CN114413420A - Control method of air conditioner and air conditioner - Google Patents
Control method of air conditioner and air conditioner Download PDFInfo
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- 238000013528 artificial neural network Methods 0.000 claims description 26
- 230000033228 biological regulation Effects 0.000 claims description 9
- 230000007613 environmental effect Effects 0.000 claims description 8
- 230000008859 change Effects 0.000 abstract description 10
- 238000004134 energy conservation Methods 0.000 abstract description 3
- 230000008569 process Effects 0.000 description 7
- 238000010586 diagram Methods 0.000 description 5
- 238000004590 computer program Methods 0.000 description 4
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- 238000004364 calculation method Methods 0.000 description 3
- 230000017525 heat dissipation Effects 0.000 description 3
- 238000004378 air conditioning Methods 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
- F24F11/46—Improving electric energy efficiency or saving
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/61—Control or safety arrangements characterised by user interfaces or communication using timers
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/64—Electronic processing using pre-stored data
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
- F24F11/72—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
- F24F11/80—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/88—Electrical aspects, e.g. circuits
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/10—Temperature
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Abstract
The application relates to a control method of an air conditioner, which comprises the following steps: acquiring a user set temperature, a user set time and an indoor environment parameter; the user-set time is the time required for the indoor temperature to reach the user-set temperature; determining a target temperature control function according to the user set temperature, the user set duration, the indoor environment parameters and the temperature prediction model; the target temperature control function represents the future temperature control data trend within the user-set time length; determining the operation parameters of the air conditioner according to the target temperature control function; and controlling the air conditioner to regulate and control the temperature according to the operation parameters. When the air conditioner controls the temperature, the air conditioner adjusts the indoor temperature according to the operation parameters corresponding to the target temperature control function, so that the change of the indoor temperature is consistent with the prediction of the target temperature control function. The air conditioner can minimize the power consumption of the air conditioner while meeting the requirements of users, and energy conservation is achieved.
Description
Technical Field
The application relates to the technical field of air conditioner control, in particular to a control method of an air conditioner and the air conditioner.
Background
In different application scenarios, the sizes and sizes of the rooms are different, and the heat dissipation amounts of different rooms are different accordingly. That is, the environmental parameters are not consistent, and in different application scenarios, the time taken by the air conditioner to complete the target and the power consumption are not consistent under the condition that the user sets the target temperature of the air conditioner and the time required for reaching the target temperature.
Meanwhile, the existing air conditioner control strategy cannot realize temperature control according to the time desired by the user.
Therefore, in order to adjust the operation parameters according to the different application scene parameters and the time desired by the user during the temperature control process of the air conditioner, and reduce the power consumption, it is necessary to design a control method of the air conditioner.
Disclosure of Invention
In order to overcome the problems in the related art, the present application provides a control method of an air conditioner, which is characterized by comprising the following steps:
acquiring a user set temperature, a user set time and an indoor environment parameter; the user-set time is the time required for the indoor temperature to reach the user-set temperature; the indoor environmental parameters include: room size, room temperature, door and window area, and room humidity;
determining a target temperature control function according to the user set temperature, the user set duration, the indoor environment parameters and the temperature prediction model; the target temperature control function represents the future temperature control data trend within the user-set time length;
determining the operation parameters of the air conditioner according to the target temperature control function;
and controlling the air conditioner to regulate and control the temperature according to the operation parameters.
In one embodiment, the determining the target temperature control function according to the user-set temperature, the user-set time, the indoor environment parameter and the temperature prediction model includes:
the user set duration is divided into N detection time periods; n is an integer greater than 1;
determining the temperature variation and the power consumption of the Nth detection time period according to the user set temperature, the user set time period, the indoor environment parameters and the temperature prediction model within the user set time period;
and fitting the temperature variation of the N detection time periods with the minimum total power consumption to obtain the target temperature control function.
In one embodiment, the temperature prediction model is generated by training a room load neural network and a room load sample training set;
the temperature prediction model comprises an input layer, a hidden layer and an output layer;
the room load sample training set comprises indoor environment parameters input as fixed values and air conditioner setting parameters input as adjustable variables; the air conditioner setting parameters comprise user set temperature and user set duration;
the output layer of the temperature prediction model comprises temperature variation and power consumption;
and performing network training on the temperature prediction model according to the room load sample training set.
