). Accordingly, the electronic device (100) can determine the driving conditions of the motor (110) for performing a target task by considering the driving state information of the motor (110).

[0039] According to various embodiments, the electronic device (100) may use a risk prediction model, which is an artificial intelligence model, to obtain driving state information of the motor (110). For example, the electronic device (100) may obtain a risk prediction model trained to determine the driving state based on sensing data related to the driving of the motor (110), and may determine the driving state of the motor (110) using the risk prediction model. For example, the electronic device (100) may obtain a training data set by processing the sensing data obtained during the driving of the motor (110) and the generated (obtained) failure data during a specified period, and may generate a risk prediction model trained to determine the driving state of the motor (110) based on the training data set. In addition, the electronic device (100) may obtain diagnostic data by processing the sensing data obtained through the sensor device (120) obtained during the driving of the motor (110), and may obtain driving state information by inputting the diagnostic data into the risk prediction model.

[0040] According to various embodiments, the sensor device (120) may represent a sensor configured to acquire a signal related to the driving state of the motor (110). Although the sensor device (120) is described as a single device for convenience of explanation, it may be composed of a plurality of sensor devices (120) or may be composed of a single sensor device (120) including a plurality of sensors. For example, the sensor device (120) may include at least one of a vibration measuring sensor, an acoustic measuring sensor, a rotational speed measuring sensor, a temperature sensing sensor, a crack sensing sensor, or a pressure sensing sensor for measuring vibration of the motor (110). Specifically, it may include an accelerometer, a speedometer, a pi-conducting sensor, a laser vibration sensor, a gyroscope, etc. for measuring vibration. Specifically, as a sensor for measuring acoustics, it may include an acoustic sensor, a wave sensor, a sound pressure sensor, a microphone, etc. Specifically, as a sensor for measuring rotational speed, it may include an encoder, a magnetic sensing sensor, a light sensing sensor, an electronic accelerometer, etc. Specifically, sensors for measuring heat may include infrared thermal imaging cameras, infrared temperature sensors, thermocouples, thermal phone sensors, thermometers, etc. Specifically, sensors for detecting cracks may include stress gauges, ultrasonic sensors, current sensors, vibration sensors, etc. Specifically, sensors for measuring pressure may include pressure sensors, stress gauges, fluid pressure sensors, pressure transmitters, etc.

[0041] According to various embodiments, the sensor device (120) is provided (e.g., in contact, mounted, attached, etc.) in association with equipment (e.g., motor (110)) for which the operating state is to be determined, so as to acquire sensing data during the operation of the motor (110). According to one embodiment, the sensor device (120) can acquire information regarding vibration, sound, rotation, temperature, cracks, pressure, etc. occurring during the operation of the motor (110). In addition, the sensor device (120) can acquire various sensing data that can be acquired during the operation of at least one motor (110) through various sensors, not limited to the examples listed above.

[0042] According to various embodiments, the sensor device (120) can transmit the acquired sensing data to the electronic device (100). For example, the sensor device (120) can transmit the acquired sensing data to the electronic device (100) by establishing a direct (e.g., wired) communication channel or a wireless communication channel.

[0043] According to various embodiments, the sensor device (120) can store various sensing data obtained during the operation of the motor (110). For example, the sensor device (120) can accumulate and store sensing data obtained by providing it to equipment of the same type. The electronic device (100) can use the sensing data obtained through the sensor device (120) to obtain information on the operating status of the motor (110) or to train (or retrain) a risk prediction model.

[0044] Figure 2 is a block diagram of an electronic device that controls the driving of a motor using an artificial intelligence agent.

[0045] Referring to FIG. 2, the electronic device (100) may include a processor (210), a storage device (220), a communication device (230), an input device (240), and / or an output device (250). The listed components may be operatively or electrically connected to each other. As an example, some of the components of the electronic device (100) shown in FIG. 2 may be modified, deleted, or added. For example, the electronic device (100) may establish a communication connection with the input device (240) and / or output device (250), which are configured as separate hardware, to transmit and receive data, thereby providing the same function as the function described below.

[0046] According to various embodiments, the electronic device (100) may include a processor (210). The processor (210) may include hardware for executing instructions, such as instructions that constitute a computer program. For example, to execute instructions, the processor (210) may retrieve (or fetch) instructions from an internal register, an internal cache, a storage device (220) (including memory), decode and execute the instructions, and then store the result in the internal register, the internal cache, and the storage device (220).

[0047] In various embodiments, the processor (210) can execute software (e.g., a computer program) to control at least one other component (e.g., a hardware or software component) of the electronic device (100) connected to the processor (210) and can perform various data processing or operations. According to various embodiments, as at least part of the data processing or operations, the processor (210) can store instructions or data received from another component (e.g., a communication device (230)) in volatile memory, process the instructions or data stored in volatile memory, and store the resulting data in non-volatile memory.

[0048] According to various embodiments, the processor (210) may include at least one of a central processing unit (CPU), a graphics processing unit (GPU), a microcontroller unit (MCU), a sensor hub, a supplementary processor, a communication processor, an application processor, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or a Neural Processing Unit (NPU), and may have multiple cores.

[0049] According to various embodiments, the processor (210) (e.g., a neural network processing unit) may include a hardware structure specialized for processing an artificial intelligence model. The artificial intelligence model may be generated through machine learning. The learning algorithm may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but is not limited to the examples described above. The artificial intelligence model may include a plurality of artificial neural network layers. The artificial neural network may be a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), deep Q-networks, or a combination of two or more of the above, but is not limited to the examples described above. In addition to the hardware structure, the artificial intelligence model may include a software structure, either additionally or substantially.

[0050] According to various embodiments, the electronic device (100) may obtain a request message from a user that includes a target task. For example, the processor (210) may obtain a request message that includes information about a target task to be performed by controlling at least one motor (e.g., the motor (110) of FIG. 1) connected to the electronic device (100). According to various embodiments, the request message may include various additional task conditions related to the target task (e.g., temperature, time, working environment, noise level, etc.).

[0051] According to various embodiments, the electronic device (100) may obtain a control signal to control the operation of the at least one motor (110) through an artificial intelligence agent based on the request message and driving status information for the at least one motor (110). For example, the processor (210) may analyze the request message to determine the user's intention. For example, the processor (210) may generate a control signal for the at least one motor (110) based on the user's intention and driving status information for the at least one motor (110) in order to provide a result that matches the user's intention.

