KR20210074162A - An artificial intelligence apparatus for providing a connection between home devices and method thereof - Google Patents

Abstract

The present invention relates to an artificial intelligence device for providing connection between a plurality of home devices. Provided is the artificial intelligence device, which includes: a communication unit configured to receive a visitor image from a door locking device including a door camera for photographing a visitor; and a processor configured to obtain visitor information output from a visitor recognition model by inputting the visitor image to the visitor recognition model and select a home device to receive the visitor image and the visitor information from among the plurality of home devices, wherein the communication unit transmits the visitor image and the visitor information to the selected home device. Accordingly, it is possible to integrally control the plurality of home devices by connecting the plurality of home devices.

Description

AN ARTIFICIAL INTELLIGENCE APPARATUS FOR PROVIDING A CONNECTION BETWEEN HOME DEVICES AND METHOD THEREOF

The present disclosure relates to an artificial intelligence apparatus and method for providing a connection between a plurality of home devices.

Artificial intelligence is a field of computer science and information technology that studies how computers can do the thinking, learning, and self-development that can be done by human intelligence. This means that it can be imitated.

Also, AI does not exist by itself, but has many direct and indirect connections with other fields of computer science. In particular, in modern times, attempts are being made to introduce artificial intelligence elements in various fields of information technology and use them to solve problems in that field.

On the other hand, technologies for recognizing and learning surrounding situations using artificial intelligence, providing information desired by a user in a desired form, or performing an operation or function desired by a user are being actively researched.

And, an electronic device that provides such various operations and functions may be called an artificial intelligence device.

On the other hand, recently, there are numerous artificial devices inside and outside of houses, offices, transportation facilities, cultural facilities, sports facilities, and various facilities.

Each device may acquire or generate various data.

Accordingly, there is an increasing need to share or manage data acquired by each device with other devices.

In addition, there is an increasing need to connect devices to share or control data between devices.

SUMMARY OF THE INVENTION The present disclosure aims to solve the above and other problems.

An object of the present disclosure is to provide an artificial intelligence apparatus and method for providing a connection between a plurality of home devices.

An object of the present disclosure is to provide an artificial intelligence device and method applicable to a smart home system.

An object of the present disclosure is to provide an artificial intelligence apparatus and method capable of enhancing entry/exit security by providing a device-to-device connection.

An object of the present disclosure is to provide an artificial intelligence apparatus and method for providing a connection between devices so that a user can conveniently control each device.

An object of the present disclosure is to provide an artificial intelligence apparatus and method for providing a connection between devices so that a user can conveniently control each device.

An embodiment of the present disclosure provides an artificial intelligence device for providing a connection between a plurality of home devices, a communication unit for receiving a visitor image from a door lock device including a door camera for photographing a visitor, and a visitor image to a visitor recognition model and a processor for inputting and obtaining visitor information output from the visitor recognition model, and selecting a home device to receive a visitor image and visitor information from among a plurality of home devices, wherein the communication unit transmits the visitor image and visitor information to the selected home device. It provides an artificial intelligence device that transmits.

In addition, the steps of receiving a visitor image from a door lock device including a door camera for photographing a visitor of the present disclosure, inputting the visitor image into a visitor recognition model to obtain visitor information output from the visitor recognition model, a plurality of homes There is provided a method for providing a connection between a plurality of home devices, comprising the steps of selecting a home device from among devices to receive the visitor image and visitor information, and transmitting the visitor image and visitor information to the selected home device.

According to an embodiment of the present disclosure, a plurality of home devices may be connected to integrally control the plurality of home devices.

In addition, according to various embodiments of the present disclosure, access security for visitors may be strengthened by connecting a plurality of home devices.

Also, according to various embodiments of the present disclosure, it is possible to identify a member and detect an abnormal activity of the member by using information collected from a plurality of home devices.

1 shows an AI device according to an embodiment of the present disclosure.
2 illustrates an AI server according to an embodiment of the present disclosure.
3 shows an AI system according to an embodiment of the present disclosure.
4 is a block diagram illustrating an AI device according to an embodiment of the present disclosure.
5 shows a smart home system according to an embodiment of the present invention.
6 is a diagram illustrating a smart home security system according to an embodiment of the present invention.
7 is a flowchart illustrating a method for providing a connection between devices for home security according to an embodiment of the present invention.
8 is a diagram illustrating a home security interface according to an embodiment of the present invention.
9 is a diagram for explaining a method for providing a connection between home devices according to an embodiment of the present invention.
10 is an exemplary diagram of an integrated information interface according to an embodiment of the present invention.
11 is an exemplary diagram of a lighting control interface according to an embodiment of the present invention.
12 is a block diagram of a home mail system according to an embodiment of the present invention.
13 is an exemplary diagram of an integrated convenience application according to an embodiment of the present invention.
14 is an exemplary diagram of a guidance system according to an embodiment of the present invention.
15 is an exemplary diagram of a guidance system according to an embodiment 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 numerals regardless of reference numerals, and overlapping descriptions 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 in the present specification is not limited by the accompanying drawings, and all changes included in the spirit and scope of the present disclosure , 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.

<Artificial Intelligence (AI)>

Artificial intelligence refers to a field that studies artificial intelligence or methodologies that can make it, and machine learning refers to a field that defines various problems dealt with in the field of artificial intelligence and studies methodologies to solve them. do. Machine learning is also defined as an algorithm that improves the performance of a certain task through constant experience.

An artificial neural network (ANN) is a model used in machine learning, and may refer to an overall model having problem-solving ability, which is composed of artificial neurons (nodes) that form a network by combining synapses. An artificial neural network may be defined by a connection pattern between neurons of different layers, a learning process that updates model parameters, and an activation function that generates an output value.

The artificial neural network may include an input layer, an output layer, and optionally one or more hidden layers. Each layer includes one or more neurons, and the artificial neural network may include neurons and synapses connecting neurons. In the artificial neural network, each neuron may output a function value of an activation function for input signals, weights, and biases input through synapses.

Model parameters refer to parameters determined through learning, and include the weight of synaptic connections and the bias of neurons. In addition, the hyperparameter refers to a parameter that must be set before learning in a machine learning algorithm, and includes a learning rate, the number of iterations, a mini-batch size, an initialization function, and the like.

The purpose of learning the artificial neural network can be seen as determining the model parameters that minimize the loss function. The loss function may be used as an index for determining optimal model parameters in the learning process of the artificial neural network.

Machine learning can be classified into supervised learning, unsupervised learning, and reinforcement learning according to a learning method.

Supervised learning refers to a method of training an artificial neural network in a state where a label for training data is given, and the label is the correct answer (or result value) that the artificial neural network should infer when the training data is input to the artificial neural network. can mean Unsupervised learning may refer to a method of training an artificial neural network in a state where no labels are given for training data. Reinforcement learning can refer to a learning method in which an agent defined in an environment learns to select an action or sequence of actions that maximizes the cumulative reward in each state.

Among artificial neural networks, machine learning implemented as a deep neural network (DNN) including a plurality of hidden layers is also called deep learning (deep learning), and deep learning is a part of machine learning. Hereinafter, machine learning is used in a sense including deep learning.

<Robot>

A robot can mean a machine that automatically handles or operates a task given by its own capabilities. In particular, a robot having a function of recognizing an environment and performing an operation by self-judgment may be called an intelligent robot.

Robots can be classified into industrial, medical, home, military, etc. depending on the purpose or field of use.

The robot may be provided with a driving unit including an actuator or a motor to perform various physical operations such as moving the robot joints. In addition, the movable robot includes a wheel, a brake, a propeller, and the like in the driving unit, and can travel on the ground or fly in the air through the driving unit.

<Self-Driving>

Autonomous driving refers to a technology that drives by itself, and an autonomous driving vehicle refers to a vehicle that runs without a user's manipulation or with a minimal user's manipulation.

For example, autonomous driving includes technology for maintaining a driving lane, technology for automatically adjusting speed such as adaptive cruise control, technology for automatically driving along a predetermined route, technology for automatically setting a route when a destination is set, etc. All of these can be included.