In one embodiment, the determining the operation parameter of the air conditioner according to the target temperature control function includes:
determining the operation parameters of the air conditioner according to the target temperature control function and the operation strategy model; and the operation strategy model is generated by the operation strategy neural network and the target temperature control function training.
In one embodiment, the operation strategy model is generated by the operation strategy neural network and the target temperature control function training, and includes:
the operation strategy model comprises an input layer, a hidden layer and an output layer;
an input layer of the operation strategy model comprises the temperature variation and the power consumption corresponding to the target temperature control function in the Nth detection time period;
the output layer of the operation strategy model is the operation parameters of the air conditioner; the operation parameters of the air conditioner include: compressor frequency, outer fan speed, inner fan speed and throttle device valve opening. In one embodiment, after controlling the air conditioner to perform temperature regulation according to the operation parameter, the method includes:
acquiring an indoor temperature difference at the end time of the current time period;
judging whether the current time period is the last detection time period of the user-set time period or not, and if so, determining the sum of the indoor temperature differences within the user-set time period; if not, executing the following steps: and acquiring the operating parameters in the next detection time period.
In one embodiment, said determining the sum of said indoor temperature differences over said user-set time period comprises:
acquiring an indoor actual temperature;
judging whether the sum of the indoor temperature differences and the temperature difference set by a user is greater than a preset threshold value or not; if yes, re-determining the temperature prediction model; if not, the current working state of the air conditioner is maintained.
In one embodiment, said re-determining said temperature prediction model comprises:
acquiring the actual temperature variation and the actual power consumption within the time set by the user;
retraining the room load neural network according to the actual temperature variation and the actual power consumption by taking the actual temperature variation and the actual power consumption as the room load sample training set; updating the connection weight of the input layer and the hidden layer, the connection weight of the hidden layer and the output layer, the hidden layer threshold and the output layer threshold in the room load neural network;
re-determining the temperature prediction model according to the re-trained room load neural network.
In one embodiment, the acquiring the user-set temperature, the user-set time and the indoor environment parameter includes:
determining the minimum value of the user-set time length according to the user-set temperature;
if the user-set temperature is smaller than a first temperature threshold, the user-set time length is larger than a first time threshold;
if the user-set temperature is greater than a first temperature threshold and less than a second temperature threshold, the user-set time length is greater than a second time threshold;
and if the user-set temperature is greater than a second temperature threshold, the user-set time length is greater than a third time threshold.
The present application provides in a second aspect an air conditioner comprising:
the data acquisition module 1 is used for acquiring user set temperature, user set duration and indoor environment parameters; the user-set time is the time required for the indoor temperature to reach the user-set temperature; the indoor environmental parameters include: room size, room temperature, door and window area, and room humidity;
the temperature prediction module 2 is used for determining a target temperature control function according to the temperature set by the user, the time set by the user, the indoor environment parameters and the temperature prediction model; the target temperature control function represents the future temperature control data trend within the user-set time length;
the operation strategy module 3 is used for determining the operation parameters of the air conditioner according to the target temperature control function;
and the temperature control module 4 is used for controlling the air conditioner to regulate and control the temperature according to the operation parameters.
The technical scheme provided by the application can comprise the following beneficial effects:
when the user turns on the air conditioner, the user can not only set the indoor temperature, but also set the temperature control duration of the air conditioner.
Before the air conditioner performs temperature control, the temperature set by a user, the time set by the user and the indoor environment parameters are acquired and input into a temperature prediction model, and a target temperature control function is output. The target temperature control function is a temperature control path with the least power consumption, and meanwhile, the database of the air conditioner stores the operation parameters corresponding to the target temperature control function.
When the air conditioner controls the temperature, the air conditioner adjusts the indoor temperature according to the operation parameters corresponding to the target temperature control function, so that the change of the indoor temperature is consistent with the prediction of the target temperature control function. Therefore, the air conditioner will adjust the indoor temperature to the user-set temperature within the user-set temperature control time period. The air conditioner can minimize the power consumption of the air conditioner while meeting the requirements of users, and energy conservation is achieved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The foregoing and other objects, features and advantages of the application will be apparent from the following more particular descriptions of exemplary embodiments of the application, as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the exemplary embodiments of the application.
