CN110726215B - Air conditioner, control method and device thereof, storage medium and processor - Google Patents
Air conditioner, control method and device thereof, storage medium and processor Download PDFInfo
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- CN110726215B CN110726215B CN201911039224.2A CN201911039224A CN110726215B CN 110726215 B CN110726215 B CN 110726215B CN 201911039224 A CN201911039224 A CN 201911039224A CN 110726215 B CN110726215 B CN 110726215B
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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
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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
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Abstract
The invention discloses an air conditioner, a control method and device thereof, a storage medium and a processor. The method comprises the following steps: acquiring first demand data, wherein the first demand data is used for indicating a first demand of a user for the performance of the air conditioner; analyzing the first demand data based on a neural network model to obtain a first operating parameter of the air conditioner, wherein the neural network model is used for establishing a mapping relation between different demand data and the operating parameter of the air conditioner; and controlling the air conditioner through the first operation parameter. The invention achieves the effect of reducing the limitation of controlling the air conditioner.
Description
Technical Field
The invention relates to the field of air conditioners, in particular to an air conditioner, a control method and a control device of the air conditioner, a storage medium and a processor.
Background
At present, when the air conditioner is controlled, the air conditioner is usually controlled by setting a temperature value, and the air conditioner cannot be flexibly controlled according to the performance requirement of a user on the air conditioner in the use process, so that the control of the air conditioner has great limitation.
Aiming at the problem of large limitation of controlling an air conditioner in the prior art, an effective solution is not provided at present.
Disclosure of Invention
The invention mainly aims to provide an air conditioner, a control method and device thereof, a storage medium and a processor, and at least solves the technical problem of great limitation of controlling the air conditioner.
In order to achieve the above object, according to one aspect of the present invention, there is provided a control method of an air conditioner. The method may include: acquiring first demand data, wherein the first demand data is used for indicating a first demand of a user for the performance of the air conditioner; analyzing the first demand data based on a neural network model to obtain a first operating parameter of the air conditioner, wherein the neural network model is used for establishing a mapping relation between different demand data and the operating parameter of the air conditioner; and controlling the air conditioner through the first operation parameter.
Optionally, when the air conditioner is controlled by the first operation parameter, the method further comprises: and outputting the operation state of the air conditioner under the control of the first operation parameter.
Optionally, analyzing the first demand data based on the neural network model to obtain a first operating parameter of the air conditioner, including: the first demand data is analyzed based on the neural network model, at least one actuator of the air conditioner associated with the first demand data is determined, and first operating parameters of the at least one actuator are obtained.
Optionally, analyzing the first demand data based on the neural network model to obtain a first operating parameter of the air conditioner, including: modifying the neural network model based on the first demand data; and analyzing the first requirement data through the corrected neural network model to obtain a first operation parameter.
Optionally, modifying the neural network model based on the first demand data includes: the weights in the neural network model corresponding to the first demand data are modified.
Optionally, analyzing the first demand data through the corrected neural network model to obtain a first operating parameter, including: and analyzing the first requirement data through the neural network model after the weight is corrected to obtain a first operation parameter.
Optionally, analyzing the first demand data through the neural network model after the weight is corrected to obtain a first operating parameter, including: acquiring a temperature correction value corresponding to the first demand data based on the neural network model after the weight correction; correcting the original temperature value set by the air conditioner through the temperature correction value to obtain a target temperature value; a first operating parameter corresponding to a target temperature value is obtained.
Optionally, before acquiring the first demand data, the method further includes: the method comprises the steps of obtaining information of the environment where the air conditioner is located, information of a use object of the air conditioner and second demand data, wherein the second demand data are used for indicating a second demand of a user for the performance of the air conditioner; analyzing the environment information, the using object information and the second demand data based on the neural network model to obtain a second operating parameter of the air conditioner; and controlling the air conditioner through the second operation parameter.
Optionally, the acquiring the first demand data includes: and acquiring first demand data in the process of controlling the air conditioner through the second operation parameter.
Optionally, before analyzing the first demand data based on the neural network model to obtain the first operating parameter of the air conditioner, the method further includes: acquiring sample data, wherein the sample data at least comprises: different demand data, and operating parameters of the air conditioner corresponding to the different demand data; and learning the sample data based on the sub-neural network model to generate the neural network model.
Optionally, the first demand data includes: evaluation data for evaluating the power consumption of the air conditioner, and/or evaluation data for evaluating the comfort of the air conditioner.
In order to achieve the above object, according to another aspect of the present invention, there is also provided a control method of an air conditioner. The method can comprise the following steps: displaying first demand data on the interactive interface, wherein the first demand data is used for indicating a first demand of a user for the performance of the air conditioner; displaying a first operation parameter on the interactive interface, wherein the first operation parameter is obtained by analyzing first demand data based on a neural network model, and the neural network model is used for establishing a mapping relation between different demand data and operation parameters of the air conditioner; and displaying the operation state of the air conditioner under the control of the first operation parameter on the interactive interface.
Optionally, the method further comprises: modifying the neural network model based on the first demand data; and analyzing the first requirement data through the corrected neural network model to obtain a first operation parameter.
Optionally, before displaying the first demand data on the interactive interface, the method further includes: displaying information of the environment where the air conditioner is located, information of a use object of the air conditioner and second demand data on the interactive interface, wherein the second demand data is used for indicating a second demand of the user for the performance of the air conditioner; displaying a second operation parameter on the interactive interface, wherein the second operation parameter is obtained by analyzing the environment information, the using object information and the second requirement data based on the neural network model; and displaying the operation state of the air conditioner under the control of the second operation parameter on the interactive interface.
