CN114488829A - Method and device for controlling household appliance and server - Google Patents
Method and device for controlling household appliance and server Download PDFInfo
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- 230000007613 environmental effect Effects 0.000 claims description 6
- 238000005265 energy consumption Methods 0.000 claims description 4
- 238000010438 heat treatment Methods 0.000 claims description 4
- 238000013528 artificial neural network Methods 0.000 claims description 3
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- 238000005057 refrigeration Methods 0.000 claims description 2
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B15/00—Systems controlled by a computer
- G05B15/02—Systems controlled by a computer electric
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
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- G05B2219/2642—Domotique, domestic, home control, automation, smart house
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
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Abstract
The application relates to the technical field of intelligent household appliances, and discloses a method for controlling household appliances, which comprises the following steps: acquiring actual running state parameters of a plurality of household appliances in real time, and constructing a virtual digital mirror image; training a reinforcement learning model by taking the virtual digital mirror image as a reinforcement learning environment and a digital twin body constructed by household appliance mapping as an intelligent body to obtain a personalized reinforcement learning model and virtual running state parameters of the intelligent body; controlling the household appliance equipment corresponding to the intelligent agent with the changed virtual operation state parameters to adjust the actual operation state parameters, so that the actual operation state parameters are updated to the corresponding virtual operation state parameters; the virtual running state parameters of different agents have an incidence relation, and the actual running state parameters and the virtual running state parameters are parallel data. The method is beneficial to controlling the household appliance according to the latest use preference of the user to the household appliance, so as to adapt to the latest personalized requirements of the user to the household appliance.
Description
Technical Field
The present application relates to the field of intelligent home appliance technologies, and for example, to a method and an apparatus for controlling a home appliance, and a server.
Background
At present, with the progress of artificial intelligence technology, people have more and more strong intelligent demands on household appliances.
The existing intelligent energy-saving control system of an air-conditioning cooling system based on a neural network comprises a data acquisition module, a neural network model training module, an artificial intelligence analysis module and a cooling system energy efficiency control module; the data acquisition module is connected with the air-conditioning cooling system and the outdoor sensor, and is used for acquiring and processing the operating parameters of the air-conditioning cooling system; the neural network model training module is connected with the data acquisition module, acquires data of the data acquisition module and trains to establish a model; the artificial intelligence analysis module is connected with the neural network model training module and analyzes by combining the obtained model with the operation data acquired by the data acquisition module; the cooling system energy efficiency control module is connected with the artificial intelligence analysis module, and the cooling system energy efficiency control module obtains analysis data from the artificial intelligence analysis module, corrects the data of the data acquisition module and automatically controls the air conditioner cooling system.
In the process of implementing the embodiments of the present disclosure, it is found that at least the following problems exist in the related art:
the parameter adjustment and setting only by using the historical data have great blindness, the purpose of intelligent control is difficult to achieve, the household appliance cannot be controlled according to the latest use preference of the user on the household appliance, and the latest personalized requirements of the user on the household appliance are difficult to adapt.
Disclosure of Invention
The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview nor is intended to identify key/critical elements or to delineate the scope of such embodiments but rather as a prelude to the more detailed description that is presented later.
The embodiment of the disclosure provides a method, a device and a server for controlling household appliances, so as to adapt to the latest personalized requirements of users on the household appliances.
In some embodiments, the method comprises:
acquiring actual running state parameters of a plurality of household appliances in real time, and constructing a virtual digital mirror image;
training a reinforcement learning model by taking the virtual digital mirror image as a reinforcement learning environment and a digital twin body constructed by household appliance mapping as an intelligent body to obtain a personalized reinforcement learning model and virtual running state parameters of the intelligent body;
controlling the household appliance equipment corresponding to the intelligent agent with the changed virtual operation state parameters to adjust the actual operation state parameters, so that the actual operation state parameters are updated to the corresponding virtual operation state parameters;
the virtual running state parameters of different agents have an incidence relation, and the actual running state parameters and the virtual running state parameters are parallel data.
