KR102623190B1 - Artificial intelligence device and artificial intelligence system for caring air state of indoor - Google Patents

Abstract

An artificial intelligence device according to an embodiment of the present invention includes a communication unit that transmits indoor air quality information received from a display and an air quality measuring device and meta information input through the display to an artificial intelligence server, outdoor air quality information, the indoor air quality information, and and a processor that receives an air quality analysis report generated based on the meta information from the artificial intelligence server through the communication unit, and displays the received air quality analysis report on the display, wherein the air quality analysis report is configured to display the air quality analysis report. A first analysis report including the results of analyzing the indoor air quality during the measurement period measured by the measuring device, and a second analysis including an air quality type according to the indoor air quality state and a solution for fine dust management according to the air quality type. May include reports.

Description

Artificial intelligence device and artificial intelligence system for managing indoor air conditions {ARTIFICIAL INTELLIGENCE DEVICE AND ARTIFICIAL INTELLIGENCE SYSTEM FOR CARING AIR STATE OF INDOOR}

The present invention relates to an artificial intelligence device that manages indoor air conditions.

In general, if people are active indoors for a long time when the interior of the building is not well ventilated from the outside, the interior cannot be maintained in a comfortable condition due to the increase in CO2 and fine dust, so the interior must be ventilated.

For this purpose, in the past, users would manually turn off the air conditioner or air purifier and then open the window to ventilate.

Additionally, recently, services that measure and provide indoor air quality have emerged.

However, conventionally, it only showed the indoor fine dust concentration value and the status according to the concentration value.

The purpose of the present invention is to provide more detailed information on the air quality status within the home, so that indoor air quality can be taken care of.

The purpose of the present invention is to improve the air quality in your home.

The artificial intelligence device according to an embodiment of the present invention displays an air quality analysis report generated based on outdoor air quality information, indoor air quality information, and meta information, and the air quality analysis report is provided during the measurement period measured by the air quality measurement device. , It may include a first analysis report including the results of analyzing the indoor air quality state, and a second analysis report including an air quality type according to the indoor air quality state and a solution for fine dust management according to the air quality type.

An artificial intelligence system according to another embodiment of the present invention receives indoor air quality information from an air quality measuring device that measures indoor air quality information, an artificial intelligence device that acquires meta information, and the air quality measuring device, and the artificial intelligence device An artificial intelligence server that receives the meta information, generates an air quality analysis report based on the outdoor air quality information, the indoor air quality information, and the meta information, and transmits the generated air quality analysis report to the artificial intelligence device; , the air quality analysis report is a first analysis report containing the results of analyzing the indoor air quality state during the measurement period measured by the air quality measuring device, an air quality type according to the indoor air quality state, and fine dust management according to the air quality type. A second analysis report containing a solution may be included.

According to various embodiments of the present invention, users can receive feedback and guidance on the air quality status in their home and take appropriate measures against air quality.

Accordingly, the air quality in the user's home improves, and the user's respiratory health can be optimized.

Figure 1 shows an AI device according to an embodiment of the present invention.
Figure 2 shows an AI server according to an embodiment of the present invention.
Figure 3 shows an AI system according to an embodiment of the present invention.
Figure 4 shows an AI device according to another embodiment of the present invention.
Figure 5 is a diagram illustrating an example of an artificial intelligence system according to another embodiment of the present invention.
Figure 6 is a ladder diagram for explaining the operation method of an artificial intelligence system according to an embodiment of the present invention.
FIG. 7A is a diagram illustrating a table mapping the operating state of the air purifier according to the air quality state and the type of the air purifier according to an embodiment of the present invention.
7B and 7B are diagrams illustrating an interface screen for performing connection settings and operation settings between a mobile terminal and an air purifier through a fine dust notification application according to an embodiment of the present invention.
Figure 8 is a ladder diagram for explaining the operation method of an artificial intelligence system according to another embodiment of the present invention.
9 and 10 are diagrams illustrating an air quality analysis report according to an embodiment of the present invention.
Figure 11 is a diagram illustrating text provided corresponding to each of a plurality of air quality types according to an embodiment of the present invention.
Figure 12 is a diagram showing the process of receiving customer meta information required to provide an air quality analysis report according to an embodiment of the present invention.
Figure 13 is a diagram showing an air quality service screen according to an embodiment of the present invention.
Figure 14 is a diagram illustrating an air quality guide table mapping a guide according to air quality according to an embodiment of the present invention.
15A and 15B are diagrams illustrating a guide according to temperature and humidity measured in a home according to an embodiment of the present invention.

<Artificial Intelligence (AI)>

Artificial intelligence refers to the field of researching artificial intelligence or methodologies to create it, and machine learning refers to the field of defining various problems dealt with in the field of artificial intelligence and researching methodologies to solve them. do. Machine learning is also defined as an algorithm that improves the performance of a task through consistent experience.

Artificial Neural Network (ANN) is a model used in machine learning. It can refer to an overall model with problem-solving capabilities that is composed of artificial neurons (nodes) that form a network through the combination of synapses. Artificial neural networks can be defined by connection patterns between neurons in different layers, a learning process that updates model parameters, and an activation function that generates output values.

An 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 synapses connecting neurons. In an artificial neural network, each neuron can output the activation function value for the input signals, weight, and bias input through the synapse.

Model parameters refer to parameters determined through learning and include the weight of synaptic connections and the bias of neurons. Hyperparameters refer to parameters that must be set before learning in a machine learning algorithm and include learning rate, number of repetitions, mini-batch size, initialization function, etc.

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

Machine learning can be classified into supervised learning, unsupervised learning, and reinforcement learning depending on the learning method.

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

Among artificial neural networks, machine learning implemented with a deep neural network (DNN) that includes multiple hidden layers is also called deep learning, and deep learning is a part of machine learning. Hereinafter, machine learning is used to include deep learning.

<Robot>

A robot can refer to a machine that automatically processes or operates a given task based on its own abilities. In particular, a robot that has the ability to recognize the environment, make decisions on its own, and perform actions can be called an intelligent robot.

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

A robot is equipped with a driving unit including an actuator or motor and can perform various physical movements such as moving robot joints. In addition, a mobile robot includes wheels, brakes, and propellers in the driving part, and can travel on the ground or fly in the air through the driving part.

<Self-Driving>

Autonomous driving refers to technology that drives on its own, and an autonomous vehicle refers to a vehicle that drives without user intervention or with minimal user intervention.

