CN110686377A - Control method for air conditioner temperature self-adaptive adjustment, computer readable storage medium and air conditioner - Google Patents
Control method for air conditioner temperature self-adaptive adjustment, computer readable storage medium and air conditioner Download PDFInfo
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- 239000000498 cooling water Substances 0.000 claims description 24
- 230000003044 adaptive effect Effects 0.000 claims description 12
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- 238000004590 computer program Methods 0.000 claims description 8
- 239000003507 refrigerant Substances 0.000 claims description 8
- 230000006870 function Effects 0.000 description 4
- 230000009467 reduction Effects 0.000 description 4
- 238000004378 air conditioning Methods 0.000 description 3
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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
- F24F11/88—Electrical aspects, e.g. circuits
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/10—Temperature
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Abstract
The invention provides a control method for air conditioner temperature self-adaptive adjustment, a computer readable storage medium and an air conditioner, wherein a system predicts the opening degree of a valve in real time by using a BP neural network in combination with historical experience information, and adjusts the opening degree of the valve by comparing and judging a predicted value and an initial value of the opening degree of the valve so as to adjust the temperature.
Description
Technical Field
The invention relates to the technical field of air conditioners, in particular to a control method for self-adaptive adjustment of air conditioner temperature, a computer readable storage medium and an air conditioner.
Background
Temperature control is a very important place in industrial automation control. In industrial processes, the temperature is generally a quantity that needs to be controlled. In agricultural production, the growth of agricultural products is also dependent on suitable temperatures. In national defense and scientific research work, the temperature is a parameter which cannot be ignored. An important realization function in the air conditioning industry is temperature control and regulation. Therefore, the research on temperature control is of great significance.
Nowadays, the air conditioning industry is increasingly competitive, in order to attract more consumers, various large electric appliance companies continuously improve products from the aspects of performance, functions and the like, the consumers are more emphatic in selecting air conditioners, the air conditioners are not satisfied with the quality of the products, and the functions of the products are more important. Therefore, the current air conditioning products tend to be more intelligent. However, currently, the air conditioner does not realize intellectualization in the most important function of temperature control adjustment, and remains in the manual adjustment stage. If the temperature is not beyond the human bearing range or the industrial requirement, almost no people actively adjust the temperature of the air conditioner, and the air conditioner is required to be capable of automatically adjusting according to a plurality of preset temperature points of people.
Disclosure of Invention
Aiming at the defects of the prior art, the control method for the self-adaptive adjustment of the air conditioner temperature provided by the invention does not need to adjust the temperature manually, only needs to preset a plurality of temperature points, and automatically adjusts the temperature according to the set temperature, the running real-time data and the like to achieve the purpose of the required temperature.
In order to achieve the purpose, the invention adopts the following technical scheme:
a control method for self-adaptive adjustment of air conditioner temperature is characterized in that a system predicts the opening of a valve in real time by combining a BP neural network with historical experience information, and adjusts the opening of the valve by comparing and judging the predicted value and an initial value of the opening of the valve, so that the temperature is adjusted. According to setting for temperature automatically regulated, can in time satisfy industrial production process temperature automatic control's development demand, the user can set for a plurality of temperature points according to self needs and automatically regulated temperature, improves the travelling comfort and the convenience of air conditioner.
Further, the predicting of the valve opening by the BP neural network specifically comprises: the method comprises the following steps of utilizing main-side heat exchange quantity, input power, cooling water inlet temperature and cooling water outlet temperature of a unit in a historical database, and utilizing refrigerant water flow and cooling water flow as input of a neural network; and taking the target opening degree at the specific temperature as the output of the neural network. For the temperature regulation of the air conditioner, the major complaints include the main side heat exchange quantity, the input power, the cooling water inlet temperature, the cooling water outlet temperature, the refrigerant water flow and the cooling water flow of the unit, and the factors are used as the input of the neural network, so that the temperature regulation accuracy can be ensured.
Further, after the BP neural network predicts the valve opening, the relation between the predicted value V of the valve opening and the initial value V0 is judged, if | V0-V | is less than a, the valve opening is adjusted to V at the rate of b, after the operation is carried out for a period of time, the current temperature Tc and the set temperature T are detected in real time and compared and judged, a and b are preset values, a is more than 0 and less than or equal to 3 percent, and b is more than 0 and less than or equal to 2 percent/min. The temperature is further compared after the opening degree of the valve is compared, the temperature serves as a main output target, whether the opening degree of the valve is adjusted correctly or not can be verified through the temperature comparison, and whether the finally output temperature is consistent with the target or not can be verified, so that the accuracy of control is improved.