In some embodiments, the apparatus comprises:
the parallel sensing module is configured to acquire actual running state parameters of a plurality of household appliances in real time and construct a virtual digital mirror image;
the reinforcement learning module is configured to train a reinforcement learning model by taking the virtual digital mirror image as a reinforcement learning environment and taking a digital twin body constructed by household appliance mapping as an intelligent body, so as to obtain a personalized reinforcement learning model and virtual running state parameters of the intelligent body;
the parallel control module is configured to control the household appliance equipment corresponding to the intelligent agent with the changed virtual running state parameters to adjust the actual running state parameters, so that the actual running state parameters are updated to the corresponding virtual running state parameters;
the virtual running state parameters of different agents have an incidence relation, and the actual running state parameters and the virtual running state parameters are parallel data.
In some embodiments, the apparatus includes a processor and a memory storing program instructions, the processor being configured to execute the method for controlling an appliance described above when executing the program instructions.
In some embodiments, the server comprises:
the device for controlling the household appliance is described above.
The method, the device and the server for controlling the household appliance provided by the embodiment of the disclosure can achieve the following technical effects:
and acquiring the actual running state parameters of the household appliances in real time based on the parallel data to form a virtual digital mirror image, and adjusting the running state parameters of the household appliances with linkage relation to the personalized requirements of the household appliances by using a reinforcement learning algorithm in the virtual digital mirror image. Therefore, the control method is beneficial to controlling the household appliances according to the latest use preference of the user to the household appliances so as to adapt to the latest personalized requirements of the user to the household appliances.
The foregoing general description and the following description are exemplary and explanatory only and are not restrictive of the application.
Drawings
One or more embodiments are illustrated by way of example in the accompanying drawings, which correspond to the accompanying drawings and not in limitation thereof, in which elements having the same reference numeral designations are shown as like elements and not in limitation thereof, and wherein:
fig. 1 is a schematic diagram of a method for controlling an electric home appliance according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram of another method for controlling an electric home appliance according to an embodiment of the present disclosure;
fig. 3 is a schematic diagram of another method for controlling an electric home appliance according to an embodiment of the present disclosure;
FIG. 4 is a schematic diagram of an application of an embodiment of the present disclosure;
fig. 5 is a schematic diagram of an apparatus for controlling a home device according to an embodiment of the present disclosure;
fig. 6 is a schematic diagram of another apparatus for controlling an electric home appliance according to an embodiment of the present disclosure.
Detailed Description
So that the manner in which the features and advantages of the embodiments of the present disclosure can be understood in detail, a more particular description of the embodiments of the disclosure, briefly summarized above, may be had by reference to the appended drawings, which are included to illustrate, but are not intended to limit the embodiments of the disclosure. In the following description of the technology, for purposes of explanation, numerous details are set forth in order to provide a thorough understanding of the disclosed embodiments. However, one or more embodiments may be practiced without these details. In other instances, well-known structures and devices may be shown in simplified form in order to simplify the drawing.
The terms "first," "second," and the like in the description and in the claims, and the above-described drawings of embodiments of the present disclosure, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged as appropriate for the embodiments of the disclosure described herein. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion.
The term "plurality" means two or more unless otherwise specified.
In the embodiment of the present disclosure, the character "/" indicates that the preceding and following objects are in an or relationship. For example, A/B represents: a or B.
The term "and/or" is an associative relationship that describes objects, meaning that three relationships may exist. For example, a and/or B, represents: a or B, or A and B.
The term "correspond" may refer to an association or binding relationship, and a corresponds to B refers to an association or binding relationship between a and B.
In the embodiment of the disclosure, the home appliance device is a home appliance product formed by introducing a microprocessor, a sensor technology and a network communication technology into the home appliance device, and has the characteristics of intelligent control, intelligent sensing and intelligent application, the operation process of the home appliance device usually depends on the application and processing of modern technologies such as internet of things, internet and an electronic chip, for example, the home appliance device can realize remote control and management of a user on the home appliance device by connecting the home appliance device with the electronic device.