Fig. 1 is a flowchart illustrating a control method of an air conditioner according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of another control method for an air conditioner according to an embodiment of the present disclosure;
FIG. 3 is a flowchart illustrating a training method for operating a strategy model according to a third embodiment;
FIG. 4 is a flowchart illustrating a method for training a temperature prediction model according to a third embodiment;
fig. 5 is a schematic diagram of a logical structure of an air conditioner according to an embodiment of the present disclosure.
Detailed Description
Preferred embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It should be understood that although the terms "first," "second," "third," etc. may be used herein to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present application. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.
Example one
In daily life, air conditioners are installed in different rooms, and the heat dissipation capacity of different rooms is different. When a user sets and adjusts the temperature, in order to enable the air conditioner to adjust the operating parameters of the air conditioner according to the heat dissipation capacity of different rooms, so that the power consumption of the air conditioner is the minimum, in the prior art, the optimal operating parameters of the air conditioner are usually calculated through a model trained by a neural network.
However, in the conventional air conditioning control method, the user cannot set the time for the temperature change by himself/herself. In order to enable the air conditioner to adjust the operation parameters according to different application scene parameters and the set temperature control time of a user in the temperature control process, and reduce the power consumption of the air conditioner, the embodiment of the application provides a control method of the air conditioner.
Fig. 1 is a flowchart illustrating a control method of an air conditioner according to an embodiment of the present disclosure.
Referring to fig. 1, the method comprises the following steps:
101. acquiring a user set temperature, a user set time and an indoor environment parameter;
specifically, the user-set time period is the time required for the indoor temperature to reach the user-set temperature.
Specifically, the indoor environmental parameters include: room size, room temperature, door and window area, and room humidity.
It should be noted that the types of the indoor environment parameters are exemplary in the embodiments of the present application, and are not limiting.
In the embodiment of the application, when the air conditioner is installed and a user starts the air conditioner for the first time, the user inputs indoor environment parameters through a mobile terminal program or a remote controller of the air conditioner.
For example, the indoor environment parameter may also be obtained by a sensor in the air conditioner indoor unit, for example, an infrared camera is used to obtain the room size; room humidity is acquired by a temperature sensor and room humidity is acquired by a humidity sensor, etc.
The acquiring of the user set temperature, the user set duration and the indoor environment parameters comprises the following steps:
determining the minimum value of the user-set time length according to the user-set temperature;
further, if the temperature set by the user is less than a first temperature threshold, the time length set by the user is greater than a first time threshold.
For example, if the user-set temperature is less than 22 degrees celsius, the user-set time period needs to be greater than or equal to 20 minutes.
Further, if the user-set temperature is greater than the first temperature threshold and less than the second temperature threshold, the user-set duration is greater than the second time threshold.
For example, if the user-set temperature is greater than 22 degrees celsius and less than 26 degrees celsius, the user-set time period needs to be greater than or equal to 10 minutes.
Further, if the temperature set by the user is greater than a second temperature threshold, the time length set by the user is greater than a third time threshold.
For example, if the user-set temperature is greater than 26 degrees celsius, the user-set time period needs to be greater than or equal to 5 minutes.
102. Determining a target temperature control function according to the user set temperature, the user set duration, the indoor environment parameters and the temperature prediction model;
specifically, the target temperature control function represents the trend of temperature control data in a future period of time.
Specifically, the temperature prediction model is a trained neural network.
In the embodiment of the application, the temperature set by a user, the time set by the user, the room size, the room temperature, the door and window area and the room humidity are input into a temperature prediction model, and a target temperature control function is output. Before the air conditioner starts temperature regulation, the temperature prediction model carries out weighted calculation on the input parameters to obtain a target temperature control function. The target temperature control function is an indoor temperature change curve within a user-set time period under the condition that the power consumption of the air conditioner is minimum.
103. Determining the operation parameters of the air conditioner according to the target temperature control function;
in the embodiment of the application, the processor adjusts the control parameter of the air conditioner, so that the indoor temperature can be controlled according to the indoor temperature change curve represented by the target temperature control function.
104. And controlling the air conditioner to regulate and control the temperature according to the operation parameters.
In the embodiment of the application, when the user turns on the air conditioner, the user can not only set the indoor temperature, but also set the temperature control duration of the air conditioner.
Before the air conditioner performs temperature control, the temperature set by a user, the time set by the user and the indoor environment parameters are acquired and input into a temperature prediction model, and a target temperature control function is output. The target temperature control function is a temperature control path with the least power consumption, and meanwhile, the database of the air conditioner stores the operation parameters corresponding to the target temperature control function.
