KR-20260062615-A - ELECTRONIC DEVICE, OPERATION METHOD, AND SYSTEM FOR CONTROLLING MOTOR DRIVE USING ARTIFICIAL INTELLIGENCE

KR20260062615AKR 20260062615 AKR20260062615 AKR 20260062615AKR-20260062615-A

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.

Inventors

송응열

Assignees

코드비전 주식회사

Dates

Publication Date: 20260507
Application Date: 20241029

Claims (20)

In electronic devices, At least one motor and a communication device for transmitting and receiving signals; At least one processor; and It includes a storage device that stores instructions, The above instructions are executed individually or collectively by the at least one processor so that the electronic device: Obtain a request message from the user that includes a target task, and A control signal is obtained to control the driving of the at least one motor through an artificial intelligence agent based on the above request message and driving status information for the at least one motor, and The control signal is transmitted to the at least one motor so that the at least one motor is driven based on the control signal, and The above driving state information is obtained based on a risk prediction model learned to diagnose the driving state of the at least one motor, in an electronic device.
In claim 1, The above request message further includes additional work conditions related to the driving of the at least one motor, and The above instructions are executed individually or collectively by the at least one processor so that the electronic device: An electronic device that obtains the control signal through the artificial intelligence agent based on at least one of the specification information for each of the above-mentioned at least one motor and the above-mentioned additional work conditions.
In claim 1, The above instructions are executed individually or collectively by the at least one processor so that the electronic device: Acquire a failure data set related to at least one motor, and A set of sensing data related to the driving state of each of the at least one motor is obtained through at least one sensor device connected to the at least one motor during a specified period, and An initial training data set is obtained by synthesizing the above sensing data set and the above failure data set, and Obtain a training data set obtained by transforming the above initial training data set into multiple dimensions, and An electronic device that obtains the risk prediction model based on the above-mentioned training 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.
In claim 3, The above driving state information is an electronic device comprising 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.
In claim 3, The above instructions are executed individually or collectively by the at least one processor so that the electronic device: An electronic device that obtains a training data set transformed into multiple dimensions using a spectrogram transformation.
In claim 1, The above control signal is an electronic device comprising the driving time of each of the at least one motor and the driving speed of each of the at least one motor.
In claim 1, The above electronic device further includes an input device, and The above instructions are executed individually or collectively by the at least one processor so that the electronic device: Obtaining the request message from the user through the input device, and An electronic device that obtains user intent information by analyzing the user's intent regarding the request message through the artificial intelligence agent.
In claim 1, The above electronic device further includes an output device, and The above instructions are executed individually or collectively by the at least one processor so that the electronic device, through the artificial intelligence agent: Based on the above driving state information and the above control signal, output information related to each of the at least one motor is generated, and The above output information is output through the above output device, and The above output information is provided in response to the above request message and includes at least one of status information, drive control information, maintenance scheduling, and resource management for each of the at least one motor, an electronic device.
In claim 8, The above instructions are executed individually or collectively by the at least one processor so that the electronic device, through the artificial intelligence agent: Based on the above driving status information, determining whether a driving abnormality event has occurred for each of the at least one motor, and Based on the above judgment result, the output device outputs a notification regarding the above driving abnormal event, and An electronic device that controls the driving of at least one motor according to the content of the driving abnormal event when it is determined that the above driving abnormal event has occurred.
In claim 1, The above instructions are executed individually or collectively by the at least one processor so that the electronic device, through the artificial intelligence agent: Sensing data is obtained through at least one sensor device for each of the above at least one motor, and The above sensing data is transformed into multiple dimensions using a spectrogram transformation, and An electronic device that inputs multidimensionally converted sensing data into the risk prediction model to obtain driving state information for at least one motor.
In a method of operation of an electronic device for controlling the driving of at least one motor, An action of obtaining a request message containing 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 above request message and driving status information for the at least one motor; and The operation includes transmitting the control signal to the at least one motor so that the at least one motor is driven based on the control signal, and A method of operation of an electronic device, wherein the above driving state information is obtained based on a risk prediction model learned to diagnose the driving state of the at least one motor.
In claim 11, The above request message further includes additional work conditions related to the driving of the at least one motor, and A method of operation of an electronic device, further comprising 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 additional work conditions.
In claim 11, The operation of acquiring a fault data set associated with at least one motor; The operation of acquiring a set of sensing data 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 during a specified period; An operation to obtain an initial training data set by synthesizing the above sensing data set and the above failure data set; The operation of obtaining a training data set obtained by converting the above initial training data set into a multidimensional form; The method further includes the operation of acquiring the risk prediction model based on the above training data set, and A method of operation of an electronic device, wherein the above-mentioned 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.
In claim 13, A method of operating an electronic device, wherein the operation of acquiring the above-mentioned training data set further includes the operation of acquiring the above-mentioned training data set obtained by transforming the above-mentioned initial training data set into a multidimensional form using a spectrogram transformation.
In claim 11, A method of operation of an electronic device, wherein the above control signal includes the driving time of each of the at least one motor and the driving speed of each of the at least one motor.
In claim 11, A method of operation of an electronic device, wherein the operation of obtaining the above request message further includes the operation of obtaining user intention information by analyzing the user's intention regarding the above request message through the above artificial intelligence agent.
In claim 11, An operation to generate output information related to each of the at least one motor based on the above driving state information and the above control signal; and It further includes an operation to output the above output information, and A method of operation of an electronic device, wherein the above output information is provided in response to the above request message and includes at least one of status information, drive control information, maintenance scheduling, and resource management for each of the at least one motor.
In claim 17, An operation to determine whether a driving abnormality event has occurred for each of the at least one motor based on the driving status information above; An operation to output a notification regarding the above-mentioned operation abnormal event based on the above-mentioned judgment result; and A method of operation of an electronic device, further comprising an operation to control the driving of at least one motor according to the content of the above-mentioned driving abnormal event.
In claim 11, The operation of acquiring sensing data through at least one sensor device for each of the above at least one motor; The operation of converting the above sensing data into a multidimensional form using spectrogram transformation; and A method of operating an electronic device, further comprising the operation of inputting multidimensionally converted sensing data into the risk prediction model to obtain driving state information for at least one motor.
An action of obtaining a request message containing a target task from a user; An operation to acquire driving state information through a risk prediction model learned to diagnose the driving state of at least one connected motor; The operation of obtaining a control signal to control the driving of at least one motor through an artificial intelligence agent based on the above request message and the above driving status information; and A computer-readable recording medium having a program for executing an operation to transmit a control signal to at least one motor so that the at least one motor is driven based on the control signal.

Description

ELECTRONIC DEVICE, OPERATION METHOD, AND SYSTEM FOR CONTROLLING MOTOR DRIVE USING ARTIFICIAL INTELLIGENCE 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. 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. 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. 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. 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. 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 structural or functional descriptions regarding various embodiments are illustrative for the purpose of explaining various embodiments, and 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. 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. 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. 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 d