With reference to fig. 1, an embodiment of the present disclosure provides a method for controlling an electrical home appliance, including:
s101, the server collects actual running state parameters of a plurality of household appliances in real time and constructs a virtual digital mirror image.
And S102, the server trains the reinforcement learning model by taking the virtual digital mirror image as a reinforcement learning environment and taking a digital twin body constructed by household appliance mapping as an intelligent body, so as to obtain the personalized reinforcement learning model and the virtual running state parameters of the intelligent body.
S103, the server controls the household electrical appliance equipment corresponding to the intelligent agent with the changed virtual running state parameters to adjust the actual running state parameters, so that the actual running state parameters are updated to the corresponding virtual running state parameters.
The virtual running state parameters of different agents have an incidence relation, and the actual running state parameters and the virtual running state parameters are parallel data.
By adopting the method for controlling the household electrical appliance provided by the embodiment of the disclosure, the actual running state parameters of the household electrical appliance can be obtained in real time based on the parallel data to form the virtual digital mirror image, and the running state parameters of the household electrical appliance with the linkage relation are adjusted according to the individual requirements of the household electrical appliance by utilizing the reinforcement learning algorithm in the virtual digital mirror image. Therefore, the control method is beneficial to controlling the household appliances according to the latest use preference of the user to the household appliances so as to adapt to the latest personalized requirements of the user to the household appliances.
Optionally, the association relationship includes: and under the condition that the virtual operating state parameter of the first agent is changed, the virtual operating parameter of the second agent which has a cooperative relationship with the first agent is changed. Therefore, the actual running state parameters of the household appliance are obtained in real time based on the parallel data to form a virtual digital mirror image, the running state parameters of the intelligent agent with the linkage relation are adjusted according to the individual requirements of the household appliance by using a reinforcement learning algorithm in the virtual digital mirror image, and then the running state parameters of the household appliance with the linkage relation are adjusted. The method is favorable for controlling the household appliances better according to the latest use preference of the user on the household appliances, so as to better adapt to the latest personalized requirements of the user on the household appliances.
Optionally, the operating state parameters include: environmental parameters of the environment in which the home device is located. And/or device parameters of the household appliance during operation. And/or the operating state of the household appliance during operation. Therefore, a data base of the running state parameters is provided for training of the reinforcement learning model, and the household appliance can be controlled better according to the latest use preference of the user on the household appliance, so that the latest personalized requirements of the user on the household appliance can be better met.
Optionally, the environmental parameters include: the ambient temperature. And/or, ambient humidity. And/or, the concentration of environmental fine particulate matter (PM 2.5). Therefore, a data basis of the environmental parameters of the environment where the household appliance is located is provided for training of the reinforcement learning model, and the household appliance can be controlled better according to the latest use preference of the user on the household appliance, so that the latest personalized requirements of the user on the household appliance can be better met.
Optionally, the device parameters include: fan speed of the device. And/or the operating frequency of the device. And/or, the amount of refrigeration or heating of the device. And/or, a humidification parameter of the device. And/or, a dust removal parameter of the device. And/or audio parameters of the device. Therefore, a data basis of the device parameters of the household appliance in the running process is provided for the training of the reinforcement learning model, and the household appliance can be controlled better according to the latest use preference of the user on the household appliance, so that the latest personalized requirements of the user on the household appliance can be better met.
Optionally, the operating state comprises: an on state of the device, or an off state of the device. Therefore, a data basis of the running state of the household appliance in the running process is provided for the training of the reinforcement learning model, and the household appliance can be controlled better according to the latest use preference of the user on the household appliance, so that the latest personalized requirement of the user on the household appliance can be better met.
Optionally, the step of controlling, by the server, the home appliance device corresponding to the agent whose virtual operating state parameter changes to adjust the actual operating state parameter includes: and the server sends an instruction for adjusting the actual operation state parameters to the household electrical appliance corresponding to the intelligent agent with the changed virtual operation state parameters. Therefore, the control method is favorable for controlling the household appliances better according to the latest use preference of the user on the household appliances, so as to better adapt to the latest personalized requirements of the user on the household appliances.
