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WO2021010520A1 - Dispositif pour fournir des informations de consommation d'énergie sur la base de l'intelligence artificielle, et son procédé de commande - Google Patents

Dispositif pour fournir des informations de consommation d'énergie sur la base de l'intelligence artificielle, et son procédé de commande Download PDF

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Publication number
WO2021010520A1
WO2021010520A1 PCT/KR2019/008795 KR2019008795W WO2021010520A1 WO 2021010520 A1 WO2021010520 A1 WO 2021010520A1 KR 2019008795 W KR2019008795 W KR 2019008795W WO 2021010520 A1 WO2021010520 A1 WO 2021010520A1
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WO
WIPO (PCT)
Prior art keywords
power usage
electronic device
user
information
data
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Ceased
Application number
PCT/KR2019/008795
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English (en)
Korean (ko)
Inventor
김동인
문곤수
신광휴
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LG Electronics Inc
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LG Electronics Inc
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Priority to PCT/KR2019/008795 priority Critical patent/WO2021010520A1/fr
Publication of WO2021010520A1 publication Critical patent/WO2021010520A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/09—Supervised learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply

Definitions

  • the present invention relates to an artificial intelligence-based power usage information providing apparatus and a control method thereof.
  • artificial intelligence does not exist by itself, but is directly or indirectly related to other fields of computer science.
  • attempts are being made very actively to introduce artificial intelligence elements in various fields of information technology and to use them for problem solving in that field.
  • Korean Laid-Open Patent Publication No. 2017-0027191 (a method and apparatus for managing energy consumption) includes the steps of: receiving information on a target rate of a user for a predetermined period from a terminal; Generating energy rate budget information for the predetermined period based on the user's energy usage history information and the target rate information received from the gateway; And transmitting the generated energy rate budget information to the terminal.
  • the problem to be solved by the present invention is to provide usability information including power usage, usage time, and usage mode of similar groups through the current status of other users using power.
  • the problem to be solved by the present invention is to generate recommended function information for power consumption reduction of a user's electronic device through information of a similar group, and provide artificial intelligence-based power use information for power reduction.
  • An apparatus for providing power usage information based on artificial intelligence includes: a communication unit for receiving power usage data and electronic device usability data for a user's electronic device; A memory for storing power usage data and electronic device usage data of each of a plurality of users; And classifying power consumption data for a predetermined period of the power consumption data of each of the plurality of users into a plurality of power use groups according to a time series analysis, and among the plurality of classified power use groups, power consumption data of the user.
  • a power usage group corresponding to is acquired, power usage data and electronic device usage data of at least one other user included in the obtained power usage group are loaded from the memory, and the loaded power usage data of at least one other user
  • a recommendation function for reducing power consumption of the user's electronic device by generating similar group power usage information based on and comparing the loaded electronic device usability data of at least one other user and the electronic device usability data of the user It may include a processor for controlling the communication unit to generate information and transmit the generated similar group power usage information and recommended function information to the user's electronic device or terminal.
  • the memory may further include location information of the electronic device or usability data of other products used in connection with the electronic device.
  • the electronic device usability data may further include at least one of a use time or a use mode of the electronic device.
  • the processor may classify the power use group according to a region located according to the time series analysis, an amount of electricity, or a household characteristic including the number of household members and age.
  • the processor provides the similar group power usage information or recommendation function information in response to a request for power usage information or recommended function information to the user's electronic device, and if there is sub-information on the user's request, an additional question Can provide.
  • the recommended function information may include a usage mode or a usage time having a small amount of power within the similar group power usage information to reduce power usage of the user's electronic device.
  • the processor may compare the generated similar group power usage information or recommended function information with all users or similar groups of the electronic device and transmit it to the terminal, and change the use mode of the electronic device according to the recommended function information. have.
  • a method of providing an apparatus for providing power usage information based on artificial intelligence includes: receiving, at a communication unit, power usage data and electronic device usability data for a user's electronic device; Power usage data and electronic device usage data of each of a plurality of users are stored in a memory, and power usage data of a predetermined period of the power usage data of each of the plurality of users is stored, and a processor uses a plurality of power usage groups according to a time series analysis. Classifying as; And similar group power usage information based on the loaded power usage data of at least one other user by loading power usage data and electronic device usage data of at least one other user included in the power usage group from the memory by the processor. It may include the step of generating.
  • the method may further include acquiring a power use group corresponding to the user's power use data from among the classified plurality of power use groups.
  • the method may further include controlling the communication unit to transmit the generated similar group power usage information or recommended function information to the user's electronic device or terminal through the processor.
  • the processor may further include classifying, by the processor, the power usage group according to a region where the electronic device is located, an amount of electricity, or a household characteristic including the number of household members and age according to the time series analysis.
  • the processor may further include providing a usage mode or usage time with a small amount of power within the similar group power usage information to reduce power usage of the user's electronic device.
  • the processor comparing the generated similar group power usage information or recommended function information with all users or similar groups of the electronic device and transmitting the generated information to the user's terminal; And changing a use mode of the electronic device according to the recommended function information.
