CN117092936A - Control method and device of household appliance, storage medium and electronic device - Google Patents
Control method and device of household appliance, storage medium and electronic device Download PDFInfo
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- G—PHYSICS
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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
- G05B19/00—Programme-control systems
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0063—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with circuits adapted for supplying loads from the battery
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/34—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering
- H02J7/35—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering with light sensitive cells
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Abstract
The application discloses a control method and device of household appliances, a storage medium and an electronic device. Wherein the method comprises the following steps: acquiring current environment information of a target household appliance and current electric quantity of a target energy storage battery, wherein the target energy storage battery is used for supplying power to the target household appliance; calling an intelligent model to obtain control information corresponding to the current environment information and the current electric quantity; and controlling the operation of the target household appliance according to the control information so that the target household appliance operates to a designated time under the power supply of the target energy storage battery, wherein the designated time is the energy storage starting time of the energy storage battery. The application solves the technical problem that the power consumption cannot be optimized according to the battery energy storage in the running process of the household appliance.
Description
Technical Field
The present application relates to the field of home appliances, and in particular, to a method and apparatus for controlling a home appliance, a storage medium, and an electronic apparatus.
Background
With the development of science and technology and the advocacy of low carbon and green, photovoltaic power generation is widely applied. However, in some remote areas (such as highland, middle east, etc.), photovoltaic energy is sufficient and power infrastructure is not developed, so that it can only meet daily power demand through photovoltaic power generation, but at night, because of no solar energy, only limited battery energy storage can be used to maintain basic operation of home appliances such as air conditioner. In general (sun-full) energy storage batteries are sufficient, but once special weather, such as rainy days, dust, haze etc. occurs, the battery is not used at night.
Aiming at the problem that the power consumption cannot be optimized according to the battery energy storage in the running process of the household appliance so as to achieve the purpose of maintaining the running to the appointed time, no effective solution is proposed at present.
Disclosure of Invention
The embodiment of the application provides a control method and device of household appliances, a storage medium and an electronic device, which at least solve the technical problem that the power consumption cannot be optimized according to battery energy storage in the running process of the household appliances.
According to an aspect of an embodiment of the present application, there is provided a control method of a home appliance, including: acquiring current environment information of a target household appliance and current electric quantity of a target energy storage battery, wherein the target energy storage battery is used for supplying power to the target household appliance; calling an intelligent model to obtain control information corresponding to the current environment information and the current electric quantity; and controlling the operation of the target household appliance according to the control information so that the target household appliance operates to a designated time under the power supply of the target energy storage battery, wherein the designated time is the energy storage starting time of the energy storage battery.
Optionally, before invoking the intelligent model to obtain the control information corresponding to the current environment information and the current electric quantity, the method further includes: pre-training the original model by using expert knowledge to obtain an intermediate model; and optimizing the intermediate model to obtain the intelligent model.
Optionally, pre-training the original model using expert knowledge to obtain an intermediate model, including: acquiring household appliance environment information, battery electric quantity of an energy storage battery and control actions, wherein the control actions are used for controlling household appliances to run to the energy storage starting time of the energy storage battery under the power supply of the energy storage battery; and training the original model by taking the household appliance environment information and the battery electric quantity of the energy storage battery as the input of the original model and taking the control brake as the expected output of the original model to obtain the intermediate model.
Optionally, optimizing the intermediate model to obtain the intelligent model includes: and updating the strategy network in the intermediate model by using a value network to obtain the intelligent model, wherein the value network term evaluates the control action output by the strategy network according to the environmental information, and updates the parameters in the strategy network according to the evaluation result.
Optionally, updating the policy network in the intermediate model by using a value network to obtain the intelligent model, including: acquiring input environment information of a strategy network in the intermediate model and output control actions of strategy network output in the intermediate model; inputting the input environment information of the strategy network in the intermediate model and the output control action of the strategy network output in the intermediate model into the value network to obtain value output, wherein the value output is used for evaluating the strategy network in the intermediate model; and updating the strategy network in the intermediate model according to the value output to obtain the intelligent model.
Optionally, the target home appliance includes an air conditioner, and acquiring current environmental information of the target home appliance includes: and acquiring the outdoor temperature, the position information, the building information, the house type information, the current time, the sunrise time and the air humidity of the air conditioner.
