CN111043726B - Intelligent air conditioner regulation and control method and air conditioner - Google Patents
Intelligent air conditioner regulation and control method and air conditioner Download PDFInfo
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- CN111043726B CN111043726B CN201911398751.2A CN201911398751A CN111043726B CN 111043726 B CN111043726 B CN 111043726B CN 201911398751 A CN201911398751 A CN 201911398751A CN 111043726 B CN111043726 B CN 111043726B
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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
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/61—Control or safety arrangements characterised by user interfaces or communication using timers
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/65—Electronic processing for selecting an operating mode
- F24F11/67—Switching between heating and cooling modes
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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
- F24F2130/00—Control inputs relating to environmental factors not covered by group F24F2110/00
- F24F2130/10—Weather information or forecasts
Abstract
The invention provides an air conditioner intelligent control method and an air conditioner. Based on the method provided by the invention, the set temperature value of the air conditioner is determined by taking the environmental characteristic data as the basis and simultaneously adopting a weighted summation mode, and all the environmental characteristic parameters of the deployed environment of the air conditioner can be comprehensively considered, so that the set temperature of the air conditioner most suitable for the currently acquired environmental characteristic parameters is efficiently and automatically determined, and the intelligent regulation and control of the air conditioner are realized, and the intelligent level of the air conditioner is further improved.
Description
Technical Field
The invention relates to the technical field of intelligent household appliances, in particular to an air conditioner intelligent regulation and control method and an air conditioner.
Background
In a certain time period, such as seven days a week or 24 hours a day, the demands of people on heat or cold are different in different stages, and at this time, the user is required to continuously adjust the set temperature of the air conditioner to meet the demands on heat or cold. The air conditioner regulation and control process is very easy to be complicated by adopting the mode, so that the use experience of a user is influenced. Therefore, how to implement an intelligent control method for an air conditioner without requiring frequent manual control by a user is an urgent problem to be solved.
Disclosure of Invention
An object of the present invention is to provide an intelligent control method for an air conditioner, so that the air conditioner can automatically determine a set temperature value based on an environmental characteristic parameter.
A further object of the present invention is to enable the air conditioner to be controlled according to the operation mode of the air conditioner.
It is another further object of the present invention to address at least one of the problems of the prior art.
According to one aspect of the present invention, there is provided an intelligent control method for an air conditioner, comprising:
acquiring a plurality of environment characteristic parameters of a space where an air conditioner is deployed;
determining a weight coefficient of each environment characteristic parameter based on a pre-established relation model;
weighting and summing the environmental characteristic parameters in combination with the corresponding weight coefficients to obtain a set temperature value of the air conditioner;
and regulating and controlling the air conditioner according to the set temperature value.
Optionally, before determining the weight coefficient of each environmental characteristic parameter based on the pre-established relationship model, the method further includes:
acquiring a plurality of historical set temperature values of the air conditioner within a preset time period;
collecting a plurality of historical environment characteristic parameters corresponding to historical set temperature values in a preset time period;
performing characteristic analysis on each historical set temperature value and a plurality of historical environment characteristic parameters corresponding to the historical set temperature value, and determining a plurality of weight coefficients of each historical environment characteristic parameter; wherein, the comprehensive value of the weighted summation of the historical environmental characteristic parameters based on the respective weight coefficients is equal to the corresponding historical set temperature value;
and establishing a relation model based on the weight coefficient.
Optionally, building a relationship model based on the weight coefficients includes:
and establishing a relation model by taking the environment characteristic parameter as an independent variable and the set temperature value as a dependent variable by taking the weight coefficient as a constant.
Optionally, after obtaining a plurality of historical set temperature values of the air conditioner within a preset time period, the method further includes:
acquiring an operation mode of the air conditioner when the air conditioner operates by taking any historical set temperature value as a target temperature;
the operation mode comprises a heating mode and a cooling mode.