When the air conditioner controls the temperature, the air conditioner adjusts the indoor temperature according to the operation parameters corresponding to the target temperature control function, so that the change of the indoor temperature is consistent with the prediction of the target temperature control function. Therefore, the air conditioner will adjust the indoor temperature to the user-set temperature within the user-set temperature control time period. The air conditioner can minimize the power consumption of the air conditioner while meeting the requirements of users, and energy conservation is achieved.
Example two
Based on the control method of the air conditioner in the first embodiment, in order to more specifically describe the calculation step of the target temperature control function and the determination step of the operation parameter of the air conditioner in the first embodiment, the embodiment of the present application further provides another control method of the air conditioner.
Fig. 2 is a flowchart illustrating another control method of an air conditioner according to an embodiment of the present disclosure.
Referring to fig. 2, the method comprises the following steps:
201. acquiring a user set temperature, a user set time and an indoor environment parameter;
specifically, the user-set time period is the time required for the indoor temperature to reach the user-set temperature.
Specifically, the indoor environmental parameters include: room size, room temperature, door and window area, and room humidity.
It should be noted that the types of the indoor environment parameters are exemplary in the embodiments of the present application, and are not limiting.
Further, the minimum value of the user-set time length is determined according to the user-set temperature.
In the embodiment of the application, when the air conditioner is started, the user determines the user set temperature through the mobile controller, and the time length of temperature adjustment can be selected as the user set time length.
202. Determining the power consumption and the temperature variation of the Nth detection time period according to the user set temperature, the user set time length, the indoor environment parameters and the temperature prediction model;
specifically, the target temperature control function represents a future temperature control data trend within the user-set time period.
Specifically, the temperature prediction model is a trained neural network.
In the embodiment of the application, the user-set duration is equally divided into N detection time periods; specifically, N is an integer greater than 1. In the process of predicting the temperature change curve of the room by the temperature prediction model, inputting the temperature set by a user, the Nth detection time period, the room size, the room temperature, the door and window area and the room humidity into the temperature prediction model, and outputting the indoor temperature variation and the air conditioner power consumption of the Nth detection time period in the future.
Further, an initial temperature and a termination temperature at the beginning of the nth detection period are determined by the indoor temperature variation.
Further, the sum of the power consumption of the air conditioner and the sum of the indoor temperature variation in the N detection time periods is used as the total power consumption and the total temperature variation in the temperature control process. And in the neural network training process of the temperature prediction model, taking the total power consumption and the total temperature variation of an ideal state as a verification set in the temperature prediction model training process.
Preferably, the sum of the air conditioner power consumption amount, the sum of the indoor temperature variation amount and the sum of the cooling/heating amount output by the temperature prediction model approach to 90% of an ideal state, and then the temperature prediction model training is completed.
A target temperature control function. Before the air conditioner starts temperature regulation, the temperature prediction model carries out weighted calculation on the input parameters to obtain a target temperature control function. The target temperature control function is an indoor temperature change curve within a user-set time period under the condition that the power consumption of the air conditioner is minimum.
203. Fitting the temperature variation of the N detection time periods with the minimum total power consumption to obtain the target temperature control function;
and within the time length set by the user, fitting the temperature variation of the N detection time periods with the minimum total power consumption to obtain the target temperature control function.
Specifically, the target temperature control function represents the trend of temperature control data in a future period of time.
Specifically, the temperature prediction model is a trained neural network.
In the embodiment of the application, the temperature set by a user, the time set by the user, the room size, the room temperature, the door and window area and the room humidity are input into a temperature prediction model, and the target temperature control function is obtained by fitting the temperature variation of N detection time periods with the minimum total power consumption.
When the air conditioner regulates and controls the temperature, the future temperature regulation and control processes of the air conditioner are N regulation and control time periods, and the regulation and control time periods are equal to the detection time periods. And the air conditioner performs temperature control by taking the target temperature control function corresponding to the Nth regulation and control time period as an indoor temperature change curve within the time period set by the user.
204. Determining the operation parameters of the air conditioner according to the target temperature control function and the operation strategy model;
specifically, the operation strategy model is generated by the operation strategy neural network and the target temperature control function training.