Optionally, after controlling the operation of the target home appliance according to the control information, so that the target home appliance operates for a specified time under the power supply of the target energy storage battery, the method further includes: and after sunrise, the target energy storage battery is charged by utilizing solar energy.
According to another aspect of the embodiment of the present application, there is also provided a control device for an electric home appliance, including: the system comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring current environment information of a target household appliance and current electric quantity of a target energy storage battery, and the target energy storage battery is used for supplying power to the target household appliance; the processing unit is used for calling the intelligent model to obtain control information corresponding to the current environment information and the current electric quantity; and the control unit is used for controlling the operation of the target household appliance according to the control information so as to enable the target household appliance to operate to a specified time under the power supply of the target energy storage battery, wherein the specified time is the energy storage starting time of the energy storage battery.
Optionally, the apparatus of the present application may further comprise: the pre-training unit is used for pre-training the original model by using expert knowledge before calling the intelligent model to obtain control information corresponding to the current environment information and the current electric quantity to obtain an intermediate model; and optimizing the intermediate model to obtain the intelligent model.
Optionally, the pre-training unit is further configured to: acquiring household appliance environment information, battery electric quantity of an energy storage battery and control actions, wherein the control actions are used for controlling household appliances to run to the energy storage starting time of the energy storage battery under the power supply of the energy storage battery; and training the original model by taking the household appliance environment information and the battery electric quantity of the energy storage battery as the input of the original model and taking the control brake as the expected output of the original model to obtain the intermediate model.
Optionally, the pre-training unit is further configured to: and updating the strategy network in the intermediate model by using a value network to obtain the intelligent model, wherein the value network term evaluates the control action output by the strategy network according to the environmental information, and updates the parameters in the strategy network according to the evaluation result.
Optionally, the pre-training unit is further configured to: acquiring input environment information of a strategy network in the intermediate model and output control actions of strategy network output in the intermediate model; inputting the input environment information of the strategy network in the intermediate model and the output control action of the strategy network output in the intermediate model into the value network to obtain value output, wherein the value output is used for evaluating the strategy network in the intermediate model; and updating the strategy network in the intermediate model according to the value output to obtain the intelligent model.
Optionally, the target household appliance includes an air conditioner, and the obtaining unit is further configured to: and acquiring the outdoor temperature, the position information, the building information, the house type information, the current time, the sunrise time and the air humidity of the air conditioner.
Optionally, the apparatus further includes: and the charging unit is used for controlling the operation of the target household appliance according to the control information so that the target household appliance can be operated for a specified time under the power supply of the target energy storage battery, and after sunrise, the target energy storage battery is charged by utilizing solar energy.
According to another aspect of the embodiments of the present application, there is also provided a storage medium including a stored program that executes the above-described method when running.
According to another aspect of the embodiments of the present application, there is also provided an electronic device including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor executing the method described above by the computer program.
In the embodiment of the application, the current environment information of the target household appliance and the current electric quantity of the target energy storage battery are obtained, and the target energy storage battery is used for supplying power to the target household appliance; calling an intelligent model to obtain control information corresponding to the current environment information and the current electric quantity; and controlling the operation of the target household appliance according to the control information so that the target household appliance operates to a designated time under the power supply of the target energy storage battery, wherein the designated time is the energy storage starting time of the energy storage battery. The application provides a control scheme of limited energy, which takes expert knowledge and reinforcement learning as the basis, takes current environmental information and the capacity of an energy storage battery as input conditions at night, takes continuous operation of an air conditioner as a target, and generates control instructions in real time, so as to reasonably change the set temperature when the energy is insufficient, control the energy consumption of the air conditioner, ensure basic requirements and solve the technical problem that the power consumption cannot be optimized according to the energy storage of the battery in the running process of the household appliance.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
fig. 1 is a flowchart of an alternative control method of a home appliance according to an embodiment of the present application;
FIG. 2 is a schematic diagram of an alternative model pre-training scheme according to an embodiment of the present application;
FIG. 3 is a schematic diagram of an alternative A2C network in accordance with an embodiment of the application;
fig. 4 is a schematic view of an alternative control device for an appliance according to an embodiment of the present application;
fig. 5 is a block diagram of a structure of a terminal according to an embodiment of the present application.