[0052] According to various embodiments, the electronic device (100) may use the artificial intelligence agent to generate a control signal to provide a result suitable for the user's intention. The artificial intelligence agent is an autonomous software system designed to optimize the driving control of the at least one motor (110), and may include an artificial intelligence system that performs various tasks to maintain the performance of the motor (110) while achieving a given goal task. The functions provided through the artificial intelligence agent will be described later with reference to FIG. 4.

[0053] According to various embodiments, the electronic device (100) can obtain the driving state information using a risk prediction model. For example, the processor (210) can obtain driving state information of a target device (e.g., the motor (110) of FIG. 1) using sensing data. For example, the processor (210) can use a risk prediction model, which is an artificial intelligence model trained to determine the driving state of the motor (110) (110). The processor (210) can obtain driving state information by inputting sensing data obtained through a sensor device (e.g., the sensor device (120) of FIG. 1) into the risk prediction model. At this time, the processor (210) can obtain driving state information of the motor (110) by inputting data (e.g., diagnostic data) in which the sensing data has been converted into a multidimensional form into the risk prediction model. According to one embodiment, the sensing data may include at least one of vibration, sound, rotational speed, temperature, cracks, and pressure occurring during the operation of the target device (e.g., the motor (110)).

[0054] According to various embodiments, the electronic device (100) can control the at least one motor (110) so that the at least one motor (110) is driven based on the control signal. For example, a processor (210) can transmit the control signal to the at least one motor (110) so that the at least one motor (110) is driven based on the control signal.

[0055] According to various embodiments, the electronic device (100) may include a storage device (220). According to various embodiments, the storage device (220) may include a mass storage for data or commands. For example, the storage device (220) may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, an optical-magnetic disk, a magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. In various embodiments, the storage device (220) may include a non-volatile, solid-state memory, or read-only memory (ROM). Such ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically changeable ROM (EAROM), or a flash memory, or a combination of two or more of these.

[0056] Although the present disclosure describes and represents a specific storage device, the present disclosure considers any suitable storage device, and the storage device (220) may be inside or outside the electronic device (100).

[0057] According to various embodiments, the processor (210) may store in the storage device (220) a module (e.g., an artificial intelligence agent (artificial intelligence system)) that provides functions related to controlling the operation of the target equipment (e.g., a motor (110)) described with reference to FIG. 4. Additionally, the storage device (220) may store a risk prediction model learned to be executed through the artificial intelligence agent to obtain information on the operation status of the motor (110).

[0058] According to various embodiments, the processor (210) may execute operations or data processing regarding the control and / or communication of at least one other component of the electronic device (200) using instructions stored in the storage device (220). According to various embodiments, the storage device (220) may store various data used by at least one component of the electronic device (200) (e.g., the processor (210)). The data may include, for example, software (e.g., a program) and input data or output data for related instructions.

[0059] According to various embodiments, the program may be stored as software in the storage device (220) and may include, for example, an operating system, middleware, or an application. According to various embodiments, the storage device (220) may store instructions that allow the processor (210) to process data or control components of the electronic device (200) to perform operations of the electronic device (200) at execution. The instructions may include code generated by a compiler or code that can be executed by an interpreter.

[0060] According to various embodiments, the storage device (220) can store various information obtained through the processor (210). For example, the storage device (220) can store sensing data of a target device (e.g., motor (110)) obtained from at least one sensor device (120). Additionally, the storage device (220) can store various artificial intelligence models. For example, the storage device (220) can store various artificial intelligence models necessary to provide the functions of the present disclosure. Furthermore, the storage device (220) can store information processed by the processor (210), such as sensing data obtained through the processor (210), a training data set and a label data set for training the risk prediction model, and a failure data set related to the operation of at least one motor (110). Additionally, the storage device (220) can store information output from the artificial intelligence agent. For example, the storage device (220) can store control signals, output information and / or driving status information for at least one motor (110) related to the output from the artificial intelligence agent.

[0061] According to various embodiments, the electronic device (100) may include a communication device (230). In various embodiments, the communication device (230) may support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between the electronic device (200) and an external electronic device (e.g., the motor (110) and sensor device (120) of FIG. 1), and the performance of communication through the established communication channel. The communication device (230) may include one or more communication processors that operate independently of the processor (210) and support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication device (230) may include a wireless communication module (e.g., a cellular communication module, a short-range wireless communication module, or a GNSS (global navigation satellite system) communication module) or a wired communication module (e.g., a LAN (local area network) communication module, or a power line communication module). The above various types of communication modules may be integrated into a single component (e.g., a single chip) or implemented as multiple separate components (e.g., multiple chips).

[0062] According to various embodiments, the electronic device (100) can transmit and receive various data with various external devices through the communication device (230). Additionally, the electronic device (100) can store the acquired data in the storage device (220). For example, the electronic device (100) can obtain a request message from a user through the communication device (230). For example, the electronic device (100) can obtain a request message from a user through the communication device (230). For example, the electronic device (100) can obtain sensing data of a target device (e.g., a motor (110)) from a sensor device (120) through the communication device (230). Additionally, the communication device (230) can transmit a control signal to the at least one motor (110) to control the operation of the at least one motor (110) obtained through the electronic device (100).

[0063] According to various embodiments, the electronic device (100) may include an input device (240). For example, the electronic device (100) may include at least one of various types of input devices (240) for obtaining a request message from a user. For example, the input device (240) may include at least one of a microphone configured to obtain a voice request message from a user, a touch screen configured to obtain a touch input request message from a user, a keyboard configured to obtain a text command request message from a user, and / or a camera configured to obtain a video request message from a user.

[0064] According to various embodiments, the electronic device (100) can determine the user's intention by analyzing a request message obtained from an input device (240) through an artificial intelligence agent.

[0065] According to various embodiments, the electronic device (100) can establish a communication connection through a physically separated input device (240) and a communication device (230) and obtain various types of request messages listed above.