An input layer of the operation strategy model comprises the temperature variation and the power consumption corresponding to the target temperature control function in the Nth detection time period; the output layer of the operation strategy model is the operation parameters of the air conditioner;
specifically, the operating parameters of the air conditioner include, but are not limited to: compressor frequency, outer fan speed, inner fan speed and throttle device valve opening.
205. And controlling the air conditioner to regulate and control the temperature according to the operation parameters.
In the embodiment of the application, when the air conditioner starts to regulate and control the temperature, the air conditioner divides the preset time of a user into a plurality of detection time periods, the room temperature variation and the power consumption of each detection time period are calculated through a temperature prediction model, and when the power consumption reaches the minimum, the air conditioner fits the temperature variation of the plurality of detection time periods in the current state into a target temperature control function. The user can learn the indoor temperature change curve in a period of time in the future through the target temperature control function, and the interactivity of the air conditioner is improved. And meanwhile, outputting the operation parameters of the air conditioner through an operation strategy model according to the temperature variation and the power consumption corresponding to the target temperature control function, and acquiring the operation parameters of the air conditioner by the air conditioner to start temperature regulation and control.
EXAMPLE III
Based on the temperature prediction model in the first embodiment or the second embodiment, an embodiment of the present application provides a training method of a temperature prediction model, as shown in fig. 4, including the following steps:
301. acquiring a room load sample training set and a room load sample verification set;
specifically, the room load sample training set includes the temperature variation and the power consumption amount corresponding to the target temperature control function in the nth detection time period output by the temperature prediction model.
Specifically, the room load sample verification set includes the temperature variation and the power consumption amount corresponding to the target temperature control function in the nth detection time period output by the temperature prediction model.
302. Inputting the room load sample training set into a room load neural network for training;
303. and verifying the output of the temperature prediction model by using the room load sample verification set to obtain the temperature prediction model.
In this embodiment, the room load sample validation set is used to adjust the connection weight between the input layer and the hidden layer, the connection weight between the hidden layer and the output layer, the hidden layer threshold, and the output layer threshold in the room load neural network.
The embodiment of the present application further provides a training method for operating a policy model, as shown in fig. 3, including the following steps:
401. acquiring an operation strategy sample training set;
the operation strategy sample training set comprises the temperature variation and the power consumption which are output by the temperature prediction model and correspond to the target temperature control function in the Nth detection time period;
402. inputting the room load sample training set into a room load neural network for training to obtain the operation strategy model;
further, after the temperature control of the air conditioner is finished, whether the sum of the indoor temperature differences and the temperature difference set by a user is greater than a preset threshold value needs to be judged; if yes, go to step 403; if not, the current working state of the air conditioner is maintained.
403. Re-determining the temperature prediction model.
In this embodiment, in order to readjust the temperature prediction model so that the temperature prediction model can more accurately output the target temperature control function, the method includes: acquiring the actual temperature variation and the actual power consumption within the time set by the user; retraining the room load neural network according to the actual temperature variation and the actual power consumption by taking the actual temperature variation and the actual power consumption as the room load sample training set; updating the connection weight of the input layer and the hidden layer, the connection weight of the hidden layer and the output layer, the hidden layer threshold and the output layer threshold in the room load neural network; re-determining the temperature prediction model according to the re-trained room load neural network.
Example four
The embodiment of the application provides an air conditioner, based on the methods in the first embodiment, the second embodiment and the third embodiment, as shown in fig. 5, the air conditioner comprises a data acquisition module 1, a data processing module and a control module, wherein the data acquisition module is used for acquiring user set temperature, user set time and indoor environment parameters; the user-set time is the time required for the indoor temperature to reach the user-set temperature; the indoor environmental parameters include: room size, room temperature, door and window area, and room humidity;
the temperature prediction module 2 is used for determining a target temperature control function according to the temperature set by the user, the time set by the user, the indoor environment parameters and the temperature prediction model; the target temperature control function represents the future temperature control data trend within the user-set time length;
the operation strategy module 3 is used for determining the operation parameters of the air conditioner according to the target temperature control function;
and the temperature control module 4 is used for controlling the air conditioner to regulate and control the temperature according to the operation parameters.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
The aspects of the present application have been described in detail hereinabove with reference to the accompanying drawings. In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments. Those skilled in the art should also appreciate that the acts and modules referred to in the specification are not necessarily required in the present application. In addition, it can be understood that the steps in the method of the embodiment of the present application may be sequentially adjusted, combined, and deleted according to actual needs, and the modules in the device of the embodiment of the present application may be combined, divided, and deleted according to actual needs.
