CN112361542A - Control method and device of kitchen air conditioner, controller and electric appliance system - Google Patents
Control method and device of kitchen air conditioner, controller and electric appliance system Download PDFInfo
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- CN112361542A CN112361542A CN202011079473.7A CN202011079473A CN112361542A CN 112361542 A CN112361542 A CN 112361542A CN 202011079473 A CN202011079473 A CN 202011079473A CN 112361542 A CN112361542 A CN 112361542A
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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
- F24F11/30—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
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
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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
- F24F2110/12—Temperature of the outside 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
- F24F2110/00—Control inputs relating to air properties
- F24F2110/20—Humidity
- F24F2110/22—Humidity of the outside 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
- F24F2120/00—Control inputs relating to users or occupants
- F24F2120/10—Occupancy
- F24F2120/14—Activity of occupants
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- Mathematical Physics (AREA)
- Air Conditioning Control Device (AREA)
Abstract
The invention relates to a control method, a control device, a controller and an electric appliance system of a kitchen air conditioner, wherein the control method of the kitchen air conditioner comprises the following steps: acquiring kitchen environment data and human body data; determining corresponding application scenes and user using conditions according to the kitchen environment data and the human body data; obtaining the optimal operation parameters of the air conditioner under the application scene and the user using condition through a machine learning model; controlling the air conditioner according to the optimal operation parameters of the air conditioner; according to the invention, the application scenes of the user are classified according to the actual kitchen use condition of the user, the use condition data of the user in different application scenes are used as input conditions for controlling the operation of the air conditioner, the optimal operation parameters of the air conditioner of the user in different application scenes and use conditions are obtained by applying a machine learning method, and the operation of the air conditioner is controlled by using the parameters, so that the use habit of the user can be met, and the accurate energy-saving operation can be realized.
Description
Technical Field
The invention relates to the technical field of air conditioner control, in particular to a kitchen air conditioner control method, a kitchen air conditioner control device, a kitchen air conditioner controller and an electric appliance system.
Background
With the development of artificial intelligence technology and the continuous innovation of household appliance technology, the technology of smart home has been advanced into the life of people, and with the continuous improvement of the living standard of people, the requirements for energy saving and self-adaptive performance are also continuously increased. At present, the intelligent air conditioner mostly adjusts the parameter according to the temperature, or adjusts the parameter based on monitoring the human body characteristic parameters of the user, for example, in patents CN110094837A and CN109595765A, although the intelligent control mode can realize energy-saving operation, the intelligent control mode cannot meet the use requirements and personalized comfort level experience of the user.
The kitchen is used as an air conditioner using place which consumes more electricity, the requirement for energy saving of air conditioner operation is relatively large, but at present, the refined energy-saving control method for the kitchen air conditioner is less, and the intelligent air conditioner which can save energy and meet the use habit of a user is fewer and fewer.
Disclosure of Invention
In view of the above, the present invention provides a method and an apparatus for controlling a kitchen air conditioner, a controller and an electrical system.
In order to achieve the purpose, the invention adopts the following technical scheme: a control method of a kitchen air conditioner, comprising:
acquiring kitchen environment data and human body data;
determining corresponding application scenes and user using conditions according to the kitchen environment data and the human body data;
obtaining the optimal operation parameters of the air conditioner under the application scene and the user using condition through a machine learning model;
and controlling the air conditioner according to the optimal operation parameters of the air conditioner.
Optionally, the kitchen environment data comprises one or more of:
kitchen interior temperature, kitchen interior humidity, outdoor ambient temperature, outdoor ambient humidity.
Optionally, the human body data includes: personnel count and human labor intensity data;
wherein the human labor intensity data comprises one or more of:
heart rate, fingertip blood flow, respiration rate, skin temperature, metabolism rate, brain wave, and perspiration rate.
Optionally, the determining the corresponding application scenario and the user usage condition according to the kitchen environment data and the human body data specifically includes:
obtaining a final application scene containing various factors according to different combinations of the kitchen environment data and the human body data;
and determining the user use condition corresponding to the application scene by combining the prior air conditioner operation data.
