Home applicance and power consumption monitoring method

Abstract

Provided in the present application is a refrigerator, which comprises a plurality of functional components and a processing apparatus. The processing apparatus is configured to: on the basis of refrigerator operation condition information, use a preconfigured first model to determine a first energy-saving operation parameter value of at least one first functional component corresponding to the refrigerator operation condition information; replace an actual operation parameter value corresponding to a first energy-saving operation parameter in the refrigerator operation condition information with the first energy-saving operation parameter value, so as to obtain refrigerator energy-saving operation condition information; on the basis of the refrigerator energy-saving operation condition information, use a preconfigured second model to determine a second energy-saving operation parameter value of at least one second functional component corresponding to the refrigerator energy-saving operation condition information; and drive the operation of the first functional component on the basis of the first energy-saving operation parameter value, and drive the operation of the second functional component on the basis of the second energy-saving operation parameter value.

Description

Household appliance and power consumption monitoring method

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to Chinese Patent Application No. 202410441999.7, filed on April 12, 2024, and Chinese Patent Application No. 202410591431.3, filed on May 13, 2024, the contents of which are incorporated herein by reference in their entirety. TECHNICAL FIELD

[0003] The present application relates to the technical field of electrical equipment, and in particular to a household appliance and a power consumption monitoring method. BACKGROUND

[0004] Household appliances such as refrigerators have brought great convenience to people's life, but the energy consumption brought by household appliances is also getting bigger and bigger. For example, a refrigerator usually operates 24 hours a day, and the cumulative consumption of electric energy cannot be underestimated. Therefore, users are also increasingly concerned about the energy consumption of household appliances. SUMMARY

[0005] The present application provides a household appliance and a power consumption monitoring method, which realizes optimization and improvement of refrigerator efficiency and reduces product energy consumption.

[0006] In some embodiments, the household appliance can be a refrigerator.

[0007] In a first aspect, some embodiments of the present application provide a refrigerator, comprising a plurality of functional components and a processing device, the processing device being configured to:

[0008] determine, based on refrigerator operating condition information, a first energy-saving operating parameter value of at least one first functional component corresponding to the refrigerator operating condition information using a pre-configured first model (also referred to as a main training model);

[0009] replace an actual operating parameter value corresponding to the first energy-saving operating parameter in the refrigerator operating condition information with the first energy-saving operating parameter value determined by the first model to generate refrigerator energy-saving operating condition information;

[0010] determine, based on the refrigerator energy-saving operating condition information, a second energy-saving operating parameter value of at least one second functional component corresponding to the refrigerator energy-saving operating condition information using a pre-configured second model (also referred to as an additional training model); and

[0011] drive the first functional component to operate based on the first energy-saving operating parameter value and drive the second functional component to operate based on the second energy-saving operating parameter value.

[0012] The processing device uses at least two models to decompose and process the energy-saving setting task, a first model is used to determine the energy-saving operation parameters of a first functional component, and a second model is used to determine the energy-saving operation parameters of a second functional component. Such decomposition can enable the first model and the second model to focus on specific tasks, improve the learning and prediction capabilities of each model, decouple and modularize the energy-saving setting task using the first model and the second model, and make the system more flexible and scalable, better cope with complex energy-saving optimization tasks, and improve energy-saving effect.

[0013] In a second aspect, some embodiments of the present application provide a power consumption monitoring method of a household appliance, applied to a cloud server, the method comprising:

[0014] determining whether the household appliance is disconnected from the network in a current monitoring period;

[0015] if the household appliance is disconnected from the network in the current monitoring period, obtaining a plurality of first historical power consumptions;

[0016] inputting the plurality of first historical power consumptions into a fourth model to obtain a first power consumption of the current monitoring period, the fourth model being trained according to a plurality of second historical power consumptions;

[0017] saving the first power consumption;

[0018] based on the first power consumption, determining an actual power consumption of the household appliance in a current preset period, wherein the current preset period includes the current monitoring period.

[0019] The method solves the problem of inaccurate actual power consumption calculation due to loss of operation parameters during disconnection of the household appliance from the network, enabling the user to more accurately understand the power consumption of the household appliance and timely adjust the usage habits of the household appliance to reduce energy consumption.

[0020] Other features and advantages of the present application will become more apparent after reading the specific embodiments of the present application in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the disclosed drawings.

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort based on these drawings.

[0023] FIG. 1 is a structural schematic block diagram of a refrigerator according to some embodiments;

[0024] FIG. 2 is a structural schematic diagram of a refrigerator according to some embodiments;

[0025] FIG. 3 is a structural schematic block diagram of a processing device according to some embodiments;

[0026] FIG. 4 is a structural schematic block diagram of a refrigerator according to some embodiments;

[0027] FIG. 5 is a structural schematic block diagram of a refrigerator according to some other embodiments;

[0028] FIG. 6 is a flowchart of a refrigerator control method performed by a processing device according to some embodiments;

[0029] FIG. 7 is a flowchart of a refrigerator control method performed by a processing device according to some other embodiments;

[0030] FIG. 8 is a flowchart of a refrigerator control method performed by a processing device according to some other embodiments;

[0031] FIG. 9 is a flowchart of a refrigerator control method performed by a processing device according to some other embodiments;

[0032] FIG. 10 is a structural schematic diagram of a model deployed in a server according to some embodiments;

[0033] FIG. 11 is a flowchart of a refrigerator control method performed by a processing device according to some embodiments;

[0034] FIG. 12 is a flowchart of a refrigerator control method performed by a processing device according to some embodiments;

[0035] FIG. 13 is a flowchart of a refrigerator control method performed by a processing device according to some embodiments;

[0036] FIG. 14 is a flowchart of a refrigerator control method performed by a processing device according to some embodiments;

[0037] FIG. 15 is a flowchart of a power consumption monitoring method of a household appliance according to some embodiments of the present application;

[0038] FIG. 16 is a schematic diagram of a monitoring period during the operation of a household appliance according to an example of some embodiments of the present application;

[0039] Fig. 17 is a flow diagram of another method for monitoring power consumption of a household appliance according to an embodiment of the present application;

[0040] Fig. 18 is a flow diagram of another method for monitoring power consumption of a household appliance according to an embodiment of the present application;

[0041] Fig. 19 is a flow diagram of another method for monitoring power consumption of a household appliance according to an embodiment of the present application;

[0042] Fig. 20 is a flow diagram of another method for monitoring power consumption of a household appliance according to an embodiment of the present application;

[0043] Fig. 21 is a flow diagram of another method for monitoring power consumption of a household appliance according to an embodiment of the present application;

[0044] Fig. 22 is a structural diagram of a device for monitoring power consumption of a household appliance according to an embodiment of the present application;

[0045] Fig. 23 is a structural diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0047] In the description of the present application, it should be understood that the terms “center”, “upper”, “lower”, “front”, “back”, “left”, “right”, “vertical”, “horizontal”, “top”, “bottom”, “inner”, “outer” and the like indicate the orientation or positional relationship shown in the drawings based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application.

[0048] The terms “first” and “second” are only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with “first” and “second” can explicitly or implicitly include one or more of the features. In the description of the present application, unless otherwise specified, the meaning of “a plurality of” is two or more.

[0049] In the description of the application, it is necessary to point out that, unless otherwise explicitly specified and limited, the terms "mounting", "connection", "linking" should be understood broadly, for example, it can be fixed connection, or detachable connection, or integrally connected; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through intermediate medium, or the communication inside two elements. For ordinary skilled in the art, the specific meaning of the above terms in the application can be understood according to the specific circumstances.

[0050] In the present application, unless otherwise explicitly specified and limited, the first feature "on" or "under" the second feature can include that the first and second features are in direct contact, or that the first and second features are not in direct contact but are in contact through another feature between them. Moreover, the first feature "on", "above" and "over" the second feature includes that the first feature is directly above and obliquely above the second feature, or only indicates that the horizontal height of the first feature is higher than that of the second feature. The first feature "under", "below" and "under" the second feature includes that the first feature is directly below and obliquely below the second feature, or only indicates that the horizontal height of the first feature is less than that of the second feature.

[0051] In the present application, "and / or" is only a description of the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can mean that A exists alone, A and B exist together, and B exists alone. In addition, the character " / " in this paper represents that the front and rear associated objects are in an "or" relationship.

[0052] The following disclosure provides many different embodiments or examples for implementing different structures of the application. In order to simplify the disclosure of the application, the components and arrangements of specific examples are described below. Of course, they are only examples, and the purpose is not to limit the application. In addition, the application can repeatedly refer to numbers and letters in different examples, and such repetition is for the purpose of simplification and clarity, which itself does not indicate the relationship between the various embodiments and arrangements discussed.

[0053] Household appliances such as refrigerators bring convenience to people's life, but the energy consumption is also increasing, and users pay more and more attention to the energy consumption of household appliances. In order to reduce energy consumption, more and more household appliances are designed to save energy. For example, for refrigerators, energy saving is generally achieved by using high-efficiency compressor, thickening insulation layer, using double door seal, using vacuum insulation board and other ways, but these measures increase the cost of products, and are not conducive to environmental protection.

[0054] Based on this, the application provides a household appliance. Based on the running state information of the household appliance in a running state, a plurality of models are used to generate energy-saving running parameters respectively determined for a plurality of functional components, and the corresponding functional components are controlled to run according to the energy-saving running parameters, so as to achieve the purpose of energy saving, reduce the cost of the product, and be conducive to environmental protection. The household appliance can include but is not limited to a refrigerator, an air conditioner, etc.

[0055] The functional component refers to an element or component in the refrigerator that realizes various functions of the refrigerator under the driving of electric power, such as a compressor, a fan, a defrosting heater, etc. The application does not limit the functional component.

[0056] The application principle of the method for determining the energy-saving running parameters in the refrigerator, the air conditioner, etc. is the same. For the convenience of description, the calculation scheme of the application is described below by taking the refrigerator as an example in combination with various embodiments, but does not constitute a limitation on the application.

[0057] In the following, the refrigerator 1 provided by the application will be introduced with reference to the accompanying drawings. In some embodiments, the overall structure of the refrigerator 1 is shown in FIG. 1.

[0058] The refrigerator 1 includes a refrigerator body 10. The refrigerator body 10 includes a cabinet for forming the appearance of the refrigerator. The cabinet is internally formed with a storage compartment for storing articles. The refrigerator body 10 also includes various elements such as a refrigeration system for realizing the functions of the refrigerator. The structure of the refrigerator body 10 will be described in detail in combination with the following embodiments.

[0059] In some embodiments, the refrigerator 1 also includes a processing device 20. The structure and function of the processing device 20 will be described in detail in combination with the following embodiments.