Detailed Description
In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In the related art, once special weather occurs, the battery energy storage is insufficient for night use, and the basic operation of the air conditioner can be maintained until the photovoltaic energy is used for generating electricity the next day after a certain comfort level is reduced. In practical application, various methods can be adopted to carry out energy-saving control. For example, a method based on data analysis, in which a suitable time node is analyzed and linear control is performed according to a threshold node of a battery, the method has a certain limitation and can only be applied to certain specific situations (i.e., situations requiring manual determination in advance).
With the development of science and technology and the development of AI, more complex calculation can be performed at the embedded end, which provides possibility for intelligent control, for example, the control parameters at the current moment can be obtained based on a pre-trained reinforcement learning model, the control parameter adjusting equipment is used for controlling the working state of indoor temperature until the temperature of a data center machine room is stable, and the method is mostly applied to the situation that energy sources are sufficient, and is considered to be systematic energy conservation for a long time, so that the method is not suitable for the specific situation of the application.
In order to solve the above problems, according to an aspect of the embodiments of the present application, an embodiment of a control method for a home appliance is provided, and a limited energy control method facing an extreme environment is provided, so that a decision can be automatically generated according to different environmental information and residual electric energy, and the operation of the device (such as a set temperature of an air conditioner) is reasonably controlled with the aim of long-time operation of the home appliance such as the air conditioner. Fig. 1 is a flowchart of an alternative method for controlling home appliances according to an embodiment of the present application, as shown in fig. 1, the method may include the steps of:
step S1, current environmental information of a target household appliance and current electric quantity of a target energy storage battery are obtained, and the target energy storage battery is used for supplying power to the target household appliance. The target household appliance can be a household air conditioner, a commercial air conditioner, a central air conditioner and the like, and the reference environmental information can report the outdoor temperature, the position information, the building information, the house type information, the current time, the sunrise time, the solar term information, the air humidity and the like of the air conditioner.
And S2, calling an intelligent model to obtain control information corresponding to the current environment information and the current electric quantity.
And step S3, controlling the operation of the target household appliance according to the control information, so that the target household appliance operates to a designated time under the power supply of the target energy storage battery, wherein the designated time is the energy storage starting time of the energy storage battery, and thus, after sunrise, the target energy storage battery can be charged by utilizing solar energy.
Optionally, before invoking the intelligent model to obtain the control information corresponding to the current environmental information and the current power, the model may be pre-trained as follows:
1) And pre-training the original model by using expert knowledge to obtain an intermediate model.
For example, acquiring household appliance environment information, battery power of an energy storage battery and a control action, wherein the control action is used for controlling household appliances to run to an energy storage starting time of the energy storage battery under the power supply of the energy storage battery; and training the original model by taking the environment information of the household appliance and the battery electric quantity of the energy storage battery as the input of the original model and taking the control action as the expected output of the original model to obtain an intermediate model.
2) And optimizing the intermediate model to obtain the intelligent model.
And updating the strategy network in the intermediate model by using the value network to obtain an intelligent model, evaluating the control action output by the strategy network by using the value network term according to the environmental information, and updating the parameters in the strategy network according to the evaluation result.
For example, obtaining input environmental information of a policy network in an intermediate model and output control actions of the policy network output in the intermediate model; inputting the input environment information of the strategy network in the intermediate model and the output control action of the strategy network output in the intermediate model into the value network to obtain value output, wherein the value output is used for evaluating the strategy network in the intermediate model; and updating the strategy network in the intermediate model according to the value output to obtain the intelligent model.
Through the steps, the current environmental information of the target household appliance and the current electric quantity of the target energy storage battery are obtained, and the target energy storage battery is used for supplying power to the target household appliance; calling an intelligent model to obtain control information corresponding to the current environment information and the current electric quantity; and controlling the operation of the target household appliance according to the control information so that the target household appliance operates to a designated time under the power supply of the target energy storage battery, wherein the designated time is the energy storage starting time of the energy storage battery. The application provides a control scheme of limited energy, which takes expert knowledge and reinforcement learning as the basis, takes current environmental information and the capacity of an energy storage battery as input conditions at night, takes continuous operation of an air conditioner as a target, and generates control instructions in real time, so as to reasonably change the set temperature when the energy is insufficient, control the energy consumption of the air conditioner, ensure basic requirements and solve the technical problem that the power consumption cannot be optimized according to the energy storage of the battery in the running process of the household appliance.