In order to achieve the above object, according to another aspect of the present invention, there is also provided a control apparatus of an air conditioner. The apparatus may include: the system comprises an acquisition unit, a control unit and a processing unit, wherein the acquisition unit is used for acquiring first demand data, and the first demand data is used for indicating a first demand of a user on the performance of the air conditioner; the analysis unit is used for analyzing the first demand data based on a neural network model to obtain a first operation parameter of the air conditioner, wherein the neural network model is used for establishing a mapping relation between different demand data and the operation parameter of the air conditioner; and the control unit is used for controlling the air conditioner through the first operation parameter.
In order to achieve the above object, according to another aspect of the present invention, there is also provided a control apparatus of an air conditioner. The apparatus may include: the first display unit is used for displaying first demand data on the interactive interface, wherein the first demand data is used for indicating a first demand of a user for the performance of the air conditioner; the second display unit is used for displaying the first operation parameter on the interactive interface, wherein the first operation parameter is obtained by analyzing the first demand data based on a neural network model, and the neural network model is used for establishing a mapping relation between different demand data and the operation parameter of the air conditioner; and the third display unit is used for displaying the running state of the air conditioner under the control of the first running parameter on the interactive interface.
In order to achieve the above object, according to another aspect of the present invention, there is also provided an air conditioner. The air conditioner can comprise the control device of the air conditioner provided by the embodiment of the invention.
In order to achieve the above object, according to another aspect of the present invention, there is also provided a storage medium. The storage medium includes a stored program, wherein the apparatus in which the storage medium is located is controlled to perform the control method of the air conditioner of the embodiment of the present invention when the program is executed.
To achieve the above object, according to another aspect of the present invention, there is also provided a processor. The processor is used for running a program, wherein the program executes the control method of the air conditioner in the embodiment of the invention when running.
According to the invention, the first demand data is acquired, wherein the first demand data is used for indicating the first demand of the user for the performance of the air conditioner; analyzing the first demand data based on a neural network model to obtain a first operating parameter of the air conditioner, wherein the neural network model is used for establishing a mapping relation between different demand data and the operating parameter of the air conditioner; and controlling the air conditioner through the first operation parameter. That is to say, the invention analyzes the demand data input by the user based on the neural network model to obtain the operation parameters for controlling the air conditioner, so as to meet the user demand, and avoids the problem that the air conditioner cannot be flexibly controlled according to the performance demand of the user on the air conditioner in the use process, thereby solving the technical problem of large limitation of controlling the air conditioner, and further achieving the technical effect of reducing the limitation of controlling the air conditioner.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a flowchart of a control method of an air conditioner according to an embodiment of the present invention;
fig. 2 is a flowchart of another control method of an air conditioner according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of determining operating parameters of an air conditioner based on a neural network model, according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of modifying an internally set temperature based on evaluation data, according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a control apparatus of an air conditioner according to an embodiment of the present invention; and
fig. 6 is a schematic view of another control apparatus of an air conditioner according to an embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. 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.
Example 1
The embodiment of the invention provides a control method of an air conditioner.
Fig. 1 is a flowchart of a control method of an air conditioner according to an embodiment of the present invention. As shown in fig. 1, the method may include the steps of:
step S102, first demand data is obtained, wherein the first demand data is used for indicating a first demand of a user for the performance of the air conditioner.
In the technical solution provided by step S102 of the present invention, in the process of operating the air conditioner, first demand data may be obtained, where the first demand data may be data for indicating a comfort demand, and the comfort may be thermal comfort, and the first demand data may also be data for indicating a power consumption demand of the air conditioner, and this is not limited here.
Optionally, the embodiment may obtain the first demand data through an input end establishing a communication connection with the air conditioner, for example, the input end may be a terminal device such as a smart phone (e.g., an Android phone, an iOS phone, etc.), a tablet computer, a palmtop computer, and a Mobile Internet Device (MID), a PAD, and optionally, the embodiment obtains the first demand data through an Application (APP for short) installed on the terminal device.
And step S104, analyzing the first demand data based on the neural network model to obtain a first operation parameter of the air conditioner.
In the technical solution provided in step S104 of the present invention, after the first demand data is obtained, the first demand data is analyzed based on the neural network model to obtain the first operating parameter of the air conditioner, wherein the neural network model is used for establishing a mapping relationship between different demand data and the operating parameter of the air conditioner.
In this embodiment, a proper neural network model may be trained in advance, and the neural network model is an intelligent module of the air conditioner itself, and may be used to establish a mapping relationship between different demand data and operation parameters of the air conditioner, that is, the operation parameters of the air conditioner may be obtained by mapping the demand data of the user.
In the embodiment, the first demand data can be used as an input layer of a neural network model, the first demand data is analyzed on the basis of the neural network model by an output layer of the first operation parameter to obtain a first operation parameter of the air conditioner, the first operation parameter is an optimal control parameter for controlling the air conditioner customized for a user according to the first demand data, the optimal control parameter can comprise optimal control parameters of actuators such as the frequency of a compressor of the air conditioner, the rotating speed of an inner fan, the angle of an air deflector, the rotating speed of an outer fan, the opening degree of an expansion valve and the like, and parameters such as indoor temperature, wind speed and the like can be adjusted, so that the air conditioner is in an optimal operation state; optionally, in this embodiment, the first demand data is subjected to linear processing such as normalization and nonlinear processing such as logarithmic transformation, square root transformation, and cubic root, and the processed first demand data is processed by a neural network algorithm through a neural network model, so as to obtain the first operating parameter of the air conditioner.
And step S106, controlling the air conditioner through the first operation parameter.
In the technical solution provided in step S106 of the present invention, after the first requirement data is analyzed based on the neural network model to obtain the first operation parameter of the air conditioner, the air conditioner is controlled according to the first operation parameter, so that the final control result can be matched with the requirement indicated by the first requirement data, thereby avoiding that the air conditioner cannot be controlled according to the requirement of the user, achieving the purpose of meeting the requirement of the user, and reducing the limitation of controlling the air conditioner.
The above method of this embodiment is further explained below.
