KR20210073058A - Vacuum cleaner and controll method thereof - Google Patents

Abstract

According to one embodiment of the present invention, disclosed is a method for controlling a cleaning device comprising: a step of collecting driving data related to operation of each cleaning device from a plurality of cleaning devices connected to a communication network for data communication, and establishing a determination policy which serves as a criterion for determining an abnormal operation of the cleaning device based on the driving data; a step of receiving the driving data from the cleaning device connected to the communication network and determining whether the cleaning device has an abnormality depending on the determination policy with the driving data; and a step of providing service information for notifying an abnormal operation of the cleaning device to a user of the cleaning device through the communication network when the cleaning device has the abnormality depending on a determination result. The present invention can accurately determine malfunctions of the cleaning device.

Description

Control method of vacuum cleaner {VACUUM CLEANER AND CONTROLL METHOD THEREOF}

The present invention relates to a control method of a cleaner that provides an abnormal operation of the cleaner to a user based on driving information obtained from cleaners connected to a communication network.

Artificial intelligence (AI) is a computing that implements human-level intelligence, a system in which a machine learns itself and improves its cognitive ability. Recently, as 5G communication becomes possible, it is drawing more attention from the market.

2. Description of the Related Art In general, a vacuum cleaner is a household appliance that sucks in small garbage or dust in a manner that uses electricity to suck air and fills the dust bin in the product, and is generally called a vacuum cleaner.

Such a vacuum cleaner may be divided into a manual cleaner in which the user directly moves the cleaner to perform cleaning, and an automatic cleaner in which the user performs cleaning while driving by themselves. Manual vacuum cleaners may be classified into canister-type cleaners, upright cleaners, handy-type cleaners, stick-type cleaners, and the like, depending on the type of cleaner.

In the past, canister-type vacuum cleaners were often used in household cleaners, but in recent years, stick cleaners with improved ease of use by providing a dust container and a cleaner body as an integral part have been increasingly used.

The canisty type vacuum cleaner is connected to the main body and the suction port by a rubber hose or pipe, and in some cases, it can be used by inserting a brush into the suction port.

A hand vacuum cleaner maximizes portability, and since it is light in weight but short in length, there may be a limitation in the cleaning area while sitting. Therefore, it is used to clean localized places such as on a desk or sofa, or in a car.

The stick vacuum cleaner can be used while standing, so you can clean without bending your back. Therefore, it is advantageous for cleaning while moving a large area. If a hand-held vacuum cleaner cleans a narrow space, the stick-type vacuum cleaner can clean a wider space than that, and can clean a high place that cannot be reached by hand. Recently, a stick cleaner is provided in a module type, and the cleaner type is actively changed and used for various objects.

In addition, recently, a product with improved user convenience by providing a handy type vacuum cleaner and a stick cleaner for use has been released.

On the other hand, it is difficult for a user without professional knowledge to know whether there is an abnormal operation during operation of such cleaners (hereinafter, unless otherwise specified, in this specification, the cleaner is collectively referred to as a canisty-type cleaner, a handy-type cleaner, and a stick cleaner) . For this reason, most users are repairing the vacuum cleaner through AS after it breaks down.

The present invention was conceived in the above technical background, and is intended to prevent malfunction of the cleaner in advance by providing a user with an abnormal operation of the cleaner based on operation information obtained from cleaners connected to a communication network.

In one embodiment of the present invention, operation data related to the operation of each cleaner is collected from a plurality of cleaners connected to a communication network capable of data communication, and a determination policy that is a criterion for determining abnormal operation of the cleaner based on the operation data is determined. establishing, receiving the driving data from a cleaner connected to the communication network, determining whether the cleaner has an abnormality based on the driving data according to the determination policy, and when it is determined that there is an abnormality, abnormal operation of the cleaner Disclosed is a control method of a cleaner comprising the step of providing service information informing the user of the cleaner through the communication network.

The step of establishing the determination policy may include: extracting the cumulative number of uses of each vacuum cleaner from the collected driving data; determining whether the extracted cumulative number of uses meets a criterion; , the driving data further includes the step of learning the artificial neural network with learning data for setting the decision policy.

The operation data includes a cleaning mode and a usage time according to a motor load of the vacuum cleaner, and the establishing of the determination policy includes selecting the plurality of cleaners based on the cleaning mode and the usage time extracted from the operation data. All driving data collected from

The driving data is information informing the usage of the battery, and includes the usage of the battery according to the cleaning mode and the operating time of the cleaner according to the cleaning mode.

The leveling is set to have an inverse relationship between the motor load of the cleaner and the use time.

The service information is provided to the user's smart device only through the communication.

The service information includes at least one of a lifespan of the battery, a replacement, and a purchase link.

According to the present invention, it is possible to establish a decision policy for judging a malfunction based on data collected in an environment used by an actual user. Therefore, it is possible to more accurately determine the malfunction of the cleaner, and as a result, it is possible to prevent an accident in advance by notifying the user of this fact before the cleaner malfunctions.