The scheme is a method for keeping the air conditioner running for a long time in various extreme environments, so that the method can automatically generate decisions according to different environmental information and residual electric energy, pre-train the model based on expert knowledge, accelerate the model construction time and reduce half of the time compared with the direct training model. As an alternative example, the technical solution of the present application is further described in detail below in conjunction with specific embodiments.
The key point of the scheme is that under the condition of insufficient energy, the decision is automatically generated according to the environmental information to keep the air conditioner running for a long time, the whole flow is divided into two steps, namely pre-training and model optimization, as shown in fig. 2 and 3, and the specific process is as follows:
1) Construction of expert knowledge
Expert knowledge is to treat environmental information and generate decisions from the perspective of an expert. For example, if the indoor temperature is 26 degrees, the outdoor temperature is 32 degrees, the battery remains 2 degrees, and if the air conditioner needs to be operated for 3 hours, the user sets the temperature to 28 degrees according to the experience of using the air conditioner for many years (at this time, the user is an expert), so that the long-time operation of the air conditioner is ensured. Accordingly, an expert dataset, i.e. expert knowledge, may be generated comprising the environmental information s: indoor and outdoor temperature, set temperature, photovoltaic condition, battery energy storage condition and weather condition, decision a: proper decision-temperature setting under different circumstances.
2) Model pre-training
The scheme of training the decision model according to expert knowledge is shown in fig. 2, the above-mentioned environmental information is used as the input of the strategy network, the output decision and the action generated by expert knowledge are compared, the error is calculated, gradient descent is performed, and the decision network is updated, so that the decision network can generate proper actions according to different environments.
It can be seen that the problem of how to ensure the long-time operation of the air conditioner under the condition of insufficient energy can be solved theoretically only by simple expert knowledge; however, the actual environment is complex and changeable (the environments of each user are different, such as location, weather, home building condition, etc.), which makes it impossible to artificially simulate each environment, and after all, the essence behind expert knowledge is the decision of artificial data output, which is statistics of limited environments, so that in order to adapt to different users and different environments, environmental information is required to be infinite, and limited artificial decisions are avoided.
3) Model optimization
The specific improvement process is shown in fig. 3, which can be divided into the following steps: constructing a rewarding environment, and constructing a value network v (s; w), wherein a strategy network pi (a|s; theta) is derived from the first step. The basic idea is that the value network takes the environment s and the rewards r as input, the network parameter w takes a specific numerical value (namely the value) as output, which is equivalent to a commentary person, and the evaluation of the current environment and the action of the commentary person judges the quality of the decision network, namely the quality of the parameter theta.
3.1 Building a rewards environment)
A reward mechanism (environment mechanism) is constructed for evaluating the quality of the action, and essentially determines the direction of the action when facing different environments. For example, when the energy is insufficient in summer, setting the heating reward (r) to +1 and setting the cooling reward to-1; when the energy is sufficient, on the premise of comfort level, setting the heating reward to be-1 and setting the cooling reward to be +1; and whether the air conditioner can last the sunrise on the next day is taken as the final rewards, if so, the air conditioner rewards +10, and cannot reward-10.
3.2 Construction of value networks
Training for value networks derives from the bellman formula:
the left hand side of the formula may be approximated as v (s t The method comprises the steps of carrying out a first treatment on the surface of the w) is the value network at time t versus V π (S t ) And (5) making an estimation. The result of the right expectation (E) is the action A with respect to the current time t And the environmental state S at the next moment t+1 Gamma is the discount factor and p is the probability distribution. Air conditioner implementation action a t Will generate a new environmental state S t+1 A corresponding r will also be generated for the bonus mechanism t . Then, to the desired Monte Carlo approximation, we get:
r t +γ·V π (S t+1 )，
further let V π (S t+1 ) Is approximately V (S) t+1 The method comprises the steps of carrying out a first treatment on the surface of the w) can be obtained:
y t ＝r t +γ·V π (S t+1 ；w)，
it may be referred to as a TD target, which is the value network versus V at time t+1 π (S t ) An estimate is made. V(s) t The method comprises the steps of carrying out a first treatment on the surface of the w) and y t Are all to action value V π (S t ) Due to y t Based in part on true observed rewards r t It can be considered that y t Ratio V(s) t The method comprises the steps of carrying out a first treatment on the surface of the w) is more reliable. So the update of the value network w (parameter) lets V(s) t The method comprises the steps of carrying out a first treatment on the surface of the w) approximating y t Then define a loss function:
the loss function gradient is defined as:
update w process, TD error delta t ＝v t -y t :
Thus, the updating of the value model is completed, and the accuracy of the judgment is ensured.