Optionally, the machine learning model is established through the following process:
the method comprises the following steps: acquiring kitchen environment data, human body data and air conditioner operation data in advance;
step two: determining an application scene of a kitchen and user using conditions under the application scene based on the kitchen environment data, the human body data and the air conditioner operation data;
step three: acquiring multiple groups of air conditioner operation data under different application scenes and user use conditions according to the first step and the second step;
step four: analyzing a plurality of groups of air conditioner operation data under the same application scene and user use conditions respectively to determine the air conditioner operation data with the minimum energy consumption under the application scene and the user use conditions, and taking the air conditioner operation data as the optimal air conditioner operation parameters under the application scene and the user use conditions;
step five: determining the optimal operation parameters of the air conditioner under each application scene and user using conditions according to the method of the fourth step;
step six: and (4) according to a machine learning method, training the machine learning model by using the optimal operation parameters of the air conditioner under each application scene and user use conditions obtained in the step five so as to establish the machine learning model.
Optionally, the control method further includes:
after the air conditioner operates for a specific time, acquiring kitchen environment data, human body data and air conditioner operation data again;
and correcting the machine learning model according to the re-acquired kitchen environment data, the human body data and the air conditioner operation data to obtain a corrected machine learning model, and acquiring the optimal operation parameters of the air conditioner according with the current application scene and the user use condition through the corrected machine learning model.
The invention also provides a controller for executing the control method of the kitchen air conditioner.
The present invention also provides a control device of a kitchen air conditioner, comprising:
the data acquisition module is used for acquiring kitchen environment data and human body data;
the first determining module is used for determining corresponding application scenes and user using conditions according to the kitchen environment data and the human body data;
the second determination module is used for obtaining the optimal operation parameters of the air conditioner under the application scene and the user using condition through a machine learning model;
and the control module is used for controlling the air conditioner according to the optimal operation parameters of the air conditioner.
The present invention also provides an electrical system comprising: the control device of the kitchen air conditioner and the kitchen air conditioner are provided.
Optionally, the electrical system further includes: a smoke exhaust system;
the control device is also used for determining the control parameters of the smoke exhaust system according to the kitchen environment data and the human body data and controlling the operation of the smoke exhaust system according to the control parameters of the smoke exhaust system.
By adopting the technical scheme, the control method of the kitchen air conditioner comprises the following steps: acquiring kitchen environment data and human body data; determining corresponding application scenes and user using conditions according to the kitchen environment data and the human body data; obtaining the optimal operation parameters of the air conditioner under the application scene and the user using condition through a machine learning model; controlling the air conditioner according to the optimal operation parameters of the air conditioner; according to the invention, the application scenes of the user are classified according to the actual kitchen use condition of the user, the use condition data of the user in different application scenes are used as input conditions for controlling the operation of the air conditioner, the optimal operation parameters of the air conditioner of the user in different application scenes and use conditions are obtained by applying a machine learning method, and the operation of the air conditioner is controlled by using the parameters, so that the use habit of the user can be met, and the accurate energy-saving operation can be realized.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a control method for a kitchen air conditioner according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a kitchen air conditioner according to a second embodiment of the present invention;
fig. 3 is a schematic structural view provided by an embodiment of a control apparatus of a kitchen air conditioner according to the present invention;
fig. 4 is a schematic structural diagram provided by an embodiment of an electrical system according to the present invention.
In the figure: 1. a data acquisition module; 2. a first determination module; 3. a second determination module; 4. a control module; 5. a control device; 6. a kitchen air conditioner; 7. provided is a smoke exhaust system.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without any inventive step, are within the scope of the present invention.
Fig. 1 is a schematic flow chart of a control method of a kitchen air conditioner according to an embodiment of the present invention.
As shown in fig. 1, the method for controlling a kitchen air conditioner according to this embodiment includes:
s11: acquiring kitchen environment data and human body data;
further, the kitchen environment data includes one or more of:
kitchen interior temperature, kitchen interior humidity, outdoor ambient temperature, outdoor ambient humidity.
Further, the human body data includes: personnel count and human labor intensity data;
wherein the human labor intensity data comprises one or more of:
heart rate, fingertip blood flow, respiration rate, skin temperature, metabolism rate, brain wave, and perspiration rate.
S12: determining corresponding application scenes and user using conditions according to the kitchen environment data and the human body data;
further, the determining the corresponding application scenario and the user use condition according to the kitchen environment data and the human body data specifically includes:
obtaining a final application scene containing various factors according to different combinations of the kitchen environment data and the human body data;
and determining the user use condition corresponding to the application scene by combining the prior air conditioner operation data.