[0066] According to various embodiments, the electronic device (100) may include an output device (250). For example, the electronic device (100) may include at least one of various types of output devices (250) configured to provide a response to a request message to a user. For example, the output device (250) may include at least one of a display configured to provide output information to a user via a screen, a speaker configured to provide output information to a user via an acoustic signal, an LED indicator configured to provide output information to a user via a visual element, and / or a vibration motor configured to provide output information to a user via vibration.

[0067] According to various embodiments, the electronic device (100) may provide output information including at least one of status information, drive control information, maintenance scheduling, and resource management for at least one motor (110) in response to a request message through the output device (250).

[0068] According to various embodiments, the electronic device (100) can establish a communication connection through a physically separated output device (250) and a communication device (230), and transmit a control signal to control the output device (250) so that output information is provided in the various types listed above.

[0070] Figure 3 is a flowchart illustrating how an electronic device controls the driving of a motor.

[0071] Figure 4 is a block diagram illustrating the functions provided through an electronic device.

[0072] Each of the operations described below may be performed in combination with one another. Additionally, among the operations described below, an operation by an electronic device (100) (e.g., the electronic device (100) of FIG. 1) may mean an operation by a processor (210) of the electronic device (100).

[0073] In addition, the term "information" described below may be interpreted as having the meaning of "data" or "signal," and the term "data" may be understood as a concept that includes both analog data and digital data.

[0074] According to various embodiments, the operations illustrated in FIG. 3 may be performed in various orders, not limited to the order illustrated. Additionally, according to various embodiments, more operations may be performed than those illustrated in FIG. 3, or at least one fewer operation may be performed.

[0075] Referring to FIG. 4, the electronic device (100) may utilize hardware and / or software modules (400) to support functions related to controlling the operation of equipment (e.g., a motor-driven valve). For example, the processor (210) may drive at least one of a UI / UX (401), an artificial intelligence agent (403), motor resource management (405), motor drive control (407), a risk prediction model (409), and a sensor measurement (411) by executing instructions stored in a storage device (220). In various embodiments, software modules different from those shown in FIG. 4 may be implemented. For example, at least two of each element may be integrated into one, or one configuration may be divided into two or more. Additionally, the hardware and software modules may share a single function to improve operational performance. For example, the electronic device (100) may include both an encoder implemented in hardware and an encoder implemented in a software module.

[0076] Hereinafter, for convenience of explanation, the present disclosure describes the case where the equipment has at least one motor (110). However, the present disclosure is not limited to the examples described herein, and the operation of the equipment can be controlled through similar functions for various types of equipment.

[0077] According to various embodiments, in operation 301, the electronic device (100) may obtain a request message including a target task from a user. For example, the electronic device (100) may obtain a request message including a target task from a user through an input device (240). According to one embodiment, the request message may include additional task conditions (e.g., temperature, time, working environment, noise level, etc.) related to the driving of the at least one motor (110).

[0078] According to various embodiments, the electronic device (100) may receive a request message through a UI / UX (401) module. The UI / UX (401) module is a software module configured to allow a user to input and verify a request message, such as a target task and / or additional task conditions, and may include a module that provides interaction between the user and the electronic device (100). According to various embodiments, the UI / UX (401) module may be designed to receive various control options, such as a target task, task speed, task temperature, task time, and task noise, through a user-friendly interface. For example, the user may set and verify the amount of work through touch input, voice commands, text input, video input, etc., via the UI / UX (401) module. Accordingly, the UI / UX (401) module may include a software module for controlling an input device (240).

[0079] According to various embodiments, in operation 303, the electronic device (100) may obtain a control signal through an artificial intelligence agent (403) based on a request message and driving status information for at least one motor (e.g., the motor (110) of FIG. 1). For example, the electronic device (100) may use the artificial intelligence agent to generate a control signal for at least one motor (110) to achieve a work target amount included in the request message.

[0080] According to various embodiments, the electronic device (100) may use various information to analyze the user's intent included in the request message based on the request message and to provide an optimal response to the user's intent. For example, the electronic device (100) may generate the control signal to provide an optimal response to the user's intent included in the request message based on driving state information for at least one motor (110). For example, the electronic device (100) may generate the control signal for the at least one motor (110) to enable the achievement of the target task according to the driving state information and the user's target task and / or task additional conditions based on the driving state information and the task additional conditions.

[0081] According to various embodiments, the electronic device (100) can generate the control signal using specification information for each of at least one motor (110).

[0082] According to various embodiments, the electronic device (100) may use a risk prediction model (409) to obtain the driving state information. For example, the electronic device (100) may use a risk prediction model (409), which is an artificial intelligence model trained to receive data related to the driving of the at least one motor (110) and output driving state information for the at least one motor (110). For example, the electronic device (100) may obtain sensing data (e.g., diagnostic data) for the at least one motor (110) by controlling a sensor device (sensor device (120) of FIG. 1) through a sensor measurement (411) module, and obtain driving state information by inputting the obtained sensing data into the risk prediction model (409).

[0083] According to various embodiments, a description of the configuration for obtaining a risk prediction model (409) will be described later with reference to FIGS. 6 and FIGS. 7.

[0084] According to various embodiments, the electronic device (100) obtaining a control signal using driving state information for at least one motor (110) may be advantageous for preventing failure of at least one motor (110) and achieving requested additional work conditions. For example, if the driving state information of at least one motor (110) indicates a first state indicating a normal driving state, the electronic device (100) may drive at a higher driving speed to achieve a target task within a short time. For example, if the driving state of at least one motor (110) indicates a second state indicating an abnormal driving state, the electronic device (100) may generate a control signal to request the user to lower the driving speed, adjust the temperature of the driving environment, or adjust the target task according to the cause of the abnormality. That is, based on the driving state information, the electronic device (100) may determine the driving conditions of the motor (110) and generate a control signal so that a target task can be achieved without failure of at least one motor (110). For example, if the driving state of at least one motor (110) is a third state indicating a dangerous state, the electronic device (100) can generate a control signal to stop the driving of the motor (110) because there is a risk of failure when driving at least one motor (110) according to the target task, and can provide information to the user as a dangerous event occurs.

[0085] However, not limited to the examples described above, the electronic device (100) can diagnose the driving state of at least one motor (110) in various states through a risk prediction model (409) and take appropriate measures in response.