  • the apparatus for providing power use information based on artificial intelligence has an advantage of providing the amount of power used, the use time, and the use mode of a user and other users.
  • the present invention has the advantage of providing additional information for power saving when a user uses a product because a user can select and compare similar group criteria.
  • the present invention provides a recommendation function through comparison with information of other users, there is an advantage of providing a new function and method of saving power to a user who uses a function that is usually repeated.
  • FIG 1 shows an AI device according to an embodiment of the present invention.
  • FIG 2 shows an AI server according to an embodiment of the present invention.
  • FIG 3 shows an AI system according to an embodiment of the present invention.
  • FIG. 4 is a block diagram of an apparatus for providing power usage information based on artificial intelligence according to an embodiment of the present invention.
  • FIG. 5 shows a data flow of an apparatus for providing power usage information based on artificial intelligence according to an embodiment of the present invention.
  • FIG. 6 is a flowchart illustrating a control method of an apparatus for providing power usage information based on artificial intelligence according to an embodiment of the present invention.
  • FIG. 7 shows a monthly accumulated power amount according to a monthly usage time according to an embodiment of the present invention.
  • FIG 9 illustrates a distribution of monthly usage of all other users similar to the model of the user's electronic device according to an embodiment of the present invention.
  • FIG. 10 shows a terminal of a user displaying contents of comparison with other users according to an embodiment of the present invention.
  • Machine learning refers to the field of researching methodologies to define and solve various problems dealt with in the field of artificial intelligence. do.
  • Machine learning is also defined as an algorithm that improves the performance of a task through continuous experience.
  • An artificial neural network is a model used in machine learning, and may refer to an overall model with problem-solving capabilities, composed of artificial neurons (nodes) that form a network by combining synapses.
  • the artificial neural network may be defined by a connection pattern between neurons of different layers, a learning process for updating model parameters, and an activation function for generating 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 an artificial neural network, each neuron can output a function of an activation function for input signals, weights, and biases input through synapses.
  • Model parameters refer to parameters determined through learning, and include weights of synaptic connections and biases of neurons.
  • hyperparameters refer to parameters that must be set before learning in a machine learning algorithm, and include a learning rate, iteration count, mini-batch size, and initialization function.
  • the purpose of learning artificial neural networks can be seen as determining model parameters that minimize the loss function.
  • the loss function can be used as an index to determine an optimal model parameter in the learning process of the artificial neural network.
  • Machine learning can be classified into supervised learning, unsupervised learning, and reinforcement learning according to the learning method.
  • Supervised learning refers to a method of training an artificial neural network when a label for training data is given, and a label indicates the correct answer (or result value) that the artificial neural network should infer when training data is input to the artificial neural network. It can mean.
  • Unsupervised learning may refer to a method of training an artificial neural network in a state where a label for training data is not given.
  • Reinforcement learning may mean a learning method in which an agent defined in a certain environment learns to select an action or action sequence that maximizes the cumulative reward in each state.
  • machine learning implemented as a deep neural network (DNN) including a plurality of hidden layers is sometimes referred to as deep learning (deep learning), and deep learning is a part of machine learning.
  • DNN deep neural network
  • machine learning is used in the sense including deep learning.
  • a robot may refer to a machine that automatically processes or operates a task given by its own capabilities.
  • a robot having a function of recognizing the environment and performing an operation by self-determining may be referred to as an intelligent robot.
  • Robots can be classified into industrial, medical, household, 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 a robot joint.
  • a driving unit including an actuator or a motor to perform various physical operations such as moving a robot joint.
  • the movable robot includes a wheel, a brake, a propeller, etc. in a driving unit, and can travel on the ground or fly in the air through the driving unit.
  • Autonomous driving refers to self-driving technology
  • autonomous driving vehicle refers to a vehicle that is driven without a user's manipulation or with a user's minimal manipulation.
  • a technology that maintains a driving lane a technology that automatically adjusts the speed such as adaptive cruise control, a technology that automatically drives along a specified route, and a technology that automatically sets a route when a destination is set, etc. All of these can be included.
  • the vehicle includes all of a vehicle having only an internal combustion engine, a hybrid vehicle including an internal combustion engine and an electric motor, and an electric vehicle including only an electric motor, and may include not only automobiles, but also trains and motorcycles.
  • the autonomous vehicle can be viewed as a robot having an autonomous driving function.
  • the extended reality collectively refers to Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR).
  • VR technology provides only CG images of real world objects or backgrounds
  • AR technology provides virtually created CG images on top of real object images
  • MR technology is a computer 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 and virtual objects together.
  • virtual objects are used in a form that complements real objects
  • MR technology virtual objects and real objects are used with equal characteristics.
  • 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 applied with XR technology are XR devices. It can be called as.
  • HMD Head-Mount Display
  • HUD Head-Up Display
  • mobile phones tablet PCs, laptops, desktops, TVs, digital signage, etc.