The vehicle includes a vehicle having only an internal combustion engine, a hybrid vehicle having both an internal combustion engine and an electric motor, and an electric vehicle having only an electric motor, and may include not only automobiles, but also trains, motorcycles, and the like.

In this case, the autonomous vehicle may be viewed as a robot having an autonomous driving function.

<Extended Reality (XR)>

The extended reality is a generic term for virtual reality (VR), augmented reality (AR), and mixed reality (MR). VR technology provides only CG images of objects or backgrounds in the real world, AR technology provides virtual CG images on top of images of real objects, and MR technology is a computer that mixes and combines virtual objects in the real world. graphic technology.

MR technology is similar to AR technology in that it shows both real and virtual objects. However, there is a difference in that in AR technology, a virtual object is used in a form that complements a real object, whereas in MR technology, a virtual object and a real object are used with equal characteristics.

XR technology can be applied to HMD (Head-Mount Display), HUD (Head-Up Display), mobile phone, tablet PC, laptop, desktop, TV, digital signage, etc. can be called

1 shows an AI device 100 according to an embodiment of the present disclosure.

AI device 100 is TV, projector, mobile phone, smartphone, desktop computer, notebook computer, digital broadcasting terminal, PDA (personal digital assistants), PMP (portable multimedia player), navigation, tablet PC, wearable device, set-top box (STB) ), a DMB receiver, a radio, a washing machine, a refrigerator, a desktop computer, a digital signage, a robot, a vehicle, etc., may be implemented as a fixed device or a movable device.

Referring to FIG. 1 , the terminal 100 includes a communication unit 110 , an input unit 120 , a learning processor 130 , a sensing unit 140 , an output unit 150 , a memory 170 and a processor 180 , and the like. may include

The communication unit 110 may transmit/receive data to and from external devices such as other AI devices 100a to 100e or the AI server 200 using wired/wireless communication technology. For example, the communication unit 110 may transmit/receive sensor information, a user input, a learning model, a control signal, and the like with external devices.

At this time, the communication technology used by the communication unit 110 includes GSM (Global System for Mobile communication), CDMA (Code Division Multi Access), LTE (Long Term Evolution), 5G, WLAN (Wireless LAN), Wi-Fi (Wireless-Fidelity) ), Bluetooth??, RFID (Radio Frequency Identification), Infrared Data Association (IrDA), ZigBee, NFC (Near Field Communication), and the like.

The input unit 120 may acquire various types of data.

In this case, the input unit 120 may include a camera for inputting an image signal, a microphone for receiving an audio signal, a user input unit for receiving information from a user, and the like. Here, the camera or microphone may be treated as a sensor, and a signal obtained from the camera or microphone may be referred to as sensing data or sensor information.

The input unit 120 may acquire training data for model training and input data to be used when acquiring an output using the training model. The input unit 120 may acquire raw input data, and in this case, the processor 180 or the learning processor 130 may extract an input feature as a preprocessing for the input data.

The learning processor 130 may train a model composed of an artificial neural network by using the training data. Here, the learned artificial neural network may be referred to as a learning model. The learning model may be used to infer a result value with respect to new input data other than the training data, and the inferred value may be used as a basis for a decision to perform a certain operation.

In this case, the learning processor 130 may perform AI processing together with the learning processor 240 of the AI server 200 .

In this case, the learning processor 130 may include a memory integrated or implemented in the AI device 100 . Alternatively, the learning processor 130 may be implemented using the memory 170 , an external memory directly coupled to the AI device 100 , or a memory maintained in an external device.

The sensing unit 140 may acquire at least one of internal information of the AI device 100 , information on the surrounding environment of the AI device 100 , and user information by using various sensors.

At this time, sensors included in the sensing unit 140 include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, an optical sensor, a microphone, and a lidar. , radar, etc.

The output unit 150 may generate an output related to visual, auditory or tactile sense.

In this case, the output unit 150 may include a display unit that outputs visual information, a speaker that outputs auditory information, and a haptic module that outputs tactile information.

The memory 170 may store data supporting various functions of the AI device 100 . For example, the memory 170 may store input data obtained from the input unit 120 , learning data, a learning model, a learning history, and the like.

The processor 180 may determine at least one executable operation of the AI device 100 based on information determined or generated using a data analysis algorithm or a machine learning algorithm. In addition, the processor 180 may control the components of the AI device 100 to perform the determined operation.

To this end, the processor 180 may request, search, receive, or utilize the data of the learning processor 130 or the memory 170, and may perform a predicted operation or an operation determined to be desirable among the at least one executable operation. It is possible to control the components of the AI device 100 to execute.

In this case, when the connection of the external device is required to perform the determined operation, the processor 180 may generate a control signal for controlling the corresponding external device and transmit the generated control signal to the corresponding external device.

The processor 180 may obtain intention information with respect to a user input and determine a user's requirement based on the obtained intention information.

In this case, the processor 180 uses at least one of a speech to text (STT) engine for converting a voice input into a character string or a natural language processing (NLP) engine for obtaining intention information of a natural language. Intention information corresponding to the input may be obtained.

At this time, at least one of the STT engine and the NLP engine may be configured as an artificial neural network, at least a part of which is learned according to a machine learning algorithm. And, at least one or more of the STT engine or the NLP engine is learned by the learning processor 130 , or learned by the learning processor 240 of the AI server 200 , or learned by distributed processing thereof. it could be

The processor 180 collects history information including the user's feedback on the operation contents or operation of the AI device 100 and stores it in the memory 170 or the learning processor 130, or the AI server 200 It can be transmitted to an external device. The collected historical information may be used to update the learning model.

The processor 180 may control at least some of the components of the AI device 100 in order to drive an application program stored in the memory 170 . Furthermore, in order to drive the application program, the processor 180 may operate two or more of the components included in the AI device 100 in combination with each other.

2 shows an AI server 200 according to an embodiment of the present disclosure.

Referring to FIG. 2 , the AI server 200 may refer to a device that trains an artificial neural network using a machine learning algorithm or uses a learned artificial neural network. Here, the AI server 200 may be configured with a plurality of servers to perform distributed processing, and may be defined as a 5G network. In this case, the AI server 200 may be included as a part of the AI device 100 to perform at least a part of AI processing together.

The AI server 200 may include a communication unit 210 , a memory 230 , a learning processor 240 , and a processor 260 .

The communication unit 210 may transmit/receive data to and from an external device such as the AI device 100 .

The memory 230 may include a model storage unit 231 . The model storage unit 231 may store a model (or artificial neural network, 231a) being trained or learned through the learning processor 240 .

The learning processor 240 may train the artificial neural network 231a using the training data. The learning model may be used while being mounted on the AI server 200 of the artificial neural network, or may be used while being mounted on an external device such as the AI device 100 .

The learning model may be implemented in hardware, software, or a combination of hardware and software. When a part or all of the learning model is implemented in software, one or more instructions constituting the learning model may be stored in the memory 230 .

The processor 260 may infer a result value with respect to new input data using the learning model, and may generate a response or a control command based on the inferred result value.

3 shows an AI system 1 according to an embodiment of the present disclosure.

Referring to FIG. 3 , the AI system 1 includes at least one of an AI server 200 , a robot 100a , an autonomous vehicle 100b , an XR device 100c , a smart phone 100d , or a home appliance 100e . It is connected to the cloud network 10 . Here, the robot 100a to which the AI technology is applied, the autonomous driving vehicle 100b, the XR device 100c, the smart phone 100d, or the home appliance 100e may be referred to as AI devices 100a to 100e.

The cloud network 10 may constitute a part of the cloud computing infrastructure or may refer to a network existing in the cloud computing infrastructure. Here, the cloud network 10 may be configured using a 3G network, a 4G or Long Term Evolution (LTE) network, or a 5G network.

That is, each of the devices 100a to 100e and 200 constituting the AI system 1 may be connected to each other through the cloud network 10 . In particular, each of the devices 100a to 100e and 200 may communicate with each other through the base station, but may also directly communicate with each other without passing through the base station.

The AI server 200 may include a server performing AI processing and a server performing an operation on big data.

The AI server 200 includes at least one or more of the AI devices constituting the AI system 1, such as a robot 100a, an autonomous vehicle 100b, an XR device 100c, a smartphone 100d, or a home appliance 100e. It is connected through the cloud network 10 and may help at least a part of AI processing of the connected AI devices 100a to 100e.