[0086] According to various embodiments, in operation 305, the electronic device (100) may transmit a control signal to drive at least one motor (110) based on the control signal. For example, the electronic device (100) may transmit a control signal for driving the motor (110), such as the driving time of each of the at least one motor (110) and the driving speed of each of the at least one motor (110), to at least one motor (110).

[0087] For example, the electronic device (100) can transmit a generated control signal to at least one motor (110) through a motor drive control (407) module. The at least one motor (110) can be driven based on the control signal.

[0088] According to various embodiments, the control signal may include the control of a valve. For example, if the motor (110) is a motor-driven valve, the electronic device (100) may generate a control signal that includes the control signal of the valve. In this case, the motor drive control (407) module may include commands for controlling the motor-driven valve.

[0089] According to various embodiments, the electronic device (100) may generate output information related to each of the at least one motor (110) based on the driving state information and / or the control signal. For example, the electronic device (100) may generate the output information to provide a response to the user's request message.

[0090] According to various embodiments, the electronic device (100) may provide output information to a user or user device through a UI / UX (401) module. The UI / UX (401) module is a software module configured to provide output information to a user (or user device), including at least one of status information, drive control information, maintenance scheduling, and resource management for each of at least one motor (110), and may include a module that provides interaction between the user and the electronic device (100). According to various embodiments, the UI / UX (401) module may be designed to provide output information in various ways through a user-friendly interface. For example, the user may receive output information through the UI / UX (401) module in various ways, such as a visual screen, sound signal, or vibration. For example, the electronic device (100) may provide the current driving status of each motor (110) or the next maintenance time graphically through the UI / UX (401) module, or provide a notification to the user when resource management is required. Additionally, the UI / UX (401) module may include interaction elements that allow the user to request additional details about specific information or adjust driving settings, thereby improving the user experience and increasing the efficiency of the system. Accordingly, the UI / UX (401) module may include a software module for controlling the output device (250).

[0091] According to various embodiments, the electronic device (100) may utilize a motor resource management (405) module in generating output information. For example, the motor resource management (405) module is a motor drive and resource management software module provided as part of an Enterprise Resource Planning (ERP) system, and can provide functions to maximize work efficiency and reduce maintenance costs by integrally managing the real-time drive status and resource usage status of the motor. For example, the motor resource management (405) module can monitor status information such as the current speed, temperature, and vibration of the motor to apply optimal drive settings according to working conditions and automatically perform adjustments if necessary. In addition, it can provide scheduling for preventive maintenance based on the drive data and work history of at least one motor, and support management optimization by tracking the usage of energy resources.

[0092] According to various embodiments, the electronic device (100) can generate a control signal for at least one motor (110) by applying an optimal driving setting according to working conditions through a motor resource management (405) module and generating a control signal based thereon.

[0093] According to various embodiments, the functions of the electronic device (100) described with reference to FIGS. 3 and FIGS. 4 may be provided through an artificial intelligence agent (403). The artificial intelligence agent (403) represents a software system designed to achieve a specific goal while operating autonomously in a given environment. The artificial intelligence agent may be configured to receive input data from a user, analyze it, make a judgment, and perform necessary tasks or provide results. The artificial intelligence agent (403) utilizes artificial intelligence technologies such as natural language processing, data analysis, and computer vision to analyze and understand the user's intentions and requirements through the request message, and can improve the user experience and automate various tasks by automatically controlling the operation of at least one motor (110) as a result based thereon.

[0094] According to various embodiments, the artificial intelligence agent (403) may be classified into at least one of reactive, autonomous, and collaborative types. For example, if the artificial intelligence agent (403) is a reactive agent, it may provide output information as an immediate response to a user request message and control the operation of at least one motor (110) according to predefined rules in a given state. For example, if the artificial intelligence agent (403) is an autonomous agent, it may make decisions independently in a more complex environment and adjust its operation according to environmental changes through a learning algorithm. For example, if at least one motor (110) is in a dangerous and / or abnormal state, the artificial intelligence agent (403) may make decisions independently despite the user's request message and generate a control signal for at least one motor (110) to adjust its operation. For example, if the artificial intelligence agent (403) is a collaborative agent, it may provide an optimal solution to the user by utilizing multiple agents. For example, the artificial intelligence agent (403) may generate control signals and / or output information using an artificial intelligence agent deployed in an industrial environment or various other artificial intelligence models. According to various embodiments, the electronic device (100) may include an artificial intelligence agent (403) that provides a combination of the functions of the various types of artificial intelligence agents described above.

[0095] According to various embodiments, the artificial intelligence agent (403) may include various software modules to provide the above functions. For example, it may include an input processing module that converts a user's request message into text data, a prompt generation module that analyzes the user's request message and generates a prompt necessary for driving control of the motor, an artificial intelligence model (e.g., Large Language Model, LLM) that determines the current motor state and optimal driving conditions to achieve a target task based on the generated prompt and generates a control signal, a result generation module that generates a final response (e.g., final information) to provide the user with a final response based on the result output from the large language model, and / or an output and feedback module that delivers the final response to the user and monitors the driving state of the at least one motor (110) to provide immediate feedback to the user regarding abnormal situations.

[0097] FIG. 5 is a diagram illustrating the acquisition of multidimensionally transformed sensing data.

[0098] Referring to FIG. 5, sensing data (510) and diagnostic data (520) obtained by converting the sensing data (510) into multiple dimensions are shown.

[0099] According to various embodiments, the electronic device (100) may acquire sensing data (510) through a sensor device (e.g., sensor device (120) of FIG. 1) that acquires sensing data related to the operation of the at least one motor (e.g., motor (110) of FIG. 1). For example, the electronic device (100) may acquire various sensing data (510) that can be acquired through the sensor device (120), such as vibration, sound, rotational speed, temperature, cracks, and / or pressure of the at least one motor (110). According to various embodiments, the sensing data (510) may be represented as a graph with the time during the operation of the at least one motor (110) on the horizontal axis and the vibration magnitude corresponding to that time on the vertical axis.

[0100] According to various embodiments, the electronic device (100) can obtain diagnostic data (520) by converting the sensing data (510) into a multidimensional form. For example, the diagnostic data (5210) may include data representing the change in sensing data per hour of at least one motor (110) in a multidimensional form (e.g., two dimensions, images). For example, the electronic device (100) can obtain diagnostic data (520) which is a graph representing the sensing data (510) (e.g., vibration, sound, rotational speed, temperature, cracks, and / or pressure, etc.) corresponding to the time on the horizontal axis in relation to the driving of at least one motor (110) in two dimensions. For example, an electronic device (100) can obtain diagnostic data (520) by converting sensing data (510) through at least one conversion method among Fourier transform, Wavelet transform, Wavelet spectrogram, and Mel-Frequency Cepstral Coefficients (MFCC).