Further, comparing the current temperature Tc with the set temperature T specifically includes: if the absolute value Tc-T < c is stable for a period of time, comparing and judging the maximum value Tmax and the minimum value Tmin of the temperature in the period of time, if the absolute value Tmax-Tmin < c is stable for a period of time, keeping the current opening, and setting a temperature point after the stable operation for a period of time, wherein c is a preset value, and c is more than 0 and less than or equal to 0.3. And c is used as the maximum value of the acceptable range of the temperature precision, if the difference value between the current temperature and the set temperature is less than c, the initial judgment is qualified, and the difference value between the maximum value and the minimum value of the temperature is further judged, so that the precision of temperature adjustment can be improved.
And further, if the Tmax-Tmin is larger than c, returning to the step of predicting the opening degree of the neural network valve. If the difference value between the maximum value and the minimum value of the temperature exceeds the error range value, the opening degree of the valve needs to be predicted again, and the prediction accuracy is guaranteed.
Further, comparing the current temperature Tc with the set temperature T specifically includes: and if the absolute value Tc-T absolute value > c is obtained, the neural network valve opening prediction is returned, the predicted value is V ', the valve opening is adjusted to V', the temperature data is detected in real time until the absolute value Tc-T absolute < c and the absolute value Tmax-Tmin absolute < c are met simultaneously, the operation is stable for a period of time, and the temperature point setting is finished. And c is used as the maximum value of the acceptable range of the temperature precision, the difference value between the current temperature and the set temperature is unqualified when the difference value exceeds c, the prediction needs to be carried out again, and the setting of the temperature point can be quickly and effectively finished by reducing the target opening degree and then carrying out the prediction again in the prediction process.
Further, after the BP neural network predicts the valve opening, the relation between the predicted value V of the valve opening and the initial value V0 is judged, if | V0-V | is greater than a, the valve opening is adjusted to V at a rate of d, after the operation is carried out for a period of time, the current temperature Tc and the set temperature T are detected in real time and compared and judged, a and d are preset values, a is greater than 0 and less than or equal to 3%, and d is greater than 0 and less than or equal to 2%/min. When the difference between the predicted value and the initial value is too large, the predicted opening degree needs to be adjusted at a faster rate, and the prediction efficiency is improved.
A computer-readable storage medium storing a computer program that, when invoked by a processor, implements the control method of air conditioner temperature adaptive adjustment described in any one of the above.
An air conditioner comprising a processor and a memory for storing a computer program which, when invoked by the processor, implements the control method of adaptive adjustment of air conditioner temperature as set forth in any one of the preceding claims.
The control method for the self-adaptive adjustment of the air conditioner temperature has the advantages that: the temperature is not required to be adjusted manually, only a plurality of temperature points are preset, and the temperature is automatically adjusted according to the set temperature, the real-time running data and the like to achieve the aim of the required temperature; in industrial production, the temperature is automatically adjusted according to the set temperature, and the development requirement of automatic temperature control in the industrial production process can be met in time; in life, people set a plurality of temperature points according to self needs to automatically adjust the temperature, and the requirements of comfort and convenience of the air conditioner are met.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is an enlarged view of the invention at A in FIG. 1.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments obtained by a person skilled in the art without making any inventive step are within the scope of the present invention.
Example 1: a control method for self-adaptive regulation of air conditioner temperature.
As shown in fig. 1, wherein the symbols have the following meanings: p1: inputting power; q1: heat exchange quantity of the main side; q 1: refrigerant water flow rate; q 2: cooling water flow rate; t1: the water inlet temperature of the cooling water; t2, the outlet water temperature of the cooling water; t: setting a temperature value; tc is the current temperature value; v is a target opening degree (empirical value); tmax is the maximum temperature; tmin is the minimum temperature value; v0 predicted valve opening value.
A control method for air conditioner temperature self-adaptive adjustment comprises the following specific steps:
1) collecting related values regulated at different temperature points for 2 months as a historical database, screening the database, removing data in the temperature regulation process, and only leaving values of valve opening, unit main side heat exchange quantity, input power, cooling water inlet temperature, cooling water outlet temperature, refrigerant water flow and cooling water flow when the temperature points stably run;
2) the heat exchange quantity, the input power, the cooling water inlet temperature, the cooling water outlet temperature, the refrigerant water flow and the cooling water flow of the main side of the unit are used as the input of a neural network; and (4) taking the valve opening as the output of the neural network, and training and testing the BP neural network. And packaging the trained network for predicting the valve opening when the unit stably operates.