Optionally, performing feature analysis on each historical set temperature value and a plurality of historical environmental feature parameters corresponding to the historical set temperature value, and determining a plurality of weight coefficients of each historical environmental feature parameter, includes:
performing characteristic analysis on each historical set temperature value and a plurality of historical environment characteristic parameters corresponding to the historical set temperature value; determining a plurality of first weight coefficients of each historical environment characteristic parameter of the air conditioner in a refrigeration mode and a heating mode respectively;
establishing a relation model based on the weight coefficients, comprising:
and establishing a plurality of first relation models respectively corresponding to the heating mode and the cooling mode based on the first weight coefficient.
Optionally, determining a weight coefficient of each environmental characteristic parameter based on a pre-established relationship model includes:
determining an operation mode of an air conditioner;
and selecting a designated first relation model from the plurality of first relation models based on the operation mode so as to determine a first weight coefficient of each environment characteristic parameter based on the designated first relation model.
Optionally, when the air conditioner operates with any historical set temperature value as a target temperature, after the operation mode of the air conditioner is obtained, the method further includes:
further acquiring the weather type of the geographical position of the space where the air conditioner is deployed;
the weather types include sunny, cloudy, rainy, and/or snowy.
Optionally, performing feature analysis on each historical set temperature value and a plurality of historical environmental feature parameters corresponding to the historical set temperature value, and determining a plurality of weight coefficients of each historical environmental feature parameter, includes:
performing characteristic analysis on each historical set temperature value and a plurality of historical environment characteristic parameters corresponding to the historical set temperature value; determining a plurality of second weight coefficients of the air conditioner which are respectively in a cooling mode and a heating mode and correspond to various historical environmental characteristic parameters of different weather types;
establishing a relation model based on the weight coefficients, comprising:
and establishing a plurality of second relation models corresponding to different weather types under the heating model and the cooling mode respectively based on the second weight coefficients.
Optionally, determining a weight coefficient of each environmental characteristic parameter based on a pre-established relationship model includes:
determining an operation mode of an air conditioner and a weather type of a geographical position of a space where the air conditioner is deployed;
and selecting a designated second relation model from the plurality of second relation models based on the operation mode and the weather type, and determining a second weight coefficient of each environment characteristic parameter based on the designated second relation model.
According to another aspect of the present invention, there is also provided an air conditioner including:
an indoor unit;
and the controller comprises a memory and a processor, wherein the memory stores a computer program, and the computer program is used for realizing the intelligent air conditioner regulation and control method according to any one of the above items when being executed by the processor.
The invention provides an air conditioner intelligent control method and an air conditioner. Based on the method provided by the invention, the set temperature value of the air conditioner is determined by taking the environmental characteristic data as the basis and simultaneously adopting a weighted summation mode, and the environmental characteristic parameters of the deployed environment of the air conditioner can be comprehensively considered, so that the set temperature of the air conditioner most suitable for the currently acquired environmental characteristic parameters is determined efficiently and quickly.
Furthermore, the invention can respectively determine a plurality of relation models according to the operation mode of the air conditioner and the weather type of the geographical position of the air conditioner, so that the set temperature value of the air conditioner can be more specific, and the intelligent regulation and control of the air conditioner are realized while the intelligent level of the air conditioner is further improved.
The above and other objects, advantages and features of the present invention will become more apparent to those skilled in the art from the following detailed description of specific embodiments thereof, taken in conjunction with the accompanying drawings.
Drawings
Some specific embodiments of the invention will be described in detail hereinafter, by way of illustration and not limitation, with reference to the accompanying drawings. The same reference numbers in the drawings identify the same or similar elements or components. Those skilled in the art will appreciate that the drawings are not necessarily drawn to scale. In the drawings:
FIG. 1 is a flow chart of an intelligent air conditioner control method according to an embodiment of the present invention;
FIG. 2 is a flow chart of an intelligent air conditioner control method according to another embodiment of the present invention;
FIG. 3 is a flow chart of an intelligent air conditioner control method according to another embodiment of the present invention;
fig. 4 is a schematic structural view of an air conditioner according to an embodiment of the present invention.