[0101] According to various embodiments, the electronic device (100) can obtain driving state information for the at least one motor (110) by inputting the diagnostic data (520) into a risk prediction model (e.g., the risk prediction model (409) of FIG. 4). For example, the electronic device (100) can obtain driving state information (e.g., current state, cause of vibration, frequency, characteristics and phenomena of vibration, countermeasures) for each of the at least one motor (110) by inputting the diagnostic data (520) into the risk prediction model (409).

[0102] According to various embodiments, the electronic device (100) can improve the accuracy of determining the operating state by clearly visualizing the characteristics of the data by preprocessing one-dimensional sensing data (510) and inputting diagnostic data (520) into a risk prediction model (409).

[0104] FIG. 6 is a flowchart (600) showing a method for generating a learned risk prediction model to determine the driving state of a motor for an electronic device.

[0105] Figure 7 is a diagram illustrating a training data set for training a risk prediction model.

[0106] Each of the operations described below may be performed in combination with one another. Additionally, among the operations described below, an operation by an electronic device (100) (e.g., the electronic device (100) of FIG. 1) may mean an operation by a processor (210) of the electronic device (100).

[0107] In addition, the term "information" described below may be interpreted as having the meaning of "data" or "signal," and the term "data" may be understood as a concept that includes both analog data and digital data.

[0108] According to various embodiments, when the electronic device (100) is implemented as a single device including a sensor device (120), the operation of the sensor device (120) can be understood as an operation of the internal configuration of the electronic device (100).

[0109] According to various embodiments, the operations illustrated in FIG. 6 may be performed in various orders, not limited to the order illustrated. Additionally, according to various embodiments, more operations may be performed than those illustrated in FIG. 6, or at least one fewer operation may be performed.

[0110] Referring to FIG. 6, in operation 601, the electronic device (100) may acquire a fault data set associated with at least one motor (110). For example, the electronic device (100) may acquire a fault data set for at least one motor (110) stored in a storage device (220) or a fault data set for various other motors (110). According to various embodiments, the fault data set may include actual fault data acquired through a sensor device (120) and / or virtual fault data generated from general sensing data.

[0111] According to various embodiments, in operation 603, the electronic device (100) can acquire a set of sensing data related to the driving state of at least one motor (110) through at least one sensor device (e.g., sensor device (120) of FIG. 1) for a specified period. For example, the electronic device (100) can acquire a set of sensing data related to the driving state of at least one motor (110) through a sensor device (120) placed in association with at least one motor (110) for a specified period (e.g., 4 weeks). Typically, sensing data acquired early after the installation of at least one motor (110) can be classified as data acquired when the driving state is normal.

[0112] According to various embodiments, in operation 605, the electronic device (100) can obtain an initial learning data set by synthesizing the sensing data set and the failure data set. For example, the electronic device (100) can synthesize the failure data set stored in the storage device (220) with the sensing data set. For example, the initial learning data (730) can be obtained by synthesizing the failure data (710) included in the failure data set and the sensing data (720) included in the sensing data set.

[0113] According to various embodiments, in operation 607, the electronic device (100) can obtain a training data set in which the initial training data set has been converted into multiple dimensions. For example, the electronic device (100) can obtain a training data set in which the initial training data set has been converted into multiple dimensions by performing the operation described with reference to FIG. 5. Hereinafter, redundant descriptions regarding the description of FIG. 5 and the acquisition of a training data set converted into multiple dimensions may be omitted.

[0114] According to various embodiments, in operation 609, the electronic device (100) may train the risk prediction model (409) to determine the driving state of at least one motor (110) based on the training data set. For example, the electronic device (100) may train the risk prediction model (409) to determine the driving state of the motor based on a training data set including training data (730). At this time, the risk prediction model (409) may be trained to output information on the driving state of at least one motor (110) as well as information on the cause of failure and countermeasures.

[0116] FIG. 8 is a flowchart showing how the driving of a motor is controlled according to a user's request.

[0117] Referring to FIG. 8, entities within a system that control the driving of a motor according to a user's request (e.g., device, user) include a user (801) (or including a user device), an electronic device (802) (e.g., the electronic device (100) of FIG. 1), a motor (803) (e.g., the motor (110) of FIG. 1) and / or a sensor device (804) (e.g., the sensor device (120) of FIG. 1). According to various embodiments, the system controlling the driving of a motor may include the driving control system described with reference to FIG. 1. Accordingly, descriptions similar to or redundant to those described with reference to FIG. 1 may be omitted below.

[0118] According to various embodiments, the operations illustrated in FIG. 8 may be performed in various orders, not limited to the order illustrated. Additionally, according to various embodiments, more operations may be performed than those illustrated in FIG. 8, or at least one fewer operation may be performed.

[0119] According to various embodiments, in operation 811, a user (801) can transmit a request message to an electronic device (802). For example, the user (801) can transmit a request message containing a target task to the electronic device (802) via a voice signal. Alternatively, the user (801) can transmit a request message to the electronic device (802) via a user device. For example, the user (801) can transmit a request message containing a voice signal of “The motor condition is bad, so please adjust the temperature so it does not exceed 50 degrees. We need to produce 500 units today.”

[0120] According to various embodiments, in operation 813, the electronic device (802) can analyze the request message. For example, the electronic device (802) can convert the user's request included in the request message into text data through an artificial intelligence agent (e.g., the artificial intelligence agent (403) of FIG. 4) and analyze it to determine the user's intent.

[0121] According to various embodiments, in operation 815, the electronic device (802) may request motor drive sensing data from the sensor device (804) to generate driving state information of at least one motor (803).

[0122] According to various embodiments, in operation 817, the sensor device (804) can transmit sensing data for at least one motor (803) to the electronic device (802).