For example, autonomous driving includes technology that maintains the driving lane, technology that automatically adjusts speed such as adaptive cruise control, technology that automatically drives along a set route, technology that automatically sets the route and drives once the destination is set, etc. All of these can be included.

Vehicles include vehicles equipped only with an internal combustion engine, hybrid vehicles equipped with both an internal combustion engine and an electric motor, and electric vehicles equipped with only an electric motor, and may include not only cars but also trains and motorcycles.

At this time, the self-driving vehicle can be viewed as a robot with self-driving functions.

< Extended Reality ( XR : eX tended Reality)>

Extended reality refers collectively to virtual reality (VR), augmented reality (AR), and mixed reality (MR). VR technology provides objects and backgrounds in the real world only as CG images, AR technology provides virtual CG images on top of images of real objects, and MR technology provides computer technology that mixes and combines virtual objects in the real world. It is a graphic technology.

MR technology is similar to AR technology in that it shows real objects and virtual objects together. However, in AR technology, virtual objects are used to complement real objects, whereas in MR technology, virtual objects and real objects are used equally.

XR technology can be applied to HMD (Head-Mount Display), HUD (Head-Up Display), mobile phones, tablet PCs, laptops, desktops, TVs, digital signage, etc., and devices with XR technology applied are called XR Devices. It can be called.

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

The AI device 100 includes TVs, projectors, mobile phones, smartphones, desktop computers, laptops, digital broadcasting terminals, PDAs (personal digital assistants), PMPs (portable multimedia players), navigation, tablet PCs, wearable devices, and set-top boxes (STBs). ), DMB receivers, radios, washing machines, refrigerators, desktop computers, digital signage, robots, vehicles, etc., can be implemented as fixed or movable devices.

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. It can be included.

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

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

The input unit 120 can acquire various types of data.

At this time, the input unit 120 may include a camera for inputting video signals, a microphone for receiving audio signals, and a user input unit for receiving information from the user. Here, the camera or microphone may be treated as a sensor, and the 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 learning and input data to be used when obtaining an output using the learning model. The input unit 120 may acquire unprocessed input data, and in this case, the processor 180 or the learning processor 130 may extract input features by preprocessing the input data.

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

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

At this time, the learning processor 130 may include 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 use various sensors to obtain at least one of internal information of the AI device 100, information about the surrounding environment of the AI device 100, and user information.

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

The output unit 150 may generate output related to vision, hearing, or tactile sensation.

At this time, 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, learning data, learning models, learning history, etc. obtained from the input unit 120.

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. Additionally, 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, retrieve, receive, or utilize data from the learning processor 130 or the memory 170, and perform an operation that is predicted or determined to be desirable among the at least one executable operation. Components of the AI device 100 can be controlled to execute.

At this time, if linkage with an external device is necessary to perform the determined operation, the processor 180 may generate a control signal to control the external device and transmit the generated control signal to the external device.

The processor 180 may obtain intent information regarding user input and determine the user's request based on the obtained intent information.

At this time, the processor 180 uses at least one of a STT (Speech To Text) engine for converting voice input into a character string or a Natural Language Processing (NLP) engine for acquiring intent information of natural language, so that the user Intent information corresponding to the input can be obtained.

At this time, at least one of the STT engine or the NLP engine may be composed of at least a portion of an artificial neural network learned according to a machine learning algorithm. And, at least one of the STT engine or the NLP engine is learned by the learning processor 130, learned by the learning processor 240 of the AI server 200, or learned by distributed processing thereof. It may be.

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

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

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

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 composed of a plurality of servers to perform distributed processing, and may be defined as a 5G network. At this time, the AI server 200 may be included as a part of the AI device 100 and may perform at least part of the AI processing.

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 can transmit and receive data with an external device such as the AI device 100.

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

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

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

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

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

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 smartphone 100d, or a home appliance 100e. It is connected to this cloud network (10). Here, a robot 100a, an autonomous vehicle 100b, an XR device 100c, a smartphone 100d, or a home appliance 100e to which AI technology is applied may be referred to as AI devices 100a to 100e.

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

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

The AI server 200 may include a server that performs AI processing and a server that performs calculations on big data.

The AI server 200 is connected to at least one of the AI devices constituting the AI system 1: 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 can assist at least some of the AI processing of the connected AI devices 100a to 100e.

At this time, the AI server 200 can train an artificial neural network according to a machine learning algorithm on behalf of the AI devices 100a to 100e, and directly store or transmit the learning model 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 for the received input data using a learning model, and provides a response or control command based on the inferred result value. It can be generated and transmitted to AI devices (100a to 100e).

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

Below, various embodiments of 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 applies AI technology and can be implemented as a guidance robot, a transport robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, etc.

The robot 100a may include a robot control module for controlling operations, and the robot control module may mean a software module or a chip implementing it as hardware.

The robot 100a uses sensor information obtained from various types of sensors to obtain status information of the robot 100a, detect (recognize) the surrounding environment and objects, generate map data, or determine movement path and driving. It can determine a plan, determine a response to user interaction, or determine an action.

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

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

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

The robot 100a determines the movement path and driving plan 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 follow the determined movement path and driving plan. The robot 100a can be driven accordingly.

The map data may include object identification information about various objects arranged in the space where the robot 100a moves. For example, map data may include object identification information for fixed objects such as walls and doors and movable objects such as flower pots and desks. Additionally, object identification information may include name, type, distance, location, etc.

Additionally, the robot 100a can perform actions or drive by controlling the driving unit based on the user's control/interaction. At this time, the robot 100a may acquire interaction intention information according to the user's motion or voice utterance, determine a response based on the acquired intention information, and perform the operation.

<AI+Autonomous Driving>

The self-driving vehicle 100b can be implemented as a mobile robot, vehicle, unmanned aerial vehicle, etc. by applying AI technology.

The autonomous vehicle 100b may include an autonomous driving control module for controlling autonomous driving functions, and the autonomous driving control module may refer to a software module or a chip implementing it as hardware. The self-driving control module may be included internally as a component of the self-driving vehicle 100b, but may also be configured as separate hardware and connected to the outside of the self-driving vehicle 100b.

The self-driving vehicle 100b uses sensor information obtained from various types of sensors to obtain status information of the self-driving vehicle 100b, detect (recognize) the surrounding environment and objects, generate map data, or You can determine the movement route and driving plan, or determine the action.