Detailed Description
Fig. 1 is a schematic flow chart of an intelligent air conditioner control method according to an embodiment of the present invention, and as can be seen from fig. 1, the intelligent air conditioner control method according to the embodiment of the present invention may include:
step S102, acquiring a plurality of environment characteristic parameters of a space where an air conditioner is deployed;
step S104, determining the weight coefficient of each environment characteristic parameter based on a pre-established relation model;
step S106, weighting and summing the environmental characteristic parameters by combining the corresponding weight coefficients to obtain a set temperature value of the air conditioner;
and S108, regulating and controlling the air conditioner according to the set temperature value.
The embodiment of the invention provides an intelligent control method of an air conditioner, which comprises the steps of obtaining a plurality of environment characteristic parameters of a space where the air conditioner is deployed, determining the weight coefficient of each environment characteristic parameter through a pre-established relation model, then carrying out weighted summation on each environment characteristic parameter to obtain a set temperature value of the air conditioner, and further regulating and controlling the air conditioner based on the set temperature. Based on the method provided by the embodiment of the invention, the set temperature value of the air conditioner is determined by taking the environmental characteristic data as a basis and simultaneously adopting a weighted summation mode, and all the environmental characteristic parameters of the deployed environment of the air conditioner can be comprehensively considered, so that the set temperature of the air conditioner most suitable for the currently obtained environmental characteristic parameters is efficiently and automatically determined, the intelligent regulation and control of the air conditioner are realized, and the intelligent level of the air conditioner is further improved.
Optionally, the environmental characteristic parameters of the space where the air conditioner is deployed according to the embodiments of the present invention may include an indoor temperature value, an outdoor temperature value, an indoor humidity value, and an outdoor humidity value. Of course, other environment characteristic parameters may be included in the practical application, and the present invention is not limited. The acquired environment characteristic parameters can be acquired in real time or at certain time intervals, and can be specifically set according to the deployment scene of the air conditioner and the environment characteristics of the air conditioner, which is not limited in the invention.
In step S104, after the environmental characteristic parameters are obtained, the weight coefficients of the environmental characteristic parameters are determined based on a pre-established relationship model. The relational model mentioned in this embodiment is constructed in advance based on historical relevant data of the air conditioner after learning the usage habits of the air conditioner by the user. Specifically, in an optional embodiment of the present invention, before the step S104, a relationship model may be further established based on the historical set temperature value of the air conditioner and the corresponding environmental characteristic parameter. In an alternative embodiment of the present invention, the relationship model may be established based on the following manner:
and S1, acquiring a plurality of historical set temperature values of the air conditioner in a preset time period. The historical set temperature value may be a temperature value that is received by the air conditioner and is autonomously adjusted by a user, the preset time period may be a historical set temperature value of the air conditioner in the past half year, one month or one week, and the preset time period may be set according to different application requirements, which is not limited in the present invention.
And S2, collecting a plurality of historical environment characteristic parameters corresponding to the historical set temperature values in a preset time period. That is to say, for each historical set temperature value, historical environment characteristic parameters corresponding to the deployment environment where the air conditioner is located when the air conditioner operates with the historical set temperature value as a target temperature are obtained, and the historical environment characteristic parameters may include an indoor temperature value, an outdoor temperature value, an indoor humidity value, and an outdoor humidity value corresponding to the historical set temperature value.
S3, performing characteristic analysis on each historical set temperature value and a plurality of corresponding historical environment characteristic parameters, and determining a plurality of weight coefficients of each historical environment characteristic parameter; and the comprehensive value obtained by weighting and summing the historical environmental characteristic parameters based on the respective weight coefficients is equal to the corresponding historical set temperature value. That is, after each of the historical set temperature values and each of the historical environmental characteristic parameters corresponding thereto are subjected to characteristic analysis, any one of the historical set temperature values T is subjected to characteristic analysis0It may be equal to the value obtained by weighted summation of the indoor temperature value, the outdoor temperature value, the indoor humidity value, and the outdoor humidity value with the respective weight coefficients.