1 is a diagram illustrating a configuration for controlling a cleaner according to an embodiment of the present specification.
2 is a control block diagram of components constituting a control system of a cleaner and a smart device.
3 is a block diagram illustrating an example of an AI device installed in a server.
4 is an exploded perspective view illustrating a cleaner according to an exemplary embodiment.
5 is a diagram illustrating a control method of a cleaner according to an exemplary embodiment.
6 is a block diagram illustrating a connection relationship of a cleaner.
7 is a view showing a coupling portion between the cleaner body and the cleaning module according to the first embodiment.
8 is a view showing a coupling portion of the cleaner body and the cleaning module according to the first embodiment, respectively.
9 is a view showing a coupling portion of the cleaner body and the cleaning module according to the second embodiment, respectively.
10 is a diagram illustrating a flow for controlling a cleaner according to an exemplary embodiment.
11 is a diagram for explaining an event and its symptoms.
12 is a diagram illustrating a process in which a server establishes a decision policy.
13 is a flowchart illustrating a process of determining whether driving data is a malfunction according to a decision policy when the driving data relates to a battery.
BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are included as a part of the detailed description to facilitate the understanding of the present invention, provide embodiments of the present invention, and together with the detailed description, explain the technical features of the present invention.

Hereinafter, the embodiments disclosed in the present specification will be described in detail with reference to the accompanying drawings, but the same or similar components are assigned the same reference numbers regardless of reference numerals, and redundant description thereof will be omitted. The suffixes "module" and "part" for the components used in the following description are given or mixed in consideration of only the ease of writing the specification, and do not have a meaning or role distinct from each other by themselves. In addition, in describing the embodiments disclosed in the present specification, if it is determined that detailed descriptions of related known technologies may obscure the gist of the embodiments disclosed in the present specification, the detailed description thereof will be omitted. In addition, the accompanying drawings are only for easy understanding of the embodiments disclosed in the present specification, and the technical spirit disclosed herein is not limited by the accompanying drawings, and all changes included in the spirit and scope of the present invention , should be understood to include equivalents or substitutes.

Terms including an ordinal number, such as first, second, etc., may be used to describe various elements, but the elements are not limited by the terms. The above terms are used only for the purpose of distinguishing one component from another.

When a component is referred to as being “connected” or “connected” to another component, it is understood that the other component may be directly connected or connected to the other component, but other components may exist in between. it should be On the other hand, when it is mentioned that a certain element is "directly connected" or "directly connected" to another element, it should be understood that the other element does not exist in the middle.

The singular expression includes the plural expression unless the context clearly dictates otherwise.

In the present application, terms such as "comprises" or "have" are intended to designate that a feature, number, step, operation, component, part, or combination thereof described in the specification exists, but one or more other features It should be understood that this does not preclude the existence or addition of numbers, steps, operations, components, parts, or combinations thereof.

1 is a diagram showing a configuration for controlling the cleaner 100 according to an embodiment of the present specification, and FIG. 2 is a control block diagram of each configuration constituting the control system of the cleaner 100 and the smart device 20 .

Referring to FIG. 1 , the control system of the cleaner 100 according to the embodiment of the present specification is a smart device 20 equipped with the cleaner 100 and an application (APP) for controlling or managing the cleaner 100 . ), a server 30 that manages applications and establishes reference data that is a basis for judging an abnormal operation based on driving data obtained from a cleaner, and a smart device 20, a cleaner 100, and a server 30 It may include the Internet 40 for inter-communication.

In addition, the server 30 may be configured to further include artificial intelligence.

Referring to FIG. 2 , the cleaner 100 includes a control unit 101 , an input unit 102 , an output unit 103 , a sensing unit 104 , a memory 105 , a communication module 106 and a power supply unit 107 . may include.

The controller 101 may include a processor. For example, it may include a micro controller (MCU).

The input unit 102 may be formed on a control panel provided near the handle of the cleaner 100 , and may be provided in the form of a touch button or a push button. Alternatively, it may be provided in the form of a microphone to recognize a voice command. In addition, an input unit including a camera or an image sensor may be provided to recognize a user's gesture.

The output unit 103 may include a display provided as an image output unit and a speaker provided as an audio output unit.

The display may be provided on the control panel or as a separate display area, and may include an LCD panel on which an image or video is output. Alternatively, it may simply include a single or a plurality of light emitting units.

The speaker may output a selection sound, a warning sound, a cleaning start or cleaning completion notification signal, and the like. In addition, the speaker may be provided in an area other than the handle that can be gripped by the user.

The sensing unit 104 senses various values acting on the load in relation to the operation of the cleaner, and inputs the results to the control unit 101 . For example, the sensing unit 104 senses various types of information required to generate operation data, such as detecting a motor load for cleaning or detecting a type of a cleaning module, and inputs it to the controller 101 .

The memory 105 may include DRAM (RAM that requires refresh), SRAM (RAM that does not require refresh), ROM, EPROM, EEPROM, and the like.

In addition, the communication module 106 may include a wired communication module including a power line communication (PLC) capable of Internet communication or a wireless communication module including a Wi-Fi. The communication module 106 may include a transceiver or an antenna. In addition, the transceiver may include a transmitter and a receiver.

In addition, the power supply unit 107 and a driving unit for operating the cleaner 100 may be further included. The driving unit may include a driving motor or a motor pump. The driving motor may include a main driving motor installed in the cleaner body to generate a suction force and an auxiliary driving motor installed in a suction nozzle provided at the suction end of the cleaner to generate rotational force of the rollers.