3) The initial valve opening is 20%, when the temperature point 1 starts to be tested, a BP neural network is combined with a historical database to predict that the predicted value of the valve opening is V when the unit stably operates at the temperature point 1, the size relation between V and 20% is judged, and the regulation is carried out according to the following steps:
(1) if V < 20%; judging the relationship between 20% and V and 3%:
(a) if 20% -V <3%, adjusting to V at the rate of 0.5%/min, and operating the unit for 2min, wherein the reduction at the fixed rate is mainly to prevent the sudden valve reduction (when the reduction amplitude is large) and the temperature from greatly fluctuating. Therefore, in the first prediction, it is desired to prevent the temperature fluctuation by decreasing at a fixed rate, and at the same time, to ensure the reliability of the unit, and to determine the relationship between the absolute value of the difference between the current temperature (Tc) and the set temperature T and 0.3:
a) if the | Tc-T | <0.3, after the stable operation is carried out for 10min, comparing the maximum value (Tmax) and the minimum value (Tmin) of the temperature within 10min, and judging the relation between the difference value of the maximum value and the minimum value and 0.3:
aa) if the Tmax-Tmin is less than 0.3, the temperature point is stable, and the unit stably operates for 1h (time preset by a user) to finish the temperature point 1;
bb) if the | Tmax-Tmin | >0.3 indicates that the temperature point is unstable, then a BP network is used for carrying out prediction again, the current values and the set temperatures of parameters such as the heat exchange quantity, the input power, the cooling water inlet temperature, the cooling water outlet temperature, the refrigerant water flow, the cooling water flow and the like of the main side of the unit are used as the input of the neural network, and the valve opening is used as the output for carrying out secondary prediction. The predicted value is V ', the valve opening is directly reduced to V', the direct reduction to the predicted value is that the difference value between the two values is small during secondary prediction, so that the predicted value can be directly reduced, and after the operation for 2min, the relation between the absolute value of the difference value between the current temperature (Tc) and the set temperature T and 0.3 is judged:
aaa) if | Tc-T | <0.3, after operating stably for 10min, comparing the maximum value (Tmax) and the minimum value (Tmin) of the temperature within 10min, and judging the relationship between the difference value of the maximum value and the minimum value and 0.3: if the Tmax-Tmin is less than 0.3, the temperature point is stable, and the report can be stored after the unit operates stably for 1h to finish the experiment; if the Tmax-Tmin is larger than 0.3, repeating the step bb) until the conditions of Tc-T <0.3 and Tmax-Tmin <0.3 are met simultaneously, stabilizing the temperature point, and finishing the temperature point 1 after the unit runs stably for 1h (time preset by a user);
bbb) if Tc-T >0.3, return to step b)
b) If the temperature point is not stable if the value is Tc-T >0.3, then a BP network is used for carrying out prediction again, the current values and the set temperature of parameters such as the heat exchange quantity, the input power, the cooling water inlet temperature, the cooling water outlet temperature, the refrigerant water flow, the cooling water flow and the like of the main side of the unit are used as the input of a neural network, and the valve opening is used as the output to carry out secondary prediction. Directly reducing the valve opening to V ' when the predicted value is V ', operating for 2min, judging the relation between the absolute value of the difference between the current temperature (Tc ') and the set temperature T and 0.3, if the value is | Tc ' -T | >0.3, repeating the current step until the value is | Tc ' -T | <0.3, stably operating for 10min, comparing the maximum value (Tmax ') and the minimum value (Tmin ') of the temperature within 10min, and judging the relation between the difference between the maximum value and the minimum value and 0.3: if the Tmax '-Tmin' | is less than 0.3, the temperature point is stable, and the report can be stored after the unit operates stably for 1h to finish the experiment; if the | Tmax ' -Tmin ' | >0.3, returning to the step bb) until | Tc ' -T | <0.3 and | Tmax ' -Tmin ' | <0.3 are simultaneously met, stabilizing the temperature point, stably operating the unit for 1h (time preset by a user) and ending the temperature point 1.
(b) If 20% -V >3%, the valve opening is reduced to N (N-V <3%) at the rate of 2%/min, then is adjusted to V at the rate of 0.5%/min, N is an intermediate value, the distance between the N value and the target value is 3%, which means that fine adjustment is needed when the distance between the N value and the target value is less than 3%, the unit operates for 2min, the relation between the absolute value of the difference between the current temperature (Tc) and the set temperature T and 0.3 is judged, and the steps a) and b in the step (a) are repeated;
(2) if V is greater than 20%; judging the relation between V-20% and 3%:
(c) v-20% <3%, repeating (a) in (1);
(d) v-20% >3%, repeating (b) in (1);
4) adjusting to a temperature point 2, and predicting the valve opening of the unit at the temperature point 2 during stable operation according to a historical database, wherein the predicted value is V1; and judging the sizes of V1 and V, and circularly judging the steps.