With reference to fig. 2, another method for controlling an electrical home device is provided in an embodiment of the present disclosure, including:
s201, a server collects actual running state parameters of a plurality of household appliances in real time and constructs a virtual digital mirror image.
S202, the server trains the reinforcement learning model by taking the virtual digital mirror image as a reinforcement learning environment and taking a digital twin body constructed by household appliance mapping as an intelligent body, so as to obtain the personalized reinforcement learning model and the virtual running state parameters of the intelligent body.
S203, the server controls the household electrical appliance corresponding to the agent with the changed virtual running state parameter to adjust the actual running state parameter, so that the actual running state parameter is updated to the corresponding virtual running state parameter.
S204, the server executes the steps circularly.
By adopting the method for controlling the household electrical appliance provided by the embodiment of the disclosure, the actual running state parameters of the household electrical appliance can be obtained in real time based on the parallel data to form the virtual digital mirror image, and the running state parameters of the household electrical appliance with the linkage relation are continuously adjusted according to the personalized requirements of the household electrical appliance by utilizing the reinforcement learning algorithm in the virtual digital mirror image. Therefore, the control method is beneficial to controlling the household appliances according to the latest use preference of the user to the household appliances so as to adapt to the latest personalized requirements of the user to the household appliances.
With reference to fig. 3, another method for controlling an electrical home device is provided in an embodiment of the present disclosure, including:
s301, the server collects actual running state parameters of a plurality of household appliances in real time and constructs a virtual digital mirror image.
And S302, the server trains the reinforcement learning model by taking the virtual digital mirror image as a reinforcement learning environment and taking a digital twin body constructed by household appliance mapping as an intelligent body, so as to obtain the personalized reinforcement learning model and the virtual running state parameters of the intelligent body.
And S303, the server controls the household electrical appliance equipment corresponding to the intelligent agent with the changed virtual running state parameter to adjust the actual running state parameter, so that the actual running state parameter is updated to the corresponding virtual running state parameter.
And S304, the server carries out intelligent analysis or intelligent control on the virtual digital mirror image according to the neural network model.
S305, the server controls the household appliance to execute the corresponding strategy according to the analysis result.
By adopting the method for controlling the household electrical appliance provided by the embodiment of the disclosure, the actual running state parameters of the household electrical appliance can be obtained in real time based on the parallel data to form the virtual digital mirror image, and the running state parameters of the household electrical appliance with the linkage relation are adjusted according to the individual requirements of the household electrical appliance by utilizing the reinforcement learning algorithm in the virtual digital mirror image. Therefore, the control method is beneficial to controlling the household appliances according to the latest use preference of the user to the household appliances so as to adapt to the latest personalized requirements of the user to the household appliances. Meanwhile, intelligent analysis or intelligent control of the equipment is realized through the neural network model, and the household appliance can be better controlled to meet the individual requirements of users.
Optionally, the virtual digital image further includes voice information collected by the device. Thus, the method is beneficial to providing voice data for reinforcement learning model training and neural network model training. On one hand, the method is beneficial to controlling the household appliances according to the latest use preference of the user to the household appliances so as to adapt to the latest personalized requirements of the user to the household appliances. On the other hand, intelligent analysis or intelligent control of the equipment is realized through the neural network model, and the household appliance equipment can be better controlled to meet the individual requirements of users.
Optionally, the server performs intelligent analysis or intelligent control on the virtual digital image according to a neural network, including: and the server takes the actual running state parameters of the household appliances in the virtual digital mirror image as input, and performs energy consumption optimization on the household appliances through an energy-saving optimization model. The server takes the actual running state parameters of the household appliance in the virtual digital mirror image as input, and carries out personalized control on the household appliance through an intelligent control model. And the server takes the actual running state parameters of the household appliance in the virtual digital mirror image as input and carries out fault diagnosis on the household appliance through a fault diagnosis model. The server takes the voice information collected by the equipment in the virtual digital mirror image as input, and controls the household appliance to have conversation with the user through a man-machine conversation model. Therefore, the control method is beneficial to controlling the household appliances according to the latest use preference of the user to the household appliances so as to adapt to the latest personalized requirements of the user to the household appliances. Meanwhile, energy consumption optimization of the household appliances is realized through the energy-saving optimization model, personalized control of the household appliances is realized through the intelligent control model, fault diagnosis of the household appliances is realized through the fault diagnosis model, and conversation between the household appliances and a user is realized through the man-machine conversation model. Therefore, intelligent analysis or intelligent control of the equipment is realized, and the household appliance equipment is controlled better to meet the individual requirements of users.