In addition, the power supply unit 107 supplies power required for the operation of the cleaner, and may be composed of a rechargeable battery.

Meanwhile, the smart device 20 may include a smart phone that a user can carry. And the smart device 20 may include a control unit 21 , an input unit 22 , a memory 23 , a power supply unit 24 , a wireless communication unit 25 , a sound output unit 26 and a display unit 27 . have.

The input unit 22 may include a touch type button for inputting a command by touching the display unit 27 .

In addition, the wireless communication unit 25 may be a wireless communication module capable of communicating with the Internet 40 .

And the sound output unit 26 may include a speaker.

According to the above configuration, the user executes an application (APP) for management or control of the cleaner 100 installed in the smart device 20, and checks the management status of the cleaner 100 or inputs a control command through the application can do. In addition, the user may receive information about the management state of the cleaner 100 stored in the server 30 through the Internet 40 to the smart device 20 . And the control command input from the smart device 20 is transmitted to the server 30 of the application through the Internet 40, and the server 30 to the communication module 106 of the cleaner 100 through the Internet 40 Control commands can be sent.

In addition, the control command received through the communication module 106 is received by the control unit 101 of the cleaner 100, the control unit 101 can control the operation of the driving unit 105 according to the received control command.

In addition, the control unit 101 of the cleaner 100 may transmit an event occurring in the cleaning process received from the sensing unit 104 through the communication module 106 by wire or wirelessly. Event information transmitted through the communication module 106 of the cleaner 100 may be transmitted to the server 30 through the Internet 40 . In addition, the server 30 may transmit the received event information to the wireless communication unit 25 of the smart device 20 through the Internet 40 .

Also, the event information received by the wireless communication unit 25 may be displayed on the display unit 27 by the control unit 21 of the smart device 20 .

3 is a diagram schematically showing the configuration of an AI device that implements artificial intelligence included in a server. This artificial intelligence may include an AI module capable of performing AI processing.

The AI processing may include all operations related to the control of the cleaner 100 illustrated in FIG. 2 . For example, the cleaner 100 may transmit driving data related to an event occurring during operation to the server, and the AI processor 31 may perform AI processing based on the received driving data to generate processing/determination and control signals. have. For example, the AI processor 31 may operate to establish reference data informing whether an abnormal operation exists based on driving data received from a number of cleaners 100 connected through a communication network.

The AI processor 31 may learn the neural network using a program stored in the memory 35 . In particular, the AI processor 31 may learn an artificial neural network for recognizing an abnormal operation that occurs during the operation of the cleaner. Here, the artificial neural network may be designed to simulate a human brain structure on a computer, and may include a plurality of network nodes having weights that simulate neurons of the human neural network. The plurality of network modes may transmit and receive data according to a connection relationship, respectively, so as to simulate a synaptic activity of a neuron in which a neuron sends and receives a signal through a synapse. Here, the artificial neural network may include a deep learning model developed from a neural network model. In a deep learning model, a plurality of network nodes may exchange data according to a convolutional connection relationship while being located in different layers. Examples of neural network models include deep neural networks (DNN), convolutional deep neural networks (CNN), Recurrent Boltzmann Machine (RNN), Restricted Boltzmann Machine (RBM), deep trust It includes various deep learning techniques such as neural networks (DBN, deep belief networks) and deep Q-networks, and can be applied to fields such as computer vision, speech recognition, natural language processing, and voice/signal processing.

Meanwhile, the processor performing the above-described function may be a general-purpose processor (eg, CPU), but may be an AI-only processor (eg, GPU) for artificial intelligence learning.

The memory 35 may store various programs and data necessary for the operation of AI processing. The memory 35 may be implemented as a non-volatile memory, a volatile memory, a flash-memory, a hard disk drive (HDD), or a solid state drive (SDD). The memory 35 is accessed by the AI processor 31 , and reading/writing/modification/deletion/update of data by the AI processor 31 may be performed. Also, the memory 35 may store a neural network model (eg, a deep learning model) generated through a learning algorithm for data classification/recognition according to an embodiment of the present specification.

Meanwhile, the AI processor 31 may include a data learning unit 32 that learns a neural network for data classification/recognition. The data learning unit 32 may learn a criterion regarding which training data to use to determine data classification/recognition and how to classify and recognize data using the training data. The data learning unit 32 may learn the deep learning model by acquiring learning data to be used for learning and applying the acquired learning data to the deep learning model.

The data learning unit 32 may be manufactured in the form of at least one hardware chip. For example, the data learning unit 32 may be manufactured in the form of a dedicated hardware chip for artificial intelligence (AI), or is manufactured as a part of a general-purpose processor (CPU) or graphics-only processor (GPU) to constitute a part of artificial intelligence. can do. Also, the data learning unit 32 may be implemented as a software module. When implemented as a software module (or a program module including instructions), the software module may be stored in a computer-readable non-transitory computer readable medium. In this case, the at least one software module may be provided by an operating system (OS) or may be provided by an application.

The data learning unit 32 may include a training data acquiring unit 33 and a model learning unit 34 .

The training data acquisition unit 33 may acquire training data required for a neural network model for classifying and recognizing data. For example, the training data acquisition unit 33 may acquire vehicle data and/or sample data to be input to the neural network model as training data.