[0123] According to various embodiments, in operation 819, the electronic device (802) can obtain operating state information using a risk prediction model (e.g., the risk prediction model (409) of FIG. 4). For example, the electronic device (802) can obtain operating state information by inputting the sensing data into the risk prediction model (409). According to one embodiment, the electronic device (802) can convert the sensing data into multiple dimensions to generate diagnostic data and input the diagnostic data into the risk prediction model (409).

[0124] According to various embodiments, in operation 821, the electronic device (802) can obtain a control signal for the motor (803). For example, the electronic device (802) can obtain a control signal for at least one motor (803) through an artificial intelligence agent based on the user's request message and the driving status information. The control signal may include a signal for controlling the driving of the at least one motor (803).

[0125] According to various embodiments, in operation 823, the electronic device (802) can transmit the control signal to the motor (803). For example, the electronic device (802) can transmit the control signal so that at least one motor (803) can be driven based on the control signal. For example, the electronic device (802) can use an artificial intelligence agent (409) to generate an appropriate response signal based on the motor's driving status information and a request message, and transmit the control signal so that at least one motor (803) is driven accordingly.

[0126] According to various embodiments, in operation 825, at least one motor (803) may be driven based on the control signal. For example, at least one motor (803) may determine various conditions related to driving, such as driving time and driving speed, based on the control signal and be driven.

[0127] According to various embodiments, in operation 827, the electronic device (802) can transmit output information to the user (801). For example, the user (801) may receive output information from the electronic device (802) in various ways, such as a visual screen, an acoustic signal, or vibration. For example, the electronic device (802) may provide a graphic of the current operating status of each motor or the next maintenance time, or provide a notification to the user if resource management is required. For example, the electronic device (802) may provide output information to the user (801) such as, “The motor has been adjusted to 3000 RPM for optimal condition. It must be operated for 5 hours to produce 500 units.”

[0128] According to various embodiments, the electronic device (802) may determine whether a driving abnormal event has occurred for each of at least one motor (803) based on the driving state information, and may output a notification regarding the driving abnormal event based on the determination result. For example, the output information may include the driving abnormal event. According to one embodiment, if the electronic device (802) determines that a driving abnormal event has occurred, it may transmit a control signal to at least one motor (803) to control the driving of the at least one motor (803) according to the content of the driving abnormal event.

[0130] As described above, the electronic device (100) includes at least one motor, a communication device for transmitting and receiving signals, at least one processor, and a storage device for storing instructions, wherein the instructions are executed individually or collectively by the at least one processor, and the electronic device obtains a request message including a target task from a user, obtains a control signal to control the driving of the at least one motor through an artificial intelligence agent based on the request message and driving status information for the at least one motor, and transmits the control signal to the at least one motor so that the at least one motor is driven based on the control signal, and the driving status information can be obtained based on a risk prediction model learned to diagnose the driving status of the at least one motor.

[0131] According to various embodiments, the request message further includes additional work conditions related to the driving of the at least one motor, and the instructions are executed individually or collectively by the at least one processor so that the electronic device can obtain the control signal through the artificial intelligence agent based on at least one of the specification information for each of the at least one motor and the additional work conditions.

[0132] According to various embodiments, the instructions may be executed individually or collectively by the at least one processor to enable the electronic device to acquire a failure data set related to the at least one motor, acquire a sensing data set related to the driving state of each of the at least one motor through at least one sensor device connected to the at least one motor for a specified period, synthesize the sensing data set and the failure data set to acquire an initial learning data set, acquire a learning data set obtained by converting the initial learning data set into a multidimensional form, and acquire the risk prediction model based on the learning data set, and the risk prediction model may be an artificial intelligence model that receives data related to the driving of the at least one motor and outputs driving state information for the at least one motor.

[0133] According to various embodiments, the driving state information may include at least one of a first state indicating a normal driving state of the at least one motor, a second state indicating an abnormal driving state of the at least one motor, and a third state indicating a dangerous driving state of the at least one motor.

[0134] According to various embodiments, the instructions may be executed individually or collectively by the at least one processor to enable the electronic device to obtain the training data set that has been transformed into multiple dimensions using a spectrogram transformation.

[0135] According to various embodiments, the control signal may include the driving time of each of the at least one motor and the driving speed of each of the at least one motor.

[0136] According to various embodiments, the electronic device further includes an input device, and the instructions are executed individually or collectively by the at least one processor so that the electronic device obtains the request message from the user through the input device and obtains user intention information by analyzing the user's intention regarding the request message through the artificial intelligence agent.

[0137] According to various embodiments, the electronic device further includes an output device, and the instructions are executed individually or collectively by the at least one processor so that the electronic device, through the artificial intelligence agent, generates output information related to each of the at least one motor based on the driving state information and the control signal, and outputs the output information through the output device, and the output information may include at least one of state information, driving control information, maintenance scheduling, and resource management for each of the at least one motor, provided in response to the request message.

[0138] According to various embodiments, the instructions may be executed individually or collectively by the at least one processor so that the electronic device determines whether a driving abnormal event has occurred for each of the at least one motor based on the driving state information through the artificial intelligence agent, and based on the determination result, the output device outputs a notification regarding the driving abnormal event, and if it is determined that the driving abnormal event has occurred, the driving of the at least one motor may be controlled according to the content of the driving abnormal event.

[0139] According to various embodiments, the instructions may be executed individually or collectively by the at least one processor to enable the electronic device to acquire sensing data through the artificial intelligence agent, through at least one sensor device for each of the at least one motor, convert the sensing data into multiple dimensions using spectrogram transformation, and input the multidimensionally converted sensing data into the risk prediction model to acquire driving state information for the at least one motor.

[0140] As described above, a method of operation of an electronic device for controlling the driving of at least one motor comprises the operation of obtaining a request message including a target task from a user, the operation of obtaining a control signal to control the driving of the at least one motor through an artificial intelligence agent based on the request message and driving state information for the at least one motor, and the operation of transmitting the control signal to the at least one motor so that the at least one motor is driven based on the control signal, wherein the driving state information may be obtained based on a risk prediction model learned to diagnose the driving state of the at least one motor.

[0141] According to various embodiments, the request message may further include additional work conditions related to the driving of at least one motor, and may further include an operation of obtaining the control signal through the artificial intelligence agent based on at least one of the specification information for each of the at least one motor and the additional work conditions.