[0060] As shown in FIG. 2, the storage compartment is constructed in the refrigerator body 10.

[0061] The storage compartment generally includes a freezing compartment and a refrigerating compartment. The temperature range of the refrigerating compartment can be 0℃ to 10℃ (for example, 4℃) to store food, medicine or biological agents in a refrigerating state. The temperature range of the freezing compartment can be -15℃ to -40℃ (for example, -18℃) to store food, medicine or biological agents in a freezing state. In some embodiments, the storage compartment can also include a variable-temperature compartment. The temperature of the variable-temperature compartment can be adjusted between the freezing temperature range and the refrigerating temperature range according to the needs of the user, for example, the temperature range of the variable-temperature compartment can be adjusted between 5℃ and -20℃.

[0062] In some embodiments, the storage compartment can also include a fruit and vegetable compartment for storing fruits and vegetables, etc. according to the division of functions of the storage compartment.

[0063] The application does not limit the type and arrangement of the storage compartment.

[0064] The refrigerator body 10 further comprises doors 11 rotatably connected to the cabinet to open and close the storage compartments.

[0065] The storage compartments have openings which can be opened and closed by the doors 11 or by pushing or pulling the drawers 12 in and out of the openings. In some embodiments, the openings of some storage compartments are opened and closed by doors, and the openings of other storage compartments are opened and closed by push-pull drawers, for example, when the storage compartments include a refrigeration compartment and a freezer compartment, the opening of the refrigeration compartment is opened and closed by a door, and the opening of the freezer compartment is opened and closed by a drawer 12.

[0066] In some embodiments, the refrigerator body 10 adopts a vapor compression refrigeration cycle system to achieve refrigeration of the refrigerator. The refrigeration cycle system includes a compressor 161 (see FIG. 4), a condenser, a throttling device, and an evaporator.

[0067] In the vapor compression refrigeration cycle system, low-temperature and low-pressure refrigerant enters the compressor 161 and is compressed by the compressor 161 into high-temperature and high-pressure refrigerant gas. The high-temperature and high-pressure refrigerant gas flows into the condenser, and the high-temperature and high-pressure refrigerant gas in the condenser condenses into a liquid phase, and the heat released during the condensation process is dissipated to the surrounding environment.

[0068] The throttling device expands the high-temperature and high-pressure liquid-phase refrigerant formed in the condenser into low-temperature and low-pressure liquid-phase refrigerant. The liquid-phase refrigerant formed in the throttling device is evaporated by the evaporator to return to the compressor 161. The evaporator can exchange heat with the material to be cooled through the latent heat of evaporation of the refrigerant to achieve the refrigeration effect. In this application, the evaporator exchanges heat with air to form air for cooling the storage compartment, thereby achieving cooling of the storage compartment. The throttling device can be a capillary tube.

[0069] A filter is further provided downstream of the condenser. The filter is used to filter impurities in the refrigerant, improve the heat exchange efficiency of the refrigeration unit, and reduce the risk of pipeline blockage.

[0070] A liquid storage tank can also be provided on the suction side of the compressor 161. The liquid storage tank is used for gas-liquid separation of the refrigerant. The liquid storage tank is a shell-shaped component, and after the gas-liquid mixed refrigerant enters the liquid storage tank, the liquid refrigerant is deposited at the bottom of the tank due to gravity, and the gaseous refrigerant enters the compressor 161 from the gas outlet, avoiding the entry of liquid refrigerant into the compressor 161 to reduce the service life of the compressor 161.

[0071] The compressor 161 and the condenser can be arranged at the lower back side of the cabinet, and the evaporator can be arranged at the back side of the cabinet corresponding to the storage compartment. The evaporator and the condenser can also be arranged at other positions of the refrigerator body 10 as needed, which will not be listed one by one here.

[0072] The refrigerator body 10 further comprises a fan 162 (see FIG. 4). The fan 162 is configured to drive the circulation of cold air in the refrigerator to ensure the uniform distribution of the cold air.

[0073] In one or more embodiments of the present application, the fan 162 comprises a refrigeration fan. The refrigeration fan is arranged close to the evaporator of the refrigeration compartment and is configured to drive the cold air generated by the evaporator to flow to the refrigeration compartment and return the air of the refrigeration compartment to the evaporator to form an air circulation.

[0074] In one or more embodiments of the present application, the fan 162 comprises a freezing fan. The freezing fan is arranged close to the evaporator of the freezing compartment and is configured to drive the cold air generated by the evaporator to flow to the freezing compartment and return the air of the freezing compartment to the evaporator to form an air circulation.

[0075] In one or more embodiments of the present application, the fan 162 can also be arranged in cooperation with the condenser.

[0076] In one or more embodiments of the present application, the evaporator can also comprise a refrigeration compartment cooler and a freezing compartment cooler corresponding to the refrigeration compartment and the freezing compartment, respectively.

[0077] The refrigerator body 10 is provided with a defrosting element configured to form heat for defrosting the evaporator to put the evaporator in a defrosting state. In one or more embodiments of the present application, the defrosting element comprises a defrosting heater 163 (see FIG. 4).

[0078] In some embodiments, the defrosting heater 163 can be an electric heating tape or an electric heater.

[0079] In other embodiments, the defrosting element can also be a combination of an electric heating tape or an electric heater and a heat exchanger or a heat exchange pipeline. When the defrosting condition is met, the heat exchange pipeline is turned on, and the high-temperature and high-pressure refrigerant discharged by the compressor 161 enters the heat exchange pipeline to exchange heat with the surrounding air, thereby increasing the temperature of the air and further providing heat to melt the frost layer on the surface of the evaporator to put the evaporator in a defrosting state. The heat exchange pipeline can be arranged below the evaporator. Since the hot air at the heat exchange pipeline has a smaller density than the surrounding cold air, the hot air will rise to the position of the evaporator to remove the ice or frost layer on the evaporator.

[0080] In other embodiments, the defrosting element formed by the electric heat tracing tape or the electric heater can be arranged at other positions around the evaporator, for example, on the upper side or one side of the evaporator.

[0081] In some embodiments, the refrigerator body 10 is provided with a display 164.

[0082] As shown in Fig. 3, the hardware configuration of the processing device 20 in some embodiments is exemplarily shown. In some embodiments, the processing device 20 comprises a processor 201. The processor 201 can be a microprocessor, a special-purpose processor, a central processing unit, etc.

[0083] In some embodiments, the processing device 20 further comprises a storage medium, for example, a volatile memory 203 and / or a non-volatile memory 202. The storage medium comprises a medium for recording information in an optical, electrical or magnetic manner, such as a CD-ROM, a floppy disk, a magneto-optical disk, etc. The storage medium can also be a semiconductor memory for recording information in an electrical manner, such as a ROM, a flash memory, etc.

[0084] The storage medium stores program instructions, and the processor 201 is configured to execute the program instructions stored in the storage medium to realize the relevant functions.

[0085] In some embodiments, the processing device 20 further comprises a display device 204. The display device 204 is configured to display various information.

[0086] In some embodiments, the processing device 20 further comprises an operation device 205. The operation device 205 is configured to be operated by a user for inputting, etc. For example, the operation device 205 can be a touch screen, a button, a keyboard, a voice recognition device, etc.

[0087] In some embodiments, the processing device 20 further comprises a communication interface 206. The communication interface 206 is configured to communicate with other elements inside the refrigerator body 10 and / or external devices.

[0088] In some embodiments, the processing device 20 further comprises a driving device 207, which is a hardware terminal interacting with the storage medium.

[0089] In some embodiments, the processor 201, the volatile memory 203, the non-volatile memory 202, the display device 204, the operation device 205, the communication interface 206, and the driving device 207 are connected to each other through a bus 208.

[0090] In one or more embodiments of the present application, the processing device 20 can be a controller 13. The controller 13 is arranged in the refrigerator body 10.

[0091] In one or more embodiments of the present application, the processing device 20 can be a device in the refrigerator body 10 independent of the controller 13, and is configured to be in communication connection with the controller 13.

[0092] In one or more embodiments of the present application, the processing device 20 can also be an electronic device independent of the refrigerator, and is configured to be in communication connection with the controller 13, for example, the functions of the processing device 20 are implemented by the terminal device 15 and / or the server 14. For example, the server 14 can be a cloud server.

[0093] In one or more embodiments of the present application, part of the functions of the processing device 20 can be implemented by the controller 13, and part of the functions can be implemented by the terminal device 15 and / or the server 14.

[0094] The controller 13 can communicate with the terminal device 15 and / or the server 14 through a network. The network between the controller 13 and the terminal device 15, or between the controller 13 and the server 14 can be the Internet, a cellular network, a Wi-Fi network, a low-power wide-area network (Low Power Wide Area) based on LoRa, Sigfox, NB-IoT, etc. standards and protocols, a wide-area network or a local-area network, etc.

[0095] The refrigerator 1 can be used in a home environment for storing daily necessities such as food and cold drinks; it can also be used in commercial places such as restaurants, hotels, supermarkets, convenience stores, etc. for storing food, food and beverages to meet customer needs; it can also be used in places such as hospitals and laboratories for storing medicines and biological samples to meet medical and scientific research needs.

[0096] The server 14 can provide various network services, such as providing resource and data access for the controller 13 and the terminal device 15 of the refrigerator 1. The server 14 has higher performance and reliability. The server 14 can connect the controllers 13 of multiple refrigerators 1, multiple terminal devices 15, and can also connect other smart home appliance terminals.

[0097] The terminal device 15 can be in communication connection with the server 14, the controller 13 and / or the processing device 20 through a network, to realize remote control, data exchange and human-computer interaction, etc. The terminal device 15 includes, but is not limited to, a smart phone, a tablet computer, a smart speaker, a wearable device, a smart home appliance (such as a smart television), a smart vehicle device, etc. The interaction mode between the terminal device 15 and the user includes, but is not limited to, operation on the screen by a finger or a touch pen, various operations by a key, voice control, gesture control, iris recognition, and face recognition, etc.

[0098] Referring to FIG. 4, in one or more embodiments of the present application, the controller 13 is communicatively connected with the sensor assembly 30. The controller 13 acquires data sensed by the sensor assembly 30 and transmits the data to the processing device 20. In other embodiments, the processing device 20 can be communicatively connected with the sensor assembly 30 to acquire the data sensed by the sensor assembly.

[0099] The sensor assembly 30 includes a plurality of sensors, at least some of which are disposed in the refrigerator body 10.

[0100] As shown in FIGS. 4 and 5, in one or more embodiments of the present application, the sensor assembly 30 includes a plurality of compartment temperature sensors 31. Exemplarily, the compartment temperature sensors 31 include one or more of a refrigeration compartment temperature sensor 311, a freezer compartment temperature sensor 312, a fruit and vegetable compartment temperature sensor 313, and a variable-temperature compartment temperature sensor 314.