S13: obtaining the optimal operation parameters of the air conditioner under the application scene and the user using condition through a machine learning model;
further, the machine learning model is built through the following process:
the method comprises the following steps: acquiring kitchen environment data, human body data and air conditioner operation data in advance;
step two: determining an application scene of a kitchen and user using conditions under the application scene based on the kitchen environment data, the human body data and the air conditioner operation data;
step three: acquiring multiple groups of air conditioner operation data under different application scenes and user use conditions according to the first step and the second step;
step four: analyzing a plurality of groups of air conditioner operation data under the same application scene and user use conditions respectively to determine the air conditioner operation data with the minimum energy consumption under the application scene and the user use conditions, and taking the air conditioner operation data as the optimal air conditioner operation parameters under the application scene and the user use conditions;
step five: determining the optimal operation parameters of the air conditioner under each application scene and user using conditions according to the method of the fourth step;
step six: and (4) according to a machine learning method, training the machine learning model by using the optimal operation parameters of the air conditioner under each application scene and user use conditions obtained in the step five so as to establish the machine learning model.
S14: and controlling the air conditioner according to the optimal operation parameters of the air conditioner.
In the actual execution of the control method described in this embodiment, in the first step, different application scenes, the number of people, and the intensity of human motion in the kitchen are classified in advance.
The kitchen application scenes are classified according to the judgment conditions of whether to cook in the kitchen, whether to cook and fire, the heat and moisture content generated after the fire and the like, and the specific classification conditions are shown in the table. It should be noted that the above classification method is only one classification method of the present application, and those skilled in the art can make other classification methods based on the above idea without departing from the scope of the present application.
The temperature in the table refers to the ambient temperature in the kitchen and the humidity refers to the ambient relative humidity in the kitchen. T is the measured temperature, T0、T1、T2Is a set classification temperature value, T0<T1<T2. RH is the measured relative humidity, RH0、RH1、RH2Is a set classification humidity value, RH0<RH1<RH2。
The person situation in the kitchen is determined synchronously when the kitchen application scenario is determined. Including determining the number of people and the intensity of the person's labor. The number of people is divided into 1,2,3,4, etc. The labor intensity of the personnel is divided into three types of weak, medium and strong, and the classification method can be based on one or more human body indexes such as heart rate, fingertip blood flow, respiration rate, skin temperature, metabolism rate, brain wave, perspiration rate and the like.
And combining the classification results of different application scenes, the number of personnel and the labor intensity of human bodies of the kitchen to obtain a final application scene containing all factors.
And secondly, acquiring kitchen environment data, human body data and air conditioner operation data. Specifically, the kitchen environment data includes kitchen interior temperature, kitchen interior humidity, outdoor environment temperature, and outdoor environment humidity, and the human body data includes the number of people and human body labor intensity, wherein the human body labor intensity data is one or more of human body indexes such as heart rate, fingertip blood flow volume, respiration rate, skin temperature, metabolism rate, brain wave, and perspiration rate. The air conditioner operation data includes, but is not limited to, compressor frequency, outlet water temperature, fan speed, water flow, air conditioner operation period, set temperature, set humidity, etc.
It can be understood that the kitchen environment data is obtained by kitchen environment monitoring devices, including temperature monitoring devices, humidity monitoring devices, and cooking monitoring devices. The human body data is obtained through personnel and physiological parameter monitoring equipment, and the personnel and physiological parameter monitoring equipment can be one or more of the following equipment: camera, infrared induction equipment, bracelet, wrist strap and other wireless induction equipment. The monitoring equipment for detecting whether the fire is on can be a camera, and a switch control signal of the smoke sensor or the cooking equipment can be directly fed back to the air conditioner.
And thirdly, determining corresponding application scenes and user using conditions according to the kitchen environment data and the human body data.