The model learning unit 34 may use the acquired training data to learn the neural network model to have a criterion for determining how to classify predetermined data. In this case, the model learning unit 34 may train the neural network model through supervised learning using at least a portion of the training data as a criterion for determination. Alternatively, the model learning unit 34 may learn the neural network model through unsupervised learning for discovering a judgment criterion by self-learning using learning data without guidance. Also, the model learning unit 54 may train the neural network model through reinforcement learning using feedback on whether the result of the situation determination according to the learning is correct. Also, the model learning unit 54 may train the neural network model by using a learning algorithm including an error back-propagation method or a gradient decent method.

When the neural network model is trained, the model learning unit 34 may store the learned neural network model in a memory. The model learning unit 34 may store the learned neural network model in a memory of a server connected to artificial intelligence and a wired or wireless network.

The data learning unit 32 further includes a training data preprocessor (not shown) and a training data selection unit (not shown) to improve the analysis result of the recognition model or to save resources or time required for generating the recognition model You may.

The learning data preprocessor may preprocess the acquired data so that the acquired data can be used for learning for situation determination. For example, the training data preprocessor may process the acquired data into a preset format so that the model learning unit 34 may use the acquired training data for image recognition learning.

In addition, the learning data selection unit may select data necessary for learning from among the learning data acquired by the learning data acquiring unit 33 or the training data preprocessed by the preprocessing unit. The selected training data may be provided to the model learning unit 34 . For example, the learning data selector may select only data about an object included in the specific region as the learning data by detecting a specific region among images acquired through the camera of the intelligent electronic device.

In addition, the data learning unit 32 may further include a model evaluation unit (not shown) in order to improve the analysis result of the neural network model.

The model evaluator may input evaluation data to the neural network model and, when an analysis result output from the evaluation data does not satisfy a predetermined criterion, may cause the model learning unit 52 to learn again. In this case, the evaluation data may be predefined data for evaluating the recognition model. As an example, the model evaluation unit may evaluate as not satisfying a predetermined criterion when, among the analysis results of the learned recognition model for the evaluation data, the number or ratio of evaluation data for which the analysis result is not accurate exceeds a preset threshold. .

The communication unit 37 may transmit the AI processing result by the AI processor 31 to the smart device.

4 is an exploded perspective view showing the cleaner 100 according to an embodiment.

Referring to FIG. 4 , the cleaner 100 includes a cleaner body 200 , a cleaning module 210 coupled to the cleaner body 200 , and a length adjusting member connecting the cleaner body 200 and the cleaning module 210 . 220 , a battery 400 coupled to the cleaner body 200 , and a cleaner holder 300 on which the cleaner body 200 is mounted.

The cleaner body 200 includes a body 201 in which a suction motor (not shown) for generating a suction force, a cyclone flow device (not shown) for separating dust from the sucked air, are installed, and the rear of the body 201 . It may include a handle part 202 that is connected to and grippable by the user, and a connection part 203 that is connected to the front of the body part 201 and to which the cleaning module 210 or the length adjustment member 220 is coupled. .

The cleaning module 210 may include a suction unit 211 for sucking dust and the like, and a coupling unit 212 coupled to the cleaner body 200 or the length adjusting member 220 .

One end of the length adjustment member 220 may be coupled to the cleaner body 200 , and the other end may be coupled to the cleaning module 210 . And the length adjustment member 220 may adopt a structure in which the length is variable. And the length adjustment member 220 may employ an elastically changeable material. In addition, one end of the length adjusting member 220 is coupled to the cleaner body 200, and a suction unit (not shown) is provided at the other end to perform a suction function without a separate cleaning module being coupled thereto.

The battery 400 may be detachably connected to the body 201 of the cleaner body 200 to supply power for driving the cleaner 100 . In addition, the battery 400 may be detachably connected to the battery accommodating part 302 of the cleaner cradle 300 to be charged. In addition, two batteries 400 are provided, one is coupled to the cleaner body 200 to supply power, and the other is coupled to the cleaner holder 300 to be charged.

The vacuum cleaner holder 300 includes a stand-type or wall-mounted body portion 301 , a battery receiving portion 302 in which the battery 400 is charged, a cleaner support portion 303 supporting the cleaner body 200 , and a cleaner It may include a charging unit 304 electrically connected to the battery 400 coupled to the body 200 .

Although the drawing shows the wall-mounted body part 301, it may include a stand-type body part (not shown) provided in a state of standing on the floor.

In addition, the battery 400 may be electrically connected to the charging unit 304 in a state in which the cleaner body 200 is supported by the cleaner support part 303 . Accordingly, the user can charge the battery 400 while the cleaner body 200 is mounted on the cleaner cradle 300 .

In addition, the cleaner holder 300 may be electrically connected to the external outlet 311 through the power line 310 . The current transmitted through the power line 310 charges the first battery accommodated in the cleaner body 200 through the charging unit 304 of the cleaner holder, and the second battery mounted in the battery accommodation unit 302 can be charged. have.

In addition, in the cleaner 100 , a suction unit for performing various functions may be mounted on the cleaner body 200 in a modular manner. That is, a plurality of cleaning modules 210 are provided for each function, and a user can use a cleaning module 210 suitable for a cleaning target by combining it with the cleaner body 200 .