Here, the autonomous vehicle 100b, like the robot 100a, may use sensor information acquired from at least one sensor among lidar, radar, and camera to determine the movement path and driving plan.

In particular, the autonomous vehicle 100b can recognize the environment or objects in an area where the view is obscured or an area over a certain distance by receiving sensor information from external devices, or receive recognized information directly from external devices. .

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

At this time, the self-driving vehicle 100b may perform operations by generating results using a direct learning model, but operates by transmitting sensor information to an external device such as the AI server 200 and receiving the results generated accordingly. You can also perform .

The autonomous vehicle 100b determines the movement path and driving plan 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 maintain the determined movement path and driving. The autonomous vehicle 100b can be driven according to a plan.

The map data may include object identification information about various objects placed in the space (eg, road) where the autonomous vehicle 100b drives. For example, map data may include object identification information for fixed objects such as streetlights, rocks, and buildings, and movable objects such as vehicles and pedestrians. Additionally, object identification information may include name, type, distance, location, etc.

Additionally, the autonomous vehicle 100b can perform operations or drive by controlling the driving unit based on the user's control/interaction. At this time, the autonomous vehicle 100b may acquire interaction intention information according to the user's motion or voice utterance, determine a response based on the acquired intention information, and perform the operation.

<AI+XR>

The XR device (100c) is equipped with AI technology and can be used for HMD (Head-Mount Display), HUD (Head-Up Display) installed in vehicles, televisions, mobile phones, smart phones, computers, wearable devices, home appliances, and digital signage. , it can be implemented as a vehicle, a fixed robot, or a mobile robot.

The XR device 100c analyzes 3D point cloud data or image data acquired through various sensors or from external devices to generate location data and attribute data for 3D points, thereby providing information about surrounding space or real objects. The XR object to be acquired and output can be rendered and output. For example, the XR device 100c may output an XR object containing additional information about the recognized object in correspondence to the recognized object.

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

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

<AI+Robot+Autonomous Driving>

The robot 100a applies AI technology and autonomous driving technology, and can be implemented as a guidance robot, a transport robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, etc.

The robot 100a to which AI technology and autonomous driving technology is applied may refer to a robot itself with autonomous driving functions or a robot 100a that interacts with an autonomous vehicle 100b.

The robot 100a with an autonomous driving function may refer to devices that move on their own along a given route without user control or move by determining the route on their own.

A robot 100a and an autonomous vehicle 100b with autonomous driving functions may use a common sensing method to determine one or more of a movement path or a driving plan. For example, the robot 100a and the autonomous vehicle 100b with autonomous driving functions can determine one or more of a movement path or a driving plan using information sensed through lidar, radar, and cameras.

The robot 100a that interacts with the self-driving vehicle 100b exists separately from the self-driving vehicle 100b and is linked to the self-driving function inside the self-driving vehicle 100b or is connected to the self-driving vehicle 100b. You can perform actions linked to the user on board.

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

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

Alternatively, the robot 100a interacting with the self-driving vehicle 100b may provide information to the self-driving vehicle 100b or assist its functions from outside the self-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, and may interact with the autonomous vehicle 100b, such as an automatic electric charger for an electric vehicle. You can also automatically connect an electric charger to the charging port.

<AI+Robot+XR>

The robot 100a applies AI technology and XR technology and can be implemented as a guidance robot, a transport robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, a drone, etc.

The robot 100a to which XR technology is applied may refer to a robot that is subject to control/interaction within an XR image. In this case, the robot 100a is distinct from the XR device 100c and may be interoperable 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 device 100c can output the generated XR image. And, this robot 100a may operate based on a control signal input through the XR device 100c or user 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. , you can control movement or driving, or check information about surrounding objects.

<AI+Autonomous Driving+XR>

The self-driving vehicle 100b can be implemented as a mobile robot, vehicle, unmanned aerial vehicle, etc. by applying AI technology and XR technology.

The autonomous vehicle 100b to which XR technology is applied may refer to an autonomous vehicle equipped with a means for providing XR images or an autonomous vehicle that is subject to control/interaction within XR images. In particular, the autonomous vehicle 100b, which is the subject of control/interaction within the XR image, is distinct from the XR device 100c and may be interoperable with each other.

An autonomous vehicle 100b equipped with a means for providing an XR image may acquire sensor information from sensors including a camera and output an XR image generated based on the acquired sensor information. For example, the self-driving vehicle 100b may be equipped with a HUD and output XR images, thereby providing occupants with XR objects corresponding to real objects or objects on the screen.

At this time, 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 toward which the passenger's gaze is directed. On the other hand, when the XR object is output to a display provided inside the autonomous vehicle 100b, at least part 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 lanes, other vehicles, traffic lights, traffic signs, two-wheeled vehicles, pedestrians, buildings, etc.

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

Figure 4 shows an AI device 100 according to an embodiment of the present invention.

Descriptions overlapping with FIG. 1 are omitted.

Referring to FIG. 4, the input unit 120 includes a camera 121 for inputting video signals, a microphone 122 for receiving audio signals, and a user input unit for receiving information from the user. 123) may be included.

Voice data or image data collected by the input unit 120 may be analyzed and processed as a user's control command.

The input unit 120 is for inputting image information (or signal), audio information (or signal), data, or information input from the user. For input of image information, the AI device 100 includes one or more Cameras 121 may be provided.

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

The microphone 122 processes external acoustic signals into electrical voice data. Processed voice data can be utilized in various ways depending on the function (or application being executed) being performed by the AI device 100. Meanwhile, various noise removal algorithms may be applied to the microphone 122 to remove noise generated in the process of receiving an external acoustic signal.

The user input unit 123 is for receiving information from the user. When information is input through the user input unit 123, the processor 180 can control the operation of the AI device 100 to correspond to the input information. .

The user input unit 123 is a mechanical input means (or mechanical key, such as a button, dome switch, jog wheel, jog switch, etc. located on the front/rear or side of the terminal 100) and It may include a touch input means. As an example, the touch input means consists of a virtual key, soft key, or visual key displayed on the touch screen through software processing, or a part other than the touch screen. It can be done with a touch key placed in .

The output unit 150 includes at least one of a display unit (151), a sound output unit (152), a haptic module (153), and an optical output unit (154). can do.