  • devices applied with XR technology are XR devices. It can be called as.
  • FIG 1 shows an AI device 100 according to an embodiment of the present invention.
  • the AI device 100 includes a TV, a projector, a mobile phone, a smartphone, a desktop computer, a laptop computer, a digital broadcasting terminal, a personal digital assistant (PDA), a portable multimedia player (PMP), a navigation system, a tablet PC, a 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, and the like.
  • PDA personal digital assistant
  • PMP portable multimedia player
  • STB set-top box
  • the terminal 100 includes a communication unit 110, an input unit 120, a running processor 130, a sensing unit 140, an output unit 150, a memory 170, and a processor 180.
  • the communication unit 110 may transmit and receive data with external devices such as other AI devices 100a to 100e or the AI server 200 using wired/wireless communication technology.
  • the communication unit 110 may transmit and receive sensor information, a user input, a learning model, and a control signal with external devices.
  • 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). ), BluetoothTM, Radio Frequency Identification (RFID), Infrared Data Association (IrDA), ZigBee, and Near Field Communication (NFC).
  • GSM Global System for Mobile communication
  • CDMA Code Division Multi Access
  • LTE Long Term Evolution
  • 5G Fifth Generation
  • WLAN Wireless LAN
  • Wi-Fi Wireless-Fidelity
  • BluetoothTM BluetoothTM
  • RFID Radio Frequency Identification
  • IrDA Infrared Data Association
  • ZigBee ZigBee
  • NFC Near Field Communication
  • the input unit 120 may acquire various types of data.
  • 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.
  • a camera or microphone for treating a camera or microphone as a sensor, 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 by using the training model.
  • the input unit 120 may obtain unprocessed input data, and in this case, the processor 180 or the running processor 130 may extract an input feature as a preprocess for the input data.
  • the learning processor 130 may train a model composed of an artificial neural network using the training data.
  • the learned artificial neural network may be referred to as a learning model.
  • the learning model can be used to infer a result value for new input data other than the training data, and the inferred value can be used as a basis for a decision to perform a certain operation.
  • the learning processor 130 may perform AI processing together with the learning processor 240 of the AI server 200.
  • the learning processor 130 may include a memory integrated or implemented in the AI device 100.
  • 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 about the surrounding environment of the AI device 100, and user information by using various sensors.
  • the 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 output related to visual, auditory or tactile sense.
  • 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.
  • the memory 170 may store input data, training data, a learning model, and a learning history acquired 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. Further, the processor 180 may perform the determined operation by controlling the components of the AI device 100.
  • the processor 180 may request, search, receive, or utilize data from the learning processor 130 or the memory 170, and perform a predicted or desirable operation among the at least one executable operation.
  • the components of the AI device 100 can be controlled to execute.
  • 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 for a user input, and determine a user's requirement based on the obtained intention information.
  • the processor 180 uses at least one of a Speech To Text (STT) engine for converting a speech 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 can be obtained.
  • STT Speech To Text
  • NLP Natural Language Processing
  • At this time, at least one or more of the STT engine and the NLP engine may be composed of an artificial neural network, at least partially trained according to a machine learning algorithm.
  • 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. Can be.
  • the processor 180 collects history information including user feedback on the operation content or operation of the AI device 100 and stores it in the memory 170 or the learning processor 130, or the AI server 200 Can be transferred to an external device.
  • the collected history 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 drive an application program stored in the memory 170. Furthermore, the processor 180 may operate by combining two or more of the components included in the AI device 100 to drive the application program.
  • FIG 2 shows an AI server 200 according to an embodiment of the present invention.
  • the AI server 200 may refer to a device that trains an artificial neural network using a machine learning algorithm or uses the learned artificial neural network.
  • the AI server 200 may be composed of a plurality of servers to perform distributed processing, or may be defined as a 5G network.
  • the AI server 200 may be included as a part of the AI device 100 to perform at least 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 and receive data with 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 trained 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 mounted on an external device such as the AI device 100 and used.
  • the learning model can be implemented in hardware, software, or a combination of hardware and software. When 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 for new input data using the learning model, and generate a response or a control command based on the inferred result value.
  • FIG 3 shows an AI system 1 according to an embodiment of the present invention.
  • 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 the cloud network 10.
  • the robot 100a to which the AI technology is applied, the autonomous vehicle 100b, the XR device 100c, the smartphone 100d, or the home appliance 100e may be referred to as the AI devices 100a to 100e.
  • the cloud network 10 may constitute a part of the cloud computing infrastructure or may mean a network that exists in the cloud computing infrastructure.
  • the cloud network 10 may be configured using a 3G network, a 4G or Long Term Evolution (LTE) network, or a 5G network.
  • LTE Long Term Evolution
  • the devices 100a to 100e and 200 constituting the AI system 1 may be connected to each other through the cloud network 10.
  • the devices 100a to 100e and 200 may communicate with each other through a base station, but may communicate with each other directly without through a base station.
  • the AI server 200 may include a server that performs AI processing and a server that performs an operation on big data.