The cleaning module 210 includes a cleaning module having a basic floor suction port, a cleaning module having a bedding exclusive suction port, a cleaning module having a mattress exclusive suction port, a cleaning module having a carpet exclusive suction port, and a wet mop. It may include a cleaning module provided. In addition, a dedicated cleaning module for performing various functions, such as for hardened dust, for bent gaps, and for cleaning the upper part, may be provided as a module.

In addition, the drawing shows that the cleaning module 221 having a 2in1 suction port and the cleaning module 222 having a gap suction port are mounted on the vacuum cleaner holder 300 . The cleaning module 221 having a 2in1 suction port can be used as a basic type when cleaning a sofa or a mattress by adjusting the length of a brush by a button operation, and can be used as a brush type when cleaning a picture frame or furniture. In addition, the cleaning module 222 having a suction port for a gap may be advantageous in that the suction port is provided in the form of a narrow nozzle and inserted into a narrow gap to suck dust or the like.

5 is a diagram illustrating a control method of the cleaner 100 according to an exemplary embodiment.

The cleaner 100 according to the embodiment of the present specification is provided in a modular type in which the cleaning module 210 is detachable and can be used while changing the appropriate cleaning module 210 as necessary.

The cleaner main body 200 may receive information and load information of the cleaning module used from the cleaning module 210 . For example, the main circuit (MCU: Micro Controller Unit) provided in the cleaner body 200 uses the current value (or voltage value) measured from the power line connected to the cleaning module 210 to the cleaning module ( 210) can be stored separately. And since the current value of the power line may vary according to the load applied to the cleaning module 210 , the main circuit may also store and use load information or torque information applied to the cleaning module 210 .

In addition, the main circuit may store information about which cleaning module 210 was used at what time and for what time, that is, use time information. And when the suction mode can be divided into strong/medium/weak according to the rotational force of the suction motor of the cleaner body 200, the main circuit may store the usage time and usage output for each suction mode used by the user. In addition, the main circuit may transmit the accumulated use time and use frequency information for each cleaning module used by the user to the server 30 together with this information.

The server 30 may provide cleaning history information to the user by using the accumulated information. In addition, the server 30 may analyze the user's cleaning pattern and recommend a cleaning type required for the smart device 20 or the cleaner 100 to notify the arrival of the cleaning time. For example, when analyzing through the accumulated data of the cleaner 100 , when two months have passed since the last cleaning of the bedding has passed, the user is informed that it is time to clean the bedding through the application of the smart device 20 . can inform

Also, the server 30 may establish reference data for determining whether the cleaner is operating normally or abnormally based on the collected driving data of the cleaner. The server 30 may receive driving data related to events occurring during operation of the cleaner from a number of vacuum cleaners connected to enable data communication through the Internet and input it to the above-described AI device. Then, the AI device can use the driving data as learning data to establish a decision policy by learning the neural network model to determine whether there is an abnormality according to the operation of the vacuum cleaner.

In addition, the server 30 may notify that the cleaning time has come for the cleaning module 210 part, or notify that the cleaning module 210 is broken or the replacement time has elapsed.

6 is a block diagram illustrating a connection relationship between the cleaner 100 .

6 (a), the cleaning module 210 and the cleaner main body 200 are physically connected through a power line, the cleaner main body 200 and the server 30 are connected by wireless communication, the server ( 30) and the smart device 20 may be connected by wireless communication.

In the coupling part of the cleaning module 210 and the cleaner body 200 , the suction power generated by the cleaner body 200 is transmitted to the cleaning module 210 , and the suction pipe serves as a passage through which the dust sucked by the cleaning module 210 moves. And, a power line for providing power to the cleaning module 210 may be provided.

And the main circuit of the cleaner body 200 receives information about which cleaning module 210 is coupled through the current value (or voltage value) of the power line, whether it is currently in use, and what the magnitude of the applied load or torque is. can be obtained

Referring to (b) of FIG. 6 , the cleaning module 210 and the cleaner body 200 are physically connected through a power line and connected through wired communication, and the cleaner body 200 and the server 30 are wirelessly connected. It is connected by communication, and the server 30 and the smart device 20 may be connected by wireless communication.

In the coupling part of the cleaning module 210 and the cleaner body 200 , the suction power generated by the cleaner body 200 is transmitted to the cleaning module 210 , and the suction pipe is a passage through which the dust sucked by the cleaning module 210 moves. And, a power line for providing power to the cleaning module 210 and a communication line for transmitting usage information of the cleaning module 210 may be provided.

In addition, the main circuit of the cleaner body 200 may acquire information about which cleaning module 210 is coupled, whether it is currently being used, and what the magnitude of the applied load is through the information of the communication line. The current (or voltage) information of the power line includes noise, and when the noise level is relatively large, it may be impossible to identify information to be obtained from them. In this case, by using a separate communication line, only information to be obtained can be transmitted through a separate line. For example, when a cleaning module dedicated to bedding is used in combination, it may be difficult to obtain usage information through the power line because the operating current is very weak. In this case, a communication line is provided separately from the power line, and the use information of the cleaning module 210 is transmitted through the communication line, so that information can be transmitted without missing information.

Referring to (c) of FIG. 6 , the cleaning module 210 and the cleaner body 200 are physically connected through a power line and connected through wireless communication, and the cleaner body 200 and the server 30 are wirelessly connected. It is connected by communication, and the server 30 and the smart device 20 may be connected by wireless communication.