The display unit 151 displays (outputs) information processed by the AI device 100. For example, the display unit 151 may display execution screen information of an application running on the AI device 100, or UI (User Interface) and GUI (Graphic User Interface) information according to such execution screen information.

The display unit 151 can implement a touch screen by forming a layered structure or being integrated with the touch sensor. This touch screen functions as a user input unit 123 that provides an input interface between the AI device 100 and the user, and can simultaneously provide an output interface between the terminal 100 and the user.

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

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 a tactile effect generated by the haptic module 153 may be vibration.

The optical output unit 154 uses light from the light source of the AI device 100 to output a signal to notify that an event has occurred. Examples of events that occur in the AI device 100 may include receiving a message, receiving a call signal, missed call, alarm, schedule notification, receiving email, receiving information through an application, etc.

Figure 5 is a diagram illustrating an example of an artificial intelligence system according to another embodiment of the present invention.

Referring to FIG. 5, the artificial intelligence system 5 according to another embodiment of the present invention may include an AI server 100 and a plurality of artificial intelligence devices 100-1 to 100-5.

The first artificial intelligence device 100-1 is an air purifier 100-1, the second artificial intelligence device 100-2 is an air conditioner 100-2, and the third artificial intelligence device 100-3 is an air conditioner 100-2. It is a voice agent device (100-3), the fourth artificial intelligence device (100-4) is a mobile terminal (100-4), and the fifth artificial intelligence device (100-5) is an air quality measurement device (100-5). You can.

The air quality measuring device 100-5 may include one or more of a dust sensor, a temperature sensor, and a humidity sensor.

The air quality measuring device 100-5 can measure the concentration of PM10, PM2.5, PM1.0, temperature, humidity, and carbon dioxide in your home.

The first to fifth artificial intelligence devices 100-1 to 100-5 shown in FIG. 5 may be installed at home.

Each of the first to fifth artificial intelligence devices 100-1 to 100-5 may include all of the components shown in FIG. 4.

Meanwhile, the artificial intelligence system 5 may further include an IoT server (not shown) that acts as a relay between the AI server 200 and each artificial intelligence device.

The IoT server can relay communication between the AI server 200 and each artificial intelligence device.

Figure 6 is a ladder diagram for explaining the operation method of an artificial intelligence system according to an embodiment of the present invention.

The processor 260 of the AI server 200 receives indoor air quality information and information about the air purifier 100-1 from the air purifier 100-1 through the communication unit 210 (S601).

Indoor air quality information may include one or more of indoor fine dust concentration, indoor ultrafine dust concentration, and indoor carbon dioxide concentration.

The concentration of ultrafine dust may include the concentration of volatile organic compounds (Voc).

One or more dust sensors provided in the sensing unit 140 of the air purifier 100-1 can measure fine dust concentration, ultrafine dust concentration, and carbon dioxide concentration in the room where the air purifier 100-1 is placed.

The processor 180 of the air purifier 100-1 may periodically transmit indoor air quality information to the AI server 200. The period may be 1 hour, but this is just an example.

Information on the air purifier 100-1 may include one or more of the model name of the air purifier 100-1, operation information of the air purifier 100-1, and the placement location of the air purifier 100-1.

The operation information of the air purifier 100-1 may include information about functions possessed by the air purifier 100-1 and operation modes that the air purifier 100-1 can perform.

According to another embodiment of the present invention, the AI server 200 may receive indoor air quality information from the fine dust notification device 100-5. The air quality measuring device 100-5 may be equipped with a dust sensor and may transmit indoor air quality information measured through the dust sensor to the AI server 200.

The processor 260 of the AI server 200 determines the operating state of the air purifier 100-1 based on the indoor air quality information and the information on the air purifier 100-1 (S603).

The processor 260 may determine the operating state of the air purifier 100-1 based on indoor air quality information, the model name of the air purifier 100-1, and operation information of the air purifier 100-1.

Specifically, the processor 260 may determine the operating state of the air purifier 100-1 according to the indoor ultrafine dust concentration and the type of the air purifier 100-1.

FIG. 7A is a diagram illustrating a table mapping the operating state of the air purifier according to the air quality state and the type of the air purifier according to an embodiment of the present invention.

The table shown in FIG. 7A may be stored in the memory 230 of the AI server 200.

Air quality conditions can include good, average, poor, and worst (very poor).

For example, a good state is a state in which the concentration of ultrafine dust is less than the first level, a normal state is a state in which the concentration of ultrafine dust is greater than the first level and less than the second level, and a bad state is a state in which the concentration of ultrafine dust is less than the first level. is greater than the second level and less than the third level, and the worst state may be a state in which the concentration of ultrafine dust is greater than the third level. The third level may be larger than the second level, and the second level may have a larger value than the first level.

The type of air purifier 100-1 can be classified according to the model name of the air purifier 100-1.

Figure 7a shows the results of measuring air quality conditions based on the concentration of ultrafine dust with a diameter of 2.5 μm or less (PM 2.5) or ultrafine dust with a diameter of 1 μm or less (PM 1.0).

Assume that the model name of the air purifier 100-1 in FIG. 6 is Montblanc D.

When the indoor air quality is poor, the processor 260 may determine the operation mode of the air purifier 100-1 as clean booster mode and the wind volume as strong wind.

When the indoor air quality is very poor, the processor 260 can set the operation mode of the air purifier 100-1 to clean booster mode, turn on left and right rotation, and set the wind speed to power wind. The wind volume of a power wind can be greater than that of a strong wind.

The processor 260 may use the operation information received from the air purifier 100-1 to determine the operating state of the air purifier 100-1.

Again, Figure 6 will be described.

The processor 260 of the AI server 200 transmits a command requesting operation in the determined operating state to the air purifier 100-1 through the communication unit 210 (S605).

The operating state may include the operation mode of the air purifier 100-1, whether the up and down or left and right rotation of the air purifier 100-1 is turned on, and the set air volume.

The processor 180 of the air purifier 100-1 sets the operation of the air purifier 100-1 to an operating state corresponding to the command received from the AI server 200 (607).

That is, the processor 180 of the air purifier 100-1 can set its own operating state according to the operation mode, rotation direction, and wind volume of the air purifier 100-1 included in the received command.

Afterwards, the processor 180 of the air purifier 100-1 acquires the indoor air quality status for a certain period of time (609).