Furthermore, the method according to the present application may also be implemented as a computer program or computer program product comprising computer program code instructions for performing some or all of the steps of the above-described method of the present application.
Alternatively, the present application may also be embodied as a non-transitory machine-readable storage medium (or computer-readable storage medium, or machine-readable storage medium) having stored thereon executable code (or a computer program, or computer instruction code) which, when executed by a processor of an electronic device (or electronic device, server, etc.), causes the processor to perform part or all of the various steps of the above-described method according to the present application.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the applications disclosed herein may be implemented as electronic hardware, computer software, or combinations of both.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems and methods according to various embodiments of the present application. 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). It should also be noted that, 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. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Having described embodiments of the present application, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (10)
1. A control method of an air conditioner is characterized by comprising the following steps:
acquiring a user set temperature, a user set time and an indoor environment parameter; the user-set time is the time required for the indoor temperature to reach the user-set temperature; the indoor environmental parameters include: room size, room temperature, door and window area, and room humidity;
determining a target temperature control function according to the user set temperature, the user set duration, the indoor environment parameters and the temperature prediction model; the target temperature control function represents the future temperature control data trend within the user-set time length;
determining the operation parameters of the air conditioner according to the target temperature control function;
and controlling the air conditioner to regulate and control the temperature according to the operation parameters.
2. The method as claimed in claim 1, wherein the determining the target temperature control function according to the user-set temperature, the user-set time, the indoor environment parameter and the temperature prediction model comprises:
the user set duration is divided into N detection time periods; n is an integer greater than 1;
determining the temperature variation and the power consumption of the Nth detection time period according to the user set temperature, the user set time period, the indoor environment parameters and the temperature prediction model within the user set time period;
and fitting the temperature variation of the N detection time periods with the minimum total power consumption to obtain the target temperature control function.
3. The control method of an air conditioner according to claim 2,
the temperature prediction model is generated by training through a room load neural network and a room load sample training set;
the temperature prediction model comprises an input layer, a hidden layer and an output layer;
the room load sample training set comprises indoor environment parameters input as fixed values and air conditioner setting parameters input as adjustable variables; the air conditioner setting parameters comprise user set temperature and user set duration;
the output layer of the temperature prediction model comprises temperature variation and power consumption;
and performing network training on the temperature prediction model according to the room load sample training set.
4. The method as claimed in claim 3, wherein the determining the operation parameters of the air conditioner according to the target temperature control function comprises:
determining the operation parameters of the air conditioner according to the target temperature control function and the operation strategy model; and the operation strategy model is generated by the operation strategy neural network and the target temperature control function training.
5. The method as claimed in claim 4, wherein the operation strategy model is generated by the operation strategy neural network and the target temperature control function training, and comprises:
the operation strategy model comprises an input layer, a hidden layer and an output layer;
an input layer of the operation strategy model comprises the temperature variation and the power consumption corresponding to the target temperature control function in the Nth detection time period;
the output layer of the operation strategy model is the operation parameters of the air conditioner; the operation parameters of the air conditioner include: compressor frequency, outer fan speed, inner fan speed and throttle device valve opening.
6. The method as claimed in claim 3, wherein after controlling the air conditioner to perform temperature regulation according to the operation parameter, the method comprises:
acquiring an indoor temperature difference at the end time of the current time period;
judging whether the current time period is the last detection time period of the user-set time period or not, and if so, determining the sum of the indoor temperature differences within the user-set time period; if not, executing the following steps: and acquiring the operating parameters in the next detection time period.
7. The control method of an air conditioner according to claim 6,
after determining the sum of the indoor temperature differences within the user-set time period, the method includes:
acquiring an indoor actual temperature;
judging whether the sum of the indoor temperature differences and the temperature difference set by a user is greater than a preset threshold value or not; if yes, re-determining the temperature prediction model; if not, the current working state of the air conditioner is maintained.
8. The control method of an air conditioner according to claim 7,
the re-determining the temperature prediction model comprises:
acquiring the actual temperature variation and the actual power consumption within the time set by the user;
retraining the room load neural network according to the actual temperature variation and the actual power consumption by taking the actual temperature variation and the actual power consumption as the room load sample training set; updating the connection weight of the input layer and the hidden layer of the room load neural network, the connection weight of the hidden layer and the output layer of the room load neural network, the hidden layer threshold value and the output layer threshold value of the room load neural network;
re-determining the temperature prediction model according to the re-trained room load neural network.
