Electronic device and method for controlling same

Abstract

An electronic device and a method for controlling same. The electronic device comprises a communication interface; a memory; and one or more processors. The one or more processors receive temperature and humidity information and operation history of a plurality of peripheral devices through a communication interface; based on a driving signal of a target device is received through the communication interface, input the temperature and humidity information and the information about the operation history of the plurality of peripheral devices into a humidity prediction model and identify a vector value output from the humidity prediction model; identify humidity prediction information for a space in which the target device is located on the basis of the identified vector value; and transmit driving information, according to which the target device is to be operated based on the driving signal, corresponding to the identified humidity prediction information to the target device.

Description

CROSS-REFERENCE TO RELATED APPLICATION

[0001] This application is a continuation application is a continuation application, under 35 U.S.C. § 111 (a), of international application No. PCT / KR2023 / 017945, filed Nov. 9, 2023, which claims priority under 35 U. S. C. § 119 to Korean Patent Application No. 10-2022-0179588, filed Dec. 20, 2022, the disclosures of which are incorporated herein by reference in their entireties.TECHNICAL FIELD

[0002] The disclosure relates to an electronic device and a method for controlling the same, and more particularly, to an electronic device that determines driving of a target device by predicting humidity on a specific time point based on temperature and humidity information received from a plurality of peripheral devices, and a method for controlling the same.BACKGROUND ART

[0003] In the case of a device that uses water or performs an operation related to drying of moisture such as a dishwasher, a humidifier, a clothes management device, or a dryer, etc., the device may be influenced by the temperature and the humidity of the space wherein the device is located.

[0004] In this case, a driving method of the device such as the driving time, the driving strength, etc. may be determined by obtaining weather information from a server, or based on the temperature and the humidity directly detected by the device.

[0005] Here, the temperature and the humidity may be changed and influenced by the time, the season, the weather, the characteristics of a space, whether there is another device, etc., and thus various attempts for obtaining correct temperature and humidity information are being performed.DISCLOSURETechnical Solution

[0006] An electronic device according to an embodiment of the disclosure may include a communication interface, a memory to store at least one instruction, and at least one processor executing the at least one instruction, wherein the at least one processor may receive temperature and humidity information and driving history information of a plurality of peripheral devices through the communication interface, and based on receiving a driving signal of a target device from the target device through the communication interface, input the temperature and humidity information and the driving history information of the plurality of peripheral devices received into a humidity prediction model and identify a vector value output from the humidity prediction model, identify humidity prediction information of a space in which the target device is located based on the identified vector value, and transmit driving information, according to which the target device is to be operated based on the driving signal, corresponding to the identified humidity prediction information to the target device.

[0007] Meanwhile, the at least one processor may receive weather information including a temperature and humidity of the outside from an external server through the communication interface, and based on receiving the driving signal of the target device, input the temperature and humidity information and the driving history information of the plurality of peripheral devices received, and the weather information received from the external server into the humidity prediction model and identify a vector value output from the humidity prediction model.

[0008] Meanwhile, the humidity prediction model is a model that was trained based on training data including a vector value, the temperature and humidity information and the driving history information of the plurality of peripheral devices received, the vector value being obtained by inputting the temperature and humidity information and the driving history information of the plurality of peripheral devices received into the humidity prediction model, and the at least one processor may identify at least one device that did not transmit temperature and humidity information among the plurality of peripheral devices, obtain alternative temperature and humidity information corresponding to the at least one device that did not transmit temperature and humidity information from the training data, and based on receiving a driving signal of the target device from the target device through the communication interface, input the temperature and humidity information and the driving history information of the plurality of peripheral devices received, and the alternative temperature and humidity information into the humidity prediction model and identify a vector value output from the humidity prediction model.

[0009] Meanwhile, the at least one processor may identify a humidity value between a first time point for the space in which the target device is located and a second time point subsequent to a predetermined time having passed from the first time point based on the identified vector value.

[0010] Meanwhile, the at least one processor may receive temperature and humidity information and driving history information of peripheral devices from the plurality of peripheral devices located within a predetermined distance from the target device through the communication interface.

[0011] Meanwhile, the at least one processor may receive temperature and humidity information and driving history information of peripheral devices from among the plurality of peripheral devices that are registered in advance to a device registration server through the communication interface.

[0012] Meanwhile, the at least one processor may receive the temperature and humidity information detected by the plurality of peripheral devices and the driving history information of the plurality of peripheral devices through the communication interface.

[0013] Meanwhile, the at least one processor may transmit driving information of the target device corresponding to the identified humidity prediction information to a user terminal device.

[0014] A method for controlling an electronic device according to an embodiment of the disclosure may include receiving temperature and humidity information and driving history information of a plurality of peripheral devices, and based on receiving a driving signal of a target device, inputting the temperature and humidity information and the driving history information of the plurality of peripheral devices received into a humidity prediction model and identifying a vector value output from the humidity prediction model, identifying humidity prediction information of a space in which the target device is located based on the identified vector value, and transmitting driving information, according to which the target device is to be operated based on the driving signal, corresponding to the identified humidity prediction information to the target device.

[0015] Meanwhile, the receiving may include receiving weather information including a temperature and humidity of the outside from an external server, and in the identifying the vector value, based on receiving the driving signal of the target device, the temperature and humidity information and the driving history information of the plurality of peripheral devices received, and the weather information received from the external server may be input into the humidity prediction model and a vector value output from the humidity prediction model may be identified.

[0016] Meanwhile, the humidity prediction model may be a model that was trained based on training data including a vector value, the temperature and humidity information and the driving history information of the plurality of peripheral devices received, the vector value being obtained by inputting the temperature and humidity information and the driving history information of the plurality of peripheral devices received into the humidity prediction model, and the receiving may include identifying at least one device that did not transmit temperature and humidity information among the plurality of peripheral devices, and obtaining alternative temperature and humidity information corresponding to the at least one device that did not transmit temperature and humidity information from the training data, and in the identifying the vector value, based on receiving a driving signal of the target device from the target device, the temperature and humidity information and the driving history information of the plurality of peripheral devices received, and the alternative temperature and humidity information may be input into the humidity prediction model and a vector value output from the humidity prediction model may be identified.