[0142] According to various embodiments, the method of operation of the electronic device further comprises the operation of acquiring a failure data set related to at least one motor, the operation of acquiring a sensing data set related to the driving state of each of the at least one motor through at least one sensor device connected to the at least one motor for a specified period, the operation of acquiring an initial learning data set by synthesizing the sensing data set and the failure data set, the operation of acquiring a learning data set obtained by converting the initial learning data set into a multidimensional form, and the operation of acquiring a risk prediction model based on the learning data set, and the risk prediction model may be an artificial intelligence model that receives data related to the driving of the at least one motor and outputs driving state information for the at least one motor.

[0143] According to various embodiments, the operation of acquiring the training data set may further include the operation of acquiring the training data set obtained by transforming the initial training data set into a multidimensional form using a spectrogram transformation.

[0144] According to various embodiments, the control signal may include the driving time of each of the at least one motor and the driving speed of each of the at least one motor.

[0145] According to various embodiments, the operation of obtaining the request message may further include the operation of obtaining user intent information by analyzing the user's intent regarding the request message through the artificial intelligence agent.

[0146] According to various embodiments, the method of operation of the electronic device further includes, based on the driving state information and the control signal, an operation of generating output information related to each of the at least one motor and an operation of outputting the output information, wherein the output information may be provided in response to the request message and may include at least one of state information, driving control information, maintenance scheduling, and resource management for each of the at least one motor.

[0147] According to various embodiments, the method of operation of the electronic device may further include, based on the driving state information, an operation of determining whether a driving abnormal event has occurred for each of the at least one motor, an operation of outputting a notification regarding the driving abnormal event based on the determination result, and an operation of controlling the driving of the at least one motor according to the content of the driving abnormal event.

[0148] According to various embodiments, the method of operation of the electronic device may further include the operation of acquiring sensing data through at least one sensor device for each of the at least one motor, the operation of converting the sensing data into multiple dimensions using spectrogram transformation, and the operation of inputting the multidimensionally converted sensing data into the risk prediction model to acquire driving state information for the at least one motor.

[0149] As described above, in a computer-readable recording medium on which a program is recorded, the program may include an operation for executing the operation of obtaining a request message including a target task from a user, an operation for obtaining driving state information through a risk prediction model learned to diagnose the driving state of at least one connected motor, an operation for obtaining a control signal to control the driving of the at least one motor through an artificial intelligence agent based on the request message and the driving state information, and an operation for transmitting the control signal to the at least one motor so that the at least one motor is driven based on the control signal.

[0150] In the present disclosure, each of the phrases such as “A or B”, “at least one of A and B”, “at least one of A or B”, “A, B or C”, “at least one of A, B and C”, and “at least one of A, B, or C” may include any one of the items listed together in the corresponding phrase, or all possible combinations thereof.

[0151] Terms such as "first," "second," or "first" or "second" may be used simply to distinguish a component from another component and do not limit the components in other aspects (e.g., importance or order).

[0152] Terms such as “part,” “module,” as used in various embodiments of the present disclosure may include units implemented in hardware, software, or firmware. For example, they may be used interchangeably with terms such as logic, logic block, component, or circuit. A module may be a component formed integrally, or a minimum unit of said component or a part thereof that performs one or more functions. Terms such as “part” and “module,” as used in various embodiments of the present disclosure, may be implemented by various programs that are stored in an addressable storage medium and can be executed by a processor.

[0153] Various embodiments of the present disclosure may be implemented as software (e.g., a program) comprising one or more instructions stored in a storage device (220) (e.g., internal memory or external memory) that can be read by a device (e.g., an electronic device (100)). The storage device (220) may be represented as a storage medium.

[0154] According to one embodiment, the method according to the various embodiments disclosed herein may be provided as included in a computer program product. The computer program product may be traded between a seller and a buyer as a product. The computer program product may be distributed in the form of a device-readable storage medium (e.g., CD-ROM (compact disc read-only memory)), or distributed online (e.g., download or upload) through an application store or directly between two user devices.

[0155] According to various embodiments, each component (e.g., module or program) of the components described above may include a singular or multiple entities, and some of the multiple entities may be separated and placed in other components. According to various embodiments, one or more of the components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Additionally or substantially, multiple components (e.g., module or program) may be integrated into a single component. In this case, the integrated component may perform one or more functions of each of the multiple components in the same or similar manner as those performed by the corresponding component among the multiple components prior to the integration.

[0156] According to various embodiments, operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

[0157] Methods according to the claims or embodiments described in the specification of the present disclosure may be implemented in the form of hardware, software, or a combination of hardware and software.

[0158] When implemented in software, a computer-readable storage medium may be provided for storing one or more programs (software modules). One or more programs stored in the computer-readable storage medium are configured for execution by one or more processors within an electronic device. One or more programs include instructions that cause the electronic device to execute methods according to the claims of the present disclosure or the embodiments described in the specification.

[0159] In the present disclosure, a function or operation performed by an electronic device may be performed by one or more processors executing one or more instructions stored in memory. A function or operation of an electronic device mentioned in the present disclosure may be performed by a single processor executing one or more instructions, or by a combination of multiple processors executing one or more instructions.