  • the AI server 200 includes at least one of a robot 100a, an autonomous vehicle 100b, an XR device 100c, a smartphone 100d, or a home appliance 100e, which are AI devices constituting the AI system 1 It is connected through the cloud network 10 and may help at least part of the AI processing of the connected AI devices 100a to 100e.
  • the AI server 200 may train an artificial neural network according to a machine learning algorithm in place of the AI devices 100a to 100e, and may directly store the learning model or transmit it to the AI devices 100a to 100e.
  • 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 generates a response or control command based on the inferred result value. It can be generated and transmitted to the AI devices 100a to 100e.
  • the AI devices 100a to 100e may infer a result value of input data using a direct learning model, and generate a response or a control command based on the inferred result value.
  • the AI devices 100a to 100e to which the above-described technology is applied will be described.
  • the AI devices 100a to 100e illustrated in FIG. 3 may be viewed as a specific example of the AI device 100 illustrated in FIG. 1.
  • the robot 100a is applied with AI technology and 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, and the like.
  • the robot 100a may include a robot control module for controlling an operation, and the robot control module may refer to a software module or a chip implementing the same as hardware.
  • the robot 100a acquires status information of the robot 100a by using sensor information acquired from various types of sensors, detects (recognizes) the surrounding environment and objects, generates map data, or moves paths and travels. It can decide a plan, decide a response to user interaction, or decide an action.
  • the robot 100a may use sensor information obtained from at least one sensor from among a lidar, a radar, and a camera in order to determine a moving route and a driving plan.
  • the robot 100a may perform the above operations using a learning model composed of at least one artificial neural network.
  • 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.
  • the learning model may be directly learned by the robot 100a or learned by an external device such as the AI server 200.
  • the robot 100a may perform an operation by generating a result using a direct learning model, but it transmits sensor information to an external device such as the AI server 200 and performs the operation by receiving the result generated accordingly. You may.
  • the robot 100a determines a movement path and a 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 determine the determined movement path and travel plan. Accordingly, the robot 100a can be driven.
  • the map data may include object identification information on various objects arranged in a space in which the robot 100a moves.
  • the map data may include object identification information on fixed objects such as walls and doors and movable objects such as flower pots and desks.
  • the object identification information may include a name, type, distance, and location.
  • the robot 100a may perform an operation or run by controlling a driving unit based on a user's control/interaction.
  • the robot 100a may acquire interaction intention information according to a user's motion or voice speech, and determine a response based on the obtained intention information to perform an operation.
  • the autonomous vehicle 100b may be implemented as a mobile robot, vehicle, or unmanned aerial vehicle 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 refer to a software module or a chip implementing the same as hardware.
  • the autonomous driving control module may be included inside as a configuration of the autonomous driving vehicle 100b, but may be configured as separate hardware and connected to the exterior of the autonomous driving vehicle 100b.
  • 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, or generates map data, It is possible to determine a travel route and a driving plan, or to determine an action.
  • the autonomous vehicle 100b may use sensor information obtained from at least one sensor from among a lidar, a radar, and a camera, similar to the robot 100a, in order to determine a moving route and a driving plan.
  • the autonomous vehicle 100b may recognize an environment or object in an area where the view is obscured or an area greater than a certain distance by receiving sensor information from external devices, or directly recognized information from external devices. .
  • the autonomous vehicle 100b may perform the above operations using a learning model composed of at least one artificial neural network.
  • the autonomous vehicle 100b may recognize a surrounding environment and an object using a learning model, and may determine a driving movement using the recognized surrounding environment information or object information.
  • the learning model may be directly learned by the autonomous vehicle 100b or learned by an external device such as the AI server 200.
  • the autonomous vehicle 100b may perform an operation by generating a result using a direct learning model, but it operates by transmitting sensor information to an external device such as the AI server 200 and receiving the result generated accordingly. You can also do
  • the autonomous vehicle 100b determines a movement path and a 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 determine 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 on various objects arranged in a space (eg, a road) in which the autonomous vehicle 100b travels.
  • 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.
  • the object identification information may include a name, type, distance, and location.
  • the autonomous vehicle 100b may perform an operation or drive by controlling a driving unit based on a user's control/interaction.
  • the autonomous vehicle 100b may acquire interaction intention information according to a user's motion or voice speech, and determine a response based on the obtained intention information to perform the operation.
  • the XR device 100c is applied with AI technology, such as HMD (Head-Mount Display), HUD (Head-Up Display) provided in the vehicle, TV, mobile phone, smart phone, computer, wearable device, home appliance, digital signage. , A vehicle, a fixed robot, or a mobile robot.
  • HMD Head-Mount Display
  • HUD Head-Up Display
  • the XR device 100c analyzes 3D point cloud data or image data acquired through various sensors or from an external device to generate location data and attribute data for 3D points, thereby providing information on surrounding spaces or real objects.
  • the XR object to be acquired and output can be rendered and output.
  • the XR apparatus 100c may output an XR object including additional information on the recognized object in correspondence with the recognized object.