As an alternative embodiment, when the air conditioner is controlled by the first operation parameter at step S106, the method further includes: and outputting the operation state of the air conditioner under the control of the first operation parameter.
In this embodiment, in the process of controlling the air conditioner through the first operation parameter, the operation state of the air conditioner under the control of the first operation parameter may be output in real time, and the operation state is also the current control state of the air conditioner controlled according to the first operation parameter, so as to achieve the purpose of feeding back the current control state to the user, so that the user determines whether the self-requirement is really met.
As an optional implementation manner, in step S104, analyzing the first demand data based on the neural network model to obtain a first operating parameter of the air conditioner, including: the first demand data is analyzed based on the neural network model, at least one actuator of the air conditioner associated with the first demand data is determined, and first operating parameters of the at least one actuator are obtained.
In this embodiment, in order to meet the demand of the user, a plurality of actuators of the air conditioner are required to participate therein. When the first demand data is analyzed based on the neural network model to obtain the first operating parameter of the air conditioner, the first demand data may be analyzed based on the neural network model to determine at least one actuator of the air conditioner, which is associated with the first demand data, for example, the actuator may be a compressor, an inner fan, an outer fan, an expansion valve, a wind shield, or the like. After determining at least one actuator of the air conditioner associated with the first demand data, obtaining first operating parameters of the at least one actuator, for example, operating parameters such as the frequency of the compressor, the rotating speed of the inner fan, the rotating speed of the outer fan, the opening degree of the expansion valve, and the angle of the wind shield, that is, the operating parameters such as the frequency of the compressor, the rotating speed of the inner fan, the rotating speed of the outer fan, the opening degree of the expansion valve, and the state of the wind shield are required to control the air conditioner together, so that the requirements of users can be met.
As an optional implementation manner, in step S104, analyzing the first demand data based on the neural network model to obtain a first operating parameter of the air conditioner, including: modifying the neural network model based on the first demand data; and analyzing the first requirement data through the corrected neural network model to obtain a first operation parameter.
In this embodiment, after obtaining first demand data, can revise the neural network model through first demand data, for example, after the air conditioner operation a period, demand data such as user's accessible APP evaluation air conditioner power consumption, indoor thermal comfort feed back it to the air conditioner, and the air conditioner revises the neural network model according to first demand data. After the neural network model is modified based on the first demand data, the first demand data is further analyzed by the modified neural network model to obtain a first operating parameter.
As an alternative embodiment, modifying the neural network model based on the first demand data includes: correcting the weight corresponding to the first demand data in the neural network model; analyzing the first demand data through the corrected neural network model to obtain a first operating parameter, including: and analyzing the first requirement data through the neural network model after the weight is corrected to obtain a first operation parameter.
In this embodiment, when modifying the neural network model based on first demand data, can modify the weight that corresponds with first demand data in the neural network model through first demand data, for example, power consumption demand data through APP input, travelling comfort demand data, the weight that the neural network model can adjust power consumption demand data by oneself and correspond, the weight that travelling comfort demand data correspond, thereby neural network model after through the correction weight carries out the analysis to first demand data, obtain first operating parameter, and then control the air conditioner according to first operating parameter, thereby satisfy individual differentiation demand.
As an optional implementation, analyzing the first demand data through the weighted neural network model to obtain a first operating parameter includes: acquiring a temperature correction value corresponding to the first demand data based on the neural network model after the weight correction; correcting the original temperature value set by the air conditioner through the temperature correction value to obtain a target temperature value; a first operating parameter corresponding to a target temperature value is obtained.
In this embodiment, the internal set temperature of the air conditioner is adjusted based on the weighted neural network model, and optionally, the embodiment obtains a temperature correction value corresponding to the first demand data based on the weighted neural network model, for example, obtains a temperature correction value corresponding to the air conditioner power consumption demand data and a temperature correction value corresponding to the comfort demand data, respectively. After a temperature correction value corresponding to the first demand data is obtained based on the neural network model after the weight correction, an original temperature value set by the air conditioner is corrected through the temperature correction value to obtain a target temperature value, wherein the original temperature value set by the air conditioner is also the internal set temperature of the air conditioner. After the original temperature value is corrected to be the target temperature value, a first operation parameter which corresponds to the target temperature value and is used for controlling the air conditioner is obtained.
As an optional implementation manner, before acquiring the first demand data in step S102, the method further includes: the method comprises the steps of obtaining information of the environment where the air conditioner is located, information of a use object of the air conditioner and second demand data, wherein the second demand data are used for indicating a second demand of a user for the performance of the air conditioner; analyzing the environment information, the using object information and the second demand data based on the neural network model to obtain a second operating parameter of the air conditioner; and controlling the air conditioner through the second operation parameter.
In this embodiment, before the first demand data is acquired, for example, when the air conditioner starts to operate, information on the environment in which the air conditioner is located, information on the object of use of the air conditioner, and the second demand data may be acquired. Alternatively, the embodiment may detect the indoor and outdoor environments through a detection sensor disposed on the air conditioner, so as to obtain the environment information; in the embodiment, the information of the use object in the environment where the air conditioner is located can be acquired through the thermal imager or the camera, for example, the information of the number of people, the positions of the people, the body temperature of the people and the like can be acquired; this embodiment can be through the second demand data of user's own input to the air conditioner, and this second demand data can be power consumption demand data, travelling comfort demand data, and wherein, power consumption demand data can be specific power consumption limiting value, and travelling comfort demand data can be the data that are used for reflecting thermal comfort demand situation.
After the information of the environment where the air conditioner is located, the information of the object used by the air conditioner and the second requirement data are obtained, the information of the environment where the air conditioner is located, the information of the object used by the air conditioner and the second requirement data are input into the neural network model for processing. Optionally, the information of the environment where the air conditioner is located, the information of the object used by the air conditioner, and the second demand data are subjected to linear processing such as normalization and nonlinear processing such as logarithmic conversion, square root conversion, and cubic root to obtain a processing result, and then the processing result is processed by adopting a neural network algorithm to obtain a second operation parameter of the air conditioner, so that the air conditioner is controlled by the second operation parameter.