[0017] Meanwhile, in the identifying the humidity prediction information, a humidity value between a first time point for the space in which the target device is located and a second time point subsequent to a predetermined time having passed from the first time point may be identified based on the identified vector value.

[0018] Meanwhile, in the receiving, temperature and humidity information and driving history information of peripheral devices may be received from among the plurality of peripheral devices located within a predetermined distance from the target device.

[0019] Meanwhile, in the receiving, temperature and humidity information and driving history information of peripheral devices may be received from among the plurality of peripheral devices that are registered in advance to a device registration server.

[0020] Meanwhile, in the receiving, the temperature and humidity information detected by the plurality of peripheral devices and the driving history information of the plurality of peripheral devices may be received.

[0021] Meanwhile, the transmitting may include transmitting driving information of the target device corresponding to the identified humidity prediction information to a user terminal device.

[0022] In a non-transitory computer-readable recording medium storing computer instructions executable by a processor of an electronic device, to cause the electronic device to perform operations, the operations may include the receiving temperature and humidity information and driving history information of a plurality of peripheral devices, and based on receiving a driving signal of a target device, inputting the temperature and humidity information and the driving history information of the plurality of peripheral devices received into a humidity prediction model and identifying a vector value output from the humidity prediction model, identifying humidity prediction information of a space in which the target device is located based on the identified vector value, and transmitting driving information, according to which the target device is to be operated based on the driving signal, corresponding to the identified humidity prediction information to the target device.DESCRIPTION OF DRAWINGS

[0023] Aspects, characteristics, and advantages of specific embodiments of the disclosure will become clearer through the following description with reference to the accompanying drawings.

[0024] FIG. 1 is a diagram for illustrating peripheral devices, an external device, an electronic device, and a target device according to an embodiment of the disclosure;

[0025] FIG. 2 is a block diagram for illustrating a configuration of an electronic device according to an embodiment of the disclosure;

[0026] FIG. 3 is a diagram for illustrating a humidity prediction model according to an embodiment of the disclosure;

[0027] FIG. 4 is a diagram for illustrating temperature and humidity information and driving history information of peripheral devices included in input data of a humidity prediction model according to an embodiment of the disclosure;

[0028] FIG. 5 is a flow chart for illustrating a modeling process of a humidity prediction model according to an embodiment of the disclosure;

[0029] FIG. 6 is a sequence diagram for illustrating a process of predicting humidity based on temperature and humidity information and driving history information received from peripheral devices and an external server, and transmitting driving information of a target device to the target device according to an embodiment of the disclosure;

[0030] FIG. 7 is a sequence diagram for illustrating a process of predicting humidity based on temperature and humidity information and driving history information received from peripheral devices and an external server, and transmitting driving information of a target device to the target device in case a humidity prediction model was implemented in an AI server but not an electronic device according to an embodiment of the disclosure; and

[0031] FIG. 8 is a flow chart for illustrating an operation of an electronic device according to an embodiment of the disclosure.MODE FOR INVENTION

[0032] Various modifications may be made to the embodiments of the disclosure, and there may be various types of embodiments. Accordingly, specific embodiments will be illustrated in drawings, and the embodiments will be described in detail in the detailed description. However, it should be noted that the various embodiments are not for limiting the scope of the disclosure to a specific embodiment, but they should be interpreted to include all modifications, equivalents, and / or alternatives of the embodiments of the disclosure. Also, with respect to the detailed description of the drawings, similar components may be designated by similar reference numerals.

[0033] Also, in describing the disclosure, in case it is determined that detailed explanation of related known functions or components may unnecessarily confuse the gist of the disclosure, the detailed explanation will be omitted.

[0034] In addition, the embodiments described below may be modified in various different forms, and the scope of the technical idea of the disclosure is not limited to the embodiments below. Rather, these embodiments are provided to make the disclosure more sufficient and complete, and to fully convey the technical idea of the disclosure to those skilled in the art.

[0035] Further, terms used in the disclosure are used only to explain specific embodiments, and are not intended to limit the scope of the disclosure. Also, singular expressions include plural expressions, unless defined obviously differently in the context.

[0036] Also, in the disclosure, expressions such as “have,”“may have,”“include,” and “may include” denote the existence of such characteristics (e.g.: elements such as numbers, functions, operations, and components), and do not exclude the existence of additional characteristics.

[0037] In addition, in the disclosure, the expressions “A or B,”“at least one of A and / or B,” or “one or more of A and / or B” and the like may include all possible combinations of the listed items. For example, “A or B,”“at least one of A and B,” or “at least one of A or B” may refer to all of the following cases: (1) including at least one A, (2) including at least one B, or (3) including at least one A and at least one B.

[0038] Further, the expressions “first,”“second,” and the like used in the disclosure may describe various elements regardless of any order and / or degree of importance. Also, such expressions are used only to distinguish one element from another element, and are not intended to limit the elements.

[0039] Meanwhile, the description in the disclosure that one element (e.g.: a first element) is “(operatively or communicatively) coupled with / to” or “connected to” another element (e.g.: a second element) should be interpreted to include both the case where the one element is directly coupled to the another element, and the case where the one element is coupled to the another element through still another element (e.g.: a third element).

[0040] In contrast, the description that one element (e.g.: a first element) is “directly coupled” or “directly connected” to another element (e.g.: a second element) can be interpreted to mean that still another element (e.g.: a third element) does not exist between the one element and the another element.

[0041] Also, the expression “configured to” used in the disclosure may be interchangeably used with other expressions such as “suitable for,”“having the capacity to,”“designed to,”“adapted to,”“made to,” and “capable of,” depending on cases. Meanwhile, the term “configured to” may not necessarily mean that a device is “specifically designed to” in terms of hardware.

[0042] Instead, under some circumstances, the expression “a device configured to” may mean that the device “is capable of” performing an operation together with another device or component. For example, the phrase “a processor configured to perform A, B, and C” may mean a dedicated processor (e.g.: an embedded processor) for performing the corresponding operations, or a generic-purpose processor (e.g.: a CPU or an application processor) that can perform the corresponding operations by executing one or more software programs stored in a memory device.