The cleaning module 210 may be provided with a transmitter for wirelessly transmitting usage information. In addition, a receiver for receiving information of the cleaning module 210 may be provided in the cleaner body 200 .

In addition, the main circuit of the cleaner body 200 may acquire information about which cleaning module 210 is coupled, whether it is currently being used, and what the magnitude of the applied load is through the information of the receiver. As a means of wireless communication that can be used, Zigbee or Bluetooth may be used.

7 is a cross-sectional view showing a coupling part between the cleaner body 200 and the cleaning module 210 according to the first embodiment, and FIG. 8 is a coupling part between the cleaner body 200 and the cleaning module 210 according to the first embodiment. Each is a plan view.

The cleaner body 200 may be connected to the front of the body 201 and a connection part 203 to which the cleaning module 210 or the length adjusting member 220 is coupled may be formed. The connection part 203 may be provided in the form of a tube protruding from the front of the body part 201 .

And one end of the cleaning module 210 or the length adjustment member 220 may be formed with a coupling portion 212 coupled to the connection portion (203). The coupling part 212 may be provided in the form of a tube in which the connection part 203 can be accommodated. At this time, the inner diameter of the coupling portion 212 may be the same as or slightly larger than the outer diameter of the connection portion 203 .

The connection part 203 and the coupling part 212 can be detachably coupled, and for example, a coupling groove 203c formed to be recessed into the outer circumferential surface of the coupling part 203 and the coupling part 212 are formed to protrude from the inner circumferential surface. It may be provided by the coupling of the coupling protrusion 212c.

In addition, the coupling protrusion 212c may be connected to the coupling part 212 by a hinge and supported by an elastic member such as a coil spring. That is, when the user inserts the connection part 203 into the space inside the coupling part 212, the coupling protrusion 212c is pressed in a state in which the elastic member is pressed, and when the insertion of the connection part 203 is completed, the restoring force of the elastic member is applied. By this, the coupling protrusion 212c is fitted into the coupling groove 203c. Accordingly, the connection part 203 and the coupling part 212 may be firmly coupled.

During separation, a pusher provided on the outer circumferential surface of the coupling part 212 may be used. When the user presses the pusher, the coupling protrusion 212c connected thereto is pressed while the elastic member is pressed. That is, the coupling protrusion 212c may be separated from the coupling groove 203c to separate the coupling part 203 from the coupling part 212 .

The connection part 203 transmits the suction power generated by the cleaner main body 200 to the cleaning module 210 , and a first suction pipe 203a that is a passage through which the dust sucked by the cleaning module 210 moves, and the cleaning module A first power connection unit 203b for providing power to the 210 may be provided.

And the coupling part 212 is the second suction pipe 212a, which is a passage through which the suction power of the connection part 203 is transmitted, and the dust sucked by the cleaning module 210 moves, and the power from the first power connection part 203b. A second power connection unit 212b for receiving .

The first and second power connection parts 203b and 212b may be provided on one side of the first and second suction pipes 203a and 212a, and may be provided in a shape in which two terminals are connected. For example, the second power connection part 212b is provided so that the positive terminal protrudes, and the first power connection part 203b is provided so that the negative terminal is recessed, and the second power connection part 212b can be inserted thereinto.

That is, while the connection part 203 and the coupling part 212 are coupled, the suction pipes 203a and 212a and the power connection parts 202b and 212b may be simultaneously connected.

9 is a plan view showing a coupling portion of the cleaner body 200 and the cleaning module 210 according to the second embodiment, respectively.

The connection part 203 transmits the suction power generated by the cleaner main body 200 to the cleaning module 210 , and a first suction pipe 203a that is a passage through which the dust sucked by the cleaning module 210 moves, and the cleaning module A first power connection unit 203b for providing power to the 210 and a first information connection unit 203d connected to a second information connection unit 212d to be described later to receive information may be provided.

And the coupling part 212 is the second suction pipe 212a, which is a passage through which the suction power of the connection part 203 is transmitted, and the dust sucked by the cleaning module 210 moves, and the power from the first power connection part 203b. A second power connection unit 212b for receiving the , and a second information connection unit 212d for transmitting information of the cleaning module 210 to the main circuit of the cleaner body 200 may be provided.

The first and second power connection parts 203b and 212b may be provided on one side of the first and second suction pipes 203a and 212a, and may be provided in a shape in which two terminals are connected. For example, the second power connection part 212b is provided so that the positive terminal protrudes, and the first power connection part 203b is provided so that the negative terminal is recessed, and the second power connection part 212b can be inserted thereinto.

In addition, the first and second information connecting units 203d and 212d may be provided adjacent to the first and second power connecting units 203b and 212b, and may be provided in a shape in which one terminal is connected. For example, the second information connection part 212d is provided so that one terminal protrudes, and the first information connection part 203d is provided so that the negative terminal is recessed, and the second power connection part 212d can be inserted thereinto.

That is, while the connection part 203 and the coupling part 212 are coupled, the suction pipes 203a and 212a, the power connection parts 202b and 212b, and the information connection parts 202d and 212d may be simultaneously connected.

Hereinafter, a method of controlling a cleaner according to an embodiment of the present invention will be described with reference to FIG. 10 .