And S4, establishing a relation model based on the weight coefficient. Further, a relationship model with the environmental characteristic parameter as an independent variable and the set temperature value as a dependent variable may be established with the weight coefficient as a constant. For example, the resulting relationship model may be as follows:
the set temperature value Ta is a + a1 indoor temperature + a2 outdoor temperature + a3 indoor humidity + a4 outdoor humidity.
Wherein, a1, a2, a3 and a4 are respectively weight coefficients corresponding to the indoor temperature value, the outdoor temperature value, the indoor humidity value and the outdoor humidity value, and are all constants, the weight coefficients of the indoor temperature value, the outdoor temperature value, the indoor humidity value and the outdoor humidity value are independent variables, the set temperature value is a dependent variable, and for any group of environment characteristic parameters, the set temperature value corresponding thereto is provided. In addition, the above embodiment further includes a constant a, which may be obtained when performing feature analysis on the environmental feature parameter, and the embodiment of the present invention is not limited. In the embodiment of the present invention, the unit of each parameter is not considered, and only the calculation of the number is considered.
Referring to step S106, after determining the sum of the weighting coefficient coefficients of each environmental characteristic parameter through the relationship model, the sum may be weighted to obtain a corresponding set temperature value, so as to regulate and control the air conditioner. In practical application, the calculated set temperature value can be rounded to obtain an integer for convenient control. In practical applications, the set temperature value is calculated and the air conditioner is adjusted at certain time intervals (for example, 15 minutes), so as to avoid the influence on the user experience caused by frequently adjusting the air conditioner. Based on the method provided by the embodiment of the invention, the requirements on the temperature at different moments can be met, manual adjustment is not needed, after the indoor temperature value, the outdoor temperature value, the indoor humidity value and the outdoor humidity value are obtained, the indoor temperature value, the outdoor temperature value, the indoor humidity value and the outdoor humidity value are input into the relation model, after the respective weight coefficients are determined through the relation model, the indoor temperature value, the outdoor temperature value, the indoor humidity value and the outdoor humidity value are combined with the respective corresponding weight coefficients to be weighted and combined to obtain the set temperature value of the air conditioner at the moment. The humidity value in this embodiment may be an absolute humidity value or a relative humidity value, and the present invention is not limited thereto. It should be noted that the calculation method in the relational model provided in this embodiment does not need to consider the unit of each value, and only needs to calculate between specific numerical values.
For example, assuming that the outdoor temperature value is 1(° c), the indoor temperature value is 18(° c), the indoor humidity value (relative humidity) is 42%, that is, 0.42, the outdoor humidity value (relative humidity) is 0.45, a is 0, a1 is 1, a2 is 1.5, a3 is 2.3, and a4 is 1.1, the set temperature Ta (° c) of the air conditioner is 20.991 ≈ 21(° c).
In an optional embodiment of the present invention, the operation modes of the air conditioner at the respective set temperature values may also be obtained, so as to provide different relationship models for the respective operation modes. That is, after the plurality of historical set temperature values of the air conditioner in the preset time period are acquired in step S1, the operation mode when the air conditioner is operated with any one of the historical set temperature values as the target temperature may be acquired. The operation mode comprises a heating mode and a cooling mode.
Furthermore, when the relation model is established, characteristic analysis can be firstly carried out on each historical set temperature value and a plurality of historical environment characteristic parameters corresponding to the historical set temperature value; determining a plurality of first weight coefficients of each historical environment characteristic parameter of the air conditioner in a refrigeration mode and a heating mode respectively; and establishing a plurality of first relation models respectively corresponding to the heating mode and the cooling mode based on the first weight coefficient.