[0043] Further, in the embodiments of the disclosure, ‘a module’ or ‘a unit’ may perform at least one function or operation, and may be implemented as hardware or software, or as a combination of hardware and software. Also, a plurality of ‘modules’ or ‘units’ may be integrated into at least one module and implemented as at least one processor, excluding ‘a module’ or ‘a unit’ that needs to be implemented as specific hardware.

[0044] Meanwhile, various elements and areas in the drawings were illustrated schematically. Accordingly, the technical idea of the disclosure is not limited by the relative sizes or intervals illustrated in the accompanying drawings.

[0045] Hereinafter, embodiments according to the disclosure will be described in detail with reference to the accompanying drawings, such that those having ordinary skill in the art to which the disclosure belongs can easily carry out the embodiments.

[0046] FIG. 1 is a diagram for illustrating peripheral devices, an external device, an electronic device, and a target device according to an embodiment of the disclosure.

[0047] Referring to FIG. 1, an electronic device 100 may predict humidity of a space or a location wherein a target device 200 operates based on temperature and humidity information and driving history information of the plurality of peripheral devices 10, 20, 30 received from a plurality of peripheral devices 10, 20, 30, or weather information including temperature and humidity information of the outside received from an external server 40, and determine a driving time (e.g.: a drying time), a driving method (e.g.: a drying mode, the drying strength), etc. of the target device 200 based on humidity prediction information (e.g.: absolute humidity, relative humidity, the time of duration of humidity, a change rate of humidity according to time), and transmit the driving time and the driving method to the target device 200. Accordingly, the target device 200 may perform operations based on the received driving time and driving method.

[0048] Other than the above, the electronic device 100 may transmit the humidity prediction information and the information on the driving time and the driving method of the target device 200 to a user terminal device (not shown) owned by a user of the target device 200, e.g., a smartphone, a tablet PC, a wearable device, etc., and make the information displayed.

[0049] The peripheral devices 10, 20, 30 may be devices or electronic devices located within a predetermined distance from the target device 200 or located in the same space. Also, the peripheral devices 10, 20, 30 may be devices or electronic devices registered in advance to a device registration server.

[0050] The peripheral devices 10, 20, 30 may be, for example, an air conditioner 10, a refrigerator 20, and a water purifier 30, but are not limited thereto, and they may be various devices or electronic devices such as a washing machine, a dryer, a TV, a computer, etc.

[0051] The external server 40 may be a device storing weather information including temperature and humidity information of the outside.

[0052] The target device 200 may be a device or an electronic device that is controlled by humidity prediction information provided by the electronic device 100 according to the disclosure.

[0053] The target device 200 may be, for example, a dishwasher, a humidifier, a clothes management device, a dryer, etc., but is not limited thereto, and it may be a device or an electronic device that uses water or performs an operation related to drying of moisture.

[0054] As described above, the electronic device 100 determines the driving time and the driving method of the target device 200 by predicting humidity of the space or the location wherein the target device 200 operates, and thereby has an effect of enabling performing of a correct and effective operation that suits the humidity, e.g., a drying operation of clothes, tableware, etc.

[0055] FIG. 2 is a block diagram for illustrating a configuration of the electronic device 100 according to an embodiment of the disclosure.

[0056] Referring to FIG. 2, the electronic device 100 may include a communication interface 110, memory 120, and at least one processor 130 (referred to as a processor hereinafter).

[0057] However, components of the electronic device 100 are not limited to the above components, and other components necessary for performing of operations of the electronic device 100 may be additionally included, or some components may be omitted.

[0058] The communication interface 110 may include a wireless communication interface, a wired communication interface, or an input interface.

[0059] A wireless communication interface may perform communication with various types of external devices by using a wireless communication technology or a mobile communication technology. As such a wireless communication technology, for example, Bluetooth, Bluetooth Low Energy, CAN communication, Wi-Fi, Wi-Fi Direct, ultrawide band (UWB) communication, Zigbee, infrared Data Association (IrDA), or Near Field Communication (NFC), etc. may be included, and as a mobile communication technology, 3GPP, Wi-Max, Long Term Evolution (LTE), 5G, etc. may be included.

[0060] A wireless communication interface may be implemented by using an antenna that can transmit electromagnetic waves to the outside or receive electromagnetic waves transmitted from the outside, a communication chip, and a substrate, etc.

[0061] A wired communication interface may perform communication with various types of external devices based on a wired communication network. Here, the wired communication network may be implemented, for example, by using a physical cable such as a pair cable, a coaxial cable, an optical fiber cable, or an Ethernet cable, etc.

[0062] Any one of the wireless communication interface or the wired communication interface may be omitted depending on embodiments. Accordingly, the electronic device may include only the wireless communication interface or include only the wired communication interface. Not only that, the electronic device may include an integrated communication interface that supports both of wireless connection by the wireless communication interface and wired connection by the wired communication interface.

[0063] The electronic device 100 is not limited to a case of including one communication interface that performs communicative connection by one method, but may include a plurality of communication interfaces that perform communicative connection by a plurality of methods.

[0064] The processor 130 may perform communicative connection with the plurality of peripheral devices 10, 20, 30 through the communication interface 110, and receive temperature and humidity information and driving history information of the plurality of peripheral devices 10, 20, 30.

[0065] The processor 130 may perform communicative connection with the external server 40 through the communication interface 110, and receive weather information including temperature and humidity information of the outside.

[0066] The processor 130 may perform communicative connection with the target device 200 through the communication interface 110, and transmit or receive information on a driving signal, the driving information, the driving time, and the driving method of the target device 200.

[0067] In case the humidity prediction model is stored in an AI server that is separately provided, but not the memory 120, the processor 130 may perform communicative connection with the AI server through the communication interface 110, and transmit the temperature and humidity information and the driving history information of the plurality of peripheral devices 10, 20, 30 received from the plurality of peripheral devices 10, 20, 30, and the weather information including the temperature and humidity information received from the external server 40. In this case, the processor 130 may perform communicative connection with the AI server through the communication interface 110, and receive humidity prediction information.

[0068] Also, the processor 130 may perform communicative connection with a user terminal device through the communication interface 110, and transmit or receive the temperature and humidity information, the weather information, the humidity prediction information, or the driving information of the target device 200.