When a user cooks in a kitchen, the cooking habits are different, and the conditions for controlling the air conditioner system are also different. The application scene where the user is located and the air conditioner use habits of the user in different time periods and corresponding application scenes can be determined according to the classification method of the first step and the data acquired in the second step. For example, the user basically cooks at six pm every day with a kitchen temperature T1<T≤T2Humidity is RH>RH2And when one person cooks at medium labor intensity, the set temperature of the air conditioner is controlled to be 23 ℃ and the humidity is controlled to be 40%, and then the corresponding application scene and the user using condition under the application scene can be determined and stored according to the classification condition, the kitchen environment data and the human body data.
And fourthly, obtaining the optimal operation parameters of the air conditioner under the application scene and the user using condition through a machine learning model.
After different application scenes, user use conditions, environmental data and air conditioner operation data are obtained, the data can be analyzed based on a self-learning method, and a machine learning model which accords with the use habits of users and can realize energy conservation under different application scenes is obtained. The machine learning model is used for establishing mapping relations among different application scenes, user using conditions and optimal operating parameters of the air conditioner. The input parameters of the model are different application scenes, user use conditions and environmental data, the output parameters are the optimal operation parameters of the air conditioner, and the optimal operation parameters include but are not limited to the parameters of compressor frequency, water outlet temperature, fan rotating speed, water flow and the like. The optimal control parameter means that the energy consumption of the air conditioner is minimum when the air conditioner is controlled to run by adopting the optimal operation parameter under the current environmental condition, application scene and user use condition.
And fifthly, controlling the air conditioner according to the optimal operation parameters of the air conditioner.
According to the control method, the application scenes of the kitchen are finely classified, so that the optimal air conditioner operation parameters can be searched in a smaller range, and more accurate control is realized; using condition data of a user in different application scenes as input conditions for controlling the operation of the air conditioner, and fully considering the personalized comfort requirement of the user; by establishing the machine learning model and controlling the operation parameters of the air conditioner according to the application scene and the use condition of the user, the aims of refining energy conservation and meeting the use habit of the user are fulfilled.
Fig. 2 is a schematic flow chart of a control method of a kitchen air conditioner according to a second embodiment of the present invention.
As shown in fig. 2, the method for controlling a kitchen air conditioner according to this embodiment includes:
s21: establishing a machine learning model according to pre-acquired kitchen environment data, human body data and air conditioner operation parameters;
s22: acquiring kitchen environment data and human body data;
s23: determining corresponding application scenes and user using conditions according to the kitchen environment data and the human body data;
s24: obtaining the optimal operation parameters of the air conditioner under the application scene and the user using condition through the machine learning model;
s25: controlling the air conditioner according to the optimal operation parameters of the air conditioner;
s26: after the air conditioner operates for a specific time, acquiring kitchen environment data, human body data and air conditioner operation data again;
s27: and correcting the machine learning model according to the re-acquired kitchen environment data, the human body data and the air conditioner operation data to obtain a corrected machine learning model, and acquiring the optimal operation parameters of the air conditioner according with the current application scene and the user use condition through the corrected machine learning model.
According to the embodiment, the application scenes of a kitchen are finely classified, the use condition data of a user in different application scenes are used as input conditions for controlling the operation of the air conditioner, the optimal operation parameters of the air conditioner of the user in different application scenes and different use conditions are obtained by applying a machine learning method, the optimal operation parameters of the air conditioner are used for controlling the operation of the air conditioner, meanwhile, after the air conditioner operates for a specific time, the learning model can be corrected, and then the optimal operation parameters of the air conditioner which accord with the current application scenes and the use conditions of the user are obtained through the corrected learning model, so that the use habits of different users can be met, the air conditioner can be accurately controlled, and energy conservation is realized.
The present invention also provides a controller for performing the control method of the kitchen air conditioner of fig. 1 or 2.
Fig. 3 is a schematic structural diagram of a control device of a kitchen air conditioner according to an embodiment of the present invention.
As shown in fig. 3, the control device for a kitchen air conditioner according to the present embodiment includes:
the data acquisition module 1 is used for acquiring kitchen environment data and human body data;
the first determining module 2 is used for determining corresponding application scenes and user using conditions according to the kitchen environment data and the human body data;
the second determining module 3 is used for obtaining the optimal operation parameters of the air conditioner under the application scene and the user using condition through a machine learning model;
and the control module 4 is used for controlling the air conditioner according to the optimal operation parameters of the air conditioner.