As an optional implementation manner, in step S102, the acquiring the first demand data includes: and acquiring first demand data in the process of controlling the air conditioner through the second operation parameter.
In this embodiment, in the process of controlling the air conditioner through the second operation parameter, if the result of controlling the air conditioner through the second operation parameter does not meet the requirement of the user, the first requirement data is obtained, for example, the air conditioner is provided with the electric quantity detection device, the electric power consumption of the air conditioner under the control of the second operation parameter, which is detected by the electric quantity detection device, can be displayed on the air conditioner, the user can check the electric power consumption, so as to determine whether to meet the requirement of the user, if not, the electric power consumption requirement data can be reset, the air conditioner obtains the electric power consumption requirement data again, and then outputs the first operation parameter aiming at the electric power consumption requirement data, thereby realizing the purpose of customizing the special operation mode for the user.
As an optional implementation manner, before analyzing the first demand data based on the neural network model to obtain the first operating parameter of the air conditioner in step S104, the method further includes: acquiring sample data, wherein the sample data at least comprises: different demand data, and operating parameters of the air conditioner corresponding to the different demand data; and learning the sample data based on the sub-neural network model to generate the neural network model.
In this embodiment, before the first demand data is analyzed based on the neural network model to obtain the first operating parameter of the air conditioner, the neural network model needs to be trained in advance. The embodiment can acquire a large amount of sample data in advance, and the sample data, namely the test data, can comprise different requirement data and the operating parameters of the air conditioner corresponding to the different requirement data.
After sample data is obtained, the sample data is learned based on a sub-neural network model, the sub-neural network model is an initially established neural network model, different required data can be used as an input layer of the sub-neural network model, operating parameters of an air conditioner corresponding to the different required data are used as an output layer of the sub-neural network model, a hidden layer can be a plurality of layers and comprises a plurality of hidden nodes, preferably, the hidden layer is one layer, and training of the full-time neural network model of each layer is performed by using a large amount of sample data.
As an optional implementation, the first demand data includes: evaluation data for evaluating the power consumption of the air conditioner, and/or evaluation data for evaluating the comfort of the air conditioner.
In this embodiment, the first demand data may be evaluation data for evaluating power consumption of the air conditioner by a user, for example, evaluation data of power consumption, general, energy saving, and the like, and each evaluation data may have a corresponding identifier, for example, the identifier of power consumption may be +2, the acceptable identifier may be +1, and the identifier of energy saving may be + 0; the evaluation data for evaluating the comfort of the air conditioner may be the evaluation data of cooler, cold, comfortable, hot, hotter, etc., the cooler may be +2, the cold may be +1, the comfortable may be +0, the hot may be-1, and the hotter may be-2.
It should be noted that the above evaluation data of this embodiment is only an example of the embodiment of the present invention, and the evaluation data that does not represent the embodiment of the present invention is only the above evaluation data, and any evaluation data that can be used for evaluating the power consumption of the air conditioner and/or evaluation data for evaluating the comfort of the air conditioner are within the scope of this embodiment, and are not illustrated here.
In the embodiment, acquiring first demand data is adopted, wherein the first demand data is used for indicating a first demand of a user for the performance of the air conditioner; analyzing the first demand data based on a neural network model to obtain a first operating parameter of the air conditioner, wherein the neural network model is used for establishing a mapping relation between different demand data and the operating parameter of the air conditioner; and controlling the air conditioner through the first operation parameter. That is to say, this embodiment is based on the neural network model and is carried out the analysis to the demand data of user's input, obtains the operating parameter that carries out control to the air conditioner to satisfy user's demand, avoided unable according to user's self demand to the performance of air conditioner in the use, come to control the air conditioner in a flexible way, thereby solved and carried out the big technical problem of limitation of control to the air conditioner, and then reached the technological effect that reduces the limitation of controlling the air conditioner.
The embodiment of the invention also provides another control method of the air conditioner from the perspective of user interaction.
Fig. 2 is a flowchart of another control method of an air conditioner according to an embodiment of the present invention. As shown in fig. 2, the method may include the steps of:
step S202, first requirement data is displayed on the interactive interface, wherein the first requirement data is used for indicating a first requirement of the user on the performance of the air conditioner.
In this embodiment, in the operation process of the air conditioner, first demand data may be acquired and displayed on the interactive interface, where the first demand data may be data for indicating a comfort demand, the comfort may be thermal comfort, and the first demand data may also be data for indicating a power consumption demand of the air conditioner, which is not limited herein.
Optionally, the embodiment may acquire the first demand data through an input end that establishes a communication connection with the air conditioner, and display the acquired first demand data on the interactive interface.
And S204, displaying a first operation parameter on the interactive interface, wherein the first operation parameter is obtained by analyzing the first requirement data based on the neural network model.
In the technical solution provided in step S204 of the present invention, after the first demand data is displayed on the interactive interface, a first operation parameter may be displayed on the interactive interface, where the first operation parameter is obtained by analyzing the first demand data based on a neural network model, and the neural network model is used to establish a mapping relationship between different demand data and operation parameters of the air conditioner.
In this embodiment, a proper neural network model may be trained in advance, and the neural network model is an intelligent module of the air conditioner itself, and may be used to establish a mapping relationship between different demand data and operation parameters of the air conditioner, that is, the operation parameters of the air conditioner may be obtained by mapping the demand data of the user.