The processor 180 of the air purifier 100-1 may obtain one or more of the concentration of fine dust, the concentration of ultrafine dust, and the concentration of carbon dioxide through the dust sensor provided in the sensing unit 140.

The processor 180 of the air purifier 100-1 determines whether the indoor air quality is good based on the obtained indoor air quality (S611).

If the concentration of ultrafine dust measured indoors is less than the first level, the processor 180 may determine the indoor air quality to be good.

When the indoor air quality is good, the processor 180 of the air purifier 100-1 sets its operation to the operating state before receiving a command from the AI server 200 (S613).

The processor 180 may store the operating state in the memory 170 before receiving a command including the operating state from the AI server 200.

For example, when the operation mode before receiving the command from the AI server 200 was auto mode, and the indoor air quality status is changed to good, the processor 180 changes the operation mode of the air purifier 100-1 to auto mode. You can reset it with .

As such, according to an embodiment of the present invention, the appropriate operating state of the air purifier (100-1) is automatically determined according to indoor air quality information and the type of the air purifier (100-1), greatly improving user convenience. It can be.

7B and 7B are diagrams illustrating an interface screen for performing connection settings and operation settings between a mobile terminal and an air purifier through a fine dust notification application according to an embodiment of the present invention.

The fine dust notification application may be installed on the mobile terminal (100-4). The fine dust notification application may be an application that provides connection settings for the air purifier (100-1) and air conditioner (100-2) provided in the home, information on indoor and outdoor fine dust, and information on fine dust management.

Figure 7b shows a connection settings screen 710 for establishing a connection between the mobile terminal 100-4 and the air purifier 100-1.

The connection settings screen 710 may include an air quality status item 711, a notification setting item 713, and a device connection setting item 715.

The air quality status item 711 may include an air quality status measured through a dust sensor provided in the air purifier 100-1 and an outdoor air quality status received from the AI server 200.

The notification setting item 713 may be an item that sets whether to provide a notification when the state of fine dust becomes bad or the state of ultrafine dust becomes bad.

The device connection setting item 715 provides a notification that a home appliance (for example, an air purifier 100-1) connected to the mobile terminal 100-4 can be operated when the indoor air quality becomes poor. This may be an item to set whether to do so or not.

Next, FIG. 7C will be described.

Figure 7c shows the operation settings screen 730 for operation settings of the air purifier 100-1.

The driving settings screen 730 may include an indoor air level setting item 731, an automatic driving setting item 733, and a storage item 735.

The indoor air level setting item 731 may be an item for setting the indoor air quality state. Indoor air quality conditions may include normal condition, poor condition, and very poor condition.

The automatic operation setting item 733 may be an item for setting whether to automatically turn on or off the operation of the air purifier 100-1 when a set air quality state is reached.

If, in the automatic operation setting item 733, the air purifier 100-1 is set to automatically turn on operation, the air purifier 100-1 can be operated in the operating state determined by the table in FIG. 7A. .

The storage item 735 may be an item for storing settings in each of the indoor air level setting item 731 and the automatic operation setting item 733.

Figure 8 is a ladder diagram for explaining the operation method of an artificial intelligence system according to another embodiment of the present invention.

In particular, Figure 8 shows an embodiment in which the analysis results of indoor air quality analyzed from the AI server 200 are provided through the mobile terminal 100-4.

The processor 260 of the AI server 200 acquires outdoor air quality information (801).

The AI server 200 may receive outdoor air quality information through the communication unit 210 from an external server that provides weather information.

The processor 260 of the AI server 200 receives indoor air quality information and user information from the mobile terminal 100-4 through the communication unit 210 (S803).

Indoor air quality information may be measured by any one of the air purifier 100-1, the air conditioner 100-2, and the air quality measurement device 100-5.

The processor 180 of the mobile terminal 100-4 can receive indoor air quality information from any one of the air purifier 100-1, the air conditioner 100-2, and the air quality measuring device 100-5. The indoor air quality information can be transmitted to the AI server 200.

Indoor air quality information may include one or more of indoor fine dust concentration, ultrafine dust concentration, indoor temperature, humidity, and carbon dioxide concentration.

Indoor air quality information may include one or more of fine dust concentration and ultrafine dust concentration measured by each of a plurality of air quality measurement devices disposed in each of a plurality of indoor spaces. The plurality of indoor spaces may be separate spaces within the home, such as a living room, kitchen, and master bedroom.

As another example, indoor air quality information may be transmitted directly to the AI server 200 by the air quality measurement device 100-5.

User information may be meta information. Meta information may include one or more of user personal information and information about the home where the user resides.

Meta information may include the user's residence type, whether the user's house is located on the side of the road or inside the road, the installation location of the air quality measuring device 100-5, etc.

The processor 260 of the AI server 200 analyzes indoor air quality based on outdoor air quality information, indoor air quality information, and user information (S805).

The processor 260 of the AI server 200 generates an air quality analysis report according to the analysis results (S807) and transmits the generated air quality analysis report to the mobile terminal 100-4 through the communication unit 210 (S809). .

The air quality analysis report may be a report that analyzes the specific state of indoor air quality based on outdoor air quality information, indoor air quality information, and user information collected over a certain period of time.

A detailed explanation of the air quality analysis report is provided later.

The processor 180 of the mobile terminal 100-4 displays an air quality analysis report through the display unit 152 (S811).

9 and 10 are diagrams illustrating an air quality analysis report according to an embodiment of the present invention.

9 and 10 are diagrams showing a first analysis report 900 and a second analysis report 1000 showing indoor air quality analysis results.

Meanwhile, the air quality analysis report may include a cover, basic content on ultrafine dust, article content on risks, and the first and second analysis reports (900, 1000).

First, Figure 9 will be described.

Referring to FIG. 9, the first analysis report 900 includes an indoor air quality measurement period item 910, a daily indoor air quality state item 920, a daily ultrafine dust concentration item 930, and an indoor air quality analysis result item 940. ), ultra-fine dust concentration item by day (950), analysis result item by day (960), ultra-fine dust concentration item by time slot (970), and analysis result item by time slot (980).

The indoor air quality measurement period item 910 may be an item representing the measurement period during which the air quality condition within the home was measured. The measurement period may be the period from the time the air quality measurement device 100-5 is installed in the home to the time it is recovered. As another example, the measurement period may be the period from the day the fine dust notification application is installed on the mobile terminal 100-4 until the end of measurement of indoor air quality.