9. The control method of an air conditioner according to claim 1,
the acquiring of the user set temperature, the user set duration and the indoor environment parameters comprises the following steps:
determining the minimum value of the user-set time length according to the user-set temperature;
if the user-set temperature is smaller than a first temperature threshold, the user-set time length is larger than a first time threshold;
if the user-set temperature is greater than a first temperature threshold and less than a second temperature threshold, the user-set time length is greater than a second time threshold;
and if the user-set temperature is greater than a second temperature threshold, the user-set time length is greater than a third time threshold.
10. An air conditioner, comprising:
the data acquisition module (1) is used for acquiring the temperature set by a user, the time set by the user and the indoor environment parameters; the user-set time is the time required for the indoor temperature to reach the user-set temperature; the indoor environmental parameters include: room size, room temperature, door and window area, and room humidity;
the temperature prediction module (2) is used for determining a target temperature control function according to the user set temperature, the user set duration, the indoor environment parameters and the temperature prediction model; the target temperature control function represents the future temperature control data trend within the user-set time length;
the operation strategy module (3) is used for determining the operation parameters of the air conditioner according to the target temperature control function;
and the temperature control module (4) is used for controlling the air conditioner to regulate and control the temperature according to the operation parameters.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116085937A (en) * | 2023-04-11 | 2023-05-09 | 湖南禾自能源科技有限公司 | Intelligent central air conditioner energy-saving control method and system |
| WO2024078034A1 (en) * | 2022-10-11 | 2024-04-18 | 广东美的制冷设备有限公司 | Method for controlling air conditioner, and air conditioner and computer-readable storage medium |
| WO2024093411A1 (en) * | 2022-10-31 | 2024-05-10 | 广东美的制冷设备有限公司 | Air conditioner control method, air conditioner, and computer readable storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106225172A (en) * | 2016-08-17 | 2016-12-14 | 珠海格力电器股份有限公司 | Air conditioning control device, method and system |
| CN106874581A (en) * | 2016-12-30 | 2017-06-20 | 浙江大学 | A kind of energy consumption of air conditioning system in buildings Forecasting Methodology based on BP neural network model |
| US20170321921A1 (en) * | 2014-11-06 | 2017-11-09 | Huawei Technologies Co., Ltd. | Thermal control apparatus and method |
| CN110726229A (en) * | 2019-10-29 | 2020-01-24 | 珠海格力电器股份有限公司 | Control method and device of air conditioner, storage medium and processor |
| CN113819607A (en) * | 2021-09-15 | 2021-12-21 | 青岛海尔空调器有限总公司 | Intelligent control method and device for air conditioner, air conditioner and electronic equipment |
-
2021
- 2021-12-24 CN CN202111604773.7A patent/CN114413420A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170321921A1 (en) * | 2014-11-06 | 2017-11-09 | Huawei Technologies Co., Ltd. | Thermal control apparatus and method |
| CN106225172A (en) * | 2016-08-17 | 2016-12-14 | 珠海格力电器股份有限公司 | Air conditioning control device, method and system |
| CN106874581A (en) * | 2016-12-30 | 2017-06-20 | 浙江大学 | A kind of energy consumption of air conditioning system in buildings Forecasting Methodology based on BP neural network model |
| CN110726229A (en) * | 2019-10-29 | 2020-01-24 | 珠海格力电器股份有限公司 | Control method and device of air conditioner, storage medium and processor |
| CN113819607A (en) * | 2021-09-15 | 2021-12-21 | 青岛海尔空调器有限总公司 | Intelligent control method and device for air conditioner, air conditioner and electronic equipment |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024078034A1 (en) * | 2022-10-11 | 2024-04-18 | 广东美的制冷设备有限公司 | Method for controlling air conditioner, and air conditioner and computer-readable storage medium |
| WO2024093411A1 (en) * | 2022-10-31 | 2024-05-10 | 广东美的制冷设备有限公司 | Air conditioner control method, air conditioner, and computer readable storage medium |
| CN116085937A (en) * | 2023-04-11 | 2023-05-09 | 湖南禾自能源科技有限公司 | Intelligent central air conditioner energy-saving control method and system |
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