For example, the first relationship model B corresponding to the cooling mode is as follows:
set temperature Tb ═ b + b1 indoor temperature + b2 outdoor temperature + b3 indoor humidity + b4 outdoor humidity
The first relationship model C corresponding to the heating mode is as follows:
set temperature value Tc ═ c + c1 indoor temperature + c2 outdoor temperature + c3 indoor humidity + c4 outdoor humidity
Further, when determining the weighting coefficients of the environmental characteristic parameters based on the pre-established relationship model in step S104, the operation mode of the air conditioner may be determined; and selecting a designated first relation model from the plurality of first relation models based on the operation mode so as to determine a first weight coefficient of each environment characteristic parameter based on the designated first relation model. That is, when the operation mode of the air conditioner is determined to be the cooling mode, the first relational model B may be adopted; when the operation mode of the air conditioner is determined to be the heating mode, the first relation model C may be adopted. Each parameter in the relationship model can be learned (such as machine learning) according to habits of users and historical related data of the air conditioner, and specific numerical values of the parameters are not limited in the invention.
Fig. 2 is a schematic flow chart of an intelligent air conditioner control method according to an alternative embodiment of the present invention, and as can be seen from fig. 2, the intelligent air conditioner control method according to the embodiment of the present invention may include:
step S202, acquiring set temperature values of the air conditioner in a historical time period, and indoor temperature values, outdoor temperature values, indoor humidity values and outdoor humidity values corresponding to the set temperature values in a refrigerating mode and a heating mode in the historical time period;
step S204, analyzing according to an indoor temperature value, an outdoor temperature value, an indoor humidity value and an outdoor humidity value corresponding to the set temperature values in different operation modes, and establishing a relation model B aiming at a refrigeration mode and a relation model C aiming at a heating mode;
step S206, acquiring an indoor temperature value, an outdoor temperature value, an indoor humidity value and an outdoor humidity value of a space where the air conditioner is deployed in real time;
step S208, acquiring a real-time operation mode of the air conditioner;
step S210, determining a specified relation model in a relation model B and a relation model C of a heating mode based on a real-time operation mode of the air conditioner;
step S212, determining weighting coefficients of the indoor temperature value, the outdoor temperature value, the indoor humidity value and the outdoor humidity value based on the specified relation model;
step S214, carrying out weighted summation based on the indoor temperature value, the outdoor temperature value, the indoor humidity value, the outdoor humidity value and respective weight coefficients to obtain the current set temperature value of the air conditioner;
and step S216, regulating and controlling the air conditioner based on the set temperature value.
Based on the method provided by the embodiment of the invention, the weight coefficients corresponding to different environment characteristic data are determined for different relation models respectively provided in a heating mode and a cooling mode, and the relation models in different air conditioner operation modes are determined simultaneously.
In another optional embodiment of the present invention, not only the operation mode of the air conditioner at each set temperature value can be obtained, but also the weather type of the air conditioner at each set temperature value can be obtained, so as to provide different relationship models for each operation mode and each weather type.
That is, after the step S1 obtains a plurality of historical set temperature values of the air conditioner within the preset time period, in addition to determining the operation mode when the air conditioner operates with any historical set temperature value as the target temperature, the weather type of the geographical location of the space where the air conditioner is deployed may be further obtained; the weather type comprises sunny days, cloudy days, rainy days and/or snowy days, and can be acquired by other equipment after networking.
Furthermore, when the relation model is established, characteristic analysis can be firstly carried out on each historical set temperature value and a plurality of historical environment characteristic parameters corresponding to the historical set temperature value; determining a plurality of second weight coefficients of the air conditioner which are respectively in a cooling mode and a heating mode and correspond to various historical environmental characteristic parameters of different weather types; and establishing a plurality of second relation models corresponding to different weather types under the heating model and the cooling mode respectively based on the second weight coefficients.
For example, in the cooling mode (which may include dehumidification), different second relationship models are provided for cloudy days, sunny days, and rainy days, respectively.