In this case, the AI server 200 may train the artificial neural network according to a machine learning algorithm on behalf of the AI devices 100a to 100e, and directly store the learning model or transmit it to the AI devices 100a to 100e.

At this time, the AI server 200 receives input data from the AI devices 100a to 100e, infers a result value with respect to the input data received using the learning model, and provides a response or control command based on the inferred result value. It can be generated and transmitted to the AI devices 100a to 100e.

Alternatively, the AI devices 100a to 100e may infer a result value with respect to input data using a direct learning model, and generate a response or a control command based on the inferred result value.

Hereinafter, various embodiments of the AI devices 100a to 100e to which the above-described technology is applied will be described. Here, the AI devices 100a to 100e shown in FIG. 3 can be viewed as specific examples of the AI device 100 shown in FIG. 1 .

<AI+Robot>

The robot 100a may be implemented as a guide robot, a transport robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, etc. to which AI technology is applied.

The robot 100a may include a robot control module for controlling an operation, and the robot control module may mean a software module or a chip implemented as hardware.

The robot 100a obtains state information of the robot 100a by using sensor information obtained from various types of sensors, detects (recognizes) the surrounding environment and objects, generates map data, moves path and travels A plan may be determined, a response to a user interaction may be determined, or an action may be determined.

Here, the robot 100a may use sensor information obtained from at least one sensor among LiDAR, radar, and camera to determine a movement path and a travel plan.

The robot 100a may perform the above-described operations using a learning model composed of at least one artificial neural network. For example, the robot 100a may recognize a surrounding environment and an object using a learning model, and may determine an operation using the recognized surrounding environment information or object information. Here, the learning model may be directly learned from the robot 100a or learned from an external device such as the AI server 200 .

At this time, the robot 100a may perform an operation by generating a result by using the direct learning model, but transmits sensor information to an external device such as the AI server 200 and receives the result generated accordingly to perform the operation You may.

The robot 100a determines a movement path and travel plan using at least one of map data, object information detected from sensor information, or object information obtained from an external device, and controls the driving unit to apply the determined movement path and travel plan. Accordingly, the robot 100a may be driven.

The map data may include object identification information for various objects disposed in a space in which the robot 100a moves. For example, the map data may include object identification information for fixed objects such as walls and doors and movable objects such as flowerpots and desks. In addition, the object identification information may include a name, a type, a distance, a location, and the like.

In addition, the robot 100a may perform an operation or drive by controlling the driving unit based on the user's control/interaction. In this case, the robot 100a may acquire intention information of an interaction according to a user's motion or voice utterance, determine a response based on the acquired intention information, and perform the operation.

<AI + Autonomous Driving>

The autonomous driving vehicle 100b may be implemented as a mobile robot, a vehicle, an unmanned aerial vehicle, etc. by applying AI technology.

The autonomous driving vehicle 100b may include an autonomous driving control module for controlling an autonomous driving function, and the autonomous driving control module may mean a software module or a chip implemented as hardware. The autonomous driving control module may be included as a component of the autonomous driving vehicle 100b, or may be configured and connected to the outside of the autonomous driving vehicle 100b as separate hardware.

The autonomous driving vehicle 100b acquires state information of the autonomous driving vehicle 100b using sensor information obtained from various types of sensors, detects (recognizes) surrounding environments and objects, generates map data, A moving route and a driving plan may be determined, or an operation may be determined.

Here, the autonomous vehicle 100b may use sensor information obtained from at least one sensor among LiDAR, radar, and camera, similarly to the robot 100a, in order to determine a moving route and a driving plan.

In particular, the autonomous vehicle 100b may receive sensor information from external devices to recognize an environment or object for an area where the field of view is blocked or an area over a certain distance, or receive information recognized directly from external devices. .

The autonomous vehicle 100b may perform the above-described operations using a learning model composed of at least one artificial neural network. For example, the autonomous driving vehicle 100b may recognize a surrounding environment and an object using a learning model, and may determine a driving route using the recognized surrounding environment information or object information. Here, the learning model may be directly learned from the autonomous vehicle 100b or learned from an external device such as the AI server 200 .

In this case, the autonomous vehicle 100b may generate a result by using a direct learning model and perform an operation, but operates by transmitting sensor information to an external device such as the AI server 200 and receiving the result generated accordingly. can also be performed.

The autonomous driving vehicle 100b determines a moving route and a driving plan by using at least one of map data, object information detected from sensor information, or object information acquired from an external device, and controls the driving unit to determine the moving path and driving. The autonomous vehicle 100b may be driven according to a plan.

The map data may include object identification information for various objects disposed in a space (eg, a road) in which the autonomous vehicle 100b travels. For example, the map data may include object identification information on fixed objects such as street lights, rocks, and buildings, and movable objects such as vehicles and pedestrians. In addition, the object identification information may include a name, a type, a distance, a location, and the like.

In addition, the autonomous vehicle 100b may perform an operation or drive by controlling the driving unit based on the user's control/interaction. In this case, the autonomous vehicle 100b may acquire intention information of an interaction according to a user's motion or voice utterance, determine a response based on the obtained intention information, and perform the operation.

<AI+XR>

The XR apparatus 100c is AI technology applied, so a head-mount display (HMD), a head-up display (HUD) provided in a vehicle, a television, a mobile phone, a smart phone, a computer, a wearable device, a home appliance, and a digital signage , a vehicle, a stationary robot, or a mobile robot.

The XR device 100c analyzes three-dimensional point cloud data or image data obtained through various sensors or from an external device to generate position data and attribute data for three-dimensional points, thereby providing information on surrounding space or real objects. It can be obtained and output by rendering the XR object to be output. For example, the XR apparatus 100c may output an XR object including additional information on the recognized object to correspond to the recognized object.

The XR apparatus 100c may perform the above operations by using a learning model composed of at least one or more artificial neural networks. For example, the XR apparatus 100c may recognize a real object from 3D point cloud data or image data using a learning model, and may provide information corresponding to the recognized real object. Here, the learning model may be directly learned from the XR device 100c or learned from an external device such as the AI server 200 .

In this case, the XR device 100c may perform an operation by generating a result using the direct learning model, but transmits sensor information to an external device such as the AI server 200 and receives the result generated accordingly to perform the operation. can also be done

<AI+Robot+Autonomous Driving>

The robot 100a may be implemented as a guide robot, a transport robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, etc. to which AI technology and autonomous driving technology are applied.

The robot 100a to which AI technology and autonomous driving technology are applied may mean a robot having an autonomous driving function or a robot 100a that interacts with the autonomous driving vehicle 100b.

The robot 100a having an autonomous driving function may collectively refer to devices that move by themselves according to a given movement line without user's control, or move by determining a movement line by themselves.

The robot 100a with the autonomous driving function and the autonomous driving vehicle 100b may use a common sensing method to determine one or more of a moving route or a driving plan. For example, the robot 100a having an autonomous driving function and the autonomous driving vehicle 100b may determine one or more of a movement route or a driving plan by using information sensed through lidar, radar, and camera.

The robot 100a interacting with the autonomous driving vehicle 100b exists separately from the autonomous driving vehicle 100b and is linked to the autonomous driving function inside the autonomous driving vehicle 100b or connected to the autonomous driving vehicle 100b. An operation associated with the user on board may be performed.

At this time, the robot 100a interacting with the autonomous driving vehicle 100b acquires sensor information on behalf of the autonomous driving vehicle 100b and provides it to the autonomous driving vehicle 100b, or obtains sensor information and obtains information about the surrounding environment or By generating object information and providing it to the autonomous driving vehicle 100b, the autonomous driving function of the autonomous driving vehicle 100b may be controlled or supported.

Alternatively, the robot 100a interacting with the autonomous driving vehicle 100b may monitor a user riding in the autonomous driving vehicle 100b or control a function of the autonomous driving vehicle 100b through interaction with the user. . For example, when it is determined that the driver is in a drowsy state, the robot 100a may activate an autonomous driving function of the autonomous driving vehicle 100b or assist in controlling a driving unit of the autonomous driving vehicle 100b. Here, the function of the autonomous driving vehicle 100b controlled by the robot 100a may include not only an autonomous driving function, but also a function provided by a navigation system or an audio system provided in the autonomous driving vehicle 100b.