[0101] In one or more embodiments of the present application, the sensor assembly 30 includes an evaporator temperature sensor 32 configured to detect a temperature of an evaporator. Exemplarily, the evaporator temperature sensor 32 includes one or more of a refrigeration compartment evaporator temperature sensor 321 and a freezer compartment evaporator temperature sensor 322.

[0102] In one or more embodiments of the present application, the sensor assembly 30 includes an ambient temperature sensor 33. The ambient temperature sensor 33 is configured to sense a temperature of an ambient environment of the refrigerator 1. For example, the ambient temperature sensor 33 includes at least one of an indoor temperature sensor 331 and an outdoor temperature sensor (not shown).

[0103] In other embodiments, the outdoor temperature can also be acquired by querying a server.

[0104] In one or more embodiments of the present application, the sensor assembly 30 further includes an electrical parameter sensor 34. The number of the electrical parameter sensor 34 is not limited, and the electrical parameter sensor 34 can be configured to detect one or more of an amount of electricity, a peak value of the amount of electricity, a valley value of the amount of electricity, an electric current, an electric voltage, and an energy efficiency.

[0105] In one or more embodiments of the present application, the sensor assembly 30 further includes a rotational speed sensor (not shown). The rotational speed sensor is configured to detect a rotational speed of a compressor, a rotational speed of a fan, or the like.

[0106] In one or more embodiments of the present application, the sensor assembly 30 can further include a humidity sensor, a door body switch sensor, a vibration sensor, a weight sensor, and other sensors, without specific limitation.

[0107] In one or more embodiments of the present application, the refrigerator body 10 further comprises functional components 16 that are driven to operate based on electricity. The functional components 16 include, but are not limited to, a compressor 161, a fan 162, a defrosting heater 163, a display 164, a water pump in an ice making module, a motor in an ice crushing module, etc. In the embodiments of the present application, the refrigerator body 10 comprises at least two functional components.

[0108] As shown in FIG. 6, a flowchart of a refrigerator control method performed by the processing device 20 in some embodiments is shown. The method comprises steps S601-S604.

[0109] In step S601, the processing device 20 determines, based on the refrigerator operating condition information, a value of a first energy-saving operating parameter (also referred to as a master energy-saving operating parameter) of at least one first functional component 16 corresponding to the refrigerator operating condition information, using a first model (also referred to as a master training model) that is pre-configured.

[0110] The refrigerator operating condition information refers to various information about the operating state of the refrigerator body 10 when the refrigerator body 10 is in a working state, representing the working condition of the refrigerator body 10. The refrigerator operating condition information can include parameters detected by each sensor in the sensor assembly 30 and working state parameters of the functional components 16. For example, the refrigerator operating condition information includes the temperature of the refrigeration compartment, the temperature of the freezer compartment, the rotation speed of the compressor 161, the rotation speed of the fan, the state of the defrosting heater 163, and the working state of the compressor. The refrigerator operating condition information can also include working mode information (including energy-saving mode, holiday mode, etc.), the opening and closing state of the door, functional state (such as network connection state, software update state), power consumption state, etc.

[0111] In some embodiments, the processing device 20 sends a query instruction to the controller 13 or the sensor assembly 30 every preset time, and the controller 13 or the sensor assembly 30 uploads the refrigerator operating condition information to the processing device 20 in response to the query instruction. In other embodiments, the sensor assembly 30 can actively upload the refrigerator operating condition information to the processing device 20. For example, the sensor assembly 30 actively sends the refrigerator operating condition information to the processing device 20 every preset time.

[0112] The first model (i.e., the master training model) refers to a model that can determine the value of the first energy-saving operating parameter of the first functional component based on the refrigerator operating condition information. The first energy-saving operating parameter output by the first model can be used to generate energy-saving operating condition information of the refrigerator, which is used as input of other models configured to determine energy-saving operating parameters of other functional components.

[0113] It should be noted that the first energy-saving operation parameter is an optimized operation parameter value output by the first model, and the first functional component can improve the energy-saving effect of the refrigerator when running at the optimized operation parameter value.

[0114] In one or more embodiments of the present application, the first functional component is the functional component with the highest energy consumption in the refrigerator body 10. In some embodiments, the first functional component is the compressor.

[0115] In one or more embodiments of the present application, the main energy-saving operation parameter is the operation parameter of the functional component 16 with the highest energy consumption in the refrigerator body 10, for example, the operation parameter of the compressor 161, including but not limited to one or more of the frequency (speed), current, voltage, electric power, or current waveform and characteristics of the compressor 161.

[0116] In one or more embodiments of the present application, the main training model can be a machine learning model or a deep learning model, such as a linear regression model, a decision tree, a random forest, and a neural network model, etc. The configuration process of the main training model includes steps such as data collection, data cleaning, modeling, training, evaluation, tuning, and deployment. The training process of the main training model can use existing technologies, which will not be described here. The input feature of the main training model is the operating condition information of the refrigerator, and the output feature is the main energy-saving operation parameter value.

[0117] In one or more embodiments of the present application, the main training model can be a reinforcement learning model. Reinforcement learning is performed by changing at least one of, for example, the compressor operating frequency (speed) and the compressor operating state as an action to obtain the main energy-saving operation parameter.

[0118] The process of constructing a reinforcement learning model is briefly introduced below. The parameters from the sensor component 30 in the refrigerator operating condition information are taken as the state space, such as the refrigeration compartment temperature, the freezing compartment temperature, the humidity, the door switch state, etc.; and the operation of the functional component 16 is taken as the action space, such as the compressor operating frequency, the fan speed, or the defrosting heater operating time, etc. A reward function of the main training model is designed to measure the effect of each action.

[0119] For example, the reward function R can be designed as: R = R B -a*P+b*R s +c*R U

[0120] where R B is a fixed basic reward under the condition of maintaining appropriate temperature and humidity, P is a penalty value generated according to the current power consumption, and the higher the power consumption, the higher the penalty value; R sR is the reward value given to each action, for example, adjusting the compressor operating frequency, fan speed, etc. U R is the reward value given according to energy efficiency, a, b, c are parameters that can be adjusted and optimized according to specific conditions to ensure that the reward function can encourage energy-saving behaviors and operations.

[0121] In one or more embodiments of the present application, the reward function R can also take other forms of empirical formula.

[0122] In some embodiments, a corresponding reinforcement learning algorithm is selected and an environment simulator is established to simulate the operating environment of the refrigerator body 10. The reinforcement learning algorithm can be Q-learning, deep Q network or policy gradient, etc. The environment simulator is configured to automatically predict the corresponding reward of the next state according to the current state and action. The environment simulator can be built using a time difference method, or a recurrent neural network, a convolutional neural network, etc. The model is trained through the environment simulator, so that the model learns how to select the optimal action to maximize the cumulative reward according to the current state. The model is evaluated and tested to verify its performance in the real environment. When the model performance reaches the preset standard, it is used as the main training model to determine the main energy-saving operating parameters.

[0123] In step S602, the processing device replaces the value of the actual operating parameter corresponding to the first energy-saving operating parameter in the refrigerator operating condition information with the value of the first energy-saving operating parameter determined by the first model to generate refrigerator energy-saving operating condition information.

[0124] For example, if the compressor operating frequency is used as the main energy-saving operating parameter, the actual compressor operating frequency corresponding to the main energy-saving operating parameter in the refrigerator operating condition information is replaced with the value of the compressor operating frequency determined by the first model to obtain the refrigerator energy-saving operating condition information. In this way, the energy-saving operating condition information of the refrigerator is the optimized energy-saving operating parameter determined by the first model, which can reflect the current energy-saving operating state.

[0125] In step S603, the processing device 20 determines the value of the second energy-saving operating parameter (also referred to as the auxiliary energy-saving operating parameter) of at least one second functional component corresponding to the refrigerator energy-saving operating condition information based on the refrigerator energy-saving operating condition information using a pre-configured second model (also referred to as an additional training model).

[0126] In one or more embodiments of the present application, the second functional component is different from the first functional component. The auxiliary energy-saving operation parameter is an operation parameter of another functional component 16 in the refrigerator body 10 that is different from the first functional component. For example, when the first functional component is a compressor and the main energy-saving operation parameter is an operation parameter of the compressor, the auxiliary energy-saving operation parameter is an operation parameter of the fan 162 or the defrosting heater 163, including but not limited to fan speed, defrosting heater operation time, defrosting heater power, and the like.

[0127] The second model (i.e., the additional training model) refers to a model that outputs an operation parameter of a second functional component, and the input value of the second model includes the operation condition information of the first energy-saving operation parameter generated by the first model. The second model outputs an optimized operation parameter value, and the second functional component operates at the optimized operation parameter value, which can improve the energy-saving effect of the refrigerator.

[0128] In one or more embodiments of the present application, the additional training model can be a machine learning or deep learning model, such as a linear regression model, a decision tree, a random forest, and a neural network model, etc. The configuration process of the additional training model includes steps such as data collection, data cleaning, modeling, training, evaluation, tuning, and deployment. The training process of the additional training model can use existing technologies, which will not be described here. The input feature of the additional training model is the energy-saving operation condition information of the refrigerator, and the output feature is the auxiliary energy-saving operation parameter.

[0129] In one or more embodiments of the present application, the additional training model can also be a reinforcement learning model.

[0130] In some embodiments of the present application, the processing device 20 uses at least two models to decompose and process the energy-saving task, the main training model is used to determine the main energy-saving operation parameter value, and the additional training model is used to determine the auxiliary energy-saving operation parameter value. Such decomposition can enable the main training model and the additional training model to focus on specific tasks, improve the learning and prediction ability of each model, obtain the features and relationships between the operation condition of the refrigerator body 10 and the functional component 16 with the highest energy consumption in the energy-saving dimension through the main training model, thereby obtaining the energy-saving operation condition information of the refrigerator and fixing the parameters of the functional component with the highest energy consumption. In the condition of relative energy saving, the features and relationships between the operation condition of the refrigerator and other functional components in the energy-saving dimension are further obtained, and the inference ability of the additional training model is improved. Using the main training model and the additional training model to decouple and modularize the energy-saving setting task can make the system more flexible and scalable, and can better cope with complex energy-saving optimization tasks and improve the energy-saving effect.

[0131] In step S604, the processing device 20 controls the first functional component to operate according to the first energy-saving operation parameter value and controls the second functional component to operate according to the second energy-saving operation parameter value.