The fine dust notification application may be an application that provides information about fine dust in your home.

The daily indoor air quality status item 920 may be an item indicating the number of days according to the air quality status. Air room conditions may include very poor condition (worst condition), poor condition, and normal condition. If the measurement period is 42 days, the number of days classified as very poor may be 9, the number of days classified as poor may be 22, and the number of days classified as normal may be 11.

Air quality conditions by day can be displayed in different colors.

If the daily representative value of ultrafine dust concentration measured indoors is less than 35㎍/㎥, it can be classified as normal, if it is 36~75 ㎍/㎥, it can be classified as bad, and if it is more than 76 ㎍/㎥, it can be classified as very bad.

A representative value of indoor ultra-fine dust concentration can be expressed as the following [Equation 1].

[Equation 1]

{Max (average indoor PM2.5 per hour) + Min (average indoor PM2.5 per hour)} / 2

As another example, the daily representative value of indoor ultra-fine dust concentration may be the maximum hourly average value.

The daily ultra-fine dust concentration item 930 may be an item that represents the value of daily ultra-fine dust concentration constituting the measurement period in a bar graph.

The daily ultrafine dust concentration item 930 may include one or more of indoor and outdoor ultrafine dust concentration values.

The representative value of the outdoor ultrafine dust concentration can be calculated using the method of calculating the representative value of the indoor ultrafine dust concentration.

Each bar can be displayed in green if the representative value of indoor ultrafine dust concentration is in a normal state, in orange if it is in a bad state, and in red if it is in a very bad state.

The daily ultra-fine dust concentration item (930) is the first baseline (931), which represents 75 ㎍/㎥, which is the standard value for distinguishing between a very bad state and a bad state, and 35 ㎍, which is a standard value for distinguishing between a normal state and a bad state. It may include a second reference line 933 representing /㎥.

The daily ultra-fine dust concentration item 930 may further include an indicator 935 indicating a dangerous state when the outdoor ultra-fine dust concentration is 35 ㎍/㎥ or more and the ultra-fine dust concentration indoors is higher than outdoors. . This is intended to express that indoor air quality is poor and ventilation is necessary, but even with ventilation, outdoor air quality is poor, so it is effective to use a fine dust management device such as an air purifier rather than opening the window.

The indoor air quality analysis result item 940 may be an item for providing the analysis result of the daily ultrafine dust concentration item 930 in text.

The ultra-fine dust concentration by day item 950 may be an item for providing the trend of ultra-fine dust concentration by day in a graph form during the measurement period.

The representative value for each day of the week can be obtained by calculating the daily representative value based on the hourly average value of indoor PM 2.5 concentration and then calculating the average value for each day of the week.

The daily fine dust concentration item (950) is the third baseline (951), which represents 75 ㎍/㎥, which is the standard value for distinguishing between a very bad state and a bad state, and 35 ㎍, which is a standard value for distinguishing between a normal state and a bad state. It may include a fourth reference line 953 representing /㎥.

The day-of-the-week analysis result item 960 may be an item for providing the analysis result of the ultra-fine dust concentration item 950 for each day of the week in text. The day of the week analysis result item 960 may include analysis results for weekdays and weekends.

The ultra-fine dust concentration item 970 by time zone may be an item to show the indoor air quality status in each of a plurality of time sections during the measurement period.

For example, the ultra-fine dust concentration item 970 by time zone can be divided into four time sections to show the indoor air quality status. The four time sections may include a preparation section for work, a general housework section, a dinner preparation section, and a bedtime section.

The mobile terminal 100-4 can calculate the representative value of each time section using Equation 2 below.

[Equation 2]

{Max (average indoor PM2.5 per hour for each time section) + Min (average indoor PM2.5 per hour for each time section)} / 2

In the above embodiment, four time sections are explained as an example, but this is only an example.

The time zone analysis result item 980 may be an item for providing the analysis result of the time zone ultrafine dust concentration item 970 in text form.

The time zone analysis result item 980 may include a user guide for air quality status and fine dust management for each of a plurality of time sections.

Meanwhile, according to another embodiment of the present invention, each item constituting the first analysis report 900 may be provided for each of the divided spaces in the home.

For example, a first analysis report 900 may be provided for each of separate spaces, such as a living room, kitchen, and bedroom. In this case, the living room, kitchen, and bedroom may each be equipped with a dust sensor that measures the concentration of ultrafine dust.

Next, FIG. 10 will be described.

Referring to FIG. 10, the second analysis report 1000 includes a first air quality comparison item 1010, a second air quality comparison item 1020, an air quality comparison analysis item 1030, an air quality type item 1040, and an air quality type analysis. It may include one or more of the item 1050 and the solution proposal item 1060.

The first air quality comparison item 1010 and the second air quality comparison item 1020 are items for comparing the air quality status between one's home and other homes.

The first air quality comparison item 1010 and the second air quality comparison item 1020 may be generated based on meta information previously input through a fine dust notification application.

Meta information may include the user's residential type (apartment or house) and residential location (roadside or inside the road).

The mobile terminal 100-4 may receive, from the AI server 200, the home air quality status having the same meta information as the input meta information.

In order to compare the air quality status between other homes and one's own home, a representative value of the indoor ultrafine dust concentration of one's home during the measurement period is required. For this, as described in FIG. 9, one representative value may be calculated first. Afterwards, the mobile terminal 100-4 can obtain a representative value of the indoor ultrafine dust concentration of each home by dividing the sum of the daily representative values by the measurement period.

Meanwhile, a representative value of the ultrafine dust concentration of another apartment-type household may be the average value of representative values collected from users living in an apartment-type residential type.

In a residential location far from the road, a representative value of the ultrafine dust concentration of another household may be the average value of representative values collected from users living in that residential location.

The meta information used in the first and second air quality comparison items (1010, 1020) is only an example, and various analysis types can be used, such as families with children/homes without children, low-rise/middle-rise/high-rise.

In the second air quality comparison item 1020, a representative air quality value of a neighboring house can be obtained for each of a plurality of spaces constituting the neighboring house. A dust sensor is provided in each of the plurality of spaces, and the ultrafine dust concentration can be measured based on the location where the dust sensor was first installed.

The air quality comparative analysis item 1030 is an item for providing text results of comparative analysis of air quality status with other households with the same meta information.