The operation principle of the control device of the kitchen air conditioner in this embodiment is the same as the operation principle of the control method of the kitchen air conditioner described in fig. 1 or fig. 2, and is not described herein again.
Fig. 4 is a schematic structural diagram provided by an embodiment of an electrical system according to the present invention.
As shown in fig. 4, the electrical system according to this embodiment includes:
a control device 5 of a kitchen air conditioner and a kitchen air conditioner 6 as shown in fig. 3.
The control device 5 may be a separate device capable of receiving and transmitting data information with the air conditioner, or may be a module integrated in the air conditioner. After the optimal operation parameters of the air conditioner are obtained, the control device 5 controls the air conditioner to execute corresponding actions under the parameters, so that the optimal operation effect of different functional areas is achieved.
Further, the electrical system further includes: a smoke exhaust system 7;
the control device 5 is also used for determining the control parameters of the smoke exhaust system according to the kitchen environment data and the human body data, and controlling the operation of the smoke exhaust system 7 according to the control parameters of the smoke exhaust system.
The kitchen air conditioner 6 can realize joint control with the smoke exhaust system 7 of the kitchen, the user does not need to manually control the smoke exhaust system 7 and the air conditioning system when cooking in the kitchen, the two systems can automatically meet the use requirements of the user, and energy conservation is realized on the basis.
The method comprises the following concrete steps:
the method comprises the following steps: the habit of the user controlling the smoke evacuation system 7 is obtained.
And the control device 5 of the kitchen air conditioner is in data communication with the control device of the smoke exhaust system 7, and the operation habit of a user on the smoke exhaust system 7 during cooking is synchronously acquired when the data of the air conditioner is acquired. The control of the smoke exhaust system 7 includes whether to turn on the smoke exhaust system 7, controlling the gear high, medium, low, etc. For example, when a user is cooking in a kitchen, the environmental temperature of the kitchen is 26 ℃, the relative humidity is 78%, the smoke concentration is medium, and the control of the smoke exhaust system 7 is high.
Step two: a machine learning model of the smoke evacuation system 7 is established.
Establishing a machine learning model controlled by different kitchen environment data and the smoke exhaust system 7 according to the acquired habit of controlling the smoke exhaust system 7 by the user and the corresponding real-time kitchen environment data (including smoke concentration) obtained from the control device 5 of the kitchen air conditioner, wherein the input condition of the model is the kitchen environment data, and the output condition is the control parameter of the smoke exhaust system. The control parameters of the smoke exhaust system comprise whether the gear is opened or not and the gear is controlled.
Step three: the control device 5 controls the operation of the air conditioner and the smoke exhaust system 7 at the same time.
Specifically, kitchen environment data and human body data of a user are obtained; obtaining corresponding output parameters from kitchen data through a model of an air conditioning system and a model of a smoke exhaust system respectively; the operation of the air conditioning system and the operation of the smoke exhaust system 7 are respectively controlled by the output parameters.
The control device 5 may participate in the control of the operation of both the air conditioning system and the smoke exhaust system 7. After obtaining the control parameters of the two systems, the control device 5 controls the kitchen air conditioner 6 and the smoke exhaust system 7 to execute corresponding actions under the parameters.
The electric appliance system described in this embodiment controls the operation parameters of the air conditioner and the smoke exhaust system 7 according to the application scene and the user use condition, so that the purposes of refining and saving energy and meeting the use habits of the user are achieved, and the intelligent degree of the kitchen is improved.
It is understood that the same or similar parts in the above embodiments may be mutually referred to, and the same or similar parts in other embodiments may be referred to for the content which is not described in detail in some embodiments.
It should be noted that the terms "first," "second," and the like in the description of the present invention are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Further, in the description of the present invention, the meaning of "a plurality" means at least two unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. A control method of a kitchen air conditioner is characterized by comprising the following steps:
acquiring kitchen environment data and human body data;
determining corresponding application scenes and user using conditions according to the kitchen environment data and the human body data;
obtaining the optimal operation parameters of the air conditioner under the application scene and the user using condition through a machine learning model;
and controlling the air conditioner according to the optimal operation parameters of the air conditioner.
2. The control method of claim 1, wherein the kitchen environment data comprises one or more of:
kitchen interior temperature, kitchen interior humidity, outdoor ambient temperature, outdoor ambient humidity.