  • the XR device 100c may perform the above operations by using a learning model composed of at least one artificial neural network.
  • the XR device 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.
  • the learning model may be directly learned by the XR device 100c or learned by an external device such as the AI server 200.
  • the XR device 100c may directly generate a result using a learning model to perform an operation, 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 can also do it.
  • 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. by applying AI technology and autonomous driving technology.
  • the robot 100a to which AI technology and autonomous driving technology are applied may refer to a robot having an autonomous driving function or a robot 100a interacting 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 the user's control or by determining the movement line by themselves.
  • the robot 100a having an 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.
  • 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 using information sensed through a lidar, a radar, and a camera.
  • the robot 100a interacting with the autonomous driving vehicle 100b exists separately from the autonomous driving vehicle 100b, and is linked to an autonomous driving function inside the autonomous driving vehicle 100b, or to the autonomous driving vehicle 100b. It is possible to perform an operation associated with the user on board.
  • 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 acquires sensor information and information about the surrounding environment or By generating object information and providing it to the autonomous vehicle 100b, it is possible to control or assist the autonomous driving function of the autonomous driving vehicle 100b.
  • the robot 100a interacting with the autonomous vehicle 100b may monitor a user in the autonomous vehicle 100b or control the function of the autonomous vehicle 100b through interaction with the user. .
  • the robot 100a may activate an autonomous driving function of the autonomous driving vehicle 100b or assist the control of a driving unit of the autonomous driving vehicle 100b.
  • the functions of the autonomous vehicle 100b controlled by the robot 100a may include not only an autonomous driving function, but also functions provided by a navigation system or an audio system provided inside the autonomous driving vehicle 100b.
  • the robot 100a interacting with the autonomous driving vehicle 100b may provide information or assist a function to the autonomous driving vehicle 100b from outside of the autonomous driving vehicle 100b.
  • 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 driving vehicle 100b, such as an automatic electric charger for an electric vehicle. You can also automatically connect an electric charger to the charging port.
  • 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., by applying AI technology and XR technology.
  • the robot 100a to which the XR technology is applied may refer to a robot that is an object of control/interaction in an XR image.
  • the robot 100a is distinguished from the XR device 100c and may be interlocked with each other.
  • the robot 100a which is the object of control/interaction in 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.
  • the XR device 100c may output the generated XR image.
  • the robot 100a may operate based on a control signal input through the XR device 100c or a user's interaction.
  • the user can check the XR image corresponding to the viewpoint of the robot 100a linked remotely through an external device such as the XR device 100c, and adjust the autonomous driving path of the robot 100a through the interaction.
  • You can control motion or driving, or check information on surrounding objects.
  • the autonomous vehicle 100b may be implemented as a mobile robot, a vehicle, or an unmanned aerial vehicle by applying AI technology and XR technology.
  • the autonomous driving vehicle 100b to which the XR technology is applied may refer to an autonomous driving vehicle including a means for providing an XR image, or an autonomous driving vehicle that is an object of control/interaction within the XR image.
  • the autonomous vehicle 100b, which is an object of control/interaction in the XR image is distinguished from the XR device 100c and may be interlocked with each other.
  • the autonomous vehicle 100b provided with a means for providing an XR image may acquire sensor information from sensors including a camera, and may output an XR image generated based on the acquired sensor information.
  • the autonomous 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.
  • the XR object when the XR object is output to the HUD, at least a part of the XR object may be output to overlap the actual object facing the occupant's gaze.
  • the XR object when the XR object is output on a display provided inside the autonomous vehicle 100b, at least a part of the XR object may be output to overlap an object in the screen.
  • the autonomous vehicle 100b may output XR objects corresponding to objects such as lanes, other vehicles, traffic lights, traffic signs, motorcycles, pedestrians, and buildings.
  • the autonomous driving vehicle 100b which is the object 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 is based on the sensor information.
  • An XR image is generated, and the XR device 100c may output the generated XR image.
  • 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.
  • FIG. 4 is a block diagram of an apparatus 200a for providing power usage information based on artificial intelligence according to an embodiment of the present invention.
  • an apparatus 200a for providing power usage information may include a communication unit 210a, a processor 260a, and a memory 230a.
  • the power usage information providing device 200a may receive a request for provision of power usage prediction information from the electronic device 100a by interlocking with the electronic device 100a, and control the electronic device 100a through the processor 260a. can do.
  • the communication unit 210a may receive power usage data and electronic device usability data for the user's electronic device 100a.
  • the electronic device usability data may include a use time and a use mode of using the electronic device 100a.
  • the use time may be recorded in units of seconds, minutes, and hours, and the use mode may include all of various modes in which the electronic device 100a can be driven.
  • the communication unit 210a may transmit and receive data with external devices such as other AI devices 100a to 100e or the AI server 200 using wired/wireless communication technology.
  • the communication unit 210a may transmit and receive sensor information, a user input, a learning model, and a control signal with external devices.