3.3 Optimized policy network)
Optimization of the policy network g (s, a; θ) with the output V of the value network π (s t ) And the current environmental state s are used as input to control the operation a by the specific air conditioner t For output, γ is the discount factor, and a similar Monte Carlo approximation is made as follows:
further to the state value function V π (s t ) Replacement by value network V(s) t ；w)：
Based on previous TD targets and TD errors:
g(s) t ,a t The method comprises the steps of carrying out a first treatment on the surface of the θ) is written as:
thereby completing the update of the policy network θ (β is a discount factor):
θ←θ+β·g(s t ,a t ；θ)，
the whole process is that the strategy network and the value network complement each other, the action generation of the strategy network takes the value network as a guide, and the update of the value network is derived from the influence degree of the action on the real environment. The method can reasonably control the set temperature of the air conditioner under various scarce electric energy conditions, thereby ensuring long-time operation.
The application provides a scheme for automatically controlling an air conditioner when energy is insufficient, so that the air conditioner can automatically control a set temperature according to the current residual electric quantity, and ensure long-time operation of the air conditioner; the scheme is based on the energy consumption data of the air conditioner, and a control strategy for automatically generating the optimal solution under different environments is constructed, so that the scheme can face a plurality of different extreme environments; training time is greatly shortened by training the model in advance through expert knowledge, and the requirement on data is reduced; an A2C (Advantage Actor-Critic) network is adopted to construct an evaluation model, and the effect of model generation decision is improved.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present application.
From the description of the above embodiments, it will be clear to a person skilled in the art that the method according to the above embodiments may be implemented by means of software plus the necessary general hardware platform, but of course also by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present application.
According to another aspect of the embodiment of the present application, there is also provided a control device for an electric appliance, for implementing the control method for an electric appliance. Fig. 4 is a schematic view of an alternative control device for home appliances according to an embodiment of the present application, as shown in fig. 4, the device may include:
an obtaining unit 41, configured to obtain current environmental information of a target home appliance and a current electric quantity of a target energy storage battery, where the target energy storage battery is used to supply power to the target home appliance;
the processing unit 42 is configured to invoke an intelligent model to obtain control information corresponding to the current environmental information and the current electric quantity;
and a control unit 43, configured to control the operation of the target home appliance according to the control information, so that the target home appliance operates under the power supply of the target energy storage battery to a specified time, where the specified time is an energy storage start time of the energy storage battery.
Optionally, the apparatus of the present application may further comprise: the pre-training unit is used for pre-training the original model by using expert knowledge before calling the intelligent model to obtain control information corresponding to the current environment information and the current electric quantity to obtain an intermediate model; and optimizing the intermediate model to obtain the intelligent model.
Optionally, the pre-training unit is further configured to: acquiring household appliance environment information, battery electric quantity of an energy storage battery and control actions, wherein the control actions are used for controlling household appliances to run to the energy storage starting time of the energy storage battery under the power supply of the energy storage battery; and training the original model by taking the household appliance environment information and the battery electric quantity of the energy storage battery as the input of the original model and taking the control brake as the expected output of the original model to obtain the intermediate model.
Optionally, the pre-training unit is further configured to: and updating the strategy network in the intermediate model by using a value network to obtain the intelligent model, wherein the value network term evaluates the control action output by the strategy network according to the environmental information, and updates the parameters in the strategy network according to the evaluation result.
Optionally, the pre-training unit is further configured to: acquiring input environment information of a strategy network in the intermediate model and output control actions of strategy network output in the intermediate model; inputting the input environment information of the strategy network in the intermediate model and the output control action of the strategy network output in the intermediate model into the value network to obtain value output, wherein the value output is used for evaluating the strategy network in the intermediate model; and updating the strategy network in the intermediate model according to the value output to obtain the intelligent model.