[0069] The memory 120 stores various types of programs or data temporarily or non-temporarily, and transmits the stored information to the processor 130 according to a call of the processor 130. Also, the memory 120 may store various types of information necessary for operations, processing, or control operations, etc. of the processor 130 in electronic formats.

[0070] The memory 120 may include, for example, at least one of a main memory device or an auxiliary memory device. The main memory device may be implemented by using a semiconductor storage medium such as ROM and / or RAM. ROM may include, for example, ROM, EPROM, EEPROM, and / or MASK-ROM, etc. which are general. RAM may include, for example, DRAM and / or SRAM, etc. The auxiliary memory device may be implemented by using at least one storage medium that can store data permanently or semi-permanently such as a flash memory device, a secure digital (SD) card, a solid state drive (SSD), a hard disc drive (HDD), a magnetic drum, a compact disc (CD), a DVD, or an optical recording medium like a laser disc, a magnetic tape, a magneto-optical disc, and / or a floppy disc, etc.

[0071] The memory 120 may store the temperature and humidity information received from the plurality of peripheral devices 10, 20, 30, and the driving history information of the plurality of peripheral devices 10, 20, 30.

[0072] The memory 120 may store the weather information including the temperature and humidity information of the outside received from the external server 40.

[0073] The memory 120 may store a humidity prediction model (a neural network model). Specifically, the memory 120 may store information on at least one layer constituting the humidity prediction model, at least one node, at least one weight, and a loss function.

[0074] The memory 120 may store a vector value output from the humidity prediction model and humidity prediction information corresponding to the output vector value.

[0075] The memory 120 may store the driving information of the target device 200 corresponding to the humidity prediction information. Here, the driving information may be the driving time and the driving method, but is not limited thereto.

[0076] The processor 130 controls overall operations of the electronic device 100. Specifically, the processor 130 may be connected with the components of the electronic device 100 including the memory 120 as described above, and control the overall operations of the electronic device 100 by executing the at least one instruction stored in the memory 120 as described above. In particular, the processor 130 may not only be implemented as one processor, but also be implemented as a plurality of processors.

[0077] The processor 130 may be implemented by various methods. For example, the at least one processor 130 may include one or more of a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), a many integrated core (MIC), a digital signal processor (DSP), a neural processing unit (NPU), a hardware accelerator, or a machine learning accelerator. The at least one processor 130 may control one or a random combination of the other components of the electronic device 100, and perform an operation related to communication or data processing. Also, the at least one processor 130 may execute one or more programs or instructions stored in the memory 120. For example, the at least one processor 130 may perform the method according to an embodiment of the disclosure by executing the one or more instructions stored in the memory 120.

[0078] In case the method according to an embodiment of the disclosure includes a plurality of operations, the plurality of operations may be performed by one processor, or performed by a plurality of processors. For example, when a first operation, a second operation, and a third operation are performed by the method according to an embodiment, all of the first operation, the second operation, and the third operation may be performed by a first processor, or the first operation and the second operation may be performed by the first processor (e.g., a generic-purpose processor), and the third operation may be performed by a second processor (e.g., an artificial intelligence-dedicated processor).

[0079] The at least one processor 130 may be implemented as a single core processor including one core, or it may be implemented as one or more multicore processors including a plurality of cores (e.g., multicores of the same kind or multicores of different kinds). In case the at least one processor 130 is implemented as multicore processors, each of the plurality of cores included in the multicore processors may include internal memory of the processor such as on-chip memory, and common cache shared by the plurality of cores may be included in the multicore processors. Also, each of the plurality of cores (or some of the plurality of cores) included in the multicore processors may independently read a program instruction for implementing the method according to an embodiment of the disclosure and perform the instruction, or the plurality of entire cores (or some of the cores) may be linked with one another, and read a program instruction for implementing the method according to an embodiment of the disclosure and perform the instruction.

[0080] In case the method according to an embodiment of the disclosure includes a plurality of operations, the plurality of operations may be performed by one core among the plurality of cores included in the multicore processors, or they may be implemented by the plurality of cores. For example, when the first operation, the second operation, and the third operation are performed by the method according to an embodiment, all of the first operation, the second operation, and the third operation may be performed by a first core included in the multicore processors, or the first operation and the second operation may be performed by the first core included in the multicore processors, and the third operation may be performed by a second core included in the multicore processors.

[0081] In the embodiments of the disclosure, the processor 130 may mean a system on chip (SoC) wherein at least one processor and other electronic components are integrated, a single core processor, a multicore processor, or a core included in the single core processor or the multicore processor. Also, here, the core may be implemented as a CPU, a GPU, an APU, a MIC, a DSP, an NPU, a hardware accelerator, or a machine learning accelerator, etc., but the embodiments of the disclosure are not limited thereto.

[0082] The processor 130 may receive the temperature and humidity information and the driving history information of the plurality of peripheral devices 10, 20, 30 from the plurality of peripheral devices 10, 20, 30.

[0083] When a driving signal of the target device 200 is received from the target device 200, the processor 130 may input the temperature and humidity information and the driving history information of the plurality of peripheral devices 10, 20, 30 received from the plurality of peripheral devices 10, 20, 30 into the humidity prediction model, and identify a vector value output from the humidity prediction model.

[0084] The processor 130 may identify humidity prediction information of the space or the location wherein the target device 200 is located based on the identified vector value.

[0085] The processor 130 may transmit the driving information of the target device 200 corresponding to the identified humidity prediction information to the target device 200 through the communication interface 110.

[0086] A control operation of the electronic device 100 by the processor 130 will be described in detail with reference to FIG. 3 to FIG. 7.

[0087] The processor 130 may receive the temperature and humidity information (e.g.: the absolute temperature, the absolute humidity, the relative humidity, the time of duration of humidity, a change rate of humidity according to time) and the driving history information (e.g., the driving time, the driving strength) of the plurality of peripheral devices 10, 20, 30 from the plurality of peripheral devices 10, 20, 30 through the communication interface 110. Here, the temperature and humidity information may be the temperature and humidity information detected by the peripheral devices 10, 20, 30.

[0088] Specifically, the processor 130 may receive the temperature and humidity information and the driving history information of the plurality of peripheral devices 10, 20, 30 from the plurality of peripheral devices 10, 20, 30 located within a predetermined distance from the target device 200 or registered in advance to the device registration server through the communication interface 110.