In the embodiment, the first demand data can be used as an input layer of a neural network model, the first demand data is analyzed by an output layer of the first operation parameter based on the neural network model to obtain a first operation parameter of the air conditioner, the first operation parameter is output to an interaction interface, the first operation parameter is displayed on the interaction interface, the first operation parameter is an optimal control parameter for controlling the air conditioner customized for a user according to the first demand data, and the optimal control parameter can comprise optimal control parameters of actuators such as the frequency of a compressor of the air conditioner, the rotating speed of an inner fan, the angle of an air deflector, the rotating speed of an outer fan, the opening degree of an expansion valve and the like; optionally, in this embodiment, the first demand data is subjected to linear processing such as normalization and nonlinear processing such as logarithmic transformation, square root transformation, and cubic root, and the processed first demand data is processed through a neural network algorithm via a neural network model, so as to obtain the first operating parameter of the air conditioner.
And step S206, displaying the running state of the air conditioner under the control of the first running parameter on the interactive interface.
In the technical solution provided in step S206 of the present invention, after the first operation parameter is displayed on the interactive interface, the air conditioner is controlled according to the first operation parameter, and the operation state of the air conditioner under the control of the first operation parameter is displayed on the interactive interface.
This embodiment can be in the in-process of controlling the air conditioner through first operating parameter, show the running state of air conditioner under the control of first operating parameter on interactive interface in real time, this running state also is the current control state of controlling the air conditioner according to first operating parameter, thereby reach the purpose of feeding back the current control state to the user, in order to make the user confirm whether the self demand that really reaches, make final control result can match with the demand that first demand data instructed, thereby avoided unable control the air conditioner according to user's self demand, thereby reach the purpose that satisfies the user demand, the limitation of controlling the air conditioner has been reduced.
As an optional implementation, the method further comprises: modifying the neural network model based on the first demand data; and analyzing the first requirement data through the corrected neural network model to obtain a first operation parameter.
In this embodiment, after the first demand data is displayed on the interactive interface, the neural network model can be corrected through the first demand data, for example, after the air conditioner runs for a period of time, the user can evaluate demand data such as power consumption and indoor thermal comfort of the air conditioner through the APP, and feed back the demand data to the air conditioner, and the air conditioner corrects the neural network model according to the first demand data. After the neural network model is corrected based on the first requirement data, the first requirement data is further analyzed through the corrected neural network model, and the obtained first operation parameters are output to an interactive interface to be displayed.
As an optional implementation manner, before displaying the first demand data on the interactive interface, the method further includes: displaying information of the environment where the air conditioner is located, information of a use object of the air conditioner and second demand data on the interactive interface, wherein the second demand data is used for indicating a second demand of the user for the performance of the air conditioner; displaying a second operation parameter on the interactive interface, wherein the second operation parameter is obtained by analyzing the environment information, the using object information and the second requirement data based on the neural network model; and displaying the operation state of the air conditioner under the control of the second operation parameter on the interactive interface.
In this embodiment, in step S202, before the first demand data is displayed on the interactive interface, for example, when the air conditioner starts to operate, information of an environment where the air conditioner is located, information of a use object of the air conditioner, and the second demand data may be acquired and displayed on the interactive interface. Alternatively, the embodiment may detect the indoor and outdoor environments through a detection sensor disposed on the air conditioner, so as to obtain the environment information; in the embodiment, the information of the use object in the environment where the air conditioner is located can be acquired through the thermal imager or the camera, for example, the information of the number of people, the positions of the people, the body temperature of the people and the like can be acquired; this embodiment can be through the second demand data of user's own input to the air conditioner, and this second demand data can be power consumption demand data, travelling comfort demand data, and wherein, power consumption demand data can be specific power consumption limiting value, and travelling comfort demand data can be the data that are used for reflecting thermal comfort demand situation.
After the information of the environment where the air conditioner is located, the information of the object used by the air conditioner and the second demand data are displayed on the interactive interface, the information of the environment where the air conditioner is located, the information of the object used by the air conditioner and the second demand data can be input into the neural network model for processing. Optionally, the information of the environment where the air conditioner is located, the information of the object used by the air conditioner, and the second demand data are subjected to linear processing such as normalization and nonlinear processing such as logarithmic conversion, square root conversion, and cubic root to obtain a processing result, and then the processing result is processed by adopting a neural network algorithm to obtain a second operation parameter of the air conditioner, and the second operation parameter is displayed on the interactive interface, so that the air conditioner is controlled by the second operation parameter.
The embodiment displays first demand data on an interactive interface, wherein the first demand data is used for indicating a first demand of a user for performance of an air conditioner, displays a first operating parameter on the interactive interface, and the first operating parameter is obtained by analyzing the first demand data based on a neural network model, wherein the neural network model is used for establishing a mapping relation between different demand data and operating parameters of the air conditioner, controls the air conditioner according to the first operating parameter, and displays an operating state of the air conditioner under the control of the first operating parameter on the interactive interface. That is to say, this embodiment is based on the neural network model and is carried out the analysis to the demand data of user's input, obtains the operating parameter that carries out control to the air conditioner to satisfy user's demand, avoided unable according to user's self demand to the performance of air conditioner in the use, come to control the air conditioner in a flexible way, thereby solved and carried out the big technical problem of limitation of control to the air conditioner, and then reached the technological effect that reduces the limitation of controlling the air conditioner.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
Example 2
The technical solution of the present invention is illustrated below with reference to preferred embodiments.
In this embodiment, the air conditioner is provided with an electric quantity detection device, which can display the electric quantity of the air conditioner, and is convenient for a user to check, the user can evaluate the electric quantity of the air conditioner through an input end such as App, for example, evaluation data such as unacceptable, acceptable, general, energy-saving and the like, and can also evaluate the starting comfort of the air conditioner, for example, evaluation data such as cold, cool, slightly cool, moderate, slightly warm, heat and the like, the evaluation data are transmitted to a neural network model in an intelligent module of the air conditioner, the data are processed through the neural network model, target operation parameters are output, an actuator such as a compressor, an inner fan, an outer fan, an expansion valve and the like of the air conditioner is controlled through the target operation parameters, so as to meet the user requirements, and the current control state can be fed back to the user, thereby achieving the purpose of.