[0132] In some embodiments, the first model and the second model are both deployed in the processing device of the refrigerator, i.e., the first model and the second model are both deployed in the refrigerator, and in this case, the processing device performs the steps shown in S601-S604.

[0133] In other embodiments, the first model and the second model are both deployed in the server 14. In this case, the processing device 20 (or the controller 13) in the refrigerator sends the operation condition information of the refrigerator to the server 14. For example, the server 14 can send an acquisition instruction to the refrigerator, and the refrigerator sends the operation condition information to the server in response to the acquisition instruction. Alternatively, the refrigerator can actively send the operation condition information to the server.

[0134] After the server 14 receives the operation condition information of the refrigerator, it performs the following operations: based on the operation condition information of the refrigerator, the first model is used to determine the value of the first energy-saving operation parameter of at least one first functional component corresponding to the operation condition information of the refrigerator; the value of the actual operation parameter corresponding to the first energy-saving operation parameter in the operation condition information of the refrigerator is replaced with the value of the first energy-saving operation parameter determined by the first model to generate the energy-saving operation condition information of the refrigerator; based on the energy-saving operation condition information of the refrigerator, the second model is used to determine the value of the second energy-saving operation parameter of at least one second functional component corresponding to the energy-saving operation condition information of the refrigerator; and the first energy-saving operation parameter and the second energy-saving operation parameter are sent to the processing device 20 (the controller 13) of the refrigerator. The processing device 20 (the controller 13) in the refrigerator controls the first functional component to operate according to the received first energy-saving operation parameter and controls the second functional component to operate according to the received second energy-saving operation parameter.

[0135] Deploying the first model and the second model in the server can reduce the requirements for storage space and computing power on the refrigerator side, and can reduce the cost of the refrigerator while achieving effective energy saving.

[0136] It can be understood that in other embodiments, when the household appliance is other than a refrigerator, the operation condition information is the operation condition information of the corresponding device. For example, when the household appliance is an air conditioner, the operation condition information is the operation condition information of the air conditioner, and the functional component is the functional component of the air conditioner.

[0137] The technical solutions of the present application will be further described below in combination with several specific application scenarios.

[0138] FIG. 7 is a flowchart of a refrigerator control method performed by the processing apparatus 20 in an embodiment. In one or more embodiments of the present application, the functional components 16 include a compressor 161 and a fan 162. Among them, the compressor 161 is a first functional component, and the fan 162 is a second functional component. The refrigerator control method performed by the processing apparatus 20 includes the following steps.

[0139] Step S701, the processing apparatus 20 determines at least one compressor operating parameter value corresponding to the refrigerator operating condition information (i.e., the first energy-saving operating parameter value) based on the refrigerator operating condition information and using a pre-configured first model.

[0140] Exemplarily, the refrigerator operating condition information includes the refrigeration compartment temperature, the freezer compartment temperature, the compressor speed, the fan speed, the defrosting heater state, and the compressor operating state. After inputting the refrigerator operating condition information into the first model, the first model outputs the first energy-saving operating parameter value as the compressor speed value.

[0141] It can be understood that the compressor speed in the refrigerator operating condition information is the actual compressor speed value in the refrigerator operation process, and the compressor speed value in the first energy-saving parameter value is the compressor speed value calculated by the first model.

[0142] Step S702, the processing apparatus 20 replaces the actual operating parameter value corresponding to the compressor operating parameter value in the refrigerator operating condition information with the first energy-saving operating parameter value determined by the first model to generate refrigerator energy-saving operating condition information.

[0143] Continuing the previous example, the actual compressor speed value in the refrigerator operating condition information is replaced with the compressor speed value (i.e., the first energy-saving operating parameter value) output by the first model to obtain the refrigerator energy-saving operating condition information.

[0144] Step S703, the processing apparatus 20 determines at least one fan operating parameter value (i.e., the second energy-saving operating parameter) corresponding to the refrigerator energy-saving operating condition information based on the refrigerator energy-saving operating condition information and using a pre-configured second model.

[0145] Continuing the previous example, after inputting the refrigerator energy-saving operating condition information into the second model, the second model outputs the second energy-saving operating parameter of the fan as the fan speed.

[0146] Step S704, the processing apparatus 20 drives the compressor 161 to operate based on the first energy-saving operating parameter value and drives the fan 162 to operate based on the second energy-saving operating parameter value.

[0147] Continuing the previous example, the processing apparatus 20 drives the compressor to operate based on the compressor speed value output by the first model and drives the fan to operate based on the fan speed value output by the second model.

[0148] FIG. 8 is a flowchart of a refrigerator control method performed by the processing device 20 in an embodiment. In one or more embodiments of the present application, the functional components 16 include a compressor 161 and a defrosting heater 163. Among them, the compressor 161 is a first functional component, and the defrosting heater is a second functional component.

[0149] S801, the processing device 20 determines at least one first energy-saving operation parameter of the compressor corresponding to the refrigerator operation condition information by using a pre-configured first model based on the refrigerator operation condition information.

[0150] Exemplarily, the refrigerator operation condition information includes a refrigeration compartment temperature, a freezing compartment temperature, a compressor speed, a fan speed, a defrosting heater state, and a compressor working state. After the refrigerator operation condition information is input into the first model, the first model outputs a first energy-saving operation parameter value as a compressor speed value.

[0151] It can be understood that the compressor speed in the refrigerator operation condition information is an actual compressor speed value in the operation process of the refrigerator, and the compressor speed value in the first energy-saving parameter value is a compressor speed value calculated by the first model.

[0152] S802, the processing device 20 replaces the actual operation parameter value corresponding to the first energy-saving operation parameter of the compressor in the refrigerator operation condition information with the first energy-saving operation parameter determined by the first model to generate refrigerator energy-saving operation condition information.

[0153] Continuing the previous example, the actual compressor speed value in the refrigerator operation condition information is replaced with the compressor speed value (i.e., the first energy-saving operation parameter value) output by the first model to obtain the refrigerator energy-saving operation condition information.

[0154] S803, the processing device 20 determines at least one second energy-saving operation parameter of the defrosting heater 163 corresponding to the refrigerator energy-saving operation condition information by using a pre-configured second model based on the refrigerator energy-saving operation condition information.

[0155] Exemplarily, after the refrigerator energy-saving operation condition information is input into the second model, the second energy-saving operation parameter of the defrosting heater 163 output by the second model is a ratio between the power-on time and the power-off time of the defrosting heater.

[0156] S804, the processing device 20 drives the compressor 161 to operate based on the first energy-saving operation parameter value and drives the defrosting heater 163 to operate based on the second energy-saving operation parameter value.

[0157] FIG. 9 is a flowchart of a refrigerator control method performed by the processing device 20 in an embodiment. In one or more embodiments of the present application, the functional components 16 include a compressor 161, a fan 162, and a defrosting heater 163.

[0158] S901, the processing device 20 determines, based on the refrigerator operating condition information, at least one first energy-saving operating parameter of the compressor corresponding to the refrigerator operating condition information by using a pre-configured first model.

[0159] S902, the processing device 20 replaces an actual operating parameter corresponding to the first energy-saving operating parameter of the compressor in the refrigerator operating condition information with the first energy-saving operating parameter determined by the first model to generate refrigerator energy-saving operating condition information.

[0160] S903, the processing device 20 determines, based on the refrigerator energy-saving operating condition information, at least one second energy-saving operating parameter (i.e., auxiliary energy-saving operating parameter) of the fan 162 corresponding to the refrigerator energy-saving operating condition information by using a pre-configured second model (also referred to as a first additional training model).

[0161] S904, the processing device 20 determines, based on the refrigerator energy-saving operating condition information, at least one second energy-saving operating parameter (i.e., auxiliary energy-saving operating parameter) of the defrosting heater 163 corresponding to the refrigerator energy-saving operating condition information by using a pre-configured third model (also referred to as a second additional training model).

[0162] S905, the compressor 161 is driven to operate based on the first energy-saving operating parameter, the fan 162 is driven to operate based on the second energy-saving operating parameter of the fan, and the defrosting heater 163 is driven to operate based on the second energy-saving operating parameter of the defrosting heater.

[0163] It can be understood that the order of steps S903 and S904 can be changed or performed simultaneously, and the present application does not limit the execution order.

[0164] In one or more embodiments of the present application, the first additional training model M2 and the second additional training model M3 can be two independent models.

[0165] Exemplarily, the main training model M1, the first additional training model M2 and the second additional training model M3 are pre-configured in the server 14, as shown in FIG. 10. Exemplarily, the refrigerator running condition information Xt is input into the main training model M1, the running speed Sc of the compressor 161 is determined through the main training model M1, and then the actual running speed of the compressor 161 in Xt is replaced by Sc to obtain the refrigerator energy-saving running condition information Xt', and then Xt' is input into the first additional training model M2 and the second additional training model M3 respectively to obtain the fan 162 running parameter and the defrosting heater running parameter. The fan 162 running parameter can be the fan speed Sf, and the defrosting heater running parameter can be the defrosting heating coefficient h.

[0166] The defrosting heating coefficient characterizes the working state time ratio of the defrosting heater 163 in a set period, that is, the ratio of the power-on time and the power-off time of the defrosting heater 163 in the set period. For example, the set period is T, and when defrosting, the defrosting heater 163 first works for h*T minutes, and then is powered off for (1-h)*T minutes, where h is less than 1. Such a cycle continues until the defrosting operation is exited.

[0167] In some embodiments, as shown in FIG. 11, the processing device 20 is further configured to perform the following steps.

[0168] S1101, the processing device 20 obtains the refrigerator running condition information to obtain the working state of the compressor 161.

[0169] S1102, it is judged whether the compressor working state is valid.

[0170] S1103, when the compressor working state is valid, based on the refrigerator running condition information, at least one compressor first energy-saving running parameter (also referred to as main energy-saving running parameter) corresponding to the refrigerator running condition information is determined by using the pre-configured first model.

[0171] S1104, the defrosting heater running parameter is corrected to a first preset value.

[0172] In some embodiments, correcting the defrosting heater running parameter to the first preset value includes setting the defrosting coefficient of the defrosting heater to 0.

[0173] S1105, the actual running parameter corresponding to the first energy-saving running parameter of the compressor in the refrigerator running condition information is replaced by the first energy-saving running parameter determined by the first model to generate refrigerator energy-saving running condition information.

[0174] S1106, based on the refrigerator energy-saving running condition information, at least one second energy-saving running parameter of the fan 162 corresponding to the refrigerator running condition information is determined by using the pre-configured second model (i.e., the first additional training model).

[0175] S1107, driving the compressor 161 to operate based on the first energy-saving parameter, driving the fan 162 to operate based on the second energy-saving operating parameter, and driving the defrosting heater 163 to operate based on the first preset value.