Referring to FIG. 10 , the user drives an app installed on the smart device 20 to be connected to the cleaner 100 through the communication network 40 so that data communication is possible, and the cleaner 100 wirelessly communicates , for example, by being connected to the communication network 40 through Wi-Fi, it may be connected to enable data communication with the smart device 20 (S11).

In this case, the cleaner 100 and the smart device 20 may be directly connected through the communication network 40 or may be connected through the mediation of the server 30 .

The user can set various functions of the vacuum cleaner 100 by using the app installed on the smart device 20. For example, the user can set to inform the life time and cleanliness of the filter installed in the vacuum cleaner, or set the motor load Various settings can be made through the app, such as setting to notify the level or not. Such setting information is transmitted to the cleaner 100 through a communication network, and the control unit 101 of the cleaner 100 receives necessary information from the sensing unit 104 based on the setting information. And, it may operate to provide a notification to the user through the app based on the received information.

The server 30 receives driving data in which events occurring while the respective cleaners are operated are recorded from the plurality of cleaners 100 connected through the Internet 40 (S11).

Examples of the event are shown in FIG. 11 . 11 , the event refers to a case in which the cleaner operates abnormally, and the characteristics of driving data according to the event have been described. For example, when the suction motor of the vacuum cleaner is overheated, the operation data shows a normal level of the motor output value (RPM, current), but the temperature excessively increases.

In addition, the operation data may include a cleaning mode, accumulated use time, use time, sensor information indicating a load of the motor, and various sensor information of a sensing unit indicating a cleaning floor. Also, in one example, the driving data may be transmitted to the server through a communication network, and used as learning data necessary for the AI device to determine an operation failure.

In the next step S12 , the server 30 receives driving data from a plurality of cleaners connected to the communication network 40 . The server 30 may be connected to vacuum cleaners of the same model sold worldwide through the communication network 400, and may also receive operation data from the connected cleaners. Sufficient learning data necessary for training the intelligence to determine the cause of a failure according to an event (or malfunction) can be acquired through the communication network 40. The artificial intelligence (or AI device) mounted on the server 30 is Through repeated learning (or deep learning) of the received learning data, it is possible to establish a decision policy that is a criterion for determining abnormal operation of the cleaner.

12 shows a process in which the server 30 establishes a decision policy.

In step S21 , the server 30 receives operation data of each cleaner from a plurality of cleaners connected to the communication network 40 . Here, the driving data relates to the battery, and is data including at least the accumulated number of uses from the factory to the present, the cleaning mode, the operation time according to the cleaning mode, and the usage of the battery.

The server 30 parses the accumulated number of uses from the driving data received from the plurality of cleaners, and determines whether the corrugated accumulated number of uses meets a condition (S22). As an example, the condition may be a case in which the cumulative number of uses of the cleaner is equal to or less than 100 times since shipment.

As a result of the determination in step S22, the server 30 determines that the driving data that meets the criteria is used as learning data for establishing a decision policy, and if it does not meet the criteria, the driving data is not referred to as learning data (S23) .

The operation described through FIG. 12 may be implemented in an AI device, and in particular, may be an operation model of the data learning unit 32 .

The learning data acquisition unit 33 extracts the accumulated number of uses from the received driving data to determine whether it meets the condition, and if it meets the condition, it is input to the model learning unit 34 to drive the model learning unit 34 input. Train a neural network model based on data.

In step S22, the driving data that meets the conditions is leveled through the following process. Here, the leveling refers to a process of making the operating conditions of the cleaners the same. For example, when there are first to third cleaners, it is assumed that the first cleaner operates in the normal mode for 21 minutes, the second cleaner operates in the strong mode for 14 minutes, and the third cleaner operates in the turbo mode for 7 minutes If it is assumed that the operation is performed, since the cleaning mode and operation time are different, it is necessary to set the standards to be the same. In the present specification, it is said that the process of matching the operating conditions in this way is equalized.

The processor 31 normalizes the first driving data received from the first cleaner, the second driving data received from the second cleaner, and the third driving data received from the third cleaner as follows.

It is assumed that 100% of the battery is used, and the battery consumption ratio for each cleaning mode may be set as normal mode: strong mode: turbo mode = 1:2:3. Here, the consumption rate of the battery may be expressed as the output of the battery per minute according to the mode. According to this standard, since the second operation data was operated for 14 minutes in the strong mode, it is normalized to be operated for 21 minutes in the normal mode. In addition, the third operation data is averaged to be operated for 7 minutes in the turbo mode and operated for 21 minutes in the normal mode.

Accordingly, the first to third cleaners may be averaged to operate for 21 minutes in the normal mode when 100% of the battery is used.

This is an example, and by extending the number of cleaners and leveling the received driving data according to the above leveling method, the operating time of each battery mode may be calculated.

Meanwhile, in the above-described example, only the case where the driving data is specialized for the battery has been described, but the reference data may be generated through the same leveling process according to an event occurring in the cleaner.

The AI device installed in the server may operate to establish a decision policy for determining whether the event is a malfunction of the cleaner according to an event with reference to the received driving data. This decision policy can actually increase its accuracy by training the neural network model of the AI device.