Fig. 3 is a schematic diagram of determining an operation parameter of an air conditioner based on a neural network model according to an embodiment of the present invention. As shown in fig. 3, the neural network model of this embodiment includes an input layer, a hidden layer, and an output layer. Wherein, the hidden layer comprises a plurality of hidden nodes, such as hidden node 1, hidden node 2 … … and hidden node n.
Firstly, the collected data are input into an input layer of the neural network model, and the data required by the power consumption of the air conditioner, the data required by thermal comfort, the data of indoor and outdoor environments, indoor personnel information, other information and the like can be input into the input layer of the neural network model. Optionally, in this embodiment, a thermal imager or a camera may be used to collect the personnel in the room where the air conditioner is located, and indoor personnel information such as the number of the personnel, the positions of the personnel, the body temperature, and the like may be acquired; the indoor and outdoor environments are detected by using a detection sensor arranged on the air conditioner, and then the power consumption requirement (a specific power consumption limit value) of the air conditioner and the thermal comfort requirement condition are fed back through the user input.
After the collected data is input to the input layer of the neural network model, the data is processed at the hidden layer. In this embodiment, the collected data is input to an intelligent module inside the air conditioner, and a neural network algorithm is adopted, that is, the data obtained after processing is processed through linear processing such as normalization and nonlinear processing such as logarithmic conversion, square root conversion, and cubic root, so as to obtain a processing result, where the processing result may include other optimal operation parameters for controlling the air conditioner, such as air conditioner compressor frequency, inner fan rotation speed, air deflector angle, outer fan rotation speed, and expansion valve opening.
After the data are processed, the processing result is output through an output layer of the neural network model, and the optimal operation parameters for controlling the air conditioner, such as the frequency of an air conditioner compressor, the rotating speed of an inner fan, the angle of an air deflector, the rotating speed of an outer fan, the opening degree of an expansion valve and the like, can be output.
In the process that the air conditioner operates according to the optimal operating parameters, the user can also feed back and correct the target set temperature. Fig. 4 is a schematic diagram of correcting an internal set temperature based on evaluation data according to an embodiment of the present invention. As shown in fig. 4, after the air conditioner operates for a period of time, a user may input power consumption evaluation data (e.g., power consumption +2, general +1, energy saving +0) and thermal comfort evaluation data (e.g., cooler +2, cooler +1, comfort +0, hot-1, and hotter-2) of the air conditioner through the APP and feed back the data to the air conditioner, and the air conditioner may obtain a corresponding temperature correction value Δ t1 based on the power consumption evaluation data, obtain a corresponding temperature correction value Δ t2 based on the thermal comfort evaluation data, and further correct the internal set temperature by the temperature correction value corresponding to the power consumption evaluation data and the temperature correction value corresponding to the thermal comfort evaluation data.
Optionally, in this embodiment, the neural network model is corrected through the evaluation data, the weight of power consumption and thermal comfort can be automatically adjusted, and then the internal set temperature is adjusted, so that the optimal operation parameters of each actuator of the air conditioner during operation are obtained, and personalized differentiation requirements are met, so that the evaluation degree of the user on the power consumption and the thermal comfort of the air conditioner is higher and higher, and the user experience is improved.
In this embodiment, the user is at the in-process that uses the air conditioner, can evaluate the indoor environment according to the thermal comfort of self, and manage the planning according to the demand of self to the power consumption of air conditioner to the electric quantity of air conditioner, the air conditioner can gather corresponding information, utilize the neural network algorithm model in the intelligent object, adjust indoor temperature, wind speed isoparametric, thereby realize that the air conditioner is in best running state, the user can be according to the demand of self like this, propose the requirement to indoor environment state and air conditioner electric quantity, thereby realize the purpose that satisfies user's electric quantity and thermal comfort's demand simultaneously.
Example 3
The invention also provides a control device of the air conditioner. It should be noted that the control device of the air conditioner of this embodiment may be used to execute the control method of the air conditioner shown in fig. 1 according to the embodiment of the present invention.
Fig. 5 is a schematic diagram of a control apparatus of an air conditioner according to an embodiment of the present invention. As shown in fig. 5, the control device 50 of the air conditioner may include: an acquisition unit 51, an analysis unit 52 and a control unit 53.
The acquiring unit 51 is configured to acquire first demand data indicating a first demand of the user for performance of the air conditioner.
The analysis unit 52 is configured to analyze the first demand data based on a neural network model to obtain a first operating parameter of the air conditioner, where the neural network model is used to establish a mapping relationship between different demand data and the operating parameter of the air conditioner.
And a control unit 53 for controlling the air conditioner by the first operation parameter.
Optionally, the apparatus further comprises: and the output unit is used for outputting the operation state of the air conditioner under the control of the first operation parameter when the air conditioner is controlled by the first operation parameter.
Optionally, the analyzing unit 52 includes: the processing module is used for analyzing the first demand data based on the neural network model, determining at least one actuator of the air conditioner associated with the first demand data, and acquiring a first operating parameter of the at least one actuator.
Optionally, the analyzing unit 52 includes: the correction module is used for correcting the neural network model based on the first requirement data; and the analysis module is used for analyzing the first requirement data through the corrected neural network model to obtain a first operation parameter.
Optionally, the correction module comprises: and the correction submodule is used for correcting the weight corresponding to the first requirement data in the neural network model.
Optionally, the analysis module comprises: and the analysis submodule is used for analyzing the first requirement data through the neural network model after the weight is corrected to obtain a first operation parameter.
Optionally, the analysis sub-module is further configured to analyze the first demand data through the weight-corrected neural network model to obtain a first operating parameter by: acquiring a temperature correction value corresponding to the first demand data based on the neural network model after the weight correction; correcting the original temperature value set by the air conditioner through the temperature correction value to obtain a target temperature value; a first operating parameter corresponding to a target temperature value is obtained.