Optionally, the target household appliance includes an air conditioner, and the obtaining unit is further configured to: and acquiring the outdoor temperature, the position information, the building information, the house type information, the current time, the sunrise time and the air humidity of the air conditioner.
Optionally, the apparatus further includes: and the charging unit is used for controlling the operation of the target household appliance according to the control information so that the target household appliance can be operated for a specified time under the power supply of the target energy storage battery, and after sunrise, the target energy storage battery is charged by utilizing solar energy.
It should be noted that the above modules are the same as examples and application scenarios implemented by the corresponding steps, but are not limited to what is disclosed in the above embodiments. It should be noted that, the above modules may be implemented in a corresponding hardware environment as part of the apparatus, and may be implemented in software, or may be implemented in hardware, where the hardware environment includes a network environment.
According to another aspect of the embodiment of the present application, there is also provided a server or a terminal for implementing the control method of the above-mentioned home appliance.
Fig. 5 is a block diagram of a terminal according to an embodiment of the present application, and as shown in fig. 5, the terminal may include: one or more (only one is shown) processors 501, memory 503, and transmission means 505, as shown in fig. 5, the terminal may further comprise input output devices 507.
The memory 503 may be used to store software programs and modules, such as program instructions/modules corresponding to the control method and apparatus of the home appliance in the embodiment of the present application, and the processor 501 executes the software programs and modules stored in the memory 503, thereby executing various functional applications and data processing, that is, implementing the control method of the home appliance described above. Memory 503 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid state memory. In some examples, the memory 503 may further include memory located remotely from the processor 501, which may be connected to the terminal via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 505 is used for receiving or transmitting data via a network, and may also be used for data transmission between the processor and the memory. Specific examples of the network described above may include wired networks and wireless networks. In one example, the transmission device 505 includes a network adapter (Network Interface Controller, NIC) that may be connected to other network devices and routers via a network cable to communicate with the internet or a local area network. In one example, the transmission device 505 is a Radio Frequency (RF) module, which is used to communicate with the internet wirelessly.
Wherein in particular the memory 503 is used for storing application programs.
The processor 501 may call an application stored in the memory 503 via the transmission means 505 to perform the following steps:
acquiring current environment information of a target household appliance and current electric quantity of a target energy storage battery, wherein the target energy storage battery is used for supplying power to the target household appliance; calling an intelligent model to obtain control information corresponding to the current environment information and the current electric quantity; and controlling the operation of the target household appliance according to the control information so that the target household appliance operates to a specified time under the power supply of the target energy storage battery, wherein the specified time is the energy storage starting time of the energy storage battery.
Alternatively, specific examples in this embodiment may refer to examples described in the foregoing embodiments, and this embodiment is not described herein.
It will be appreciated by those skilled in the art that the structure shown in fig. 5 is only illustrative, and the terminal may be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a palmtop computer, a mobile internet device (Mobile Internet Devices, MID), a PAD, etc. Fig. 5 is not limited to the structure of the electronic device. For example, the terminal may also include more or fewer components (e.g., network interfaces, display devices, etc.) than shown in fig. 5, or have a different configuration than shown in fig. 5.
Those of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be implemented by a program for instructing a terminal device to execute in association with hardware, the program may be stored in a computer readable storage medium, and the storage medium may include: flash disk, read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), magnetic or optical disk, and the like.
The embodiment of the application also provides a storage medium. Alternatively, in the present embodiment, the above-described storage medium may be used for executing the program code of the control method of the home appliance.
Alternatively, in this embodiment, the storage medium may be located on at least one network device of the plurality of network devices in the network shown in the above embodiment.
Alternatively, in the present embodiment, the storage medium is configured to store program code for performing the steps of:
acquiring current environment information of a target household appliance and current electric quantity of a target energy storage battery, wherein the target energy storage battery is used for supplying power to the target household appliance; calling an intelligent model to obtain control information corresponding to the current environment information and the current electric quantity; and controlling the operation of the target household appliance according to the control information so that the target household appliance operates to a specified time under the power supply of the target energy storage battery, wherein the specified time is the energy storage starting time of the energy storage battery.
Alternatively, specific examples in this embodiment may refer to examples described in the foregoing embodiments, and this embodiment is not described herein.