Optionally, the fault diagnosis model is based on federal learning. Therefore, each household appliance used by a user is used as a node in the federal learning method, the privacy and the safety of data are guaranteed through the federal learning, and meanwhile, a globally optimal fault diagnosis model is obtained through training under the condition that the model precision is not lost, so that the fault diagnosis and prediction of the household appliances are realized. Therefore, intelligent analysis or intelligent control of the equipment can be better realized, and the household appliance can be better controlled to meet the individual requirements of users.
Optionally, the human-machine dialogue model is based on a knowledge graph. Specifically, the multi-turn dialogue model takes a knowledge graph as an underlying knowledge base. Therefore, the knowledge graph is fused into the conversation, so that the generated conversation contains the fact basis, and the conversation between the household appliance and the user can be better realized through a man-machine conversation model. Therefore, intelligent analysis or intelligent control of the equipment is better realized, and the household appliance equipment is further better controlled to meet the individual requirements of users.
Optionally, the design of the knowledge-graph-based multi-turn dialogue model comprises: the server uses a man-machine conversation model to extract semantic information sent by residents and converts the semantic information into a feature vector which can be understood by a machine. And matching the characteristic vector with the triple consisting of the entities and the relations in the knowledge graph by the server. The server searches for the answer in the knowledge graph, and the triple information contained in the answer is generated into a language understood by human through the feature vector, so that one conversation is realized. The server circularly executes the steps to realize multiple rounds of conversations. Therefore, the knowledge graph is fused into the conversation, so that the generated conversation contains the fact basis, and the conversation between the household appliance and the user can be better realized through a man-machine conversation model. Therefore, intelligent analysis or intelligent control of the equipment is better realized, and the household appliance is better controlled to meet the individual requirements of users.
In practical application, as shown in fig. 4, the server collects actual operating state parameters of a plurality of home appliances in real time, and constructs a virtual digital mirror image. The server trains the reinforcement learning model by taking the virtual digital mirror image as a reinforcement learning environment and taking a digital twin body constructed by household appliance mapping as an intelligent body, so as to obtain the personalized reinforcement learning model and the virtual running state parameters of the intelligent body. And the server controls the household appliance corresponding to the intelligent agent with the changed virtual operation state parameters to adjust the actual operation state parameters, so that the actual operation state parameters are updated to the corresponding virtual operation state parameters. The virtual running state parameters of different agents have an incidence relation, and the actual running state parameters and the virtual running state parameters are parallel data. And the server takes the actual running state parameters of the household appliances in the virtual digital mirror image as input, and performs energy consumption optimization on the household appliances through an energy-saving optimization model. The server takes the actual running state parameters of the household appliance in the virtual digital mirror image as input, and carries out personalized control on the household appliance through an intelligent control model. And the server takes the actual running state parameters of the household appliance in the virtual digital mirror image as input and carries out fault diagnosis through the household appliance with the fault diagnosis model. The server takes the voice information collected by the equipment in the virtual digital mirror image as input, and controls the household appliance to have conversation with the user through a man-machine conversation model.