[0089] Also, the processor 130 may receive the weather information including the temperature and the humidity of the outside from the external server 40 through the communication interface 110.

[0090] Here, the processor 130 may identify at least one device that did not transmit the temperature and humidity information among the plurality of peripheral devices 10, 20, 30, and obtain alternative temperature and humidity information corresponding to the at least one device that did not transmit the temperature and humidity information from training data that was used in advance for training of the humidity prediction model.

[0091] Accordingly, regarding a peripheral device that existed at the time of training of the humidity prediction model 320, but does not exist currently, the processor 130 may predict humidity by inputting the alternative information into the humidity prediction model 320.

[0092] When a driving signal of the target device 200 is received from the target device 200 through the communication interface 110, the processor 130 may input the temperature and humidity information and the driving history information of the plurality of peripheral devices 10, 20, 30 received from the plurality of peripheral devices 10, 20, 30, the alternative temperature and humidity information, or the weather information received from the external server into the humidity prediction model, and identify a vector value output from the humidity prediction model.

[0093] The processor 130 may identify humidity prediction information of the space wherein the target device 200 is located based on the identified vector value.

[0094] Specifically, the processor 130 may identify a humidity value between a first time point of the space wherein the target device 200 is located and a second time point when a predetermined time passed from the first time point, e.g., a humidity value between 1 p.m. and 2 p.m. based on the identified vector value.

[0095] The humidity prediction model 320 used by the processor 130 will be described in detail with reference to FIG. 3 to FIG. 5.

[0096] FIG. 3 is a diagram for illustrating the humidity prediction model 320 according to an embodiment of the disclosure.

[0097] Referring to FIG. 3, the humidity prediction model 320 may be a neural network model that has a similar structure to a human neural network consisting of neurons. Specifically, the humidity prediction model 320 may consist of at least one layer, at least one node, and weights corresponding to each layer or node.

[0098] The at least one layer may consist of at least one node, and may be an input layer, a hidden layer, or an output layer. However, the disclosure is not limited thereto, and the humidity prediction model 320 may include additional layers, or some layers may be omitted.

[0099] The processor 130 may input the input data 310 into the humidity prediction model 320. Here, the input data 310 may be the temperature and humidity information and the driving history information of the peripheral devices 10, 20, 30 received from the peripheral devices 10, 20, 30, and the weather information including the temperature and humidity information of the outside received from the external server 40 through the communication interface 110, but is not limited thereto.

[0100] FIG. 4 is a diagram for illustrating temperature and humidity information and driving history information of the peripheral devices 10, 20, 30 included in the input data 310 of the humidity prediction model 320 according to an embodiment of the disclosure.

[0101] Referring to FIG. 4, the processor 130 may input a data set consisting of temperature and humidity information and driving history information 410 of an air conditioner, temperature and humidity information and driving history information 420 of a refrigerator, temperature and humidity information and driving history information 430 of a water purifier, etc. into the humidity prediction model 320.

[0102] Each data set may include information on a set temperature, a control temperature, an outside air temperature, outside air humidity, a driving time, driving strength, etc., or some of them may be omitted.

[0103] The humidity prediction model 320 may perform a real number operation by adding a weight to each of the input data 310, and output a random vector value located in a real number space. The processor 130 may identify humidity prediction information 330 corresponding to the output vector value, and identify driving information of the target device 200 corresponding to the humidity prediction information 330.

[0104] The processor 130 may train the humidity prediction information 320 by using the input data 310 and the output vector value as training data.

[0105] Specifically, the processor 130 may train the humidity prediction model 320 based on training data including a vector value, the temperature and humidity information, the driving history information of the plurality of peripheral devices 10, 20, 30 received from the plurality of peripheral devices 10, 20, 30, and the weather information received from the external server 40. Here, the vector value is obtained by inputting the temperature and humidity information, the driving history information of the plurality of peripheral devices 10, 20, 30 received from the plurality of peripheral devices 10, 20, 30, or the weather information received from the external server 40 into the humidity prediction model 320.

[0106] The processor 130 may perform training by a method of identifying loss of a vector value output by inputting the input data 310 into the humidity prediction model 320, and changing the weights included in the humidity prediction model 320 through a backpropagation method, but this is merely an embodiment of training of the humidity prediction model 320 by the processor 130, and training of the humidity prediction model 320 is not limited to the above method.

[0107] Other than the above, a process wherein the processor 130 obtains the humidity prediction model 320 through a modeling process of the humidity prediction model 320 is explained by each operation as follows.

[0108] FIG. 5 is a flow chart for illustrating a modeling process of the humidity prediction model 320 according to an embodiment of the disclosure.

[0109] Referring to FIG. 5, the processor 130 may perform communicative connection with the peripheral devices 10, 20, 30 through the communication interface 110, and obtain temperature and humidity information of the peripheral devices 10, 20, 30 and driving history information of the peripheral devices 10, 20, 30 in the operation S510.

[0110] The processor 130 may perform communicative connection with the external server 40 through the communication interface 110, and obtain weather information including temperature and humidity information of the outside in the operation S520.

[0111] The processor 130 may remove invalid information among the obtained information in the operation S530. Here, the invalid information may be information that has a difference greater than or equal to a threshold value in terms of overall tendency among the temperature and humidity information, the driving history information, and the weather information, but is not limited thereto.

[0112] The aforementioned process may be deemed as a pre-processing process of training data for the processor 130 to train the humidity prediction model 320.

[0113] Afterwards, the processor 130 may analyze and optimize the correlation between each information by using the obtained information as a parameter in the operation S540. This may be deemed as a process wherein the processor 130 models the humidity prediction model 320.

[0114] Ultimately, the processor 130 may verify the validity of the parameter, and verify the humidity prediction model 320 in the operation S550.

[0115] Through the aforementioned modeling process, the processor 130 may obtain the humidity prediction model 320.

[0116] Here, the humidity prediction model 320 may be stored in the electronic device 100, but is not limited thereto, and it may be stored in a separate AI server. In this case, the processor 130 may perform communicative connection with the AI server through the communication interface 110, and transmit the temperature and humidity information and the driving history information of the plurality of peripheral devices 10, 20, 30 received from the plurality of peripheral devices 10, 20, 30 to the AI server. Also, the processor 130 may perform communicative connection with the AI server through the communication interface 110, and receive humidity prediction information corresponding to a vector value output from the humidity prediction model 320 from the AI server.