Optionally, the apparatus further comprises: the air conditioner management system comprises a first acquisition unit, a second acquisition unit and a control unit, wherein the first acquisition unit is used for acquiring information of the environment where the air conditioner is located, information of a using object of the air conditioner and second demand data before acquiring the first demand data, and the second demand data is used for indicating a second demand of a user on the performance of the air conditioner; the first analysis unit is used for analyzing the environment information, the using object information and the second demand data based on the neural network model to obtain a second operation parameter of the air conditioner; and the first control unit is used for controlling the air conditioner through the second operation parameter.
Alternatively, the acquisition unit 51 includes: and the acquisition module is used for acquiring the first demand data in the process of controlling the air conditioner through the second operation parameter.
Optionally, the apparatus further comprises: the second obtaining unit is configured to obtain sample data before the first demand data is analyzed based on the neural network model to obtain a first operating parameter of the air conditioner, where the sample data at least includes: different demand data, and operating parameters of the air conditioner corresponding to the different demand data; and the training unit is used for learning the sample data based on the sub-neural network model to generate the neural network model.
Optionally, the first requirement data of this embodiment includes: evaluation data for evaluating the power consumption of the air conditioner, and/or evaluation data for evaluating the comfort of the air conditioner.
The device obtains first demand data through an obtaining unit 51, wherein the first demand data is used for indicating a first demand of a user for performance of the air conditioner, the first demand data is analyzed through an analyzing unit 52 based on a neural network model to obtain a first operating parameter of the air conditioner, the neural network model is used for establishing a mapping relation between different demand data and the operating parameter of the air conditioner, and the air conditioner is controlled through a control unit 53 through the first operating parameter. That is to say, this embodiment is based on the neural network model and is carried out the analysis to the demand data of user input, obtains the operating parameter that controls the air conditioner to satisfy user's demand, solved the technical problem that the limitation is big that controls the air conditioner, reached the technological effect that reduces the limitation that controls the air conditioner.
The invention also provides another control device of the air conditioner. It should be noted that the control device of the air conditioner of this embodiment may be used to execute the control method of the air conditioner shown in fig. 2 according to the embodiment of the present invention.
Fig. 6 is a schematic view of another control apparatus of an air conditioner according to an embodiment of the present invention. As shown in fig. 6, the control device 60 of the air conditioner may include: a first display unit 61, a second display unit 62, and a third display unit 63.
The first display unit 61 is configured to display first demand data on the interactive interface, where the first demand data is used to indicate a first demand of the user for performance of the air conditioner.
And the second display unit 62 is configured to display the first operation parameter on the interactive interface, where the first operation parameter is obtained by analyzing the first demand data based on a neural network model, and the neural network model is used to establish a mapping relationship between different demand data and the operation parameter of the air conditioner.
And the third display unit 63 is used for displaying the operation state of the air conditioner under the control of the first operation parameter on the interactive interface.
Optionally, the apparatus further comprises: the correcting unit is used for correcting the neural network model based on the first requirement data; and the analysis unit is used for analyzing the first requirement data through the corrected neural network model to obtain a first operation parameter.
Optionally, the apparatus further comprises: the fourth display unit is used for displaying the information of the environment where the air conditioner is located, the information of the using object of the air conditioner and second demand data on the interactive interface before the first demand data is displayed on the interactive interface, wherein the second demand data is used for indicating a second demand of the user on the performance of the air conditioner; the fifth display unit is used for displaying a second operation parameter on the interactive interface, wherein the second operation parameter is obtained by analyzing the environment information, the using object information and the second requirement data based on the neural network model; and the sixth display unit is used for displaying the operation state of the air conditioner under the control of the second operation parameter on the interactive interface.
In the embodiment, first requirement data is displayed on an interactive interface through a first display unit 61, wherein the first requirement data is used for indicating a first requirement of a user on the performance of the air conditioner, and first operation parameters are displayed on the interactive interface through a second display unit 62, wherein the first operation parameters are obtained by analyzing the first requirement data based on a neural network model, the neural network model is used for establishing a mapping relation between different requirement data and operation parameters of the air conditioner, and an operation state of the air conditioner under the control of the first operation parameters is displayed on the interactive interface through a third display unit 63. That is to say, this embodiment is based on the neural network model and is carried out the analysis to the demand data of user input, obtains the operating parameter that controls the air conditioner to satisfy user's demand, solved the technical problem that the limitation is big that controls the air conditioner, reached the technological effect that reduces the limitation that controls the air conditioner.
Example 4
The embodiment of the invention also provides an air conditioner, which can comprise the device for determining the outlet air temperature of the air conditioner.
Example 5
The embodiment of the invention also provides a storage medium. The storage medium comprises a stored program, wherein when the program runs, the equipment where the storage medium is located is controlled to execute the method for determining the outlet air temperature of the air conditioner.
Example 6
The embodiment of the invention also provides a processor. The processor is used for running a program, wherein the program executes the method for determining the outlet air temperature of the air conditioner in the embodiment of the invention when running.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and they may alternatively be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, or fabricated separately as individual integrated circuit modules, or fabricated as a single integrated circuit module from multiple modules or steps. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (16)
1. A method of controlling an air conditioner, comprising:
acquiring first demand data, wherein the first demand data is used for indicating a first demand of a user for the performance of the air conditioner;
analyzing the first demand data based on a neural network model to obtain a first operating parameter of the air conditioner, wherein the neural network model is used for establishing a mapping relation between different demand data and the operating parameter of the air conditioner;
controlling the air conditioner through the first operation parameter;
wherein, when the air conditioner is controlled by the first operation parameter, the method further comprises: outputting the running state of the air conditioner under the control of the first running parameter;
analyzing the first demand data based on a neural network model to obtain a first operating parameter of the air conditioner, including: modifying the neural network model based on the first demand data; and analyzing the first requirement data through the corrected neural network model to obtain the first operation parameter.