The content of the text may vary depending on whether the representative value of ultrafine dust concentration is greater than that of the neighboring house.

The air quality type item 1040 is an item for providing the air quality type within the home according to comparison between the air quality state within the home and the outdoor air quality state during the measurement period.

Air quality types can be divided into four types depending on indoor air quality and outdoor air quality.

For the first air quality type, the average value of daily representative values of indoor ultra-fine dust concentration (based on the measurement period) exceeds 35 ㎍/㎥, and the average value of daily representative values of outdoor ultra-fine dust concentration exceeds 35 ㎍/㎥. This is the type of case.

The second air quality type is when the average value of daily representative values of indoor ultra-fine dust concentration (based on the measurement period) exceeds 35 ㎍/㎥ and the average value of daily representative values of outdoor ultra-fine dust concentration is 35 ㎍/㎥ or less. It is a type of

The third air quality type is one in which the average value of daily representative values of indoor ultra-fine dust concentration (based on the measurement period) is 35 ㎍/㎥ or less, and the average value of daily representative values of outdoor ultra-fine dust concentration is more than 35 ㎍/㎥. This is the type of case.

The fourth air quality type is when the average value of daily representative values of indoor ultra-fine dust concentration (based on the measurement period) is 35 ㎍/㎥ or less, and the average value of daily representative values of outdoor ultra-fine dust concentration is 35 ㎍/㎥ or less. It is a type.

The mobile terminal 100-4 can determine one of a plurality of air quality types according to the average value of indoor and outdoor ultrafine dust concentration.

The air quality type analysis item 1050 is an item to provide a detailed description of the air quality type item 1040 in text.

The air quality type analysis item 1050 may include text corresponding to the actually determined air quality type item 1040. This will be explained with reference to FIG. 11 .

Figure 11 is a diagram illustrating text provided corresponding to each of a plurality of air quality types according to an embodiment of the present invention.

Referring to Figure 11, text corresponding to each of the four air quality types is shown.

The mobile terminal 100-4 may provide text corresponding to the determined air quality type through the air quality type analysis item 1050.

Again, Fig. 10 will be described.

The solution suggestion item (1060) is an item to recommend a fine dust management solution suitable for the determined air quality type.

The solution suggestion item 1060 may include a model of an air purifier or a model of a related home appliance that matches the determined air quality type.

Figure 12 is a diagram showing the process of receiving customer meta information required to provide an air quality analysis report according to an embodiment of the present invention.

Each of the screens in Figure 12 is an interface screen displayed on the display of the manager's mobile terminal for fine dust care.

The manager screen 1210 in FIG. 12 may be a management screen visible only to the manager who manages fine dust in the home.

When an item for managing customer meta information is selected through the management screen 1210, a meta information input screen 1230 may be displayed.

The meta information input screen 1230 is a screen for a manager to input meta information about a specific customer.

The customer's meta information includes the installation date of the dust sensor or air purifier, the collection date of the dust sensor or air purifier, the customer's residence type, the customer's personal information, the installation location of the dust sensor or air purifier, and whether the customer's residence is on the road. , may include whether it is inside the road, etc.

The customer's meta information may be used to provide the air quality analysis report described in FIGS. 9 and 10.

Figure 13 is a diagram showing an air quality service screen according to an embodiment of the present invention.

The air quality service screen in FIG. 13 may be another form of an air quality analysis report.

The air quality service screen may include outdoor air quality information 1310, air quality information 1330 for a specific space within the home, and an air quality management guide 1350.

Outdoor air quality information 1310 may include information indicating outdoor air quality.

Air quality information 1330 for a specific space within the home may include information about the air quality measured in the specific space. The specific space may be a space where the air quality measurement device 100-5 is installed.

The air quality management guide 1350 may include text for air quality management tailored to the air quality information 1330 of a specific space.

Figure 14 is a diagram illustrating an air quality guide table mapping a guide according to air quality according to an embodiment of the present invention.

The air quality guide table 1400 shown in FIG. 14 includes the memory 230 of the AI server 200, the memory 170 of the voice agent device 100-3, the memory 170 of the mobile terminal 100-4, It may be stored in the air quality measurement device 100-5.

The air quality guide table may be a table that maps guides according to outdoor air quality conditions and indoor air quality conditions.

When the outdoor air quality state and the indoor air quality state are identified, the processor 180 of the mobile terminal 100-4 may extract a guide corresponding to the corresponding outdoor air quality state from the air quality guide table 1400.

The processor 180 of the mobile terminal 100-4 can output the extracted guide as a voice through a speaker.

The processor 180 of the mobile terminal 100-4 can output the text guide as a voice using a text-to-speech engine.

As another example, the voice agent device 100-3 or the air quality measurement device 100-5 may also extract a guide according to the outdoor air quality state and the indoor air quality state and output the extracted guide as a voice.

15A and 15B are diagrams illustrating a guide according to temperature and humidity measured in a home according to an embodiment of the present invention.

Each of the temperature and humidity within the home can be measured by a temperature sensor and a humidity sensor provided in any one of the air purifier 100-1, the air conditioner 100-2, and the air quality measuring device 100-5.

Referring to FIG. 15A, a comfort state diagram 1510 according to humidity and temperature is shown.

The comfort state diagram 1510 can be classified according to comfort level. The comfort level may include a comfortable state, a slightly comfortable state, a low temperature state, an unpleasant dry state, an unpleasant high humidity state, and a high temperature state.

Each of the states representing comfort can be distinguished by a different color.

The mobile terminal 100-4 can determine the level of comfort within the home based on the temperature and humidity measured within the home. The mobile terminal 100-4 can receive temperature and humidity from any one of the air purifier 100-1, the air conditioner 100-2, and the air quality measuring device 100-5.

The mobile terminal 100-4 can display a guide corresponding to the determined comfort level in text or output it in voice.

Referring to FIG. 15B, a comfort level guide 1530 representing a guide according to comfort level is shown. The comfort guide 1530 may be stored in the memory 170.

The mobile terminal 100-4 may determine the comfort level according to temperature and humidity, and extract a guide corresponding to the determined comfort level from the comfort level table 1530.

The mobile terminal 100-4 can display the extracted guide as text or output it as voice.

The above-described present invention can be implemented as computer-readable code on a program-recorded medium. Computer-readable media includes all types of recording devices that store data that can be read by a computer system. Examples of computer-readable media include HDD (Hard Disk Drive), SSD (Solid State Disk), SDD (Silicon Disk Drive), ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc. There is. Additionally, the computer may include a processor 180 of an artificial intelligence device.