[0117] The processor 130 may identify driving information of the target device 200 corresponding to the identified humidity prediction information 330.

[0118] For example, in case the target device 200 is a dishwasher, the processor 130 may identify the drying mode and the drying time of the dishwasher.

[0119] The processor 130 may transmit the driving information of the target device 200 corresponding to the identified humidity prediction information 330 to the target device 200 through the communication interface 110.

[0120] Also, the processor 130 may transmit the driving information of the target device 200 corresponding to the identified humidity prediction information 330 to a user terminal device through the communication interface 110, such that a GUI or a text regarding the identified humidity prediction information 330 and the driving information of the target device 200 corresponding to the humidity prediction information 330 is displayed on the user terminal device. Accordingly, the user can easily identify information on control of the target device 200 through the user terminal device, and easily control the target device 200 through the user terminal device.

[0121] For example, in case the predicted humidity and duration time of humidity are greater than or equal to a predetermined value, the target device 200 may start energy saving drying if an AI automatic drying option was selected, and if the AI automatic drying option was not turned on, the target device 200 may provide recommendation of energy saving drying to the user.

[0122] Also, in case the predicted humidity and duration time of humidity are smaller than the predetermined value, the target device 200 may perform strong drying if the AI automatic drying option was selected, and if the AI automatic drying option was not turned on, the target device 200 may provide recommendation of strong drying to the user.

[0123] Through the aforementioned process, the processor 130 may predict humidity based on the received temperature and humidity information, driving history information, or weather information, and transmit effective driving information corresponding to the predicted humidity to the target device 200, and can thereby make the target device 200 perform a correct and effective operation that suits the humidity.

[0124] FIG. 6 is a sequence diagram for illustrating a process of predicting humidity based on temperature and humidity information and driving history information received from peripheral devices and an external server, and transmitting driving information of a target device to the target device according to an embodiment of the disclosure.

[0125] Referring to FIG. 6, the peripheral devices 10, 20, 30 may transmit temperature and humidity information and driving history information of the peripheral devices 10, 20, 30 to the electronic device 100 through the communication interface 110, and the electronic device 100 may receive the temperature and humidity information and the driving history information of the peripheral devices 10, 20, 30 through the communication interface 110 in the operation S610.

[0126] The external server 40 may transmit weather information including temperature and humidity information of the outside to the electronic device 100 through the communication interface 110, and the electronic device 100 may receive the weather information including the temperature and humidity information of the outside through the communication interface 110 in the operation S620.

[0127] The electronic device 100 may receive a driving signal of the target device 200 from the target device 200 through the communication interface 110 in the operation S630.

[0128] When the driving signal of the target device 200 is received, the electronic device 100 may input the temperature and humidity information and the driving history information of the plurality of peripheral devices 10, 20, 30 received from the plurality of peripheral devices 10, 20, 30, and the weather information received from the external server 40 into the humidity prediction model 320 in the operation S640.

[0129] The electronic device 100 may identify a vector value in a random real number space output from the humidity prediction model 320 in the operation S650.

[0130] The electronic device 100 may identify humidity prediction information 330 of the space wherein the target device 200 is located based on the identified vector value in the operation S660.

[0131] The electronic device 100 may transmit driving information of the target device 200 corresponding to the humidity prediction information 330 to the target device 200 through the communication interface 110 in the operation S670.

[0132] Other than the above, the electronic device 100 may transmit driving information of the target device 200 corresponding to the humidity prediction information 330 to a user terminal device through the communication interface 110.

[0133] Unlike in the aforementioned case, in case the humidity prediction model 320 is stored in a separate AI server but not the electronic device 100, the operation of the electronic device 100 may be indicated as in FIG. 7.

[0134] FIG. 7 is a sequence diagram for illustrating a process of predicting humidity based on temperature and humidity information and driving history information received from peripheral devices and an external server, and transmitting driving information of a target device to the target device in case a humidity prediction model was implemented in an AI server but not an electronic device according to an embodiment of the disclosure.

[0135] Referring to FIG. 7, the peripheral devices 10, 20, 30 may transmit temperature and humidity information and driving history information of the peripheral devices 10, 20, 30 to the electronic device 100 through the communication interface 110, and the electronic device 100 may receive the temperature and humidity information and the driving history information of the peripheral devices 10, 20, 30 through the communication interface 110 in the operation S705.

[0136] The external server 40 may transmit weather information including temperature and humidity information of the outside to the electronic device 100 through the communication interface 110, and the electronic device 100 may receive the weather information including the temperature and humidity information of the outside through the communication interface 110 in the operation S710.

[0137] The electronic device 100 may receive a driving signal of the target device 200 from the target device 200 through the communication interface 110 in the operation S715.

[0138] When the driving signal of the target device 200 is received, the electronic device 100 may transmit a signal requesting humidity prediction, the temperature and humidity information of the peripheral devices 10, 20, 30, the driving history information of the peripheral devices 10, 20, 30, and the weather information received from the external server 40 to the AI server 700 through the communication interface 110 in the operation S720.

[0139] However, the disclosure is not limited thereto, and the peripheral devices 10, 20, 30 may directly transmit the temperature and humidity information of the peripheral devices 10, 20, 30 and the driving history information of the peripheral devices 10, 20, 30 to the AI server 700 through the communication interface 110. Also, the external server 40 may directly transmit the weather information to the AI server 700 through the communication interface 110.

[0140] The AI server 700 may input the temperature and humidity information and the driving history information of the plurality of peripheral devices 10, 20, 30 received from the plurality of peripheral devices 10, 20, 30, and the weather information received from the external server 40 into the humidity prediction model 320 in the operation S725.

[0141] The AI server 700 may identify a vector value in a random real number space output from the humidity prediction model 320 in the operation S730.

[0142] The AI server 700 may identify humidity prediction information 330 of the space wherein the target device 200 is located based on the identified vector value in the operation S735.

[0143] The AI server 700 may transmit the humidity prediction information 330 to the electronic device 100 through the communication interface 110, and the electronic device 100 may receive the humidity prediction information 330 through the communication interface 110 in the operation S740.