[0176] In one embodiment, as shown in FIG. 12, the processing device is further configured to perform the following steps.

[0177] S1201, when the working state of the compressor is invalid, respectively correcting the compressor operating parameter to a second preset value and the fan 162 operating parameter to a third preset value.

[0178] S1202, obtaining information of the running state of the refrigerator, and determining the working state of the defrosting heater 163.

[0179] S1203, determining whether the working state of the defrosting heater 163 is valid.

[0180] S1204, when the working state of the defrosting heater 163 is valid, based on the information of the energy-saving running state of the refrigerator, determining at least one second energy-saving operating parameter of the defrosting heater corresponding to the information of the energy-saving running state of the refrigerator according to a third model (i.e., a second additional training model) configured in advance, i.e., an auxiliary energy-saving operating parameter.

[0181] S1205, driving the compressor 161 to operate based on the second preset value, driving the fan 162 to operate based on the third preset value, and driving the defrosting heater 163 to operate based on the second energy-saving operating parameter of the defrosting heater.

[0182] As shown in FIG. 13, in one or more embodiments of the present application, the processing device is further configured to perform the following steps.

[0183] S1301, when the working state of the defrosting heater 163 is invalid, correcting the defrosting heater operating parameter to a first preset value.

[0184] S1302, driving the compressor 161 to operate based on the second preset value, driving the fan 162 to operate based on the third preset value, and driving the defrosting heater 163 to operate based on the first preset value.

[0185] In one or more embodiments of the present application, the defrosting heater operating parameter corresponding to the information of the energy-saving running state of the refrigerator includes a defrosting heating coefficient, which represents a working state time ratio of the defrosting heater 163 in a set period.

[0186] The defrosting heating coefficient represents the ratio of the working time of the defrosting heater 163 in a set period, i.e., the ratio of the on time and the off time of the defrosting heater 163 in the set period. For example, the set period is T, and when defrosting, the defrosting heater 163 works for h*T minutes, and then is off for (1-h)*T minutes. Such a cycle is repeated until the defrosting operation is exited.

[0187] In one or more embodiments of the present application, the first preset value can be that the defrosting heating coefficient h is 0.

[0188] In one or more embodiments of the present application, the second preset value can be that the rotation speed of the compressor 161 is 0.

[0189] In one or more embodiments of the present application, the third preset value can be that the rotation speed of the fan is 0.

[0190] Exemplarily, the main training model M1, the first additional training model M2 and the second additional training model M3 are pre-configured in the server 14.

[0191] As shown in FIG. 14, it is a flowchart of an exemplary refrigerator control method, which comprises the following steps.

[0192] S1401, first acquire the refrigerator running condition information Xt, such as one or more frames of communication data.

[0193] S1402, read the compressor working state in the refrigerator running condition information Xt, for example, read the data corresponding to the compressor 161 state bit therein, and determine whether the compressor state bit is 1. For example, when the compressor 161 state bit is 1, it represents that the compressor 161 is in the working state; when the compressor 161 state bit is 0, it represents that the compressor 161 is in the non-working state.

[0194] S1403, when the compressor 161 state bit is 1, it is confirmed that the compressor working state is valid, the refrigerator running condition information Xt is input into the main training model M1 to obtain the running rotation speed Sc of the compressor 161, and the defrosting heater running parameter is corrected to the first preset value, for example, the defrosting heating coefficient h is set to 0. Then, step S1404 is performed.

[0195] S1404, the actual running rotation speed of the compressor 161 in Xt is replaced by Sc to obtain the refrigerator energy-saving running condition information Xt'.

[0196] S1405, input Xt' into the first additional training model M2 to obtain the fan rotation speed Sf.

[0197] S1406, control the compressor 161 based on Sc, and control the fan based on Sf.

[0198] S1407, when the compressor 161 state bit is 0, it is confirmed that the compressor working state is invalid, the compressor 161 is in the shutdown state, then the compressor running parameter and the fan 162 running parameter are respectively corrected to the second preset value and the third preset value, for example, the rotating speed of the compressor 161 is set to 0, and the rotating speed of the fan is set to 0. Then step S1408 is executed.

[0199] S1408, obtain the refrigerator running condition information Xt, read the working state of the defrosting heater 163 in Xt, for example, read the data corresponding to the defrosting state bit, for example, when the defrosting state bit is 1, it represents that the defrosting heater 163 is in the working state; when the defrosting state bit is 0, it represents that the defrosting heater 163 is in the non-working state.

[0200] S1409, when the defrosting heater 163 is in the working state, that is, the defrosting state bit is 1, the information Xt of the energy-saving running condition of the refrigerator is input to the second additional training model M3 configured in advance, and the defrosting heater running parameter corresponding to the running condition information of the refrigerator body 10, for example, the defrosting heater 163 coefficient h, is determined as the auxiliary running parameter. Then step S1411 is executed.

[0201] S1410, when the defrosting state bit is 0, the defrosting heating coefficient h is set to 0. Then step S1411 is executed.

[0202] S1411, based on the defrosting heater 163 coefficient h, the control of the defrosting heater 163 is executed.

[0203] The processing device 20 in the refrigerator 1 provided by the embodiment determines which operation to execute by confirming the working state of the compressor, when the working state of the compressor is invalid, the compressor running parameter and the fan 162 running parameter are corrected to the second preset value and the third preset value, which ensures the control effect, saves the computing power and improves the response speed, and better optimizes the energy efficiency performance of the refrigerator 1. When the working state of the compressor is invalid, but the working state of the defrosting heater 163 is valid, the energy consumption of the defrosting heater 163 is further optimized, which can ensure that the system adjusts in real time and adapts to different working states in the dynamic environment, further optimizes the defrosting effect and the energy-saving effect, and improves the overall performance and efficiency of the system.

[0204] In some embodiments, the first functional component can be the compressor and the second functional component can be the fan. The first model can be configured to determine the first energy-saving operation parameter of the compressor based on the refrigerator operation condition information. The second model can be configured to determine the second energy-saving operation parameter of the fan based on the energy-saving operation condition information.

[0205] In the above solution, the input data of the second model involves the data output by the first model, so the first model needs to be trained first and then the second model needs to be trained in the training process of the first model and the second model. In order to further save the model training time, some embodiments of the present application also provide a refrigerator, which inputs the refrigerator operation condition information into a plurality of pre-configured models respectively, determines the energy-saving operation parameters of a plurality of functional components by using the plurality of models respectively, and then controls the operation of the corresponding functional components according to the energy-saving operation parameters corresponding to each functional component. In this way, the plurality of models can be trained at the same time, saving the model training time and improving the efficiency.

[0206] In some embodiments, the refrigerator comprises a plurality of functional components and a processing device 20, wherein the processing device 20 is configured to:

[0207] obtain the operation condition information of the refrigerator, and input the operation condition information of the refrigerator into at least two models respectively, wherein the at least two models are configured to determine the energy-saving operation parameters of at least two functional components based on the operation condition information of the refrigerator, and different models determine the energy-saving operation parameters of different functional components;

[0208] control the operation of the corresponding functional components according to the energy-saving operation parameters of the at least two functional components respectively.

[0209] The refrigerator operation condition information and the training method of the model can refer to the previous embodiments, which will not be repeated here.

[0210] In one example, the functional components include a compressor and a fan, and the at least two pre-configured models include a first model and a second model, wherein the first model is configured to determine the energy-saving operation parameter of the compressor based on the refrigerator operation condition information, such as the rotation speed of the compressor, and the second model is configured to determine the energy-saving operation parameter of the fan based on the refrigerator operation condition information, such as the rotation speed of the fan. The processing device is configured to:

[0211] obtain the operation condition information of the refrigerator;

[0212] input the refrigerator operating condition information into the first model and the second model, determine the energy-saving operation parameter of the compressor by the first model, and determine the energy-saving operation parameter of the fan by the second model; and

[0213] control the operation of the compressor according to the energy-saving operation parameter of the compressor, and control the operation of the fan according to the energy-saving operation parameter of the fan.

[0214] In another example, the functional components include a compressor, a fan and a defrosting heater, and the at least two pre-set models include a first model, a second model and a third model. The first model is configured to determine the energy-saving operation parameter of the compressor, such as the rotation speed of the compressor, according to the refrigerator operating condition information. The second model is configured to determine the energy-saving operation parameter of the fan, such as the rotation speed of the fan, according to the refrigerator operating condition information. The third model is configured to determine the energy-saving operation parameter of the defrosting heater, such as the defrosting coefficient of the defrosting heater, according to the refrigerator operating condition information. The processing device is configured to:

[0215] acquire the operating condition information of the refrigerator;

[0216] input the refrigerator operating condition information into the first model, the second model and the third model, determine the energy-saving operation parameter of the compressor by the first model, determine the energy-saving operation parameter of the fan by the second model, and determine the energy-saving operation parameter of the defrosting heater by the third model; and

[0217] control the operation of the compressor according to the energy-saving operation parameter of the compressor, control the operation of the fan according to the energy-saving operation parameter of the fan, and control the operation of the defrosting heater according to the energy-saving operation parameter of the defrosting heater.

[0218] In addition to the energy-saving design of the refrigerator, the power consumption of the refrigerator is displayed to the user, so that the user can learn the power consumption in time and adjust the use habit of the refrigerator in time, which is also an effective means to save energy. However, there is a large gap between the actual power consumption of household appliances and the power consumption under standard working conditions, so it is necessary to monitor the actual power consumption of household appliances and display it to the user, so that the user can learn the actual power consumption and adjust the use habit of the household appliances in time to reduce energy consumption.

[0219] In the related art, a power consumption monitoring model is deployed in a household appliance to calculate the power consumption instead of an electric energy metering module installed on the refrigerator. However, since the monitoring model has high requirements for storage space and computing power, most of the hardware resources of household appliances cannot meet the requirements.

[0220] In other implementations, the monitoring model can be deployed on a cloud server, and the running parameters of the household appliance are reported to the cloud server by using the networking function of the household appliance. The cloud server inputs the received running parameters into the monitoring model, and outputs the power consumption.

[0221] However, if the household appliance is disconnected from the network for a certain period of time, the actual power consumption of the household appliance will be greatly deviated due to the data loss caused by the inability of the household appliance to upload the running data during the network disconnection to the cloud server.

[0222] Therefore, the present application also provides a method for monitoring the power consumption of a household appliance. If the household appliance is disconnected from the network during the current monitoring period, a plurality of first historical power consumptions are input into the fourth model, so that the fourth model outputs the first power consumption of the current monitoring period. The problem of inaccurate calculation of the actual power consumption caused by the loss of running parameters during the network disconnection of the household appliance is solved.