3. The control method according to claim 1, wherein the human body data includes: personnel count and human labor intensity data;
wherein the human labor intensity data comprises one or more of:
heart rate, fingertip blood flow, respiration rate, skin temperature, metabolism rate, brain wave, and perspiration rate.
4. The control method according to claim 1, wherein the determining the corresponding application scenario and the user usage condition according to the kitchen environment data and the human body data specifically comprises:
obtaining a final application scene containing various factors according to different combinations of the kitchen environment data and the human body data;
and determining the user use condition corresponding to the application scene by combining the prior air conditioner operation data.
5. The control method according to claim 1, characterized in that the machine learning model is established by:
the method comprises the following steps: acquiring kitchen environment data, human body data and air conditioner operation data in advance;
step two: determining an application scene of a kitchen and user using conditions under the application scene based on the kitchen environment data, the human body data and the air conditioner operation data;
step three: acquiring multiple groups of air conditioner operation data under different application scenes and user use conditions according to the first step and the second step;
step four: analyzing a plurality of groups of air conditioner operation data under the same application scene and user use conditions respectively to determine the air conditioner operation data with the minimum energy consumption under the application scene and the user use conditions, and taking the air conditioner operation data as the optimal air conditioner operation parameters under the application scene and the user use conditions;
step five: determining the optimal operation parameters of the air conditioner under each application scene and user using conditions according to the method of the fourth step;
step six: and (4) according to a machine learning method, training the machine learning model by using the optimal operation parameters of the air conditioner under each application scene and user use conditions obtained in the step five so as to establish the machine learning model.
6. The control method according to any one of claims 1 to 5, characterized by further comprising:
after the air conditioner operates for a specific time, acquiring kitchen environment data, human body data and air conditioner operation data again;
and correcting the machine learning model according to the re-acquired kitchen environment data, the human body data and the air conditioner operation data to obtain a corrected machine learning model, and acquiring the optimal operation parameters of the air conditioner according with the current application scene and the user use condition through the corrected machine learning model.
7. A controller for performing the method of controlling a kitchen air conditioner according to any one of claims 1 to 6.
8. A control apparatus for a kitchen air conditioner, comprising:
the data acquisition module is used for acquiring kitchen environment data and human body data;
the first determining module is used for determining corresponding application scenes and user using conditions according to the kitchen environment data and the human body data;
the second determination module is used for obtaining the optimal operation parameters of the air conditioner under the application scene and the user using condition through a machine learning model;
and the control module is used for controlling the air conditioner according to the optimal operation parameters of the air conditioner.
9. An electrical system, comprising: the control device for a kitchen air conditioner and the kitchen air conditioner as set forth in claim 8.
10. The electrical system of claim 9, further comprising: a smoke exhaust system;
the control device is also used for determining the control parameters of the smoke exhaust system according to the kitchen environment data and the human body data and controlling the operation of the smoke exhaust system according to the control parameters of the smoke exhaust system.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113531853A (en) * | 2021-06-29 | 2021-10-22 | 青岛海尔空调器有限总公司 | Kitchen air conditioner control method, device and equipment |