Returning to FIG. 10 , after the determination policy is set as described above, in step S14 , the server 30 determines whether the received driving data is malfunctioning according to the determination policy. For example, the received driving data is input to the neural network model, and the neural network model learned based on the driving data determines whether the cleaner is operating abnormally based on the input driving data and outputs the result. The accuracy of judgment can be improved by training the neural network model. The neural network model can increase its accuracy by repeating the process of classifying input driving data into similar cases and outputting the results, and by repeatedly calculating similar data.

Meanwhile, FIG. 13 is a flowchart illustrating a process of determining whether driving data is a malfunction according to a decision policy when the driving data relates to a battery.

Referring to FIG. 13 , the server 30 receives operation data from cleaners connected to the communication network 30 .

The server 30 extracts information on events generated by the operation of the cleaner, such as a driving mode and battery usage, from the received driving data.

On the other hand, since the received driving data has different variables and is generated according to operation, the standards are different. Therefore, it is necessary to convert all received operation data under the same conditions to obtain accurate output.

To this end, the server 30 extracts battery usage from the received driving data, and converts them all based on the same standard. For example, all driving data received from the server may be leveled to a state in which 100% of the battery is used. For example, if the eleventh driving data indicates that the vacuum cleaner operates for 10 minutes and uses 50% of the battery, the eleventh driving data is normalized to be used for 20 minutes when normalized based on 100% use ( S32).

Next, the server 30 corrects all cleaning modes to the same standard (S33). For example, some vacuum cleaners may operate only in the normal mode, and other vacuum cleaners may operate in the normal mode and turbo mode depending on the vacuum cleaner. Therefore, in one example, all driving data received for the same comparison may be converted to a normal mode basis.

For example, if the vacuum cleaner operates for 5 minutes in the normal mode and 5 minutes in the turbo mode, the operation data generated by this vacuum cleaner is based on the normal mode. It can be converted to 20 minutes.

As described above, through steps S32 and S33, all driving data received to the server 30 may be converted into usage time based on the same standard, for example, 100% use of the battery, and the driving mode based on the normal mode. Hereinafter, the operation data changed according to the same standard is referred to as conversion data.

As such, after converting all driving data received through steps S32 and S33 into converted data, the server 30 extracts the converted data in which the converted data has a remaining battery life below the first condition. As an example, the first condition may say that the life of the battery is less than 30% (S34).

For example, when the lifespan of the standardized battery is 21 minutes of operation in the normal mode when setting the determination policy as described above, batteries that are less than 30% of the determination policy are selected. For example, a battery whose maximum use time of the battery is less than 6.3 minutes may be determined to have a remaining lifespan of less than 30% compared to the initial period.

Next, in step S35, the identification number of the user's smart device can be identified from the recorded user information extracted to the server based on the data extracted in step S34, and service information can be transmitted to the corresponding number. This service information may be provided in the form of a push notification through an app installed on a smart device possessed by the user, or may be transmitted through an SNS message such as a general text message or a KakaoTalk message. Here, the message may be configured to provide general information related to the battery, such as battery life, usable time, replacement, and purchase link.

The present invention described above can be implemented as computer-readable code on a medium in which a program is recorded. The computer-readable medium includes all kinds of recording devices in which data readable by a computer system is stored. Examples of computer-readable media include Hard Disk Drive (HDD), Solid State Disk (SSD), Silicon Disk Drive (SDD), ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc. There is also a carrier wave (eg, transmission over the Internet) that is implemented in the form of. Accordingly, the above detailed description should not be construed as restrictive in all respects but as exemplary. The scope of the present invention should be determined by a reasonable interpretation of the appended claims, and all modifications within the equivalent scope of the present invention are included in the scope of the present invention.

Claims (7)

collecting operation data on the operation of each cleaner from a plurality of cleaners connected to a communication network capable of data communication, and establishing a determination policy as a criterion for determining abnormal operation of the cleaner based on the operation data;
receiving the driving data from a cleaner connected to the communication network and determining whether the cleaner is abnormal according to the decision policy based on the driving data; And,
providing service information for notifying an abnormal operation of the cleaner to a user of the cleaner through the communication network when it is determined that there is an abnormality as a result of the determination;
A control method of a cleaner comprising a. According to claim 1,
The step of establishing the decision policy is,
extracting the cumulative number of uses of each vacuum cleaner from the collected driving data;
determining whether the extracted cumulative number of uses meets a criterion; And,
if the determination result satisfies the criterion, the driving data is the learning data for setting the determination policy;
A control method of a cleaner further comprising a. According to claim 1,
The operation data includes a cleaning mode and a usage time according to a motor load of the cleaner,
The step of establishing the decision policy is,
A control method of a cleaner that equalizes all the driving data collected from the plurality of cleaners under the same condition based on the cleaning mode extracted from the driving data and the usage time. 4. The method of claim 3,
The driving data is information indicating battery usage, and includes a usage time of the battery according to the cleaning mode and an operating time of the cleaner according to the cleaning mode. 5. The method of claim 4,
The leveling is a control method of a cleaner set to have an inverse relationship between the motor load of the cleaner and the use time. 5. The method of claim 4,
The control method of a cleaner in which the service information is provided to the user's smart device only through the communication. 7. The method of claim 6,
The service information may include at least one of a lifespan of the battery, a replacement, and a purchase link.

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