Alternatively, the robot 100a interacting with the autonomous driving vehicle 100b may provide information or assist a function to the autonomous driving vehicle 100b from the outside of the autonomous driving vehicle 100b. For example, the robot 100a may provide traffic information including signal information to the autonomous vehicle 100b, such as a smart traffic light, or interact with the autonomous vehicle 100b, such as an automatic electric charger for an electric vehicle. An electric charger can be automatically connected to the charging port.

<AI+Robot+XR>

The robot 100a may be implemented as a guide robot, a transport robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, a drone, etc. to which AI technology and XR technology are applied.

The robot 100a to which the XR technology is applied may mean a robot that is a target of control/interaction within an XR image. In this case, the robot 100a is distinguished from the XR device 100c and may be interlocked with each other.

When the robot 100a, which is the object of control/interaction within the XR image, acquires sensor information from sensors including a camera, the robot 100a or the XR device 100c generates an XR image based on the sensor information. and the XR apparatus 100c may output the generated XR image. In addition, the robot 100a may operate based on a control signal input through the XR device 100c or a user's interaction.

For example, the user can check the XR image corresponding to the viewpoint of the remotely linked robot 100a through an external device such as the XR device 100c, and adjust the autonomous driving path of the robot 100a through interaction or , control motion or driving, or check information of surrounding objects.

<AI+Autonomous Driving+XR>

The autonomous vehicle 100b may be implemented as a mobile robot, a vehicle, an unmanned aerial vehicle, etc. by applying AI technology and XR technology.

The autonomous driving vehicle 100b to which the XR technology is applied may mean an autonomous driving vehicle equipped with a means for providing an XR image or an autonomous driving vehicle subject to control/interaction within the XR image. In particular, the autonomous driving vehicle 100b, which is the target of control/interaction in the XR image, is distinguished from the XR device 100c and may be interlocked with each other.

The autonomous driving vehicle 100b having means for providing an XR image may obtain sensor information from sensors including a camera, and output an XR image generated based on the acquired sensor information. For example, the autonomous driving vehicle 100b may provide an XR object corresponding to a real object or an object in a screen to the occupant by outputting an XR image with a HUD.

In this case, when the XR object is output to the HUD, at least a portion of the XR object may be output to overlap the actual object to which the passenger's gaze is directed. On the other hand, when the XR object is output to the display provided inside the autonomous vehicle 100b, at least a portion of the XR object may be output to overlap the object in the screen. For example, the autonomous vehicle 100b may output XR objects corresponding to objects such as a lane, other vehicles, traffic lights, traffic signs, two-wheeled vehicles, pedestrians, and buildings.

When the autonomous driving vehicle 100b, which is the subject of control/interaction in the XR image, acquires sensor information from sensors including a camera, the autonomous driving vehicle 100b or the XR device 100c performs An XR image is generated, and the XR apparatus 100c may output the generated XR image. In addition, the autonomous vehicle 100b may operate based on a control signal input through an external device such as the XR device 100c or a user's interaction.

4 is a block diagram illustrating an artificial intelligence device related to the present invention.

A description overlapping with FIG. 1 will be omitted.

The communication unit 110 may include at least one of a broadcast reception module 111 , a mobile communication module 112 , a wireless Internet module 113 , a short-range communication module 114 , and a location information module 115 .

The broadcast reception module 111 receives a broadcast signal and/or broadcast related information from an external broadcast management server through a broadcast channel.

The mobile communication module 112 includes technical standards or communication methods for mobile communication (eg, Global System for Mobile communication (GSM), Code Division Multi Access (CDMA), Code Division Multi Access 2000 (CDMA2000), EV -DO (Enhanced Voice-Data Optimized or Enhanced Voice-Data Only), WCDMA (Wideband CDMA), HSDPA (High Speed Downlink Packet Access), HSUPA (High Speed Uplink Packet Access), LTE (Long Term Evolution), LTE-A (Long Term Evolution-Advanced, etc.) transmits/receives a radio signal to and from at least one of a base station, an external terminal, and a server on a mobile communication network constructed in accordance with the above.

The wireless Internet module 113 refers to a module for wireless Internet access, and may be built-in or external to the artificial intelligence device 100 . The wireless Internet module 113 is configured to transmit and receive wireless signals in a communication network according to wireless Internet technologies.

As wireless Internet technologies, for example, WLAN (Wireless LAN), Wi-Fi (Wireless-Fidelity), Wi-Fi (Wireless Fidelity) Direct, DLNA (Digital Living Network Alliance), WiBro (Wireless Broadband), WiMAX (World Interoperability for Microwave Access), High Speed Downlink Packet Access (HSDPA), High Speed Uplink Packet Access (HSUPA), Long Term Evolution (LTE), Long Term Evolution-Advanced (LTE-A), and the like.

The short-range communication module 114 is for short-range communication, and includes Bluetooth™, Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Ultra Wideband (UWB), ZigBee, NFC. At least one of (Near Field Communication), Wireless-Fidelity (Wi-Fi), Wi-Fi Direct, and Wireless Universal Serial Bus (USB) technologies may be used to support short-range communication.

The location information module 115 is a module for acquiring the location (or current location) of the mobile artificial intelligence device, and a representative example thereof includes a Global Positioning System (GPS) module or a Wireless Fidelity (WiFi) module. For example, if the artificial intelligence device utilizes the GPS module, it may acquire the location of the mobile artificial intelligence device by using a signal transmitted from a GPS satellite.

The input unit 120 may include a camera 121 for inputting an image signal, a microphone 122 for receiving an audio signal, and a user input unit 123 for receiving information from a user.

The camera 121 processes an image frame such as a still image or a moving image obtained by an image sensor in a video call mode or a photographing mode. The processed image frame may be displayed on the display unit 151 or stored in the memory 170 .

The microphone 122 processes an external sound signal as electrical voice data. The processed voice data may be variously utilized according to a function (or a running application program) being performed by the artificial intelligence device 100 . Meanwhile, various noise removal algorithms for removing noise generated in the process of receiving an external sound signal may be implemented in the microphone 122 .

The user input unit 123 is for receiving information from a user, and when information is input through the user input unit 123 , the processor 180 may control the operation of the artificial intelligence device 100 to correspond to the input information. have.

The user input unit 123 may include a mechanical input means (or a mechanical key, for example, a button located on the front/rear or side of the artificial intelligence device 100 , a dome switch, a jog wheel, and a jog). switch, etc.) and a touch input means. As an example, the touch input means consists of a virtual key, a soft key, or a visual key displayed on the touch screen through software processing, or a part other than the touch screen. It may be made of a touch key (touch key) disposed on the.

The output unit 150 is for generating an output related to visual, auditory or tactile sense, and includes at least one of a display unit 151 , a sound output unit 152 , a haptip module 153 , and an optical output unit 154 . can do.

The display unit 151 displays (outputs) information processed by the artificial intelligence device 100 . For example, the display unit 151 may display execution screen information of an application driven in the artificial intelligence device 100 or UI (User Interface) and GUI (Graphic User Interface) information according to the execution screen information. have.

The display unit 151 may implement a touch screen by forming a layer structure with the touch sensor or being formed integrally with the touch sensor. Such a touch screen may function as the user input unit 123 providing an input interface between the artificial intelligence device 100 and the user, and may provide an output interface between the artificial intelligence device 100 and the user.

The sound output unit 152 may output audio data received from the communication unit 110 or stored in the memory 170 in a call signal reception, a call mode or a recording mode, a voice recognition mode, a broadcast reception mode, and the like.

The sound output unit 152 may include at least one of a receiver, a speaker, and a buzzer.

The haptic module 153 generates various tactile effects that the user can feel. A representative example of the tactile effect generated by the haptic module 153 may be vibration.

The light output unit 154 outputs a signal for notifying the occurrence of an event by using the light of the light source of the artificial intelligence device 100 . Examples of the event generated by the artificial intelligence device 100 may be message reception, call signal reception, missed call, alarm, schedule notification, email reception, information reception through an application, and the like.