[0223] The method for monitoring the power consumption of a household appliance provided by the embodiments of the present application can be applied to the application scenario shown in FIG. 2. Please refer to FIG. 2. The scenario includes a server 14, a household appliance 1 and a terminal device 15.

[0224] The server 14 can be a cloud server or a server cluster. In the following, the server 14 is taken as an example of a cloud server.

[0225] The cloud server 14, the household appliance 1 and the terminal device 15 can communicate with each other through the Internet. During the operation of the household appliance 1, the cloud server 14 can monitor the power consumption of the household appliance 1 in each monitoring period, and send the power consumption information to the household appliance 1 or the terminal device 15 of the user, so that the user can conveniently check the actual power consumption of the household appliance.

[0226] The fourth model is deployed in the cloud server 14, and a plurality of first historical power consumptions of the household appliance 1 are stored in the cloud server 14. If the household appliance 1 is disconnected from the network during the current monitoring period, the cloud server can determine the power consumption of the household appliance 1 in the current monitoring period according to the plurality of first historical power consumptions and the fourth model deployed on the cloud server.

[0227] For example, the household appliance 101 can be a refrigerator, an air conditioner, a water heater or the like, which is not limited in the present application.

[0228] The technical solutions of the power consumption monitoring method of the present application will be described in detail in combination with specific embodiments. The following specific embodiments can be combined with each other, or can exist independently. The same or similar concepts or processes can not be described in detail in some embodiments. The embodiments of the present application will be described in combination with the drawings.

[0229] FIG. 15 is a flow diagram of a method for monitoring power consumption of a household appliance according to an embodiment of the present application. The method can be performed by a cloud server. As shown in FIG. 15, the method comprises the following steps.

[0230] In S1501, it is determined whether the household appliance is disconnected from the network in a current monitoring period.

[0231] After the household appliance is powered on and configured with the networking function, the cloud server can determine whether the household appliance is disconnected from the network in a current monitoring period during the operation of the household appliance.

[0232] In a possible implementation, the cloud server can send first information to the household appliance, and then monitor whether the household appliance sends operation parameters to determine whether the household appliance is disconnected from the network. If the cloud server receives the operation parameters sent by the household appliance, it is determined that the household appliance is not disconnected from the network. If the cloud server does not receive the operation parameters sent by the household appliance, it is determined that the household appliance is disconnected from the network.

[0233] FIG. 16 is a schematic diagram of a monitoring period during the operation of a household appliance according to an embodiment of the present application. As shown in FIG. 16, the cloud server monitors the power consumption of the household appliance according to a monitoring period, which can be in the following two ways:

[0234] Way 1

[0235] The cloud server can monitor the power consumption of the household appliance according to a monitoring period. For example, the monitoring period can be 30 minutes, 1 hour or other time length, which is not limited in the present application. Taking the monitoring period of 1 hour as an example, the cloud server can count the power consumption of the household appliance in the last 1 hour every 1 hour.

[0236] Way 2

[0237] The cloud server can monitor the power consumption of the household appliance according to different monitoring periods in different time periods. Taking a refrigerator as an example, the user uses the refrigerator more frequently (for example, the number of times the refrigerator door is opened) from 7:00 to 9:00 in the morning and from 18:00 to 22:00 in the evening. For example, the monitoring period in these two time periods can be 30 minutes, and the monitoring period in other time periods can be 2 hours.

[0238] Specifically, the cloud server can determine a time period to which the current time belongs, and then monitor the power consumption of the household appliance according to a monitoring period corresponding to the time period. By monitoring the power consumption of the household appliance according to different monitoring periods in different time periods, the cloud server can then count the power consumption in different time periods and provide the user with the actual power consumption of the household appliance in different time periods, so that the user can more finely understand the power consumption of the household appliance, and thus can accurately adjust the use habit of the household appliance to reduce energy consumption.

[0239] S1502, if the household appliance is disconnected from the network in the current monitoring period, a plurality of first historical power consumptions are obtained.

[0240] If the household appliance is disconnected from the network in the current monitoring period, the cloud server can obtain a plurality of first historical power consumptions in the preset storage.

[0241] In a possible implementation, the plurality of first historical power consumptions can include a predicted power consumption, which is determined by the cloud server based on the power consumption of the monitoring period before the current monitoring period, using a fourth model.

[0242] In a possible implementation, for any one of the plurality of first historical power consumptions, the first historical power consumption can be determined according to the operating parameters of the household appliance in the monitoring period corresponding to the first historical power consumption. By predicting the first power consumption of the current monitoring period from the plurality of first historical power consumptions, the accuracy can be improved.

[0243] For example, the plurality of first historical power consumptions can be the power consumptions of a plurality of monitoring periods determined according to the operating parameters of the household appliance in the previous month.

[0244] S1503, inputting the plurality of first historical power consumptions into the fourth model to obtain the first power consumption of the current monitoring period.

[0245] The fourth model is obtained by training a plurality of second historical power consumptions.

[0246] In some embodiments, the cloud server can use the plurality of second historical power consumptions as training data, input the training data into a first preset model for training, and obtain the fourth model.

[0247] That is, the fourth model is obtained by training the plurality of second historical power consumptions in advance.

[0248] In a possible implementation, the plurality of second historical power consumptions does not include the plurality of first historical power consumptions, the plurality of first historical power consumptions is the power consumption in a latest time period before the current monitoring period, and the plurality of second historical power consumptions can be the power consumption of the household appliance in an earlier period.

[0249] In a possible implementation, the plurality of second historical power consumptions can be the power consumption of the household appliance in a test phase, in which a tester tests the household appliance according to the home environment and use habit of the user, and then collects the power consumption of the household appliance in a plurality of monitoring periods by using a hardware module, and determines the plurality of power consumptions as the plurality of second historical power consumptions. For example, the hardware power consumption module can be a smart socket or an electric meter with power consumption monitoring function.

[0250] It can be understood that a large number of first historical power consumptions can be included in the training data, which can improve the accuracy of the fourth model.

[0251] S1504, save the first power consumption.

[0252] After obtaining the first power consumption, the first power consumption can be stored in a preset memory, so as to facilitate subsequent statistics of the actual power consumption in a preset period.

[0253] S1505, determine the actual power consumption of the household appliance in the current preset period based on the first power consumption.

[0254] After obtaining the first power consumption, the cloud server can determine the actual power consumption of the household appliance in the current preset period based on the first power consumption. The cloud server can send the actual power consumption in the current preset period to the terminal device of the user or the household appliance, so that the user can know the power consumption of the household appliance in the current preset period.

[0255] The current preset period includes the current monitoring period. That is, the preset period can include one or more monitoring periods. The cloud server can determine the actual power consumption of the household appliance in the current preset period according to the monitoring periods included in the preset period.

[0256] It can be understood that if the household appliance is still in the offline state in the next monitoring period of the current monitoring period, the first power consumption can be used as the historical power consumption for inputting into the first model to predict the power consumption in the next monitoring period.

[0257] In this embodiment, the cloud server can determine whether the household appliance is offline in the current monitoring period. If yes, a plurality of first historical power consumptions are obtained, for any one of the historical power consumptions, the historical power consumption is determined according to the operating parameters of the household appliance in the monitoring period corresponding to the historical power consumption. Then the plurality of first historical power consumptions are input into the fourth model to obtain the first power consumption of the current monitoring period, and the fourth model is obtained by training a plurality of second historical power consumptions. The cloud server saves the first power consumption, and then determines the actual power consumption of the household appliance in the current preset period based on the first power consumption. Thus, the problem that the calculation of the actual power consumption has a large deviation due to the loss of operating parameters during the offline period of the household appliance is solved.

[0258] FIG. 17 is a flowchart of another method for monitoring the power consumption of a household appliance provided by an embodiment of the present application, which can be executed by a cloud server. As shown in FIG. 17, the method comprises the following steps.

[0259] S1701, determining whether the household appliance is offline in the current monitoring period.

[0260] S1702, if the household appliance is offline in the current monitoring period, obtaining a plurality of first historical power consumptions.

[0261] S1703, inputting the plurality of first historical power consumptions into the fourth model to obtain the first power consumption of the current monitoring period.

[0262] S1704, saving the first power consumption.

[0263] For specific descriptions of the above steps, reference can be made to the above embodiments, which will not be described here.

[0264] S1705, determining whether the preset period includes a plurality of monitoring periods.

[0265] If the preset period includes a plurality of monitoring periods, S406 is executed. It can be understood that the plurality of monitoring periods include the current monitoring period.

[0266] If the preset period includes one monitoring period, i.e., the current preset period is the current monitoring period, S408 is executed.

[0267] S1706, obtaining a plurality of second power consumptions.

[0268] S1707, determining the actual power consumption of the household appliance in the current preset period according to the plurality of second power consumptions and the first power consumption.

[0269] The plurality of second power consumptions are the power consumptions corresponding to the monitoring periods in the plurality of monitoring periods except the current monitoring period.

[0270] In a possible implementation, the cloud server can add the plurality of second power consumptions and the first power consumption to obtain an actual power consumption of the household appliance in the current preset period.

[0271] In a possible implementation, for any one of the plurality of second power consumptions, the cloud server can determine a preset time period to which a monitoring period corresponding to the second power consumption belongs. The second power consumptions of the monitoring periods belonging to the same preset time period are added to obtain power consumptions of the preset time periods. Then, the power consumptions corresponding to the preset time periods are added to obtain the actual power consumption.

[0272] The time length of each preset time period can be the same or different. For example, 7:00 to 9:00 in the morning as a time period, 18:00 to 22:00 in the evening as a time period, and other time as a time period. Or, 00:00 to 8:00 in the morning as a time period, 8:00 to 16:00 as a time period, and 16:00 to 00:00 as a time period. The application does not limit this.

[0273] In this way, after the cloud server obtains the actual power consumption, the power information can further include the power consumptions of the household appliance in the preset time periods when the power information is sent to the terminal device of the user or the household appliance.

[0274] S1708, determining the first power consumption as the actual power consumption of the household appliance in the current preset period.

[0275] In this embodiment, if the preset period includes one monitoring period, the power consumption of the current monitoring period can be determined as the actual power consumption in the preset period. If the preset period includes a plurality of monitoring periods, the actual power consumption in the preset period can be determined according to the power consumptions of the plurality of monitoring periods.

[0276] In a possible implementation, after the cloud server determines the actual power consumption, the cloud server can send power information to the terminal device of the user and / or the household appliance, and the power information includes the actual power consumption of the household appliance in the current preset period and a time period corresponding to the actual power consumption.