Claims

Claim 1 An electronic device comprising: at least one motor and a communication device for transmitting and receiving signals; at least one processor; and a storage device for storing instructions, wherein the instructions are executed individually or collectively by the at least one processor and the electronic device: obtains a request message including a target task from a user, obtains a control signal to control the driving of the at least one motor through an artificial intelligence agent based on the request message and driving state information for the at least one motor, and transmits the control signal to the at least one motor so that the at least one motor is driven based on the control signal, wherein the driving state information is obtained based on a risk prediction model learned to diagnose the driving state of the at least one motor, and the risk prediction model is an artificial intelligence model learned to diagnose the driving state of the at least one motor based on an initial learning data set that synthesizes a failure data set related to the at least one motor and a sensing data set related to the driving state of each of the at least one motor obtained through at least one sensor device connected to the at least one motor during a specified period. Claim 2 An electronic device according to claim 1, wherein the request message further includes work addition conditions related to the driving of the at least one motor, and the instructions are executed individually or collectively by the at least one processor so that the electronic device acquires the control signal through the artificial intelligence agent based on at least one of the specification information for each of the at least one motor and the work addition conditions. Claim 3 The electronic device according to claim 1, wherein the instructions are executed individually or collectively by the at least one processor to: acquire a learning data set obtained by converting the initial learning data set into a multidimensional form, and acquire the risk prediction model based on the learning data set, wherein the risk prediction model is an artificial intelligence model that receives data related to the driving of the at least one motor and outputs driving state information for the at least one motor. Claim 4 An electronic device according to claim 3, wherein the driving state information comprises at least one of a first state indicating a normal driving state of the at least one motor, a second state indicating an abnormal driving state of the at least one motor, and a third state indicating a dangerous driving state of the at least one motor. Claim 5 An electronic device according to claim 3, wherein the instructions are executed individually or collectively by the at least one processor to enable the electronic device to: acquire the training data set that has been transformed into multiple dimensions using a spectrogram transformation. Claim 6 An electronic device according to claim 1, wherein the control signal comprises a driving time of each of the at least one motor and a driving speed of each of the at least one motor. Claim 7 The electronic device according to claim 1, wherein the electronic device further comprises an input device, and the instructions are executed individually or collectively by the at least one processor such that the electronic device: obtains the request message from the user through the input device, and obtains user intention information by analyzing the user's intention regarding the request message through the artificial intelligence agent. Claim 8 The electronic device according to claim 1 further comprises an output device, wherein the instructions are executed individually or collectively by the at least one processor so that the electronic device, through the artificial intelligence agent: generates output information related to each of the at least one motor based on the driving state information and the control signal, and outputs the output information through the output device, wherein the output information is provided in response to the request message and includes at least one of state information, driving control information, maintenance scheduling, and resource management for each of the at least one motor. Claim 9 An electronic device according to claim 8, wherein the instructions are executed individually or collectively by the at least one processor, and the electronic device, through the artificial intelligence agent: determine whether a driving abnormal event has occurred for each of the at least one motor based on the driving state information, and based on the result of the determination, the output device outputs a notification regarding the driving abnormal event, and if it is determined that the driving abnormal event has occurred, controls the driving of the at least one motor according to the content of the driving abnormal event. Claim 10 An electronic device according to claim 1, wherein the instructions are executed individually or collectively by the at least one processor, and the electronic device, through the artificial intelligence agent: acquires sensing data through at least one sensor device for each of the at least one motor, converts the sensing data into multiple dimensions using spectrogram transformation, and inputs the multidimensionally converted sensing data into the risk prediction model to acquire driving state information for the at least one motor. Claim 11 A method of operation of an electronic device for controlling the operation of at least one motor, comprising: an operation of obtaining a request message including a target task from a user; an operation of obtaining a control signal to control the operation of the at least one motor through an artificial intelligence agent based on the request message and driving state information for the at least one motor; and an operation of transmitting the control signal to the at least one motor so that the at least one motor is driven based on the control signal, wherein the driving state information is obtained based on a risk prediction model learned to diagnose the driving state of the at least one motor, and the risk prediction model is an artificial intelligence model learned to diagnose the driving state of the at least one motor based on an initial learning data set that synthesizes a failure data set related to the at least one motor and a sensing data set related to the driving state of each of the at least one motor obtained through at least one sensor device connected to the at least one motor during a specified period. Claim 12 A method of operation of an electronic device according to claim 11, wherein the request message further includes work additional conditions related to the driving of the at least one motor, and further includes the operation of obtaining the control signal through the artificial intelligence agent based on at least one of the specification information for each of the at least one motor and the work additional conditions. Claim 13 A method of operation of an electronic device according to claim 11, further comprising: an operation of acquiring a failure data set associated with at least one motor; an operation of acquiring a sensing data set associated with the driving state of each of the at least one motor through at least one sensor device connected to the at least one motor during a specified period; an operation of acquiring an initial learning data set by synthesizing the sensing data set and the failure data set; an operation of acquiring a learning data set obtained by converting the initial learning data set into a multidimensional form; and an operation of acquiring a risk prediction model based on the learning data set, wherein the risk prediction model is an artificial intelligence model that receives data associated with the driving of the at least one motor and outputs driving state information for the at least one motor. Claim 14 A method of operation of an electronic device according to claim 13, wherein the operation of acquiring the training data set further includes the operation of acquiring the training data set obtained by converting the initial training data set into a multidimensional form using a spectrogram transformation. Claim 15 A method of operation of an electronic device according to claim 11, wherein the control signal comprises the driving time of each of the at least one motor and the driving speed of each of the at least one motor. Claim 16 A method of operation of an electronic device according to claim 11, wherein the operation of obtaining the request message further includes the operation of obtaining user intention information by analyzing the user's intention regarding the request message through the artificial intelligence agent. Claim 17 A method of operation of an electronic device according to claim 11, further comprising: an operation of generating output information related to each of the at least one motor based on the driving state information and the control signal; and an operation of outputting the output information, wherein the output information is provided in response to the request message and includes at least one of state information, driving control information, maintenance scheduling, and resource management for each of the at least one motor. Claim 18 A method of operation of an electronic device according to claim 17, further comprising: an operation of determining whether a driving abnormal event occurs for each of the at least one motor based on the driving state information; an operation of outputting a notification for the driving abnormal event based on the determination result; and an operation of controlling the driving of the at least one motor according to the content of the driving abnormal event. Claim 19 A method of operation of an electronic device according to claim 11, further comprising: an operation of acquiring sensing data through at least one sensor device for each of the at least one motor; an operation of converting the sensing data into multiple dimensions using spectrogram conversion; and an operation of inputting the multidimensionally converted sensing data into a risk prediction model to acquire driving state information for the at least one motor. Claim 20 A computer-readable recording medium having a program for executing the operation of: obtaining a request message including a target task from a user; obtaining driving state information through a risk prediction model learned to diagnose the driving state of at least one connected motor, wherein the risk prediction model is an artificial intelligence model learned to diagnose the driving state of at least one motor based on an initial learning data set that synthesizes a failure data set related to the at least one motor and a sensing data set related to the driving state of each of the at least one motor obtained through at least one sensor device connected to the at least one motor during a specified period; obtaining a control signal to control the driving of the at least one motor through an artificial intelligence agent based on the request message and the driving state information; and transmitting the control signal to the at least one motor so that the at least one motor is driven based on the control signal.

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