The interface unit 160 serves as a passage with various types of external devices connected to the artificial intelligence device 100 . This interface unit 160, a wired / wireless headset port (port), an external charger port (port), a wired / wireless data port (port), a memory card (memory card) port, for connecting a device equipped with an identification module It may include at least one of a port, an audio I/O (Input/Output) port, a video I/O (Input/Output) port, and an earphone port. In response to the connection of the external device to the interface unit 160 , the artificial intelligence device 100 may perform appropriate control related to the connected external device.

On the other hand, the identification module is a chip storing various information for authenticating the use right of the artificial intelligence device 100, a user identification module (UIM), a subscriber identity module (subscriber identity module; SIM), general-purpose user It may include a universal subscriber identity module (USIM) and the like. A device equipped with an identification module (hereinafter, 'identification device') may be manufactured in the form of a smart card. Accordingly, the identification device may be connected to the artificial intelligence device 100 through the interface unit 160 .

The power supply unit 190 receives external power and internal power under the control of the processor 180 to supply power to each component included in the artificial intelligence device 100 . The power supply 190 includes a battery, and the battery may be a built-in battery or a replaceable battery.

Meanwhile, as described above, the processor 180 controls the operation related to the application program and the general operation of the artificial intelligence device 100 . For example, if the state of the artificial intelligence device satisfies a set condition, the processor 180 may execute or release a lock state that restricts input of a user's control command to applications.

5 shows a smart home system 10 according to an embodiment of the present invention.

The smart home system 10 may be an embodiment of the AI system 1 .

The smart home system 10 is a system that can be applied to the inside and outside of various buildings, and is integrated into devices and sensors existing inside and outside such as houses, offices, transportation facilities, cultural facilities, sports facilities, and various facilities. It is a system that can be applied to

In addition, the smart home system 10 is a system that provides convenience to a user by using various data collected from a plurality of devices or sensors, allowing various devices or sensors to share, and controlling various devices or sensors. can

In addition, the smart home system 10 may be a system for building a smart city. A smart city may refer to a city that provides information necessary to efficiently manage assets and resources by using devices (or sensors) that collect various types of electronic data.

Meanwhile, the smart home system 10 may include one or more home devices. Each of the home devices 100_1 , 100_2 , 100_3 , 100_4 , 100_5 , 100_6 , and 100_7 may be an embodiment of the artificial intelligence apparatus 100 . Home devices include smart wallpads, smart switches, TVs, door locks, remote controllers, cameras, home security cameras, lighting devices, temperature control devices, lighting control devices, air conditioners, wired/wireless video, audio or text data transmission and reception devices, and heating and cooling devices. , wired/wireless gateway, temperature/humidity/illuminance measuring device, projector, mobile phone, smartphone, desktop computer, laptop computer, digital broadcasting terminal, PDA (personal digital assistants), PMP (portable multimedia player), navigation device, tablet PC, wearable device , a set-top box (STB), a DMB receiver, a radio, a washing machine, a refrigerator, a desktop computer, a digital signage, a robot, a vehicle, etc., a stationary device or a mobile device, and the like.

6 is a diagram illustrating a smart home security system 20 according to an embodiment of the present invention.

The smart home security system 20 may be an embodiment of the smart home system 10 .

The smart home security system 20 may be configured by connecting the artificial intelligence device 100 , the door lock device 300 , and the plurality of home devices 100_1 , 100_2 , 100_N-1 , and 100_N through a wired/wireless network.

The door lock device 300 may be a device installed on a door of a building to control opening and closing of the door. Also, the door device 300 may be a type of home device.

In addition, the door lock device 300 may include a door camera 301 for photographing the visitor. The door camera 301 may be installed to photograph the periphery of the door. The door camera 301 may be set to start photographing when an event in which the door bell is activated occurs, or may be set to always photograph the vicinity of the door.

In addition, the door lock device 300 may include a door speaker 302 capable of outputting an audio signal.

Meanwhile, the artificial intelligence device 100 may be a smart wall pad. The smart wall pad may be a device that is installed on the wall of a building to collect and display data from various devices and control the various devices.

7 is a flowchart illustrating a method for providing a connection between devices for home security according to an embodiment of the present invention.

The communication unit 110 may receive the visitor image from the door lock device 300 including the door camera 301 for photographing the visitor (S7001).

The door lock device 300 may transmit an image around the door captured by the door camera 301 to the artificial intelligence device 100 .

For example, the door lock device 300 causes the door camera 301 to start photographing the visitor when an event in which the door bell is activated by the visitor occurs, and displays the captured visitor image to the artificial intelligence device 100 . can be sent to

Meanwhile, the processor 180 may obtain visitor information output from the face recognition model by inputting the visitor image into the face recognition model ( S7002 ).

The processor 180 may provide the visitor image to the visitor recognition model to obtain visitor information of a person included in the visitor image.

The visitor recognition model may compare the visitor's face included in the input image with the previously registered visitor's face, specify who the visitor is, and output visitor information.

The visitor information may include at least one of visitor face information, visitor identity information, visit time information, and visit number information.

For example, the visitor identity information is information for specifying the identity of the visitor, and information such as a family member, a security guard, and a delivery person may be specified. If the visitor identity is not verified, it may be identified as an unidentified visitor.

Meanwhile, the visitor recognition model may be an artificial neural network (ANN) model used in machine learning. The visitor recognition model may be composed of artificial neurons (nodes) that form a network by synaptic bonding. An image recognition model may be defined by a connection pattern between neurons of different layers, a learning process that updates model parameters, and an activation function that generates an output value.

The visitor recognition model may include an input layer, an output layer, and optionally one or more hidden layers. Each layer includes one or more neurons, and the artificial neural network may include neurons and synapses connecting neurons. In the artificial neural network, each neuron may output a function value of an activation function for input signals, weights, and biases input through synapses.

The visitor recognition model may be generated through supervised learning, unsupervised learning, or reinforcement learning according to a learning method.

For example, when a visitor recognition model is generated through supervised learning, it may be trained in a state in which a label for the training data is given. The label may mean a correct answer (or result value) that the artificial neural network should infer when the training data is input to the artificial neural network.

The learning processor 130 may designate a label that specifies the visitor's facial image. For example, a face image of a family member, a face image of a security guard, a face image of a courier, and other unidentified face images may be individually labeled and designated.

Accordingly, the learning processor 130 may train the visitor recognition model to identify the visitors by labeling the face image received when registering the visitor information. Accordingly, when a new image file is input, the visitor's face can be identified by determining the label of the face image included in the image file.

In addition, the visitor recognition model is an unsupervised learning that trains an artificial neural network in a state where no labels are given to the training data, or an agent defined in an environment is trained to select an action or sequence of actions that maximizes the cumulative reward in each state. It can also be learned through reinforcement learning.

Meanwhile, the processor 180 may select a home device to receive the visitor image and visitor information from among the plurality of home devices ( S7003 ).

When there are two or more home devices, the processor 180 may select a home device to which the visitor image and visitor information are to be transmitted. Therefore, it is not necessary to transmit the visitor image and visitor information to all of the plurality of home devices included in the smart home security system 20, but to transmit the visitor image and visitor information to a specific home device to prevent the operation of all devices from being disturbed. can

Also, the processor 180 may select a home device to receive the visitor image and visitor information from among the plurality of home devices based on the operation state of each of the plurality of home devices.

For example, the processor 180 determines the home device used by the user based on whether each of the plurality of home devices operates, and sets the home device used by the user as the home device to receive the visitor image and visitor information. You can choose.

For example, when a parcel delivery arrives while the user is watching TV, which is one of a plurality of home devices, the door lock device 300 may transmit a visitor image photographed by the courier to the artificial intelligence apparatus 100 . The processor 180 may acquire visitor information output through the visitor recognition model. If the courier is a registered courier, visitor information may be obtained. The processor 180 may select a TV, which is a home device used by a user, as a home device to receive a visitor image and visitor information among a plurality of home devices.

Also, when there are a plurality of home devices used by the user, the processor 180 may select a home device to receive the visitor image and the visitor information based on the size of the display of each home device used by the user.