  • the communication unit 210a includes Global System for Mobile communication (GSM), Code Division Multi Access (CDMA), Long Term Evolution (LTE), 5G, Wireless LAN (WLAN), Wireless-Fidelity (Wi-Fi), and Bluetooth. (BluetoothTM), Radio Frequency Identification (RFID), Infrared Data Association (IrDA), ZigBee, and NFC (Near Field Communication) can transmit and receive.
  • GSM Global System for Mobile communication
  • CDMA Code Division Multi Access
  • LTE Long Term Evolution
  • 5G Fifth Generation
  • WLAN Wireless LAN
  • Wi-Fi Wireless-Fidelity
  • Bluetooth BluetoothTM
  • IrDA Infrared Data Association
  • ZigBee ZigBee
  • NFC Near Field Communication
  • the processor 260a classifies power usage data for a predetermined period of the power usage data of each of the plurality of users into a plurality of power usage groups according to a time series analysis, and among the plurality of classified power usage groups, the A power usage group corresponding to the user's power usage data may be acquired.
  • the processor 260a may determine an operation and perform the operation based on information determined or generated using a data analysis algorithm or a machine learning algorithm. To this end, the processor 260a may request, search, receive, or utilize data from the learning processor or the memory 230a.
  • the processor 260a acquires corresponding intention information by changing a user's program, or an STT (Speech To Text) engine for converting a speech input into a character string, or a natural language processing (NLP) for obtaining intention information of a natural language. ) At least one of the engines may be used to obtain intention information corresponding to a user input.
  • STT Seech To Text
  • NLP natural language processing
  • the memory 230a stores power usage data and electronic device usage data of each of a plurality of users, and may be learned using machine learning or deep learning algorithms.
  • the electronic device usability data may further include at least one of a use time or a use mode of the electronic device 100a.
  • the memory 230a may further include location information of the electronic device 100a or other product usability data used in conjunction with the electronic device 100a to be stored.
  • the processor 260a may collect history information including the user's feedback on the operation content or the operation of the AI device 100, and an external server (not shown) or an external server (not shown) through the communication unit 210a. It can be transmitted to the database 300. The collected history information can be used to update the learning model.
  • the power consumption data may collectively refer to information related to various amounts of power generated by using the measured amount of power. For example, based on the measured amount of power, it may include all information related to the amount of power, such as power amount fluctuation status, standard deviation analysis result, variance deviation analysis result, trend line information, past accumulated power amount change information, future accumulated power amount expected change information, and the like.
  • FIG 5 shows a data flow of an apparatus 200a for providing power usage information based on artificial intelligence according to an embodiment of the present invention.
  • the power usage information providing apparatus (200a).
  • Various data may be provided by interworking with the electronic device 100a and stored in the database 300.
  • the processor 260a may classify the power usage data for a predetermined period of the power usage data of each of the plurality of users into a plurality of power usage groups according to a time series analysis.
  • the processor 260a may obtain a power usage group corresponding to the user's power usage data from among a plurality of classified power usage groups, and, for example, classify the power usage group by region, power amount, and household characteristic.
  • Areas can be organized by division into administrative districts such as neighboring wards, cities, provinces, including the area where the user's electronic device 100a is located, and power can be arranged by dividing into wh/kwh units.
  • Household characteristics can be divided by the number of household members and by age or age group, but are not limited thereto, and can include any method that can be classified to process data.
  • the processor 260a may load power consumption data of at least one other user included in the obtained power use group, that is, the power amount of a similar user and product usability data of a similar user from the memory 230a.
  • the processor 260a may calculate the nearest user group through clustering of the power usage data and the electronic device usage data stored in the database 300, and divide them into similar groups to generate a power usage group.
  • the processor 260a generates similar group power usage information based on the loaded power usage data of at least one other user, which means the average amount of power of the similar user.
  • the processor 260a provides recommended function information for reducing power consumption of the electronic device 100a of the user through comparison between the loaded electronic device usability data of at least one other user and the electronic device usability data of the user. Can produce
  • the processor 260a provides the similar group power usage information or recommendation function information in response to a request for power usage information or recommendation function information to the user's electronic device 100a, and sub-information on the user's request is If so, you can provide additional questions.
  • the processor 260a may ask an additional question. For example, if there is additional information related to this, if you ask the user'Would you like to tell me more,' and if you agree, another user in dong 00 will drive in tropical night mode from 8 pm to 11 pm this month. It can provide information, saying, '15kwh, 3,000 won was saved compared to the normal operation used by the user.'
  • the recommended function information can additionally guide'It is recommended to drive 00 hours in tropical night mode', and'when operating in tropical night mode, power will be reduced by 00 and electricity cost will be reduced by 00. I can guide you.
  • the recommended function information may include a usage mode or usage time with a small amount of power in the similar group power usage information in order to reduce power usage of the electronic device 100a of the user.
  • the processor 260a may transmit the generated similar group power usage information and recommended function information to the user's electronic device 100a or the terminal. According to an embodiment, the processor 260a recommends the power amount, usability, and N recommended use functions of the similar user group to which the user belongs, predicts the amount of power reduction when using the recommendation function, and converts it into a monthly fee. This is possible.