In yin-sky, the relationship model D may be as follows:
the set temperature value Td is d + d1 indoor temperature + d2 outdoor temperature + d3 indoor humidity + d4 outdoor humidity
In sunny days, the relationship model E may be as follows:
set temperature Te ═ e + e1 indoor temperature + e2 outdoor temperature + e3 indoor humidity + e4 outdoor humidity
In rain, the relationship model F may be as follows:
set temperature value Tf ═ f + f1 indoor temperature + f2 outdoor temperature + f3 indoor humidity + f4 outdoor humidity
In the heating mode (which may include dehumidification), different second relationship models are provided for cloudy days, sunny days, and rainy days, respectively, and in addition, the same relationship model may be used for rainy days and snowy days.
In yin-sky, the relationship model G may be as follows:
the set temperature value Tg is g + g1 indoor temperature + g2 outdoor temperature + g3 indoor humidity + g4 outdoor humidity
In sunny days, the relationship model H may be as follows:
the set temperature Th is h + h1 indoor temperature + h2 outdoor temperature + h3 indoor humidity + h4 outdoor humidity
In rain (or snow), the relationship model K may be as follows:
set temperature value Tk ═ k + k1 indoor temperature + k2 outdoor temperature + k3 indoor humidity + k4 outdoor humidity
Each parameter in the relationship model can be learned (such as machine learning) according to habits of users and historical related data of the air conditioner, and specific numerical values of the parameters are not limited in the invention.
Further, when determining the weighting coefficients of the environmental characteristic parameters based on the pre-established relationship model in step S104, the operation mode of the air conditioner and the weather type of the geographic location of the space where the air conditioner is deployed may be determined; and selecting a designated second relation model from the plurality of second relation models based on the operation mode and the weather type, and determining a second weight coefficient of each environment characteristic parameter based on the designated second relation model. In this embodiment, when determining the weight coefficient, it is necessary to first determine the current operation mode of the air conditioner, and then determine the current weather type of the geographic location, so as to further select a designated second relationship model corresponding to the current operation mode and the weather type from the plurality of second relationship models, and thus determine the weight coefficients of the indoor temperature value, the outdoor temperature value, the indoor humidity value, and the outdoor humidity value based on the designated second relationship model.
Fig. 3 is a schematic flow chart of an intelligent air conditioner control method according to an alternative embodiment of the present invention, and as can be seen from fig. 3, the intelligent air conditioner control method according to the embodiment of the present invention may include:
step S302, acquiring set temperature values of the air conditioner in the historical time period, and indoor temperature values, outdoor temperature values, indoor humidity values and outdoor humidity values corresponding to the set temperature values in different weather types and different operation conditions in the historical time period; the weather type can comprise sunny days, cloudy days and rainy days, and the operation mode can comprise a heating mode and a cooling mode;
step S304, analyzing according to indoor temperature values, outdoor temperature values, indoor humidity values and outdoor humidity values corresponding to the set temperature values in different weather types and different operation modes, and establishing a plurality of relation models X aiming at different operation modes and different weather types;
step S306, acquiring an indoor temperature value, an outdoor temperature value, an indoor humidity value and an outdoor humidity value of a space where the air conditioner is deployed in real time;
step S308, acquiring a real-time operation mode of the air conditioner and a weather type of a geographical position of a deployment space where the air conditioner is currently located;
step S310, determining a designated relation model in a plurality of relation models X based on the real-time operation mode of the air conditioner and the current weather type;
step S312, determining weighting coefficients of the indoor temperature value, the outdoor temperature value, the indoor humidity value and the outdoor humidity value based on the specified relation model;
step S314, carrying out weighted summation based on the indoor temperature value, the outdoor temperature value, the indoor humidity value, the outdoor humidity value and respective weight coefficients to obtain the current set temperature value of the air conditioner;
step S316, the air conditioner is controlled based on the set temperature value.