And training a reinforcement learning model through the formed virtual digital mirror image under the condition that the server acquires that the ambient temperature of the air conditioner changes. In the reinforcement learning model, the refrigerating or heating capacity of the equipment of the first agent corresponding to the air conditioner, the operating frequency of the equipment and the humidifying parameters of the equipment are changed. Meanwhile, the dust removal parameters of the equipment of the second intelligent agent in the association relationship with the first intelligent agent are changed, and the second intelligent agent is the intelligent agent corresponding to the air purifier in the association relationship with the air conditioner. The server controls the refrigerating or heating quantity of the air conditioner adjusting equipment, the running frequency of the equipment and the humidifying parameter of the equipment to be the virtual running state parameter of the corresponding intelligent body, and controls the dust removing parameter of the humidifier adjusting equipment to be the virtual running state parameter of the corresponding intelligent body.
Therefore, the actual running state parameters of the household electrical appliance are obtained in real time based on the parallel data to form a virtual digital mirror image, and the running state parameters of the household electrical appliance with the linkage relation are adjusted according to the individual requirements of the household electrical appliance by using a reinforcement learning algorithm in the virtual digital mirror image. Therefore, the control method is beneficial to controlling the household electrical appliance according to the latest use preference of the user to the household electrical appliance so as to adapt to the latest personalized requirements of the user on the household electrical appliance.
As shown in fig. 5, an apparatus for controlling an electrical home device according to an embodiment of the present disclosure includes a parallel sensing module 501, a reinforcement learning module 502, and a parallel control module 503. The parallel sensing module 501 is configured to collect actual operating state parameters of a plurality of home appliances in real time, and construct a virtual digital image. The reinforcement learning module 502 is configured to train a reinforcement learning model by taking the virtual digital mirror image as a reinforcement learning environment and taking a digital twin body constructed by home appliance mapping as an agent, so as to obtain a personalized reinforcement learning model and a virtual operating state parameter of the agent. And the parallel control module 503 is configured to control the home appliance device corresponding to the agent with the changed virtual operating state parameter to adjust the actual operating state parameter, so that the actual operating state parameter is updated to the corresponding virtual operating state parameter. The virtual running state parameters of different agents have an incidence relation, and the actual running state parameters and the virtual running state parameters are parallel data.
The device for controlling the household appliances, provided by the embodiment of the disclosure, is beneficial to acquiring the actual running state parameters of the household appliances in real time based on parallel data to form a virtual digital mirror image, and adjusting the running state parameters of the household appliances with linkage relation to the personalized requirements of the household appliances by using a reinforcement learning algorithm in the virtual digital mirror image. Therefore, the control method is beneficial to controlling the household appliances according to the latest use preference of the user to the household appliances so as to adapt to the latest personalized requirements of the user to the household appliances.
Optionally, the apparatus for controlling a home device further includes a data intelligent analysis module. The data intelligent analysis module is configured to perform intelligent analysis or intelligent control on the virtual digital mirror image according to the neural network model; and controlling the household appliance to execute a corresponding strategy according to the analysis result. Therefore, the actual running state parameters of the household electrical appliance are obtained in real time based on the parallel data to form a virtual digital mirror image, and the running state parameters of the household electrical appliance with linkage relation are adjusted according to the individual requirements of the household electrical appliance by using a reinforcement learning algorithm in the virtual digital mirror image. Therefore, the control method is beneficial to controlling the household appliances according to the latest use preference of the user to the household appliances so as to adapt to the latest personalized requirements of the user to the household appliances. Meanwhile, intelligent analysis or intelligent control of the equipment is realized through the neural network model, and the household appliance can be better controlled to meet the individual requirements of users.
As shown in fig. 6, an apparatus for controlling an electrical home device according to an embodiment of the present disclosure includes a processor (processor)600 and a memory (memory) 601. Optionally, the apparatus may also include a Communication Interface 602 and a bus 603. The processor 600, the communication interface 602, and the memory 601 may communicate with each other via a bus 603. The communication interface 602 may be used for information transfer. The processor 600 may call the logic instructions in the memory 601 to perform the method for controlling the electric home appliance of the above-described embodiment.
In addition, the logic instructions in the memory 601 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products.
The memory 601 is a computer-readable storage medium, and can be used for storing software programs, computer-executable programs, such as program instructions/modules corresponding to the methods in the embodiments of the present disclosure. The processor 600 executes functional applications and data processing by executing program instructions/modules stored in the memory 601, that is, implements the method for controlling the home appliance in the above-described embodiments.