[0144] The electronic device 100 may identify driving information of the target device 200 corresponding to the humidity prediction information 330 in the operation S745.

[0145] The electronic device 100 may transmit the driving information of the target device 200 corresponding to the humidity prediction information 330 to the target device 200 in the operation S750.

[0146] Other than the above, the electronic device 100 may transmit the driving information of the target device 200 corresponding to the humidity prediction information 330 to a user terminal device through the communication interface 110.

[0147] FIG. 8 is a flow chart for illustrating an operation of an electronic device according to an embodiment of the disclosure.

[0148] Referring to FIG. 8, the electronic device 100 may receive temperature and humidity information and driving history information of the plurality of peripheral devices 10, 20, 30 from the plurality of peripheral devices 10, 20, 30 through the communication interface 110 in the operation S810. Other than the above, the electronic device 100 may receive weather information including temperature and humidity information of the outside from the external server 40 through the communication interface 110.

[0149] Specifically, the electronic device 100 may receive temperature and humidity information and driving history information of the plurality of peripheral devices 10, 20, 30 from the plurality of peripheral devices 10, 20, 30 located within a predetermined distance from the target device 200 or registered in advance to a device registration server through the communication interface 110.

[0150] Here, the temperature and humidity information may be temperature and humidity information detected by the peripheral devices 10, 20, 30.

[0151] When a driving signal of the target device 200 is received from the target device 200, the electronic device 100 may input the temperature and humidity information and the driving history information of the plurality of peripheral devices 10, 20, 30 received from the plurality of peripheral devices 10, 20, 30, or the weather information received from the external server 40 into a humidity prediction model, and identify a vector value output from the humidity prediction model in the operation S820.

[0152] The electronic device 100 may identify humidity prediction information of the space or the location wherein the target device 200 is located based on the identified vector value in the operation S830.

[0153] The electronic device 100 may identify a humidity value between a first time point of the space wherein the target device 200 is located and a second time point when a predetermined time passed from the first time point based on the identified vector value.

[0154] The electronic device 100 may transmit driving information of the target device 200 corresponding to the identified humidity prediction information to the target device 200 through the communication interface 110 in the operation S840.

[0155] Functions related to artificial intelligence according to the disclosure are operated through the processor and the memory of the electronic device.

[0156] The processor may consist of one or a plurality of processors. Here, the one or plurality of processors may include at least one of a central processing unit (CPU), a graphics processing unit (GPU), or a neural processing unit (NPU), but the processors are not limited to the aforementioned examples of processors.

[0157] A CPU is a generic-purpose processor that can perform not only general operations but also artificial intelligence operations, and it can effectively execute a complex program through a multilayer cache structure. A CPU is advantageous for a serial processing method that enables a systemic linking between the previous calculation result and the next calculation result through sequential calculations. A generic-purpose processor is not limited to the aforementioned examples excluding cases wherein it is specified as the aforementioned CPU.

[0158] A GPU is a processor for mass operations such as a floating point operation used for graphic processing, etc., and it can perform mass operations in parallel by massively integrating cores. In particular, a GPU may be advantageous for a parallel processing method such as a convolution operation, etc. compared to a CPU. Also, a GPU may be used as a co-processor for supplementing the function of a CPU. A processor for mass operations is not limited to the aforementioned examples excluding cases wherein it is specified as the aforementioned GPU.

[0159] An NPU is a processor specialized for an artificial intelligence operation using an artificial neural network, and it can implement each layer constituting an artificial neural network as hardware (e.g., silicon). Here, the NPU is designed to be specialized according to the required specification of a company, and thus it has a lower degree of freedom compared to a CPU or a GPU, but it can effectively process an artificial intelligence operation required by the company. Meanwhile, as a processor specialized for an artificial intelligence operation, an NPU may be implemented in various forms such as a tensor processing unit (TPU), an intelligence processing unit (IPU), a vision processing unit (VPU), etc. An artificial intelligence processor is not limited to the aforementioned examples excluding cases wherein it is specified as the aforementioned NPU.

[0160] Also, the one or plurality of processors may be implemented as a system on chip (SoC). Here, in the SoC, the memory, and a network interface such as a bus for data communication between the processor and the memory, etc. may be further included other than the one or plurality of processors.

[0161] In case the plurality of processors are included in the system on chip (SoC) included in the electronic device, the electronic device may perform an operation related to artificial intelligence (e.g., an operation related to learning or inference of the artificial intelligence model) by using some processors among the plurality of processors. For example, the electronic device may perform an operation related to artificial intelligence by using at least one of a GPU, an NPU, a VPU, a TPU, or a hardware accelerator specified for artificial intelligence operations such as a convolution operation, a matrix product operation, etc. among the plurality of processors. However, this is merely an example, and the electronic device can obviously process an operation related to artificial intelligence by using the generic-purpose processor such as a CPU, etc.

[0162] Also, the electronic device may perform operations regarding functions related to artificial intelligence by using a multicore (e.g., a dual core, a quad core, etc.) included in one processor. In particular, the electronic device may perform artificial intelligence operations such as a convolution operation, a matrix product operation, etc. in parallel by using the multicore included in the processor.

[0163] The one or plurality of processors perform control such that input data is processed according to predefined operation rules or an artificial intelligence model stored in the memory. The predefined operation rules or the artificial intelligence model are characterized in that they are made through learning.

[0164] Here, being made through learning means that a learning algorithm is applied to a plurality of training data, and predefined operation rules or an artificial intelligence model having desired characteristics are thereby made. Such learning may be performed in a device itself wherein artificial intelligence is performed according to the disclosure, or through a separate server / system.

[0165] An artificial intelligence model may consist of a plurality of neural network layers. At least one layer has at least one weight value, and performs an operation of the layer through the operation result of the previous layer and at least one defined operation. As examples of a neural network, there are a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), a restricted Boltzmann Machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), deep Q-networks, and a transformer, but the neural network in the disclosure is not limited to the aforementioned examples excluding specified cases.

[0166] A learning algorithm is a method of training a specific subject device (e.g., a robot) by using a plurality of training data and thereby making the specific subject device make a decision or make prediction by itself. As examples of learning algorithms, there are supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but learning algorithms in the disclosure are not limited to the aforementioned examples excluding specified cases.