And the rest can be done in the same way until the operation is finished at all the temperature points.
The step of predicting the valve opening degree by using the neural network in fig. 1 is the same as that shown in fig. 2.
Example 2: a computer readable storage medium.
A computer-readable storage medium for storing a computer program that implements the control method of adaptive adjustment of air conditioner temperature as described in embodiment 1 when called by a processor.
Example 3: an air conditioner.
An air conditioner comprising a processor and a memory for storing a computer program that, when invoked by the processor, implements the control method of adaptive adjustment of air conditioner temperature as described in embodiment 1.
The above description is only for the preferred embodiment of the present invention, but the present invention should not be limited to the embodiment and the disclosure of the drawings, and therefore, all equivalent or modifications that do not depart from the spirit of the present invention are intended to fall within the scope of the present invention.
Claims (9)
1. A control method for self-adaptive adjustment of air conditioner temperature is characterized in that a system predicts the opening of a valve in real time by combining a BP neural network with historical experience information, and adjusts the opening of the valve by comparing and judging the predicted value with an initial value of the opening of the valve so as to realize the adjustment of the temperature.
2. The control method of adaptive adjustment of air conditioner temperature according to claim 1, characterized in that: the BP neural network prediction valve opening specifically comprises the following steps: the method comprises the following steps of utilizing main-side heat exchange quantity, input power, cooling water inlet temperature and cooling water outlet temperature of a unit in a historical database, and utilizing refrigerant water flow and cooling water flow as input of a neural network; and taking the target opening degree at the specific temperature as the output of the neural network.
3. The control method of adaptive adjustment of air conditioner temperature according to claim 1, characterized in that: after the BP neural network predicts the valve opening, the relation between a predicted value V of the valve opening and an initial value V0 is judged, if | V0-V | is less than a, the valve opening is adjusted to V at a rate of b, after the operation is carried out for a period of time, the current temperature Tc and the set temperature T are detected in real time and compared and judged, a and b are preset values, a is more than 0 and less than or equal to 3 percent, and b is more than 0 and less than or equal to 2 percent/min.
4. The control method of adaptive adjustment of air conditioner temperature according to claim 3, characterized in that: comparing the current temperature Tc with the set temperature T specifically is: if the absolute value Tc-T < c is stable for a period of time, comparing and judging the maximum value Tmax and the minimum value Tmin of the temperature in the period of time, if the absolute value Tmax-Tmin < c is stable for a period of time, keeping the current opening, and finishing the temperature setting after the stable operation for a period of time, wherein c is a preset value, and c is more than 0 and less than or equal to 0.3.
5. The control method of adaptive adjustment of air conditioner temperature according to claim 4, characterized in that: and if the Tmax-Tmin is larger than c, returning to the step of predicting the opening degree of the neural network valve.
6. The control method of adaptive adjustment of air conditioner temperature according to claim 3, characterized in that: comparing the current temperature Tc with the set temperature T specifically is: and if the absolute value Tc-T absolute value > c is obtained, the neural network valve opening prediction is returned, the predicted value is V ', the valve opening is adjusted to V', the temperature data is detected in real time until the absolute value Tc-T absolute < c and the absolute value Tmax-Tmin absolute < c are met simultaneously, the operation is stable for a period of time, and the temperature setting is finished.
7. The control method of adaptive adjustment of air conditioner temperature according to claim 1, characterized in that: after the BP neural network predicts the valve opening, the relation between a predicted value V of the valve opening and an initial value V0 is judged, if | V0-V | is larger than a, the valve opening is adjusted to V at a rate of d, after the operation is carried out for a period of time, the current temperature Tc and the set temperature T are detected in real time and compared and judged, a and d are preset values, a is larger than 0 and smaller than or equal to 3%, and d is larger than 0 and smaller than or equal to 2%/min.
8. A computer-readable storage medium storing a computer program, characterized in that: the computer program realizes the control method of the adaptive adjustment of the air conditioner temperature according to any one of claims 1 to 7 when being called by a processor.