Based on the method provided by the embodiment of the invention, the weight coefficients corresponding to different environment characteristic data are determined for different relation models respectively provided for different operation modes of the air conditioner and the current weather type of the air conditioner respectively, and meanwhile, the weight coefficients are determined for the relation models in different operation modes of the air conditioner and different weather types, when the air conditioner is adjusted in real time subsequently, on the basis of obtaining the environment characteristic data, the method not only can be combined with the operation modes of the air conditioner, but also can consider the different relation models in real time according to the weather conditions, so that the set temperature value can be adaptively adjusted along with the operation modes of the air conditioner and the current weather conditions, the adjusted and controlled temperature of the air conditioner can further accord with the real-time requirements of users, and the use experience of the users is further improved.
In an optional embodiment of the present invention, if the set temperature value calculated by the environmental characteristic parameters and the weighting coefficients thereof has multiple decimal places, the set temperature value may be rounded and then an integer or a decimal place may be reserved, so as to more conveniently control the air conditioner. In addition, assuming that the calculated set temperature value is greater than the first preset temperature value (e.g., 30), the first preset temperature value (e.g., 30) is used as the set temperature value, and assuming that the calculated set temperature value is less than the second preset temperature value (e.g., 16), the second preset temperature value (e.g., 16) is used as the set temperature value. The first preset temperature value and the second preset temperature value may be adjusted according to the highest ambient temperature and the lowest ambient temperature that the user can adapt to after learning the user habit, which is not limited in the present invention.
In addition, each relation model can be updated in the actual use process, so that the set temperature value of the air conditioner can be adjusted according to the needs of a user, and the comfort level of the user is further improved.
Based on the same inventive concept, an embodiment of the present invention further provides an air conditioner, as shown in fig. 4, the air conditioner 400 may include:
an indoor unit 410;
the controller 420 includes a memory 421 and a processor 422, where the memory 421 stores a computer program, and the computer program is executed by the processor 422 to implement the intelligent air conditioner control method according to any of the embodiments.
The embodiment of the invention provides an intelligent control method of an air conditioner and the air conditioner. Based on the method provided by the embodiment of the invention, the set temperature value of the air conditioner is determined by taking the environmental characteristic data as a basis and simultaneously adopting a weighted summation mode, and all the environmental characteristic parameters of the deployed environment of the air conditioner can be comprehensively considered, so that the set temperature of the air conditioner most suitable for the currently acquired environmental characteristic parameters is efficiently and automatically determined.
Furthermore, the embodiment of the invention can also respectively determine a plurality of relation models according to the operation mode of the air conditioner and the weather type of the geographical position of the air conditioner, so that the set temperature value of the air conditioner can be more applicable when being determined, the set temperature value can be adaptively adjusted along with the operation mode of the air conditioner and the current weather condition, the regulated and controlled temperature of the air conditioner can further meet the real-time requirements of users, the intelligent regulation and control of the air conditioner can be realized, and the intelligent level of the air conditioner can be further improved.
Thus, it should be appreciated by those skilled in the art that while a number of exemplary embodiments of the invention have been illustrated and described in detail herein, many other variations or modifications consistent with the principles of the invention may be directly determined or derived from the disclosure of the present invention without departing from the spirit and scope of the invention. Accordingly, the scope of the invention should be understood and interpreted to cover all such other variations or modifications.
Claims (10)
1. An intelligent regulation and control method of an air conditioner comprises the following steps:
acquiring a plurality of environment characteristic parameters of a space where an air conditioner is deployed;
determining a weight coefficient of each environment characteristic parameter based on a pre-established relation model;
weighting and summing the environmental characteristic parameters in combination with the corresponding weight coefficients to obtain a set temperature value of the air conditioner;
regulating and controlling the air conditioner according to the set temperature value;
after the environmental characteristic parameters are combined with the corresponding weight coefficients to perform weighted summation to obtain the set temperature value of the air conditioner, the method further comprises the following steps:
judging whether the set temperature value falls within the range from a second preset temperature value to a first preset temperature value or not;
regulating and controlling the air conditioner according to the set temperature value, comprising:
if the set temperature value falls within the range from the second preset temperature value to the first preset temperature value, regulating and controlling the air conditioner according to the set temperature value;
if the set temperature value is greater than the first preset temperature value, regulating and controlling the air conditioner according to the first preset temperature value;
and if the set temperature is lower than the second preset temperature value, regulating and controlling the air conditioner according to the second preset temperature value.