  • the processor 260a compares the generated similar group power usage information or recommended function information with all users or similar groups of the electronic device 100a to transmit to the terminal, and according to the recommended function information, the electronic device ( You can change the mode of use of 100a).
  • the processor 260a may change the usage mode of the electronic device 100a by transmitting a signal to the user's electronic device 100a when the user receives a command to change the tropical night according to the recommended function information.
  • FIG. 6 is a flowchart illustrating a control method of an apparatus 200a for providing power usage information based on artificial intelligence according to an embodiment of the present invention.
  • the method of controlling the apparatus 200a for providing power usage information based on artificial intelligence comprises: receiving power usage data and electronic device usability data (S10); Classifying the power usage data into a plurality of power usage groups (S20); And generating similar group power usage information (S30).
  • the step of receiving power usage data and electronic device usability data (S10) is performed by the communication unit 210a, and the power consumption data includes the amount of power consumed by the electronic device 100a for a predetermined period, and electronic device usability data May include usage time and usage mode.
  • the memory 230a stores power usage data and electronic device usage data of each of a plurality of users, and may learn about a usage pattern for each user using machine learning or deep learning algorithms.
  • the processor 260a In the step of classifying the power usage data into a plurality of power usage groups (S20), the processor 260a is involved, and the processor 260a is based on information determined or generated using a data analysis algorithm or a machine learning algorithm. , Determine the action and perform the action.
  • the power usage data of each electronic device 100a stored as big data may be used using a data cube or the like based on the power usage data collected by the processor 260a.
  • the power used of each electronic device 100a is recorded by region, amount of power, and household characteristics, and may be classified into the same or similar electronic device 100a, and the amount of power may be recorded for each period or for each type. .
  • the type of the electronic device 100a is a type related to power use, and may be recorded in various forms.
  • the electronic device 100a is set as an air conditioner, and a wall-mounted type, a stand type, a system type, It can be classified and saved by models such as cassette type.
  • the processor 260a may sample and store the power consumption of each electronic device 100a.
  • the sampling unit may be set in a predetermined time unit (eg, 10 seconds, 30 seconds, 1 minute, 5 minutes, 10 minutes, 30 minutes, 1 hour, etc.). It can be sampled by power consumption or operation status within the sampling unit time.
  • FIG. 7 shows a monthly accumulated power amount according to a monthly usage time according to an embodiment of the present invention.
  • a monthly usage time and monthly accumulated power amount may be calculated for a predetermined period.
  • the data collection period is from June 6, 2018 to November 2018, and represents data collected for 364 electronic devices 100a.
  • the power consumption sampling for each electronic device 100a is sampled according to whether or not it is operated within a corresponding sampling unit time, and the power consumption sampling for all electronic devices 100a or all electronic devices by type is a corresponding sampling unit. It can be sampled as the total amount of power consumed in time.
  • the processor 260a may reduce the amount of stored data by sampling the power consumption of each electronic device 100a. As described above, the measured power usage data and electronic device usage data may be accumulated and stored in the database 300 as a large amount of data, which may be referred to as big data.
  • the processor 260a may acquire a power usage group corresponding to the user's power usage data, that is, a similar user group to which the user belongs, from among a plurality of classified power usage groups.
  • Table 1 shows how the electronic device 100a based on similar power consumption is calculated.
  • the processor 260a stores power usage data representing the amount of power of a similar user of at least one other user included in the obtained power use group and electronic device usability data representing the usability of a similar user product to the memory 230a.
  • the similar group power usage information may be loaded from and based on the loaded power usage data of at least one other user.
  • Tables 2 and 3 show the generation of similar group power usage information of other users loaded through Table 1.
  • the amount of power of similar users of other users included in the power use group is calculated as 1 kw as the rated power consumption of a specific stand model according to an embodiment of the present invention, and a minimum of 0.8 to a maximum of 1.2 kw of power is consumed. Can be.
  • a specific stand-like user power amount (0.8 kw to 1.2 kw) calculated according to an embodiment of the present invention, other models are excluded and 1.08 kw may be extracted as an average value.
  • FIG. 8 shows the distribution of monthly usage of all other users not related to the model of the user's electronic device 100a
  • FIG. 9 is a view of all other users similar to the model of the user's electronic device 100a according to an embodiment of the present invention. It shows the distribution of monthly usage.
  • FIG. 8 since it is a distribution of model energy usage across the corresponding area, it shows the average monthly usage distribution including all models such as wall mounts, stands, systems, and cassettes.Therefore, the difference in power consumption by model is large and the distribution of monthly usage The range is wide. Therefore, despite the change in monthly usage of a specific model, it is not easy to extract similar groups because it is determined as total power.
  • the power usage group corresponding to the user's power usage data is extracted from the monthly usage distribution of other user groups similar to the user's electronic device 100a, the usage level can be compared and reflected and calculated.