The processor 180 may determine a home device having a display greater than or equal to a predetermined size among a plurality of home devices, and select a home device having a display greater than or equal to a predetermined size as the home device to receive the visitor image and visitor information.

Also, the processor 180 may select a home device having the largest display among the plurality of home devices as the home device to receive the visitor image and visitor information.

Meanwhile, the communication unit 110 may transmit the visitor image and visitor information to the selected device (S7004).

The processor 180 may transmit the visitor image and visitor information to the selected home device through the communication unit 110 .

Accordingly, the selected home device may provide a home security interface that displays the received visitor image and visitor information so that the user can check the visitor.

8 is a diagram illustrating a home security interface according to an embodiment of the present invention.

The home device 400 may receive the visitor image 406 and the visitor information 407 from the artificial intelligence apparatus 100 . The visitor information 407 may include visitor number information. The home device 400 may include a TV.

The home device 400 may provide the home security interface 410 through the display. The home device 400 may receive a door control command regarding the door lock device 300 input through the home security interface 410 . The home device 400 may transmit a door control command for the door locking device 300 to the artificial intelligence apparatus 100 .

The home security interface 410 may include control buttons 401 , 402 , 403 , and 404 for controlling the operation of the door locking device 300 . In addition, the home security interface 410 may include a screen displaying the visitor image 406 and the visitor information 407 . In addition, the home security interface 410 may include a box button 405 for selecting a setting to cause the door lock device 300 to perform the same operation for the same visitor.

Meanwhile, the home device 400 may be controlled by the remote control apparatus 500 .

The remote control device 500 may include control buttons 401 , 402 , 403 , and 404 of the home security interface 410 and corresponding physical buttons 501 , 502 , 503 , and 504 .

For example, the control button for controlling the operation of the door lock device 300 includes a door opening button 401 for unlocking a lock, a call button 402 for making a call with a visitor, and outputting a pre-stored voice. an auto-response button 403 and an ignore button 404 that stops the door bell sound and exits the home security interface 410 after a predetermined time. The first physical button 501 of the remote control device may correspond to the door open button 401, the second physical button 502 may correspond to the call button 402, and the third physical button 503 is automatic It may correspond to the response button 403 , and the fourth physical button 504 may correspond to the ignore button 404 . The home device 400 may receive an input for a control button corresponding to a physical button from the remote control apparatus 500 , and may receive a control command for controlling the door lock device 300 .

Meanwhile, the processor 180 may receive a control command for the door lock device 300 from the home device 400 that has provided the home security interface 410 through the communication unit 110 .

When receiving an unlock control command for the door lock device 300 from the home device 400 , the processor 180 may control the door lock device 300 to unlock the door.

In addition, when the processor 180 receives a call mode control command from the home device 400 to allow the door lock device 300 to enter a call mode for making a call with the visitor, the door speaker of the door lock device 300 . 302 and door microphone 303 may be activated.

In addition, the processor 180 may control the door lock device 300 to output the voice data received from the selected home device 400 through the door speaker 300 included in the door lock device 300 . have.

In addition, when receiving an automatic response control command for causing the door lock device 300 to output the previously stored voice data, the processor 180 transmits the pre-stored voice data to the door lock device 300 to the door speaker 302 . ) may control the door lock device 300 to output pre-stored voice data.

Meanwhile, when the ignore button 404 is input in the home security interface 410 , the processor 180 may control the door lock device 300 to stop the door bell sound.

On the other hand, when a setting for allowing the door fund device 300 to perform the same operation for the same visitor is input in the home security interface 410 , the processor 180 matches the door control command received with the visitor information to the memory 170 ) can be stored in

The processor 180 inputs a visitor image to the visitor recognition model, obtains visitor information output from the visitor recognition model, determines whether a door control command matching the visitor information exists, and provides a door control command matching the visitor information If present, the door lock device 300 may be controlled with the corresponding door control command.

Meanwhile, when the visitor's face is not identified from the visitor image and visitor information cannot be obtained, the processor 100 may control the locking device to enter the safe mode for a predetermined time.

The safe mode is a mode in which a warning notification is output or a warning notification is transmitted to an external server when it is determined that a visitor whose face is not identified stays for a predetermined time or revisits a predetermined number of times or more within a predetermined time. can be

After entering the safe mode, the processor 180 may acquire the time during which a visitor whose face is not identified based on the visitor image stays.

In addition, the processor 180 may control to output an emergency notification through a door speaker when a visitor whose face is not identified for more than a preset period stays.

In addition, the communication unit 110 receives the audio data obtained by the door microphone of the door lock device, and the processor 180 sends an emergency notification to the speaker when a sound of more than a preset decibel is detected in the audio data received after entering the safe mode. output can be controlled through .

Meanwhile, the processor 180 may obtain visitor information output from the visitor recognition model and obtain visit history information. The visit history information may be information that stores the time each visitor visits, whether the visitor is a registered visitor allowed to visit, and the like.

The processor 180 may determine whether the visitor is a pre-registered visitor based on the visitor information. When the visitor is not a pre-registered visitor, the processor 180 may determine whether there is a history of repeating a visit for a preset number of times for a preset time. Accordingly, the processor 180 may determine a case in which a visitor who is not permitted to visit visits multiple times. When determining an unregistered user who has repeatedly visited a preset number of times for a preset time, the processor 180 may control the door lock device to enter the safe mode for a preset time.

On the other hand, when a plurality of door lock devices are installed in a collective building facility such as an apartment, the artificial intelligence device 100 may receive and store visitor information from each of the external door lock devices, and may use it for a security service.

For example, the communication unit 110 may receive face information of a visitor whose visit is rejected from the first external door lock device.

The memory 170 may store face information of a visitor whose visit is rejected.

The processor 180 may determine whether the face information obtained from the visitor image received from the door lock device 300 matches the face information of the visitor whose visit is rejected. Accordingly, the processor 180 may determine whether a person is a person whose visit is rejected at another location.

When the face information obtained from the visitor image matches the face information of the rejected visitor, the processor 180 may control the door locking device to enter the safe mode for a predetermined time.

Also, the communication unit 110 may transmit face information of the rejected visitor to the second external door locking device. Accordingly, the artificial intelligence apparatus 100 may share information about a dangerous person between devices.

Meanwhile, the processor 180 may determine whether the visitor is an allowed member based on the visitor information. Access allowed members may refer to members allowed access, such as family members, friends, relatives, and employees.

The artificial intelligence apparatus 100 may provide member information (eg, family information), input/exit history information, use information of each home device, sleep information of members, and environment information of the home, based on data received from a plurality of home devices. information can be obtained.

For example, the artificial intelligence device 100 may acquire member information based on the number of portable devices accessing the home network, visitor information photographed when entering and exiting, and the like. Member information may be input by a user. A person registered as a member may be allowed access.

In addition, when the visitor is a member allowed to enter, the processor 180 may update the member's entry/exit history. The processor 180 may store an input/exit time for each member in the memory 170 .

The processor 180 may acquire activity information of the member based on the updated input/exit history and operation information of a plurality of home devices.

The activity information may refer to a time/cycle/pattern for a member to stay at home and a time/period/pattern to use each of the home devices.

For example, the processor 180 may generate an entry/exit pattern for each month, day of the week, time, weather, or predetermined period based on the entry/exit history of each member. Accordingly, the processor 180 may obtain activity information of each member.

Also, when each member stays in the house, the processor 180 may obtain activity information of the member based on the type and time of home devices used.

For example, when the home device is a TV, activity information of a member may be obtained based on operation time information of the member's TV.

The processor 180 may detect an abnormal activity based on the member's activity information.

The processor 180 compares the previously acquired activity information of the member with the current member's activity information, and when it does not match the previously acquired member's activity information, the processor 180 may detect an abnormal activity.

For example, the processor 180 may detect that an abnormal activity has occurred when a member who goes out once a week does not go out and does not have time to use the TV even though they are not going out.

When an abnormal activity is detected, the processor 180 may provide a notification to an external device.

9 is a diagram for explaining a method for providing a connection between home devices according to an embodiment of the present invention.