[0277] In a possible implementation, if the cloud server adds the second power consumptions of the monitoring periods belonging to the same preset time period to obtain the power consumptions of the preset time periods, the power information can include the actual power consumption of the household appliance in the current preset period, the time period corresponding to the actual power consumption, and the power consumptions of the household appliance in the preset time periods.

[0278] FIG. 18 is a flow diagram of another method for monitoring power consumption of a household appliance according to an embodiment of the present application. The method can be performed by a cloud server. As shown in FIG. 18, the method includes the following steps.

[0279] S1801. At the end of a current monitoring period, the cloud server sends first information to the household appliance.

[0280] The first information is used to obtain an operating parameter of the household appliance in the current monitoring period.

[0281] S1802. If the operating parameter is not received, it is determined that the household appliance is disconnected from the network in the current monitoring period.

[0282] S1803. If the operating parameter is received, it is determined that the household appliance is not disconnected from the network in the current monitoring period.

[0283] For example, after the cloud server sends the first information to the household appliance, if the operating parameter is not received within a preset time period, it can be determined that the household appliance is disconnected from the network in the current monitoring period. If the operating parameter is received, it can be determined that the household appliance is not disconnected from the network in the current monitoring period.

[0284] In a possible implementation, the preset time period can be set based on a maximum value of the time difference between the cloud server sending the first information and receiving the operating parameter sent by the household appliance, so as to avoid misjudgment that the household appliance is disconnected from the network due to the network transmission speed.

[0285] It can be understood that if the cloud server receives the operating parameter sent by the household appliance, the operating parameter can be stored in a preset storage for subsequent determination of power consumption.

[0286] In this embodiment, at the end of each monitoring period, the cloud server can send first information to the household appliance to request the operating parameter of the household appliance in the monitoring period. If the operating parameter sent by the household appliance can be received, it can be determined that the household appliance is not disconnected from the network. If the operating parameter cannot be received, it can be determined that the household appliance is disconnected from the network.

[0287] If the household appliance is not disconnected from the network in the current monitoring period, the power consumption of the household appliance in the current monitoring period can be monitored in the manner shown in FIG. 19.

[0288] FIG. 19 is a flow diagram of another method for monitoring power consumption of a household appliance according to an embodiment of the present application. The method can be performed by a cloud server. As shown in FIG. 19, the method includes the following steps.

[0289] S1901. It is determined whether the household appliance is disconnected from the network in a current monitoring period.

[0290] S1902, if the household appliance is not disconnected from the network in the current monitoring period, obtaining the running parameters of the household appliance in the current monitoring period.

[0291] S1903, inputting the plurality of running parameters into the fifth model to obtain the third power consumption in the current monitoring period.

[0292] The fifth model is trained according to a plurality of sets of historical running parameters and power consumption corresponding to each set of historical running parameters.

[0293] In some embodiments, the cloud server can use the plurality of sets of historical running parameters and the power consumption corresponding to each set of historical running parameters as training data, wherein each set of historical running parameters can be used as an input feature, and the corresponding power consumption can be used as a label.

[0294] The training data is input into the second preset model for training to obtain the fifth model.

[0295] That is, the fifth model is trained in advance according to the plurality of sets of historical running parameters and the power consumption corresponding to each set of historical running parameters.

[0296] In one possible implementation, the training data can be obtained by having a test personnel control the running of the household appliance according to the home environment and usage habits of the user during the test phase, and then obtaining the running parameters of the household appliance in a plurality of monitoring periods and the power consumption corresponding to each set of running parameters. For example, the power consumption of the household appliance in each monitoring period can be collected by a hardware module, for example, a smart socket or an electric meter with power monitoring function. Then, the running parameters in the plurality of monitoring periods and the power consumption corresponding to each set of running parameters are used as the training data of the second preset model.

[0297] Taking a refrigerator as an example, the running parameters can include the working power of each load, the running time in the current monitoring period, the environmental temperature, the set temperature of the refrigerator, etc.

[0298] S1904, saving the third power consumption.

[0299] S1905, determining the actual power consumption of the household appliance in the current preset period based on the third power consumption.

[0300] For the cloud server to determine the actual power consumption of the household appliance in the current preset period based on the third power consumption, reference can be made to the determination of the actual power consumption of the household appliance in the current preset period based on the first power consumption in the above embodiments, which will not be repeated here.

[0301] It can be understood that the first power consumption of the household appliance in the current monitoring period under the network interruption condition and the third power consumption under the non-network interruption condition are approximately the same, that is, the difference is small.

[0302] In the embodiment, if the household appliance is not interrupted, the power consumption of the current monitoring period can be calculated according to the running parameter and the fifth model, and the use of the hardware structure such as the smart socket or the electric meter with the power consumption statistics function to detect the power consumption of the household appliance can be avoided, and the household appliance cost is reduced.

[0303] FIG. 20 is a flowchart of another method for monitoring the power consumption of a household appliance provided by an embodiment of the application, which can be executed by a cloud server. As shown in FIG. 20, the method comprises the following steps.

[0304] S2001, obtaining first training data, the first training data comprising a plurality of sets of running parameters and power consumption corresponding to each set of running parameters.

[0305] Among the plurality of sets of running parameters, the running parameters of the current monitoring period are included.

[0306] S2002, updating the fifth model according to the first training data to obtain an updated fifth model.

[0307] For example, the cloud server can update the fifth model every preset time interval. Specifically, the cloud server can input the first training data into the fifth model to train the fifth model and obtain an updated fifth model.

[0308] The updated fifth model is used to predict the power consumption of the household appliance in the monitoring period during the non-network interruption period.

[0309] In the embodiment, the fifth model can be updated to improve the accuracy of the fifth model in determining the power consumption.

[0310] FIG. 21 is a flowchart of another method for monitoring the power consumption of a household appliance provided by an embodiment of the application, which can be executed by a cloud server. As shown in FIG. 21, the method comprises the following steps.

[0311] S2101, obtaining second training data, the second training data comprising a plurality of second historical power consumptions and a plurality of first historical power consumptions.

[0312] S2102, updating the fourth model according to the second training data to obtain an updated fourth model.

[0313] Exemplarily, the cloud server can update the fourth model every preset time interval. Specifically, the cloud server can input the second training data into the fourth model to train the fourth model, and obtain an updated fourth model.

[0314] The updated fourth model is used to predict the power consumption of the household appliance in the monitoring period during the network interruption.

[0315] In this embodiment, the fourth model can be updated to improve the accuracy of the fourth model in determining the power consumption.

[0316] FIG. 22 is a structural schematic diagram of a power consumption monitoring device of a household appliance provided by the present application. As shown in FIG. 22, the device 220 includes:

[0317] The first determination module 2201 is configured to determine whether the household appliance is interrupted from the network in the current monitoring period.

[0318] The acquisition module 2202 is configured to acquire a plurality of first historical power consumptions if the household appliance is interrupted from the network in the current monitoring period.

[0319] The prediction module 2203 is configured to input the plurality of first historical power consumptions into the fourth model to obtain a first power consumption of the current monitoring period, wherein the fourth model is trained according to a plurality of second historical power consumptions.

[0320] The storage module 2204 is configured to save the first power consumption.

[0321] The second determination module 2205 is configured to determine the actual power consumption of the household appliance in a preset period based on the first power consumption, wherein the current preset period includes the current monitoring period.

[0322] In a possible implementation, the second determination module 2205 is configured to:

[0323] If the current preset period is the current monitoring period, the first power consumption is determined as the actual power consumption of the household appliance in the current preset period.

[0324] If the current preset period includes a plurality of monitoring periods, a plurality of second power consumptions are acquired, and the actual power consumption of the household appliance in the current preset period is determined according to the plurality of second power consumptions and the first power consumption. The plurality of second power consumptions are power consumptions corresponding to the monitoring periods except the current monitoring period in the plurality of monitoring periods.

[0325] In a possible implementation, the first determination module 2201 is configured to:

[0326] At the end of the current monitoring period, the first information is sent to the household appliance, and the first information is used to obtain the operation parameter of the household appliance in the current monitoring period.

[0327] If the operation parameter is not received, it is determined that the household appliance is disconnected from the network in the current monitoring period.

[0328] If the operation parameter is received, it is determined that the household appliance is not disconnected from the network in the current monitoring period.

[0329] In a possible implementation, the apparatus 220 further includes an online prediction module configured to:

[0330] If it is determined that the household appliance is not disconnected from the network in the current monitoring period, the operation parameter of the household appliance in the current monitoring period is input into the fifth model to obtain a third power consumption of the current monitoring period, wherein the fifth model is trained according to a plurality of groups of historical operation parameters and power consumption corresponding to each group of historical operation parameters.

[0331] The third power consumption is saved.

[0332] Based on the third power consumption, the actual power consumption of the household appliance in the current preset period is determined.

[0333] In a possible implementation, the apparatus 220 further includes a first training module configured to:

[0334] Obtain first training data, the first training data including a plurality of groups of operation parameters and power consumption corresponding to each group of operation parameters, wherein the plurality of groups of operation parameters include the operation parameter of the current monitoring period;

[0335] According to the first training data, the fifth model is updated to obtain an updated fifth model, and the updated fifth model is configured to predict the power consumption of the monitoring period during which the household appliance is not disconnected from the network.

[0336] In a possible implementation, the apparatus 220 further includes a second training module configured to:

[0337] Obtain second training data, the second training data including a plurality of second historical power consumptions and a plurality of first historical power consumptions;

[0338] According to the second training data, the fourth model is updated to obtain an updated fourth model, and the updated fourth model is configured to predict the power consumption of the monitoring period during which the household appliance is disconnected from the network.

[0339] In a possible implementation, the apparatus 220 further includes a sending module configured to:

[0340] The power consumption information is sent to the terminal device and / or the household appliance of the user, and the power consumption information includes actual power consumption of the household appliance in a current preset period and a time period corresponding to the actual power consumption.

[0341] The power consumption monitoring device of the household appliance provided by the embodiments of the present application can execute the power consumption monitoring method of the household appliance in the method embodiments, and has similar implementation principles and technical effects, which will not be described here.

[0342] It should be noted that the division of each module shown in FIG. 22 is only a schematic, and the division of each module and the naming of each module are not limited by the present application. Each module in the above device can be implemented by software, hardware and a combination thereof in whole or in part. Each module can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to each module.

[0343] FIG. 23 is a structural schematic diagram of an electronic device according to an embodiment of the present application. As shown in the figure, the electronic device 2300 can include at least one processor 2301 and a memory 2302.

[0344] The memory 2302 is configured to store a program. The program can include program code including computer operation instructions.

[0345] The memory 2302 can include a random access memory (RAM) and can also include a non-volatile memory such as at least one disk memory.