9. An air conditioner comprising a processor and a memory for storing a computer program, characterized in that: the computer program realizes the control method of the adaptive adjustment of the air conditioner temperature according to any one of claims 1 to 7 when being called by the processor.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111797980A (en) * | 2020-07-20 | 2020-10-20 | 房健 | Self-adaptive learning method for personalized floor heating use habits |
| CN112503725A (en) * | 2020-12-08 | 2021-03-16 | 珠海格力电器股份有限公司 | Air conditioner self-cleaning control method and device and air conditioner |
| CN114060961A (en) * | 2021-10-28 | 2022-02-18 | 青岛海尔空调器有限总公司 | Method and device for dehumidifying air conditioner, storage medium and air conditioner |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000304286A (en) * | 1999-04-20 | 2000-11-02 | Sanyo Electric Co Ltd | Temperature controller and temperature control method for floor heater |
| CN103309370A (en) * | 2013-06-01 | 2013-09-18 | 中南林业科技大学 | Incubating room humidity control method based on BP (back-propagation) neural network |
| CN103487172A (en) * | 2013-07-08 | 2014-01-01 | 浙江工业大学 | Heat metering wireless temperature control system based on fuzzy control algorithm |
| CN104482269A (en) * | 2014-12-19 | 2015-04-01 | 重庆川仪自动化股份有限公司 | Accurate control method and accurate control system for piezoelectric valve |
| CN104729024A (en) * | 2015-04-08 | 2015-06-24 | 南京优助智能科技有限公司 | Air conditioning load prediction method based on indoor average temperature |
| CN106369834A (en) * | 2016-09-02 | 2017-02-01 | 南通大学 | Directly-heated type heat pump system constant-temperature flow control method based on neural network |
| JP2018105571A (en) * | 2016-12-27 | 2018-07-05 | 株式会社大気社 | Heat exchange system, controller and construction method of neural network |
| CN109270842A (en) * | 2018-10-25 | 2019-01-25 | 浙江大学 | A kind of district heating model predictive control system and method based on Bayesian network |
| CN110187727A (en) * | 2019-06-17 | 2019-08-30 | 武汉理工大学 | A kind of Glass Furnace Temperature control method based on deep learning and intensified learning |
| CN110686350A (en) * | 2019-09-20 | 2020-01-14 | 珠海格力电器股份有限公司 | Control method for predicting self-regulating temperature in real time based on BP neural network, computer readable storage medium and air conditioner |
-
2019
- 2019-09-20 CN CN201910892761.5A patent/CN110686377A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000304286A (en) * | 1999-04-20 | 2000-11-02 | Sanyo Electric Co Ltd | Temperature controller and temperature control method for floor heater |
| CN103309370A (en) * | 2013-06-01 | 2013-09-18 | 中南林业科技大学 | Incubating room humidity control method based on BP (back-propagation) neural network |
| CN103487172A (en) * | 2013-07-08 | 2014-01-01 | 浙江工业大学 | Heat metering wireless temperature control system based on fuzzy control algorithm |
| CN104482269A (en) * | 2014-12-19 | 2015-04-01 | 重庆川仪自动化股份有限公司 | Accurate control method and accurate control system for piezoelectric valve |
| CN104729024A (en) * | 2015-04-08 | 2015-06-24 | 南京优助智能科技有限公司 | Air conditioning load prediction method based on indoor average temperature |
| CN106369834A (en) * | 2016-09-02 | 2017-02-01 | 南通大学 | Directly-heated type heat pump system constant-temperature flow control method based on neural network |
| JP2018105571A (en) * | 2016-12-27 | 2018-07-05 | 株式会社大気社 | Heat exchange system, controller and construction method of neural network |
| CN109270842A (en) * | 2018-10-25 | 2019-01-25 | 浙江大学 | A kind of district heating model predictive control system and method based on Bayesian network |
| CN110187727A (en) * | 2019-06-17 | 2019-08-30 | 武汉理工大学 | A kind of Glass Furnace Temperature control method based on deep learning and intensified learning |
| CN110686350A (en) * | 2019-09-20 | 2020-01-14 | 珠海格力电器股份有限公司 | Control method for predicting self-regulating temperature in real time based on BP neural network, computer readable storage medium and air conditioner |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111797980A (en) * | 2020-07-20 | 2020-10-20 | 房健 | Self-adaptive learning method for personalized floor heating use habits |
| CN112503725A (en) * | 2020-12-08 | 2021-03-16 | 珠海格力电器股份有限公司 | Air conditioner self-cleaning control method and device and air conditioner |
| CN114060961A (en) * | 2021-10-28 | 2022-02-18 | 青岛海尔空调器有限总公司 | Method and device for dehumidifying air conditioner, storage medium and air conditioner |
| CN114060961B (en) * | 2021-10-28 | 2022-12-20 | 青岛海尔空调器有限总公司 | Method and device for dehumidifying air conditioner, storage medium and air conditioner |
| WO2023071198A1 (en) * | 2021-10-28 | 2023-05-04 | 青岛海尔空调器有限总公司 | Dehumidification method and device of air conditioner, storage medium, and air conditioner |
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