A plurality of home devices may be connected to the artificial intelligence apparatus 100 through a wired/wireless network to configure a home network system.

The artificial intelligence apparatus 100 may be a gateway device connected to a plurality of home devices. Accordingly, it is possible to receive data from each of the plurality of home devices and transmit data to each of the other plurality of home devices.

The artificial intelligence device 100 may receive a request for data transmission from the first home device 100_1 located in the basement space 701 of the building to the second home device 100_2 located in the second floor space 702 of the building. have.

The communication unit 110 may receive a connection request from the first home device 100_1 to the second home device 100_2 .

The processor 180 may acquire a first communication protocol used by the first home device 100_1 and acquire a second communication protocol used by the second home device 100_2 .

For example, when the first home device 100_1 uses the Bluetooth protocol and the second home device 100_2 uses the Wi-fi protocol, the second home device 100_2 from the first home device 100_1 ) to send data directly. Accordingly, the processor 180 of the artificial intelligence apparatus 100 may transmit data received from the first home device 100_1 to the second home device 100_2 using a Wi-Fi communication protocol.

Accordingly, the artificial intelligence apparatus 100 may provide a connection between a plurality of home devices in the smart home system 10 including a plurality of devices using different communication protocols.

Accordingly, among a plurality of home devices using a single communication protocol, the artificial intelligence apparatus 100 may perform a function of converting one protocol into another protocol.

Meanwhile, the artificial intelligence apparatus 100 may display data collected from a plurality of home devices or display an integrated information interface 1000 capable of operating each of the plurality of home devices through the display unit 151 .

10 is an exemplary diagram of an integrated information interface according to an embodiment of the present invention.

The integrated information interface 1000 may include a temperature display interface 1001 that displays temperature information and humidity information collected from a home device capable of measuring and adjusting temperature and humidity.

In addition, the integrated information interface 1000 includes an indoor temperature display interface 1002 that displays indoor temperature information collected from a home device that can measure and adjust the indoor temperature and a home device that functions as a boiler, and information about the boiler operation state. may include

In addition, the integrated information interface 1000 may include a visitor display interface 1003 that displays the visitor image collected from the home device photographing the visitor.

Also, the integrated information interface 1000 may display operation mode interfaces 1004 and 1005 that display operation modes of indoor home devices. The operation mode interfaces 1004 and 1005 may include a home mode interface 1004 indicating that the user has entered the house and a wake-up mode interface 1005 indicating that it is time to wake up in the morning.

In addition, the integrated information interface 1000 may include a lighting information interface 1006 capable of specifying the location of the home device having the lighting means and controlling the home device having the lighting means.

In addition, the integrated information interface 1000 may include a message information interface 1007 capable of displaying a message notification when a home device capable of receiving a message receives a message.

Meanwhile, the artificial intelligence apparatus 100 may display a lighting control interface 1100 capable of controlling a plurality of home devices having lighting means through the display unit 151 .

11 is an exemplary diagram of a lighting control interface according to an embodiment of the present invention.

The lighting control interface 1100 may display an on/off state of a lighting device installed in each room of the room and provide an interface capable of setting the illuminance of the entire lighting device.

For example, the lighting control interface 1100 may include an interface 1101 that displays the current overall illuminance setting information in the form of a bar. Also, when the user lowers or increases the overall illuminance setting, the bar may be displayed differently ( 1102 and 1103 ).

In addition, the lighting control interface 1100 may provide an interface 1104 capable of setting the illuminance of a lighting device installed in a specific room.

Meanwhile, the artificial intelligence device 100 may be linked with the home mail system 30 .

12 is a block diagram of a home mail system 30 according to an embodiment of the present invention.

The home mail system 30 may include a sender device 1201 , a home mail transmission server 1202 , a customer information database 1203 , and an artificial intelligence device 100 .

The home mail system 30 may be a system for replacing paper mail delivered to a house in which the artificial intelligence device 100 is installed with an electronic mail.

The mail transmission server 1202 may generate a unique identification mail address based on the address of the place where the artificial intelligence device 100 is installed. The unique identification e-mail address may be an e-mail address that can be identified for each address based on a plurality of offline addresses. Accordingly, the mail transmission server 1202 may assign a different email address to each address where the artificial intelligence device 100 is installed.

The customer information database 1203 may store an address of a place where the artificial intelligence device 100 is installed, a unique identification mail address corresponding to the address, and information on whether to consent to reception.

The sender device 1201 may be a device used by a sender who wants to send a digital mail to the artificial intelligence device 100 . For example, the sender may be a mobile communication company, a securities company, an insurance company, an organization, and the like. On the other hand, digital mail can replace the existing paper mail. Existing paper mail may include various promotional materials in the apartment complex, statement of maintenance fees, notices in the apartment complex, and the like. In addition, the digital mail can be produced in a form that can receive votes from residents on various issues in the apartment complex, and can be a form that can receive participation in the second-hand market. In addition, the digital postal mail may include promotional materials for shops around the address where the artificial intelligence device 100 is installed, promotional materials for newly established shops, or discount information. In addition, the digital mail may include information on items delivered to the address where the artificial intelligence device 100 is installed, information on delivery status, arrival information, and the like.

For example, the sender device 1201 sends a digital mail that replaces the content of the paper mail with a unique identification mail address corresponding to an offline address to receive the paper mail through the mail transmission server 1202 to the artificial intelligence device 100 ) can be sent.

The mail transmission server 1202 receives the sending request for the digital mail from the sender device 1201, and obtains a unique identification mail address corresponding to the offline address of the place where the artificial intelligence device 100 is installed from the customer information database 1203. obtained, and the digital mail can be sent to the artificial intelligence device 100 based on the unique identification mail address. Accordingly, the sender can send the digital mail only to the offline address of the place where the artificial intelligence device 100 is installed.

Therefore, the sender can increase customer satisfaction by using digital content that has a better content delivery effect than the existing paper mail, and can reduce cost waste by replacing paper mail.

Meanwhile, the mail transmission server 1202 may receive feedback information about the digital mail sent by the sender device 1201 from the artificial intelligence device 100 . The feedback information may be an unsubscribe request for digital mail. When the mail transmission server 1202 receives a reception rejection request from the artificial intelligence device 100 , the mail transmission server 1202 may update the customer information database 1203 , and may block the future sending of the digital mail sent by the sender device 1201 .

13 is an exemplary diagram of an integrated convenience application according to an embodiment of the present invention.

Meanwhile, the smart home system 10 may be connected to one or more user devices 1300 . The user device 1300 may be a device that can access various services provided within the area where the artificial intelligence apparatus 100 is installed. For example, user devices may include smartphones and vehicles.

The user device 1300 may be a device capable of executing an integrated convenience application. The integrated convenience application may be an application that provides a service for conveniently using culture, medical care, shopping facilities, etc. in a user's residential area.

The user device 1300 may execute an integrated convenience application 1301 that provides information on nearby reservable hospitals or restaurants, provides information on cultural events in the vicinity, and provides information on health care centers and living support centers. have.

In addition, the user device 1300 provides a service that allows you to reserve or easily call various mobile groups such as taxis, taxis for the disabled, and car sharing services so that anyone, including the elderly and the disabled, can move more conveniently in the town. to execute the integrated convenience application 1302 . In addition, the integrated convenience application 1302 may recognize the user device 1300 to enable automatic parking settlement for the user device 1300 .

In addition, the user device 1300 provides an integrated convenience application 1303 that provides an online space where residents in a residential area can purchase baby products, toys, books, household goods, groceries, etc. as a group, trade used goods, or share for free can run

In addition, the user device 1300 supports various meetings and activities for residents in the residential area, and conveniently provides various inconveniences or suggestions/inquiries that occur not only in residential spaces in town but also in commercial and office spaces, neighborhood facilities, etc. An integrated convenience application 1304 that provides a shareable online communication space may be executed.

On the other hand, when the user device 1300 is a vehicle, a vehicle payment system may be installed so that payment can be made in the vehicle. For example, a beacon device of a store may detect the user device 1300 and provide a menu corresponding to the store so that the user can make an order payment.