The memory 601 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal device, and the like. In addition, the memory 601 may include a high speed random access memory, and may also include a non-volatile memory.
The embodiment of the disclosure provides a server, which comprises the device for controlling the household appliance.
Embodiments of the present disclosure provide a computer-readable storage medium storing computer-executable instructions configured to perform the above-described method for controlling an electric home appliance.
Embodiments of the present disclosure provide a computer program product comprising a computer program stored on a computer-readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the above-mentioned method for controlling an electric home appliance.
The computer-readable storage medium described above may be a transitory computer-readable storage medium or a non-transitory computer-readable storage medium.
The technical solution of the embodiments of the present disclosure may be embodied in the form of a software product, where the computer software product is stored in a storage medium and includes one or more instructions to enable a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method of the embodiments of the present disclosure. And the aforementioned storage medium may be a non-transitory storage medium comprising: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes, and may also be a transient storage medium.
The above description and drawings sufficiently illustrate embodiments of the disclosure to enable those skilled in the art to practice them. Other embodiments may incorporate structural, logical, electrical, process, and other changes. The examples merely typify possible variations. Individual components and functions are optional unless explicitly required, and the sequence of operations may vary. Portions and features of some embodiments may be included in or substituted for those of others. Furthermore, the words used in the specification are words of description only and are not intended to limit the claims. As used in the description of the embodiments and the claims, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. Similarly, the term "and/or" as used in this application is meant to encompass any and all possible combinations of one or more of the associated listed. Furthermore, the terms "comprises" and/or "comprising," when used in this application, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Without further limitation, an element defined by the phrase "comprising an …" does not exclude the presence of other like elements in a process, method or apparatus that comprises the element. In this document, each embodiment may be described with emphasis on differences from other embodiments, and the same and similar parts between the respective embodiments may be referred to each other. For methods, products, etc. of the embodiment disclosures, reference may be made to the description of the method section for relevance if it corresponds to the method section of the embodiment disclosure.
Those of skill in the art would appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software may depend upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the disclosed embodiments. It can be clearly understood by the skilled person that, for convenience and simplicity of description, the specific working processes of the above-described systems, apparatuses, and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments disclosed herein, the disclosed methods, products (including but not limited to devices, apparatuses, etc.) may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units may be merely a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form. The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to implement the present embodiment. In addition, functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. In the description corresponding to the flowcharts and block diagrams in the figures, operations or steps corresponding to different blocks may also occur in different orders than disclosed in the description, and sometimes there is no specific order between the different operations or steps. For example, two sequential operations or steps may in fact be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved. Each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Claims (10)
1. A method for controlling an appliance, comprising:
acquiring actual running state parameters of a plurality of household appliances in real time, and constructing a virtual digital mirror image;
training a reinforcement learning model by taking the virtual digital mirror image as a reinforcement learning environment and a digital twin body constructed by household appliance mapping as an intelligent body to obtain a personalized reinforcement learning model and virtual running state parameters of the intelligent body;
controlling the household appliance equipment corresponding to the intelligent agent with the changed virtual operation state parameters to adjust the actual operation state parameters, so that the actual operation state parameters are updated to the corresponding virtual operation state parameters;
the virtual running state parameters of different agents have an incidence relation, and the actual running state parameters and the virtual running state parameters are parallel data.
2. The method of claim 1, wherein the associating comprises:
and under the condition that the virtual operating state parameter of the first agent is changed, the virtual operating parameter of the second agent which has a cooperative relationship with the first agent is changed.
3. The method of claim 1, wherein the operating state parameters comprise:
environmental parameters of the environment in which the household appliance is located; and/or the presence of a gas in the gas,
device parameters of the household appliance during operation; and/or the presence of a gas in the gas,
the running state of the household appliance in running.
4. The method of claim 3, wherein the environmental parameters comprise:
ambient temperature; and/or the presence of a gas in the gas,
ambient humidity; and/or the presence of a gas in the gas,
the ambient fine particulate matter PM2.5 concentration.
