Electronic device and controlling method of electronic device
An electronic device includes: at least one memory configured to store information of a first neural network model trained to predict an operation of a refrigerator, and information of a second neural network model trained to obtain information associated with a defrosting of the refrigerator; and at least one processor configured to: obtain first data regarding an operation history of the refrigerator, input the first data to the first neural network model, and obtain, from the first neural network model, second data regarding a prediction result for a future operation of the refrigerator, and input the second data to the second neural network model, and obtain, from the second neural network model, third data including information regarding a degree of frost formation based on an operation of the refrigerator being performed according to the second data, and information regarding controlling a defrost operation of the refrigerator.
1. An electronic device comprising:
at least one memory configured to store information of a first neural network model trained to predict an operation of a refrigerator, and information of a second neural network model trained to obtain information associated with a defrosting of the refrigerator; and
at least one processor configured to:
obtain first data regarding an operation history of the refrigerator,
input the first data to the first neural network model, and obtain, from the first neural network model, second data regarding a prediction result for a future operation of the refrigerator, and
input the second data to the second neural network model, and obtain, from the second neural network model, third data comprising information regarding a degree of frost formation based on an operation of the refrigerator being performed according to the second data, and information regarding controlling a defrost operation of the refrigerator.
2. The electronic device of claim 1 , further comprising:
a communicator,
wherein the at least one processor is further configured to control, based on the third data being obtained from the second neural network model, the communicator to transmit the third data to the refrigerator as instructions for the refrigerator to perform the defrost operation according to the third data.
3. The electronic device of claim 2 , wherein the information regarding controlling the defrost operation of the refrigerator comprises information regarding a cycle of the defrost operation, information regarding a period of the defrost operation per cycle, and information regarding an intensity of the defrost operation per cycle.
4. The electronic device of claim 3 , wherein the at least one processor is further configured to:
obtain, based on the cycle of the defrost operation according to the information regarding controlling the defrost operation being less than a threshold value, fourth data by changing the information regarding the cycle of the defrost operation to the threshold value in the third data, and
control the communicator to transmit the fourth data to the refrigerator.
5. The electronic device of claim 4 , wherein the at least one processor is further configured to:
obtain information regarding at least one reason associated with a deterioration in efficiency of the refrigerator, based on the first data, and
control the communicator to transmit, to the refrigerator, information regarding a guide message corresponding to the at least one reason.
6. The electronic device of claim 5 , wherein the at least one processor is further configured to, identify the information regarding the at least one reason based on at least one from among information regarding a temperature range of the refrigerator, information regarding a number of door openings and closings of the refrigerator, information regarding a difference between a maximum temperature inside the refrigerator and a control temperature, or information regarding an outdoor temperature of the refrigerator, and
wherein the first data comprises the information regarding the temperature range of the refrigerator.
7. The electronic device of claim 1 , wherein the first data comprises information on a revolution per minute (RPM) of a fan of the refrigerator, information of a power consumption of a compressor of the refrigerator, and information of a temperature inside the refrigerator, and
wherein the first neural network model is trained to obtain the second data based on the first data, data on a surrounding environment of the refrigerator, and information on a user of the refrigerator.
8. The electronic device of claim 7 , wherein the second data comprises a first prediction value of the RPM of the fan, a second prediction value of the power consumption of the compressor, and a third prediction value of the temperature inside the refrigerator, and
wherein the second neural network model is trained to obtain information regarding a degree of excessive frost formation based on the first prediction value, the second prediction value, and the third prediction value.
9. The electronic device of claim 1 , wherein the first neural network model and the second neural network model are implemented as one integrated neural network model, and the integrated neural network model is trained according to an end-to-end learning method to obtain the third data based on the first data.
10. The electronic device of claim 1 , wherein the electronic device and the refrigerator are implemented as one integrated device, and
wherein the at least one processor is further configured to perform, based on the third data being obtained from the second neural network model, the defrost operation of the refrigerator based on the third data.
11. A method of controlling an electronic device, the method comprising:
obtaining first data regarding an operating history of a refrigerator;
obtaining, based on inputting the first data to a first neural network model trained to predict an operation of the refrigerator, second data regarding a prediction result for a future operation of the refrigerator; and
obtaining, based on inputting the second data to a second neural network model trained to obtain information associated with a defrosting of the refrigerator, third data comprising information regarding a degree of frost formation based on an operation of the refrigerator being performed according to the second data, and information regarding controlling a defrost operation of the refrigerator.
12. The method of claim 11 , further comprising transmitting, based on the third data being obtained from the second neural network model, the third data to the refrigerator as instructions for the refrigerator to perform the defrost operation according to the third data.
13. The method of claim 12 , wherein the information regarding controlling the defrost operation of the refrigerator comprises information regarding a cycle of the defrost operation, information regarding a period of the defrost operation per cycle, and information regarding an intensity of the defrost operation per cycle.
14. The method of claim 13 , further comprising:
obtaining, based on the cycle of the defrost operation according to the information regarding controlling the defrost operation being less than a threshold value, fourth data by changing the information regarding the cycle of the defrost operation to the threshold value in the third data; and
transmitting the fourth data to the refrigerator.
15. The method of claim 14 , further comprising:
obtaining information regarding at least one reason associated with a deterioration in efficiency of the refrigerator, based on the first data; and
transmitting information on a guide message corresponding to the at least one reason to the refrigerator.
16. A non-transitory computer readable recording medium storing a program that is executed by at least one processor of an electronic device to perform a method of controlling the electronic device, the method comprising:
obtaining first data regarding an operating history of a refrigerator;
obtaining, based on inputting the first data to a first neural network model trained to predict an operation of the refrigerator, second data regarding a prediction result for a future operation of the refrigerator; and
obtaining, based on inputting the second data to a second neural network model trained to obtain information associated with a defrosting of the refrigerator, third data comprising information regarding a degree of frost formation based on an operation of the refrigerator being performed according to the second data, and information regarding controlling a defrost operation of the refrigerator.
17. The non-transitory computer readable recording medium of claim 16 , wherein the method further comprises transmitting, based on the third data being obtained from the second neural network model, the third data to the refrigerator as instructions for the refrigerator to perform the defrost operation according to the third data.
18. The non-transitory computer readable recording medium of claim 17 , wherein the information regarding controlling the defrost operation of the refrigerator comprises information regarding a cycle of the defrost operation, information regarding a period of the defrost operation per cycle, and information regarding an intensity of the defrost operation per cycle.
19. The non-transitory computer readable recording medium of claim 18 , wherein the method further comprises:
obtaining, based on the cycle of the defrost operation according to the information regarding controlling the defrost operation being less than a threshold value, fourth data by changing the information regarding the cycle of the defrost operation to the threshold value in the third data; and
transmitting the fourth data to the refrigerator.
20. The non-transitory computer readable recording medium of claim 19 , wherein the method further comprises:
obtaining information regarding at least one reason associated with a deterioration in efficiency of the refrigerator, based on the first data; and
transmitting information on a guide message corresponding to the at least one reason to the refrigerator.