| WO2023142510A1 (en) * | 2022-01-28 | 2023-08-03 | 青岛海尔空调器有限总公司 | Method and apparatus for scenario simulation and air conditioner |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104214888A (en) * | 2013-06-05 | 2014-12-17 | 海尔集团公司 | Air conditioner and temperature control method and device thereof |
| CN106152408A (en) * | 2016-07-06 | 2016-11-23 | 北京地平线机器人技术研发有限公司 | Intelligent air conditioner controller, control method and air-conditioner |
| CN106369739A (en) * | 2016-08-23 | 2017-02-01 | 海信（山东）空调有限公司 | Air conditioner control method, air conditioner controller and air conditioner system |
| CN106482189A (en) * | 2016-11-29 | 2017-03-08 | 广东万家乐燃气具有限公司 | The system and method that a kind of range hood is interacted with Intelligent bracelet |
| CN106556111A (en) * | 2016-11-24 | 2017-04-05 | 广东美的制冷设备有限公司 | Household electric appliance control method, device, server and air-conditioner |
| CN108361927A (en) * | 2018-02-08 | 2018-08-03 | 广东美的暖通设备有限公司 | A kind of air-conditioner control method, device and air conditioner based on machine learning |
| CN108870679A (en) * | 2018-07-02 | 2018-11-23 | 珠海格力电器股份有限公司 | A kind of control method of air-conditioning, device, storage medium and air-conditioning |
| CN109631255A (en) * | 2018-12-10 | 2019-04-16 | 珠海格力电器股份有限公司 | A kind of air conditioning control method, device, storage medium and air-conditioning |
| CN109974224A (en) * | 2019-04-01 | 2019-07-05 | 珠海格力电器股份有限公司 | The air conditioner of adaptive place use demand, the method and device for controlling air conditioner |
| CN110094837A (en) * | 2019-04-30 | 2019-08-06 | 珠海格力电器股份有限公司 | Intelligent control apparatus for air-conditioner and method |
| CN110650200A (en) * | 2019-09-25 | 2020-01-03 | 佛山市顺德区美的洗涤电器制造有限公司 | Control method, kitchen appliance system, terminal, kitchen appliance and storage medium |
| CN111664565A (en) * | 2020-05-07 | 2020-09-15 | 华帝股份有限公司 | Control system and control method of kitchen air conditioner |
| CN111750462A (en) * | 2020-06-04 | 2020-10-09 | 华帝股份有限公司 | Control method of range hood air conditioner and range hood air conditioner |
-
2020
- 2020-10-10 CN CN202011079473.7A patent/CN112361542B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104214888A (en) * | 2013-06-05 | 2014-12-17 | 海尔集团公司 | Air conditioner and temperature control method and device thereof |
| CN106152408A (en) * | 2016-07-06 | 2016-11-23 | 北京地平线机器人技术研发有限公司 | Intelligent air conditioner controller, control method and air-conditioner |
| CN106369739A (en) * | 2016-08-23 | 2017-02-01 | 海信（山东）空调有限公司 | Air conditioner control method, air conditioner controller and air conditioner system |
| CN106556111A (en) * | 2016-11-24 | 2017-04-05 | 广东美的制冷设备有限公司 | Household electric appliance control method, device, server and air-conditioner |
| CN106482189A (en) * | 2016-11-29 | 2017-03-08 | 广东万家乐燃气具有限公司 | The system and method that a kind of range hood is interacted with Intelligent bracelet |
| CN108361927A (en) * | 2018-02-08 | 2018-08-03 | 广东美的暖通设备有限公司 | A kind of air-conditioner control method, device and air conditioner based on machine learning |
| CN108870679A (en) * | 2018-07-02 | 2018-11-23 | 珠海格力电器股份有限公司 | A kind of control method of air-conditioning, device, storage medium and air-conditioning |
| CN109631255A (en) * | 2018-12-10 | 2019-04-16 | 珠海格力电器股份有限公司 | A kind of air conditioning control method, device, storage medium and air-conditioning |
| CN109974224A (en) * | 2019-04-01 | 2019-07-05 | 珠海格力电器股份有限公司 | The air conditioner of adaptive place use demand, the method and device for controlling air conditioner |
| CN110094837A (en) * | 2019-04-30 | 2019-08-06 | 珠海格力电器股份有限公司 | Intelligent control apparatus for air-conditioner and method |
| CN110650200A (en) * | 2019-09-25 | 2020-01-03 | 佛山市顺德区美的洗涤电器制造有限公司 | Control method, kitchen appliance system, terminal, kitchen appliance and storage medium |
| CN111664565A (en) * | 2020-05-07 | 2020-09-15 | 华帝股份有限公司 | Control system and control method of kitchen air conditioner |
| CN111750462A (en) * | 2020-06-04 | 2020-10-09 | 华帝股份有限公司 | Control method of range hood air conditioner and range hood air conditioner |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113531853A (en) * | 2021-06-29 | 2021-10-22 | 青岛海尔空调器有限总公司 | Kitchen air conditioner control method, device and equipment |
| WO2023273732A1 (en) * | 2021-06-29 | 2023-01-05 | 青岛海尔空调器有限总公司 | Control method, apparatus and device for kitchen air conditioner |
| WO2023142510A1 (en) * | 2022-01-28 | 2023-08-03 | 青岛海尔空调器有限总公司 | Method and apparatus for scenario simulation and air conditioner |
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|---|---|
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