5. The method of claim 3, wherein the device parameters comprise:
the fan speed of the device; and/or the presence of a gas in the atmosphere,
the operating frequency of the device; and/or the presence of a gas in the gas,
refrigeration or heating capacity of the equipment; and/or the presence of a gas in the gas,
humidification parameters of the device; and/or the presence of a gas in the gas,
dedusting parameters of the equipment; and/or the presence of a gas in the atmosphere,
audio parameters of the device.
6. The method according to any one of claims 1 to 5, wherein after the constructing the virtual digital image, further comprising:
carrying out intelligent analysis or intelligent control on the virtual digital mirror image according to the neural network model;
and controlling the household appliance to execute a corresponding strategy according to the analysis result.
7. The method of claim 6, wherein the virtual digital image further comprises voice information collected by a device; the intelligent analysis or intelligent control of the virtual digital mirror image according to the neural network comprises the following steps:
taking the actual running state parameters of the household appliances in the virtual digital mirror image as input, and performing energy consumption optimization on the household appliances through an energy-saving optimization model;
the actual running state parameters of the household appliances in the virtual digital mirror image are used as input, and the household appliances are subjected to individualized control through an intelligent control model;
taking the actual running state parameters of the household electrical appliance in the virtual digital mirror image as input, and carrying out fault diagnosis on the household electrical appliance through a fault diagnosis model;
and voice information acquired by the equipment in the virtual digital mirror image is used as input, and the household appliance equipment is controlled to have conversation with the user through a man-machine conversation model.
8. An apparatus for controlling an appliance, comprising:
the parallel sensing module is configured to acquire actual running state parameters of a plurality of household appliances in real time and construct a virtual digital mirror image;
the reinforcement learning module is configured to train a reinforcement learning model by taking the virtual digital mirror image as a reinforcement learning environment and taking a digital twin body constructed by household appliance mapping as an intelligent body, so as to obtain a personalized reinforcement learning model and virtual running state parameters of the intelligent body;
the parallel control module is configured to control the household appliance equipment corresponding to the intelligent agent with the changed virtual running state parameters to adjust the actual running state parameters, so that the actual running state parameters are updated to the corresponding virtual running state parameters;
the virtual running state parameters of different agents have an incidence relation, and the actual running state parameters and the virtual running state parameters are parallel data.
9. An apparatus for controlling an appliance comprising a processor and a memory storing program instructions, wherein the processor is configured to perform the method for controlling an appliance of any of claims 1 to 7 when executing the program instructions.
10. A server, characterized in that it comprises a device for controlling an electric household appliance according to claim 8 or 9.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202210010578.XA CN114488829A (en) | 2022-01-05 | 2022-01-05 | Method and device for controlling household appliance and server |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202210010578.XA CN114488829A (en) | 2022-01-05 | 2022-01-05 | Method and device for controlling household appliance and server |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115524990A (en) * | 2022-06-13 | 2022-12-27 | 青岛海尔智能家电科技有限公司 | Intelligent household control method, device, system and medium based on digital twins |
| CN116088325A (en) * | 2022-12-05 | 2023-05-09 | 广州视声智能股份有限公司 | Digital twinning-based household equipment control method and device and storage medium |
| WO2024099582A1 (en) * | 2022-11-09 | 2024-05-16 | E.G.O. Elektro-Gerätebau GmbH | A water bearing appliance, control methods, and system |
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2022
- 2022-01-05 CN CN202210010578.XA patent/CN114488829A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115524990A (en) * | 2022-06-13 | 2022-12-27 | 青岛海尔智能家电科技有限公司 | Intelligent household control method, device, system and medium based on digital twins |
| WO2024099582A1 (en) * | 2022-11-09 | 2024-05-16 | E.G.O. Elektro-Gerätebau GmbH | A water bearing appliance, control methods, and system |
| CN116088325A (en) * | 2022-12-05 | 2023-05-09 | 广州视声智能股份有限公司 | Digital twinning-based household equipment control method and device and storage medium |
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