Alternatively, in the present embodiment, the storage medium may include, but is not limited to: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing embodiment numbers of the present application are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
The integrated units in the above embodiments may be stored in the above-described computer-readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing one or more computer devices (which may be personal computers, servers or network devices, etc.) to perform all or part of the steps of the method described in the embodiments of the present application.
In the foregoing embodiments of the present application, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In several embodiments provided by the present application, it should be understood that the disclosed client may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, such as the division of the units, is merely a logical function division, and may be implemented in another manner, for example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate units may or may not be physically separate, and units shown 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 may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The foregoing is merely a preferred embodiment of the present application and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present application, which are intended to be comprehended within the scope of the present application.
Claims (10)
1. A control method of an electric home appliance, comprising:
acquiring current environment information of a target household appliance and current electric quantity of a target energy storage battery, wherein the target energy storage battery is used for supplying power to the target household appliance;
calling an intelligent model to obtain control information corresponding to the current environment information and the current electric quantity;
and controlling the operation of the target household appliance according to the control information so that the target household appliance operates to a specified time under the power supply of the target energy storage battery, wherein the specified time is the energy storage starting time of the energy storage battery.
2. The method of claim 1, wherein prior to invoking the smart model to obtain control information corresponding to the current environmental information and the current power, the method further comprises:
pre-training the original model by using expert knowledge to obtain an intermediate model;
and optimizing the intermediate model to obtain the intelligent model.
3. The method of claim 2, wherein pre-training the original model using expert knowledge to obtain an intermediate model comprises:
acquiring household appliance environment information, battery electric quantity of an energy storage battery and control actions, wherein the control actions are used for controlling household appliances to run to the energy storage starting time of the energy storage battery under the power supply of the energy storage battery;
and training the original model by taking the household appliance environment information and the battery electric quantity of the energy storage battery as the input of the original model and taking the control brake as the expected output of the original model to obtain the intermediate model.
4. The method of claim 2, wherein optimizing the intermediate model to obtain the intelligent model comprises:
and updating the strategy network in the intermediate model by using a value network to obtain the intelligent model, wherein the value network term evaluates the control action output by the strategy network according to the environmental information, and updates the parameters in the strategy network according to the evaluation result.
5. The method of claim 4, wherein updating the policy network in the intermediate model with a value network to obtain the intelligent model comprises:
acquiring input environment information of a strategy network in the intermediate model and output control actions of strategy network output in the intermediate model;
inputting the input environment information of the strategy network in the intermediate model and the output control action of the strategy network output in the intermediate model into the value network to obtain value output, wherein the value output is used for evaluating the strategy network in the intermediate model;
and updating the strategy network in the intermediate model according to the value output to obtain the intelligent model.
6. The method according to any one of claims 1 to 5, wherein the target appliance includes an air conditioner, and acquiring current environmental information of the target appliance includes:
and acquiring the outdoor temperature, the position information, the building information, the house type information, the current time, the sunrise time and the air humidity of the air conditioner.
7. The method according to any one of claims 1 to 5, wherein after controlling the operation of the target home appliance in accordance with the control information so that the target home appliance operates under the power of the target energy storage battery for a specified time, the method further comprises:
and after sunrise, the target energy storage battery is charged by utilizing solar energy.
8. A control device for an electric home appliance, comprising:
the system comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring current environment information of a target household appliance and current electric quantity of a target energy storage battery, and the target energy storage battery is used for supplying power to the target household appliance;
the processing unit is used for calling the intelligent model to obtain control information corresponding to the current environment information and the current electric quantity;
and the control unit is used for controlling the operation of the target household appliance according to the control information so as to enable the target household appliance to operate to a specified time under the power supply of the target energy storage battery, wherein the specified time is the energy storage starting time of the energy storage battery.
9. A storage medium comprising a stored program, wherein the program when run performs the method of any one of the preceding claims 1 to 7.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor performs the method of any of the preceding claims 1 to 7 by means of the computer program.
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|---|---|---|---|
| CN202311074759.XA CN117092936A (en) | 2023-08-24 | 2023-08-24 | Control method and device of household appliance, storage medium and electronic device |
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| CN202311074759.XA CN117092936A (en) | 2023-08-24 | 2023-08-24 | Control method and device of household appliance, storage medium and electronic device |
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