2. The method according to claim 1, wherein before determining the weighting coefficient of each of the environmental characteristic parameters based on the pre-established relationship model, further comprising:
acquiring a plurality of historical set temperature values of the air conditioner within a preset time period;
collecting a plurality of historical environment characteristic parameters corresponding to the historical set temperature values in the preset time period;
performing characteristic analysis on each historical set temperature value and a plurality of historical environment characteristic parameters corresponding to the historical set temperature value to determine a plurality of weight coefficients of each historical environment characteristic parameter; the comprehensive value of each historical environment characteristic parameter which is subjected to weighted summation based on the respective weight coefficient is equal to the corresponding historical set temperature value;
and establishing the relation model based on the weight coefficient.
3. The method of claim 2, wherein the building the relationship model based on the weight coefficients comprises:
and establishing a relation model by taking the environment characteristic parameter as an independent variable and the set temperature value as a dependent variable by taking the weight coefficient as a constant.
4. The method of claim 2, wherein after obtaining a plurality of historical set temperature values of the air conditioner within a preset time period, the method further comprises:
acquiring an operation mode of the air conditioner when the air conditioner operates by taking any historical set temperature value as a target temperature;
wherein the operation mode comprises a heating mode and a cooling mode.
5. The method of claim 4, wherein,
performing feature analysis on each historical set temperature value and a plurality of historical environment characteristic parameters corresponding to the historical set temperature value to determine a plurality of weight coefficients of each historical environment characteristic parameter, including:
performing characteristic analysis on each historical set temperature value and a plurality of historical environment characteristic parameters corresponding to the historical set temperature value; determining a plurality of first weight coefficients of each historical environment characteristic parameter when the air conditioner is respectively in a cooling mode and a heating mode;
the establishing the relationship model based on the weight coefficients comprises:
and establishing a plurality of first relation models respectively corresponding to a heating mode and a cooling mode based on the first weight coefficient.
6. The method of claim 5, wherein the determining a weighting factor for each of the environmental characteristic parameters based on a pre-established relationship model comprises:
determining an operation mode of the air conditioner;
selecting a designated first relational model from the plurality of first relational models based on the operation mode, and determining a first weight coefficient of each environmental characteristic parameter based on the designated first relational model.
7. The method of claim 4, wherein after obtaining the operation mode of the air conditioner when the air conditioner operates with any one of the historical set temperature values as the target temperature, the method further comprises:
further acquiring the weather type of the geographical position of the space where the air conditioner is deployed;
the weather types include sunny, cloudy, rainy, and/or snowy.
8. The method of claim 7, wherein,
performing feature analysis on each historical set temperature value and a plurality of historical environment characteristic parameters corresponding to the historical set temperature value to determine a plurality of weight coefficients of each historical environment characteristic parameter, including:
performing characteristic analysis on each historical set temperature value and a plurality of historical environment characteristic parameters corresponding to the historical set temperature value; determining a plurality of second weight coefficients of each historical environment characteristic parameter of the air conditioner which is respectively in a cooling mode and a heating mode and corresponds to different weather types;
the establishing the relationship model based on the weight coefficients comprises:
and establishing a plurality of second relational models corresponding to different weather types in the heating model and the cooling mode respectively based on the second weight coefficients.
9. The method of claim 8, wherein the determining a weighting factor for each of the environmental characteristic parameters based on a pre-established relationship model comprises:
determining an operation mode of the air conditioner and a weather type of a geographical position of a space where the air conditioner is deployed;
selecting a designated second relational model from the plurality of second relational models based on the operation mode and the weather type, so as to determine a second weight coefficient of each environmental characteristic parameter based on the designated second relational model.
10. An air conditioner comprising:
an indoor unit;
a controller comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, is for implementing the air conditioner intelligent regulation method according to any one of claims 1 to 9.
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