[0167] According to an embodiment of the disclosure, the method according to the various embodiments described in the disclosure may be provided while being included in a computer program product. A computer program product refers to a product, and it can be traded between a seller and a buyer. A computer program product can be distributed in the form of a storage medium that is readable by machines (e.g.: compact disc read only memory (CD-ROM)), or may be distributed directly between two user devices (e.g.: smartphones), and distributed on-line (e.g.: download or upload) through an application store (e.g.: Play Store™). In the case of on-line distribution, at least a portion of a computer program product (e.g.: a downloadable app) may be stored in a storage medium such as the server of the manufacturer, the server of the application store, and the memory of the relay server at least temporarily, or may be generated temporarily.

[0168] Also, while preferred embodiments of the disclosure have been shown and described, the disclosure is not limited to the aforementioned specific embodiments, and it is apparent that various modifications may be made by those having ordinary skill in the technical field to which the disclosure belongs, without departing from the gist of the disclosure as claimed by the appended claims. Further, it is intended that such modifications are not to be interpreted independently from the technical idea or prospect of the disclosure.

Claims

1. An electronic device comprising:a communication interface;a memory to store at least one instruction; andat least one processor executing the at least one instruction stored in the memory to:receive temperature and humidity information and driving history information of a plurality of peripheral devices through the communication interface,based on receiving a driving signal of a target device through the communication interface, input the temperature and humidity information and the driving history information of the plurality of peripheral devices into a humidity prediction model and identify a vector value output from the humidity prediction model,identify humidity prediction information of a space in which the target device is located based on the identified vector value, andtransmit driving information, according to which the target device is to be operated based on the driving signal, corresponding to the identified humidity prediction information to the target device.

2. The electronic device of claim 1,wherein the at least one processor is configured to:receive weather information including temperature and humidity outside, from an external server, through the communication interface,based on receiving the driving signal of the target device, input the temperature and humidity information and the driving history information of the plurality of peripheral devices received, and the weather information received from the external server into the humidity prediction model and identify a vector value output from the humidity prediction model.

3. The electronic device of claim 1,wherein the humidity prediction model is a model that was trained based on training data including a vector value, the temperature and humidity information and the driving history information of the plurality of peripheral devices received, the vector value being obtained by inputting the temperature and humidity information and the driving history information of the plurality of peripheral devices received into the humidity prediction model, andthe at least one processor is configured to:identify at least one device that did not transmit temperature and humidity information among the plurality of peripheral devices,obtain alternative temperature and humidity information corresponding to the at least one device that did not transmit temperature and humidity information from the training data, andbased on receiving the driving signal of the target device, input the temperature and humidity information and the driving history information of the plurality of peripheral devices received, and the alternative temperature and humidity information into the humidity prediction model and identify a vector value output from the humidity prediction model.

4. The electronic device of claim 1,wherein the at least one processor is configured to:identify a humidity value between a first time point for the space in which the target device is located and a second time point subsequent to a predetermined time having passed from the first time point based on the identified vector value.

5. The electronic device of claim 1,wherein the at least one processor is configured to:receive temperature and humidity information and driving history information of peripheral devices from among the plurality of peripheral devices located within a predetermined distance from the target device through the communication interface.

6. The electronic device of claim 1,wherein the at least one processor is configured to:receive temperature and humidity information and driving history information of peripheral devices from among the plurality of peripheral devices that are registered in advance to a device registration server through the communication interface.

7. The electronic device of claim 1,wherein the at least one processor is configured to:receive the temperature and humidity information detected by the plurality of peripheral devices and the driving history information of the plurality of peripheral devices through the communication interface.

8. The electronic device of claim 1,wherein the at least one processor is configured to:transmit driving information of the target device corresponding to the identified humidity prediction information to a user terminal device.

9. A method for controlling an electronic device, the method comprising:receiving temperature and humidity information and driving history information of a plurality of peripheral devices;based on receiving a driving signal of a target device, inputting the temperature and humidity information and the driving history information of the plurality of peripheral devices received into a humidity prediction model and identifying a vector value output from the humidity prediction model;identifying humidity prediction information of a space wherein the target device is located based on the identified vector value; andtransmitting driving information, according to which the target device is to be operated based on the driving signal, corresponding to the identified humidity prediction information to the target device.

10. The method for controlling of claim 9,wherein the receiving comprises:receiving weather information including a temperature and humidity of outside from an external server, andthe identifying the vector value comprises:based on receiving the driving signal of the target device, inputting the temperature and humidity information and the driving history information of the plurality of peripheral devices received, and the weather information received from the external server into the humidity prediction model and identifying a vector value output from the humidity prediction model.

11. The method for controlling of claim 9,wherein the humidity prediction model is a model that was trained based on training data including a vector value, the temperature and humidity information and the driving history information of the plurality of peripheral devices received, the vector value being obtained by inputting the temperature and humidity information and the driving history information of the plurality of peripheral devices received into the humidity prediction model, andthe receiving comprises:identifying at least one device that did not transmit temperature and humidity information among the plurality of peripheral devices; andobtaining alternative temperature and humidity information corresponding to the at least one device that did not transmit temperature and humidity information from the training data, andthe identifying the vector value comprises:based on receiving the driving signal of the target device, inputting the temperature and humidity information and the driving history information of the plurality of peripheral devices received, and the alternative temperature and humidity information into the humidity prediction model and identifying a vector value output from the humidity prediction model.

12. The method for controlling of claim 9,wherein the identifying the humidity prediction information comprises:identifying a humidity value between a first time point for the space in which the target device is located and a second time point subsequent to a predetermined time having passed from the first time point based on the identified vector value.

13. The method for controlling of claim 9,wherein the receiving comprises:receiving temperature and humidity information and driving history information of peripheral devices from among the plurality of peripheral devices located within a predetermined distance from the target device.

14. The method for controlling of claim 9,wherein the receiving comprises:receiving temperature and humidity information and driving history information of peripheral devices from among the plurality of peripheral devices that are registered in advance to a device registration server.

15. The method for controlling of claim 9,wherein the receiving comprises:receiving the temperature and humidity information detected by the plurality of peripheral devices and the driving history information of the plurality of peripheral devices.

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