  • the processor 260a may extract the power usage group and generate similar group power usage information as shown in Tables 1 to 3.
  • the processor 260a acquires a power use group corresponding to the user's power use data from among the classified plurality of power use groups, and is obtained through the process of Tables 1 to 3 described above. Similar group power usage information may be generated based on power usage data of other users included in the power usage group.
  • the processor 260a may transmit the generated similar group power usage information and recommended function information to the user's electronic device 100a or the terminal.
  • 1 summary of my usage 2 based on inquiry month and similar group (power amount, usage time, number of members), 3 overall average, similar group, user, 4 recommended function information, etc. 1
  • the information of to 4 can be transmitted to the terminal.
  • the transmission information transmitted to the terminal includes the user's power consumption data and electronic device usability data indicating usability, a monthly usage history can be checked, and a similar group can be selected.
  • the processor 260a may classify and provide any one of all users, similar groups, or users, and recommend a power saving operation mode to the user through recommended function information.
  • the apparatus 200a for providing power usage information based on artificial intelligence provides the user and other users' power usage, usage time, and usage mode, and allows the user to select and compare similar group criteria. It has the advantage of providing additional information for power savings when using the product.
  • the present invention provides a recommended function through comparison with other users' information to the terminal, so it is convenient for the user, and provides a new function and method for saving power to the user who uses the usual repetitive function. There is this.

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Abstract

La présente invention concerne un dispositif pour fournir des informations de consommation d'énergie sur la base de l'intelligence artificielle, et son procédé de commande. La présente invention concerne un dispositif pour fournir des informations de consommation d'énergie sur la base de l'intelligence artificielle, et son procédé de commande, le dispositif comprenant : une unité de communication pour recevoir des données de facilité d'utilisation de dispositif électronique et des données de consommation d'énergie pour un dispositif électronique d'un utilisateur ; une mémoire pour stocker des données de facilité d'utilisation de dispositif électronique et des données de consommation d'énergie pour chacun de multiples utilisateurs ; et un processeur pour : diviser des données de consommation d'énergie pendant une période prédéterminée, dans les données de consommation d'énergie pour chacun des multiples utilisateurs, en de multiples groupes de consommation d'énergie selon une analyse de série chronologique ; acquérir un groupe de consommation d'énergie correspondant aux données de consommation d'énergie de l'utilisateur parmi les multiples groupes de consommation d'énergie divisés ; charger, à partir de la mémoire, des données de facilité d'utilisation de dispositif électronique et des données de consommation d'énergie d'au moins un autre utilisateur, qui sont incluses dans le groupe de consommation d'énergie acquis ; générer des informations de consommation d'énergie de groupe similaire sur la base des données de consommation d'énergie chargées de l'au moins un autre utilisateur ; générer des informations de fonction recommandée pour une réduction de consommation d'énergie du dispositif électronique de l'utilisateur, par l'intermédiaire d'une comparaison entre les données de facilité d'utilisation de dispositif électronique chargées de l'au moins un autre utilisateur et les données de facilité d'utilisation de dispositif électronique de l'utilisateur ; et commander l'unité de communication pour transmettre les informations de consommation d'énergie de groupe similaire et les informations de fonction recommandée générées au dispositif électronique ou à un terminal de l'utilisateur.
PCT/KR2019/008795 2019-07-16 2019-07-16 Dispositif pour fournir des informations de consommation d'énergie sur la base de l'intelligence artificielle, et son procédé de commande Ceased WO2021010520A1 (fr)

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JP2012048503A (ja) * 2010-08-26 2012-03-08 Panasonic Electric Works Co Ltd 電気量管理システムおよびセンタサーバ
JP2012048511A (ja) * 2010-08-27 2012-03-08 Toyota Home Kk 省エネアドバイス装置
JP2015230571A (ja) * 2014-06-04 2015-12-21 一般財団法人電力中央研究所 省エネアドバイス生成装置、省エネアドバイス生成方法及び省エネアドバイス生成プログラム
KR20160011109A (ko) * 2014-07-21 2016-01-29 주식회사 타이드 에너지 절감을 위한 다각적 스마트그리드 유닛과 그 운영방법

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2011221971A (ja) * 2010-04-14 2011-11-04 Mitsubishi Electric Corp 電気機器管理装置、電気機器管理システム、電気機器管理方法及びプログラム
JP2012048503A (ja) * 2010-08-26 2012-03-08 Panasonic Electric Works Co Ltd 電気量管理システムおよびセンタサーバ
JP2012048511A (ja) * 2010-08-27 2012-03-08 Toyota Home Kk 省エネアドバイス装置
JP2015230571A (ja) * 2014-06-04 2015-12-21 一般財団法人電力中央研究所 省エネアドバイス生成装置、省エネアドバイス生成方法及び省エネアドバイス生成プログラム
KR20160011109A (ko) * 2014-07-21 2016-01-29 주식회사 타이드 에너지 절감을 위한 다각적 스마트그리드 유닛과 그 운영방법

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