Claims (20)

In artificial intelligence devices,
display;
a communication unit that transmits indoor air quality information received from an air quality measuring device and meta information input through the display to an artificial intelligence server; and
It includes a processor that receives an air quality analysis report generated based on the outdoor air quality information, the indoor air quality information, and the meta information from the artificial intelligence server through the communication unit, and displays the received air quality analysis report on the display. do,
The air quality analysis report is
A first analysis report containing the results of analyzing the indoor air quality status during the measurement period measured by the air quality measuring device, and
Contains a second analysis report including an air quality type according to the indoor air quality condition and a solution for fine dust management according to the air quality type,
The meta information is
Including the residential type of the house where the user lives, whether the house where the user lives is located on the side of the road, and the date of installation and collection date of the air quality measuring device.
Artificial intelligence device. According to paragraph 1,
The first analysis report is
Daily indoor air quality status item indicating the number of days with air quality status,
A daily ultra-fine dust concentration item that graphs the daily ultra-fine dust concentration during the measurement period,
During the measurement period, an ultra-fine dust concentration item for each day of the week indicating the trend of the ultra-fine dust concentration for each day of the week, and
During the measurement period, in each of a plurality of time intervals, including one or more of the ultra-fine dust concentration items by time zone showing the indoor air quality status.
Artificial intelligence device. According to paragraph 2,
The air quality condition is
Is one of the following: Normal, Poor, and Very Poor,
The normal state is a state when a representative value of the ultrafine dust concentration is less than a first value, and the bad state is a state when a representative value of the ultrafine dust concentration is less than a second value greater than the first value. And the very bad state is a state when a representative value of the ultrafine dust concentration is greater than or equal to the second value.
Artificial intelligence device. According to paragraph 3,
The daily representative value of the ultrafine dust concentration is
Calculated by the following [Equation 1]
[Equation 1]
(Max(average indoor PM2.5 per hour) + Min(average indoor PM2.5 per hour))/2
Artificial intelligence device. According to paragraph 3,
The representative value of ultrafine dust in each of the plurality of time sections is calculated by the following [Equation 2]
[Equation 2]
(Max (average indoor PM2.5 per hour in each time section) + Min (average indoor PM2.5 per hour in each time section)) / 2
Artificial intelligence device. According to paragraph 1,
The second analysis report is
an air quality type item for providing the air quality type determined according to a comparison between the indoor air quality state and the outdoor air quality state; and
Containing a solution proposal item including a model of air purifier suitable for the air quality type determined above
Artificial intelligence device. According to paragraph 1,
The indoor air quality information above is
Contains one or more of fine dust concentration, ultra-fine dust concentration, temperature, humidity, and carbon dioxide concentration measured within the home.
Artificial intelligence device. delete According to paragraph 1,
The indoor air quality information above is
Containing one or more of fine dust concentration and ultrafine dust concentration measured in each of a plurality of air quality measurement devices placed in each of a plurality of indoor spaces
Artificial intelligence device. According to clause 6,
The second analysis report is
It further includes an air quality comparison item including a comparison result between the indoor air quality state and the indoor air quality state of another home.
Artificial intelligence device. In artificial intelligence systems,
Air quality measurement device that measures indoor air quality information;
Artificial intelligence device that acquires meta information; and
Receive the indoor air quality information from the air quality measuring device, receive the meta information from the artificial intelligence device, and generate and generate an air quality analysis report based on the outdoor air quality information, the indoor air quality information, and the meta information. It includes an artificial intelligence server that transmits the air quality analysis report to the artificial intelligence device,
The air quality analysis report is
A first analysis report containing the results of analyzing the indoor air quality status during the measurement period measured by the air quality measuring device, and
Contains a second analysis report including an air quality type according to the indoor air quality condition and a solution for fine dust management according to the air quality type,
The meta information is
Including the residential type of the house where the user lives, whether the house where the user lives is located on the side of the road, and the date of installation and collection date of the air quality measuring device.
artificial intelligence system. According to clause 11,
The first analysis report is
Daily indoor air quality status item indicating the number of days with air quality status,
A daily ultra-fine dust concentration item that graphs the daily ultra-fine dust concentration during the measurement period,
During the measurement period, an ultra-fine dust concentration item for each day of the week indicating the trend of the ultra-fine dust concentration for each day of the week, and
During the measurement period, in each of a plurality of time intervals, including one or more of the ultra-fine dust concentration items by time zone showing the indoor air quality status.
artificial intelligence system. According to clause 12,
The air quality condition is
Is one of the following: Normal, Poor, and Very Poor,
The normal state is a state when a representative value of the ultrafine dust concentration is less than a first value, and the bad state is a state when a representative value of the ultrafine dust concentration is less than a second value greater than the first value. And the very bad state is a state when a representative value of the ultrafine dust concentration is greater than or equal to the second value.
artificial intelligence system According to clause 13,
The daily representative value of the ultrafine dust concentration is
Calculated by the following [Equation 1]
[Equation 1]
(Max(average indoor PM2.5 per hour) + Min(average indoor PM2.5 per hour))/2
artificial intelligence system According to clause 13,
The representative value of ultrafine dust in each of the plurality of time sections is calculated by the following [Equation 2]
[Equation 2]
(Max (average indoor PM2.5 per hour in each time section) + Min (average indoor PM2.5 per hour in each time section)) / 2
artificial intelligence system According to clause 11,
The second analysis report is
an air quality type item for providing the air quality type determined according to a comparison between the indoor air quality state and the outdoor air quality state; and
Containing a solution proposal item including a model of air purifier suitable for the air quality type determined above
artificial intelligence system. According to clause 11,
The indoor air quality information above is
Contains one or more of fine dust concentration, ultra-fine dust concentration, temperature, humidity, and carbon dioxide concentration measured within the home.
artificial intelligence system. delete According to clause 11,
The indoor air quality information above is
Containing one or more of fine dust concentration and ultrafine dust concentration measured in each of a plurality of air quality measurement devices placed in each of a plurality of indoor spaces
artificial intelligence system. According to clause 16,
The second analysis report is
It further includes an air quality comparison item including a comparison result between the indoor air quality state and the indoor air quality state of another home.
artificial intelligence system.

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