Meanwhile, when the user device 1300 is a vehicle, a vehicle security service may be provided. For example, when a vehicle is parked by someone other than the owner of the vehicle, a service capable of preventing vehicle theft and accidents may be provided. The user device 1300 may operate in a valet parking mode. In the valet parking mode, the vehicle may be operated so that parking is possible without a key of the vehicle. When the vehicle's gear shifts to P mode, an interface for entering valet parking mode may be displayed on the display. The vehicle may store an image of a driver providing a valet parking service. In addition, the speed of the vehicle may be limited to a certain speed or less. In addition, a camera for photographing the interior of the vehicle and a microphone for recording a sound may be activated. In addition, by transmitting the captured image to an external server, it is possible to detect the abnormal behavior of the driver who parks the vehicle. In addition, the vehicle may provide a notification to the owner of the vehicle after the valet parking is completed. In addition, the valet parking mode of the vehicle may be set to be released at any time by the owner of the vehicle.

Meanwhile, the artificial intelligence apparatus 100 may provide a service for guiding a road to a destination by controlling the lighting color and lighting on/off of one or more home devices having a lighting means.

14 and 15 are exemplary views of a guidance system 40 according to an embodiment of the present invention.

The user 1401 may input a desired destination 1403 into the artificial intelligence device 100 to receive directions.

The artificial intelligence device 100 may photograph the user 1401 through a camera, and recognize and store the user 1401's face.

The artificial intelligence device 100 may continuously acquire the location of the user 1401 by acquiring an image from a camera or CCTV installed in a building and tracking an object matching the face of the user 1401 in the image.

The artificial intelligence apparatus 100 may guide the user 1401 from the location of the user 1401 to the destination 1403 by controlling the lighting color and lighting on/off of the lighting device 1402 .

For example, the lighting of the lighting device 1402 installed on the road from the location of the user 1401 to the destination 1403 may be turned on. In addition, if a plurality of users 1401 input a different destination, it is possible to control the lighting device 1402 so as to guide the road for each user in different colors. The lighting device 1402 may include an LCD or LED panel. On the other hand, the lighting device 1402 may generate a sound, and when the user 1401 approaches, may guide the way with the sound.

In addition, the lighting device 1402 may be turned off on the way the user 1401 has passed, and a different color may guide the way for other users.

Meanwhile, the lighting device may include a beam projector installed on a ceiling of a building to guide a road by emitting light to a floor surface.

Referring to FIG. 15 , the artificial intelligence device may control the beam projector so that the guide display 1404 is visible in front of the user based on the location of the user 1401 .

The present disclosure described above can be implemented as computer-readable code on a medium in which a program is recorded. The computer-readable medium includes all types 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 this. In addition, the computer may include the processor 180 of the artificial intelligence device.

Claims (22)

An artificial intelligence device that provides a connection between a plurality of home devices, comprising:
a communication unit for receiving a visitor image from a door lock device including a door camera for photographing the visitor; and
A processor for inputting the visitor image into a visitor recognition model to obtain visitor information output from the visitor recognition model, and selecting a home device to receive the visitor image and the visitor information from among the plurality of home devices,
The communication unit,
transmitting the visitor image and the visitor information to the selected home device,
artificial intelligence device. According to claim 1,
The processor is
determining a home device used by the user based on whether each of the plurality of home devices operates, and selecting the home device used by the user as a home device to receive the visitor image and the visitor information,
artificial intelligence device. 3. The method of claim 2,
The processor is
When the number of home devices used by the user is plural, selecting a home device to receive the visitor image and the visitor information based on a display size of each home device used by the user;
artificial intelligence device. According to claim 1,
The communication unit,
receiving a door control command for a door locking device from the selected home device,
The processor is
controlling the door locking device based on the door control command;
artificial intelligence device. According to claim 1,
The processor is
controlling the door lock device to output the voice data received from the selected home device through a door speaker included in the door lock device,
artificial intelligence device. According to claim 1,
The processor is
When the visitor's face is not identified from the visitor image and visitor information cannot be obtained, controlling the locking device to enter the safe mode for a predetermined time,
artificial intelligence device. 7. The method of claim 6,
The processor is
After entering the safe mode, the time for which the visitor whose face is not identified stays is obtained based on the visitor image, and when the visitor whose face is not identified stays for more than a preset time, an emergency notification is output through the door speaker. to control,
artificial intelligence device. 7. The method of claim 6,
The communication unit receives the audio data obtained by the door microphone of the door lock device,
The processor is
After entering the safety mode, when a sound of more than a preset decibel is detected in the audio data, controlling to output an emergency notification through a door speaker,
artificial intelligence device. According to claim 1,
The processor is
Acquire visit history information based on the visitor information, and if the visitor is not a registered visitor and there is a history of repeated visits for a preset number of times for a preset time, the locking device is placed in the safe mode for a preset time. to control entry into
artificial intelligence device. According to claim 1,
The communication unit,
Receive face information of a visitor whose visit is rejected from the first external door lock device,
The processor is
When the face information obtained from the visitor image matches the face information of the rejected visitor, controlling the lock device to enter the safe mode for a predetermined time,
The communication unit,
transmitting face information of the rejected visitor to a second external door locking device,
artificial intelligence device. According to claim 1,
The communication unit,
receiving a connection request from the first home device to the second home device,
The processor is
acquiring a first communication protocol used by the first home device, acquiring a second communication protocol used by the second home device, and obtaining the first home device based on the first communication protocol and the second communication protocol Transmitting the data received from the device to the second home device through the communication unit,
artificial intelligence device. According to claim 1,
The processor is
Determining whether the visitor is an allowed member based on the visitor information, updating the member's entry/exit history if the visitor is an allowed member, and the updated entry/exit history and operation of the plurality of home devices To obtain the activity information of the member based on the information,
artificial intelligence device. 13. The method of claim 12,
The processor is
Detecting an abnormal activity based on the member's activity information,
artificial intelligence device. Receiving a visitor image from a door lock device including a door camera for photographing the visitor;
inputting the visitor image into a visitor recognition model to obtain visitor information output from the visitor recognition model;
selecting a home device to receive the visitor image and the visitor information from among a plurality of home devices; and
and transmitting the visitor image and the visitor information to the selected home device. 15. The method of claim 14,
The step of selecting the home device comprises:
determining a home device used by a user based on whether each of the plurality of home devices operates; and
A method of providing a connection between a plurality of home devices, wherein a home device used by the user is selected as a home device to receive the visitor image and the visitor information. 16. The method of claim 15,
The step of selecting the home device comprises:
When the number of home devices used by the user is plural, the method further comprising: selecting a home device to receive the visitor image and the visitor information based on a display size of each home device used by the user; A method for providing connectivity between a plurality of home devices. 15. The method of claim 14,
receiving a door control command for a door locking device from the selected home device; and
The method of providing a connection between a plurality of home devices, further comprising the step of controlling the door locking device based on the door control command. 15. The method of claim 14,
If the visitor's face is not identified from the visitor image and visitor information cannot be obtained, further comprising the step of controlling the lock device to enter the safe mode for a predetermined time, providing a connection between a plurality of home devices Way. 19. The method of claim 18,
After entering the safe mode, the step of obtaining a time for a visitor whose face is not identified based on the visitor image to stay; and
The method of providing a connection between a plurality of home devices, further comprising the step of controlling to output an emergency notification through a door speaker when a visitor whose face is not identified stays for more than a preset time. 19. The method of claim 18,
receiving audio data obtained by a door microphone of the door locking device; and
After entering the safe mode, when a sound greater than a preset decibel is detected in the audio data, the method further comprising the step of controlling to output an emergency notification through a door speaker. 15. The method of claim 14,
obtaining visit history information based on the visitor information; and
When the visitor is not a pre-registered visitor and there is a history of repeated visits for a preset number of times for a preset time, the method further comprising controlling the lock device to enter the safe mode for a preset time. How to provide connectivity between devices. 15. The method of claim 14,
receiving face information of a visitor whose visit is rejected from the first external door locking device;
when the face information obtained from the visitor image matches the face information of the rejected visitor, controlling the locking device to enter a safe mode for a predetermined time; and
The method of providing a connection between a plurality of home devices, further comprising the step of transmitting the face information of the rejected visitor to a second external door locking device.

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