2. The method of claim 1, wherein analyzing the first demand data based on a neural network model to obtain a first operating parameter of the air conditioner comprises:
analyzing the first demand data based on the neural network model, determining at least one actuator of the air conditioner associated with the first demand data, and acquiring the first operating parameter of the at least one actuator.
3. The method of claim 1, wherein modifying the neural network model based on the first demand data comprises:
modifying weights in the neural network model corresponding to the first demand data.
4. The method of claim 3, wherein analyzing the first demand data through the modified neural network model to obtain the first operating parameter comprises:
and analyzing the first requirement data through the neural network model after the weight is corrected to obtain the first operation parameter.
5. The method of claim 4, wherein analyzing the first demand data through the neural network model after modifying the weights to obtain the first operating parameter comprises:
acquiring a temperature correction value corresponding to the first demand data based on the neural network model after the weight is corrected;
correcting the original temperature value set by the air conditioner through the temperature correction value to obtain a target temperature value;
and acquiring the first operating parameter corresponding to the target temperature value.
6. The method of claim 1, wherein prior to obtaining the first demand data, the method further comprises:
acquiring information of an environment where the air conditioner is located, information of a use object of the air conditioner and second demand data, wherein the second demand data is used for indicating a second demand of a user for the performance of the air conditioner;
analyzing the environment information, the using object information and the second demand data based on the neural network model to obtain a second operating parameter of the air conditioner;
and controlling the air conditioner through the second operation parameter.
7. The method of claim 6, wherein obtaining the first demand data comprises:
and acquiring the first demand data in the process of controlling the air conditioner through the second operation parameter.
8. The method of any one of claims 1 to 7, wherein before analyzing the first demand data based on a neural network model to obtain a first operating parameter of the air conditioner, the method further comprises:
obtaining sample data, wherein the sample data at least comprises: different demand data, and operating parameters of the air conditioner corresponding to the different demand data;
and learning the sample data based on a sub-neural network model to generate the neural network model.
9. The method of any one of claims 1 to 7, wherein the first demand data comprises: evaluation data for evaluating power consumption of the air conditioner, and/or evaluation data for evaluating comfort of the air conditioner.
10. A method of controlling an air conditioner, comprising:
displaying first demand data on an interactive interface, wherein the first demand data is used for indicating first demands of users on the performance of the air conditioner;
displaying a first operation parameter on the interactive interface, wherein the first operation parameter is obtained by analyzing the first demand data based on a neural network model, and the neural network model is used for establishing a mapping relation between different demand data and operation parameters of the air conditioner;
displaying the running state of the air conditioner under the control of the first running parameter on the interactive interface;
wherein, when the air conditioner is controlled by the first operation parameter, the method further comprises: outputting the running state of the air conditioner under the control of the first running parameter;
wherein the method further comprises: modifying the neural network model based on the first demand data; and analyzing the first requirement data through the corrected neural network model to obtain the first operation parameter.
11. The method of claim 10, wherein prior to displaying the first demand data on an interactive interface, the method further comprises:
displaying information of the environment where the air conditioner is located, information of a use object of the air conditioner and second demand data on the interactive interface, wherein the second demand data is used for indicating second demands of users on the performance of the air conditioner;
displaying a second operation parameter on the interactive interface, wherein the second operation parameter is obtained by analyzing the environment information, the using object information and the second demand data based on the neural network model;
and displaying the operation state of the air conditioner under the control of the second operation parameter on the interactive interface.
12. A control apparatus of an air conditioner, comprising:
the system comprises an acquisition unit, a control unit and a processing unit, wherein the acquisition unit is used for acquiring first demand data, and the first demand data is used for indicating a first demand of a user on the performance of the air conditioner;
the analysis unit is used for analyzing the first demand data based on a neural network model to obtain a first operating parameter of the air conditioner, wherein the neural network model is used for establishing a mapping relation between different demand data and the operating parameter of the air conditioner;
the control unit is used for controlling the air conditioner through the first operation parameter;
the device is used for outputting the running state of the air conditioner under the control of a first running parameter when the air conditioner is controlled by the first running parameter;
wherein the analysis unit analyzes the first demand data based on a neural network model to obtain a first operating parameter of the air conditioner by: modifying the neural network model based on the first demand data; and analyzing the first requirement data through the corrected neural network model to obtain the first operation parameter.
13. A control apparatus of an air conditioner, comprising:
the first display unit is used for displaying first demand data on an interactive interface, wherein the first demand data is used for indicating a first demand of a user on the performance of the air conditioner;
the second display unit is used for displaying a first operation parameter on the interactive interface, wherein the first operation parameter is obtained by analyzing the first demand data based on a neural network model, and the neural network model is used for establishing a mapping relation between different demand data and operation parameters of the air conditioner;
the third display unit is used for displaying the running state of the air conditioner under the control of the first running parameter on the interactive interface;
the device is used for outputting the running state of the air conditioner under the control of a first running parameter when the air conditioner is controlled by the first running parameter;
wherein the apparatus is further configured to modify the neural network model based on the first demand data; and analyzing the first requirement data through the corrected neural network model to obtain the first operation parameter.
14. An air conditioner characterized by comprising the control device of the air conditioner of claim 12 or 13.
15. A storage medium, characterized in that the storage medium comprises a stored program, wherein the program, when executed, controls an apparatus in which the storage medium is located to perform the method of any one of claims 1 to 11.
16. A processor, characterized in that the processor is configured to run a program, wherein the program when running performs the method of any of claims 1 to 11.
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| CN104110767B (en) * | 2013-05-30 | 2017-07-11 | 广东美的制冷设备有限公司 | A kind of air-conditioner power consumption control method, apparatus and system |
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