[0346] The processor 2301 is configured to execute the computer operation instructions stored in the memory 2302 to implement the method described in the foregoing method embodiments. The processor 2301 can be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0347] In some embodiments, the electronic device 2300 can further include a communication interface 2303. In a specific implementation, if the communication interface 2303, the memory 2302 and the processor 2301 are implemented independently, the communication interface 2303, the memory 2302 and the processor 2301 can be connected to each other through a bus and complete communication between each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc., but it does not mean that there is only one bus or one type of bus.

[0348] In a specific implementation, if the communication interface 2303, the memory 2302 and the processor 2301 are integrated on a chip, the communication interface 2303, the memory 2302 and the processor 2301 can complete communication through an internal interface.

[0349] The electronic device 2300 can be a cloud server, etc.

[0350] The electronic device of the embodiment can be used to execute the technical solutions shown in the method embodiments, and the specific implementation manners and technical effects are similar, which will not be described here.

[0351] The application also provides a computer readable storage medium, which can include: a U disk, a mobile hard disk, a read-only memory (ROM), a RAM, a magnetic disk or an optical disk, and various media that can store program codes, and the like. Specifically, the computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by a computer to implement the technical solutions shown in the above method embodiments.

[0352] The application also provides a program product, which includes execution instructions stored in a computer readable storage medium. When the execution instructions are executed by a computer, the technical solutions shown in the above method embodiments are executed, and the specific implementation manners and technical effects are similar, which will not be described here.

[0353] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A refrigerator comprising: Multiple functional components; and A processing device configured to: Based on the refrigerator operating status information, using a pre-configured first model, determining a first energy-saving operating parameter value of at least one first functional component corresponding to the refrigerator operating status information; Replacing the actual operating parameter value corresponding to the first energy-saving operating parameter in the refrigerator operating status information with the first energy-saving operating parameter value determined by the first model to generate refrigerator energy-saving operating status information; Based on the energy-saving operation status information of the refrigerator, using a pre-configured second model, determining a second energy-saving operation parameter value of at least one second functional component corresponding to the energy-saving operation status information of the refrigerator; and The first functional component is driven to operate based on the first energy-saving operation parameter value, and the second functional component is driven to operate based on the second energy-saving operation parameter value.

2. The refrigerator according to claim 1, wherein: The first functional component includes a compressor; The processing device is configured to determine, based on the refrigerator operating status information and according to a preconfigured first model, a first energy-saving operating parameter value of at least one compressor corresponding to the refrigerator operating status information.

3. The refrigerator according to claim 2, wherein: The second functional component includes a fan; The processing device is further configured to: replacing an actual operating parameter value corresponding to the first energy-saving operating parameter value of the compressor in the refrigerator operating status information with the first energy-saving operating parameter value determined by the first model to generate refrigerator energy-saving operating status information; Based on the energy-saving operation status information of the refrigerator, using the pre-configured second model, determining a second energy-saving operation parameter value of the fan corresponding to the energy-saving operation status information of the refrigerator; The compressor is driven to operate based on the first energy-saving operation parameter value, and the fan is driven to operate based on the second energy-saving operation parameter value.

4. The refrigerator according to claim 2, wherein: The second functional component includes a defrost heater; The processing device is configured to replace the actual operating parameter value corresponding to the first energy-saving operating parameter value of the compressor in the refrigerator operating status information with the first energy-saving operating parameter determined by the first model to generate the refrigerator energy-saving operating status information; Based on the refrigerator energy-saving operation status information, using a pre-configured second model, determining a second energy-saving operation parameter value of a defrost heater corresponding to the refrigerator energy-saving operation status information; The compressor is driven to operate based on the first energy-saving operation parameter value, and the defrost heater is driven to operate based on the second energy-saving operation parameter value.

5. The refrigerator according to claim 2, wherein: The second functional component includes a fan and a defrost heater; The processing device is configured to: replacing an actual operating parameter value corresponding to the first energy-saving operating parameter of the compressor in the refrigerator operating status information with the first energy-saving operating parameter value determined by the first model to generate refrigerator energy-saving operating status information; Based on the refrigerator energy-saving operation status information, using a pre-configured second model, determining a second energy-saving operation parameter of the fan corresponding to the refrigerator energy-saving operation status information; based on the refrigerator energy-saving operation status information, using a pre-configured third model, determining a second energy-saving operation parameter of the defrost heater corresponding to the refrigerator energy-saving operation status information; The compressor is driven to operate based on the first energy-saving operation parameter, the fan is driven to operate based on the second energy-saving operation parameter of the fan, and the defrost heater is driven to operate based on the second energy-saving operation parameter of the defrost heater.

6. The refrigerator according to claim 4 or 5, wherein: The second energy-saving operation parameter of the defrost heater includes a defrost heating coefficient; the defrost heating coefficient represents a working state time ratio of the defrost heater within a set period.

7. A refrigerator comprising: compressor; Fan; Defrost heater; and A processing device configured to: Obtain refrigerator operating status information, obtain the working status of the compressor, and confirm whether the compressor working status is valid; When the compressor operating state is valid, determining a first energy-saving operating parameter of at least one compressor corresponding to the refrigerator operating status information using a pre-configured first model based on the refrigerator operating status information, and correcting a defrost heater operating parameter to a first preset value; replacing an actual operating parameter value corresponding to the first energy-saving operating parameter of the compressor in the refrigerator operating status information with the first energy-saving operating parameter determined by the first model to generate refrigerator energy-saving operating status information; Based on the energy-saving operation status information of the refrigerator, using a pre-configured second model, determining a second energy-saving operation parameter of at least one fan corresponding to the energy-saving operation status information of the refrigerator; and The compressor is driven to operate based on the first energy-saving parameter, the fan is driven to operate based on the second energy-saving operating parameter, and the defrost heater is driven to operate based on the first preset value.

8. The refrigerator according to claim 7, wherein: The processing device is further configured to: When the compressor working state is invalid, the compressor operating parameter and the fan operating parameter are respectively corrected to the second preset value and the third preset value; Obtain refrigerator operating status information, obtain the working status of the defrost heater, and confirm whether the working status of the defrost heater is valid; When the defrost heater is in a valid working state, determining, based on the refrigerator energy-saving operation status information, a second energy-saving operation parameter of at least one defrost heater corresponding to the refrigerator energy-saving operation status information using a pre-configured third model; and The compressor is driven to operate based on the second preset value, the fan is driven to operate based on the third preset value, and the defrost heater is driven to operate based on the second energy-saving operation parameter of the defrost heater.

9. The refrigerator according to claim 8, wherein The processing device is configured to: When the working state of the defrost heater is invalid, the operating parameter of the defrost heater is corrected to a first preset value; The compressor is driven to operate based on the second preset value, the fan is driven to operate based on the third preset value, and the defrost heater is driven to operate based on the first preset value.

10. The refrigerator according to claim 9, wherein: The second energy-saving operating parameter of the defrost heater includes a defrost heating coefficient; the defrost heating coefficient represents the working state time ratio of the defrost heater within a set cycle.

11. A method for monitoring the power consumption of household appliances, applied to a cloud server, the method comprising: Determining whether the household appliance is disconnected from the network during a current monitoring period; If the household appliance is disconnected from the network during the current monitoring period, obtaining a plurality of first historical power consumptions; Inputting the plurality of first historical power consumptions into a fourth model to obtain the first power consumption of the current monitoring period, wherein the fourth model is trained based on the plurality of second historical power consumptions; saving the first power consumption; Based on the first power consumption, actual power consumption of the household appliance in a current preset period is determined, wherein the current preset period includes the current monitoring period.

12. The method according to claim 11, wherein The determining, based on the first power consumption, actual power consumption of the household appliance in a current preset cycle includes: If the current preset period is the current monitoring period, determining the first power consumption as the actual power consumption of the household appliance in the current preset period; If the current preset cycle includes multiple monitoring cycles, multiple second power consumptions are obtained, and the actual power consumption of the household appliance within the current preset cycle is determined based on the multiple second power consumptions and the first power consumption; wherein the multiple second power consumptions are the power consumptions corresponding to the monitoring cycles in the multiple monitoring cycles except the current monitoring cycle.

13. The method according to claim 11 or 12, wherein: The determining whether the household appliance is disconnected from the network during the current monitoring period includes: At the end of the current monitoring period, sending first information to the household appliance, where the first information is used to obtain operating parameters of the household appliance during the current monitoring period; If the operating parameter is not received, determining that the household appliance is disconnected from the network during the current monitoring period; If the operating parameters are received, it is determined that the household appliance is not disconnected from the network during the current monitoring period.

14. The method according to claim 11, wherein The method further comprises: If it is determined that the household appliance is not disconnected from the network during the current monitoring period, inputting the operating parameters of the household appliance during the current monitoring period into a fifth model to obtain a third power consumption during the current monitoring period, wherein the fifth model is trained based on multiple sets of historical operating parameters and the power consumption corresponding to each set of historical operating parameters; saving the third power consumption; Based on the third power consumption, actual power consumption of the household appliance in a current preset cycle is determined.

15. The method according to claim 14, wherein The method further comprises: Acquire first training data, where the first training data includes multiple sets of operating parameters and power consumption corresponding to each set of operating parameters, wherein the multiple sets of operating parameters include the operating parameters of the current monitoring period; The fifth model is updated based on the first training data to obtain an updated fifth model, and the updated fifth model is used to predict the power consumption of the household appliance during a monitoring period when the household appliance is not disconnected from the network.

16. The method according to claim 11, wherein The method further comprises: Acquire second training data, where the second training data includes a plurality of second historical power consumptions and a plurality of first historical power consumptions; The fourth model is updated based on the second training data to obtain an updated fourth model, and the updated fourth model is used to predict the power consumption of the household appliance during a monitoring period when the household appliance is disconnected from the network.

17. The method according to any one of claims 11 to 14, wherein: The method further comprises: The power information is sent to the user's terminal device and / or the household appliance, where the power information includes the actual power consumption of the household appliance in the current preset cycle and the time period corresponding to the actual power consumption.

18. A device for monitoring power consumption of a household appliance, comprising: A first determining module, configured to determine whether the household appliance is disconnected from the network during a current monitoring period; An acquisition module, configured to acquire a plurality of first historical power consumptions if the household appliance is disconnected from the network during the current monitoring period; a prediction module, configured to input the plurality of first historical power consumptions into a fourth model to obtain the first power consumption of the current monitoring period, wherein the fourth model is trained based on the plurality of second historical power consumptions; a storage module, configured to store the first power consumption; The second determining module is configured to determine the actual power consumption of the household appliance within a current preset period based on the first power consumption, wherein the current preset period includes the current monitoring period.

19. An electronic device comprising: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 11 to 17.

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