Artificial intelligent refrigerator

Patent metadata

Field	Value
Publication number	US-11629909-B2
Application number	US-201916687595-A
Country	US
Kind code	B2
Filing date	Nov 18, 2019
Priority date	Aug 5, 2019
Publication date	Apr 18, 2023
Grant date	Apr 18, 2023

Abstract

An artificial intelligent refrigerator is disclosed. The artificial intelligent refrigerator includes: one or more first temperature sensor that senses refrigerating compartment-internal temperature in a refrigerating compartment of the refrigerator; one or more second temperature sensor that senses freezing compartment-internal temperature in a freezing compartment of the refrigerator; and a refrigerator processor that calculates a load accumulation amount for food put in the refrigerator on the basis of the refrigerating compartment-internal temperature or the freezing compartment-internal temperature, and performs a load correspondence operation using the calculated load accumulation amount. According to the artificial intelligent refrigerator of the present disclosure, one or more of a user terminal, and a server of the present disclosure may be associated with an artificial intelligence module, a drone ((Unmanned Aerial Vehicle, UAV), a robot, an AR (Augmented Reality) device, a VR (Virtual Reality) device, a device associated with 5G services, etc.

First claim

What is claimed is:

1. An artificial intelligent refrigerator comprising:
a first temperature sensor configured to sense a first temperature of a refrigerating compartment;
a second temperature sensor configured to sense a second temperature of a freezing compartment; and
a processor configured to:
calculate a load accumulation amount of items stored in the refrigerating compartment and the freezing compartment based on at least one of the first temperature or the second temperature; and
perform an operation corresponding to a load using the calculated load accumulation amount,

wherein the processor is further configured to:
calculate the load accumulation amount by learning a variation of the first temperature, a variation of the second temperature, at least one of an external air temperature or humidity of the artificial intelligent refrigerator, and an operation state of the artificial intelligent refrigerator;
set a step range of the calculated load accumulation amount, the step range being a percentage of an amount of change in load accumulation divided into control responses at a stepped basis;
change a cooling ability value of load correspondence according to the set step range of the calculated load accumulation amount; and
generate a notice signal that an overload accumulation amount has been generated based on the load accumulation amount being out of the set step range of the load accumulation amount and being maintained for a set time,
wherein the step range includes a plurality of groups using different cooling abilities, each group of the plurality of groups includes a plurality of steps using the same cooling ability, and at least two groups among the plurality of groups that use the different cooling abilities use a same fan speed.

2. The artificial intelligent refrigerator of claim 1 , further comprising:
a first compressor and a second compressor that are coupled and configured to compress a refrigerant;
a condenser coupled to a discharge side of the second compressor positioned downstream in a flow direction of the refrigerant;
a first evaporator diverging from the condenser and coupled to an intake side of the first compressor positioned upstream in the flow direction of the refrigerant;
a second evaporator diverging from the condenser and coupled to a connecting portion between a discharge side of the first compressor and an intake side of the second compressor; and
a refrigerant switch valve located at a point diverting from an outlet side of the condenser to the first evaporator and the second evaporator, and configured to control the flow direction of the refrigerant.

3. The artificial intelligent refrigerator of claim 1 , wherein the processor comprises:
a first calculator configured to calculate a variation of the first temperature periodically; and
a second calculator configured to calculate a variation of the second temperature periodically.

4. The artificial intelligent refrigerator of claim 1 , further comprising a memory configured to store a variation of the first temperature and a variation of the second temperature.

5. The artificial intelligent refrigerator of claim 2 , wherein the processor is further configured to maintain a current cooling ability value of the first and second compressors when a variation of the first temperature or a variation of the second temperature is equal to or greater than a pre-set reference variation.

6. The artificial intelligent refrigerator of claim 2 , wherein the processor is further configured to increase a current cooling ability value of the first and second compressors when a variation of the first temperature or a variation of the second temperature is equal to or less than a pre-set reference variation.

7. The artificial intelligent refrigerator of claim 1 , wherein a thermal capacity of food or a thermal load required for cooling based on an external air environment condition is added to the load accumulation amount.

8. The artificial intelligent refrigerator of claim 1 , wherein the processor is further configured to:
receive a Downlink Control Information (DCI), which is used to schedule transmission of temperature information sensed by the artificial intelligent refrigerator, from a network; and
transmit the sensed temperature information to the network based on the DCI.

9. The artificial intelligent refrigerator of claim 8 , wherein the processor is further configured to:
perform a procedure of initial connection with the network based on a Synchronization signal block (SSB); and
transmit the sensed temperature information to the network through a physical uplink shared channel (PUSCH), and
wherein demodulation reference signals (DM-RS) of the SSB and the PUSCH has undergone quasi co-located (QCLed) for a QCL type D.

10. The artificial intelligent refrigerator of claim 8 , wherein the network comprises a 5G network.

11. The artificial intelligent refrigerator of claim 8 , wherein the step range includes steps 1 to 10 in which a load change accumulation amount is sequentially increased by 10% from 0% to 100%, and
wherein the processor is further configured to:
in the step 1 to the step 3, use a load correspondence cooling ability corresponding to 90% of a basic cooling ability and drive a fan at a first speed,
in the step 4 to the step 6, use a load correspondence cooling ability corresponding to 100% of the basic cooling ability and drive the fan at the first speed,
in the step 7 to the step 8, use a load correspondence cooling ability corresponding to 105% of the basic cooling ability and drive the fan at the first speed, and
in the step 9 to the step 10, use a load correspondence cooling ability corresponding to 110% of the basic cooling ability and drive the fan at a second speed higher that the first speed.

Assignees

• Lg Electronics Inc

Inventors

• Han Junsoo
• Yang Younghun
• Eom Yonghwan
• Jeong Junseong
• Han Cholok

Classifications

• G06N3/084

  Backpropagation, e.g. using gradient descent · CPC title

• F25D2700/122

  of freezer compartments · CPC title

• F25D2700/121

  of particular compartments · CPC title

• F25D29/003 Primary

  for movable devices · CPC title

• F25D29/00 Primary

  Arrangement or mounting of control or safety devices · CPC title