CN114076381B - Air conditioner control method and device, air conditioner and storage medium - Google Patents
Air conditioner control method and device, air conditioner and storage medium Download PDFInfo
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- CN114076381B CN114076381B CN202010853855.4A CN202010853855A CN114076381B CN 114076381 B CN114076381 B CN 114076381B CN 202010853855 A CN202010853855 A CN 202010853855A CN 114076381 B CN114076381 B CN 114076381B
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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
- F24F11/32—Responding to malfunctions or emergencies
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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
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- F24F11/63—Electronic processing
- F24F11/65—Electronic processing for selecting an operating mode
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
- F24F11/72—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
- F24F11/80—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air
- F24F11/86—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air by controlling compressors within refrigeration or heat pump circuits
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- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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- 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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- 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
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Abstract
The embodiment of the invention relates to an air conditioner control method, an air conditioner control device, an air conditioner and a storage medium, wherein the method comprises the following steps: acquiring environmental information corresponding to an air conditioner and current operating parameters of the air conditioner; determining a first cooling/heating capacity and a first energy consumption of the air conditioner based on the environmental information and the operating parameters; adjusting the first refrigeration/heat quantity and the first energy consumption according to an energy-saving strategy to obtain a second refrigeration/heat quantity and a second energy consumption; generating an adjustment parameter for the air conditioner based on the environmental information, the second cooling/heating amount, and the second energy consumption; the operation state of the air conditioner is adjusted based on the adjustment parameters, so that the air conditioner can determine optimal operation parameters more intelligently and adjust according to the optimal operation parameters, and the energy consumption of the air conditioner is saved on the premise of not influencing the comfort.
Description
Technical Field
The embodiment of the invention relates to the field of intelligent air conditioners, in particular to an air conditioner control method and device, an air conditioner and a storage medium.
Background
Along with the rapid development of economy and science and the improvement of the comfort level requirement of people on the living environment, the application of the air conditioner is more and more common, and accordingly, under the form that energy conservation and emission reduction is greatly promoted in China, the application of artificial intelligence in the field of adjusting the energy conservation of the air conditioner shows a leap-going progress.
At present, the method adopted in the aspect of air conditioning energy is to adjust the operation mode of the air conditioner by collecting logic among all parameters of the air conditioner to judge whether the threshold value is reached or not, so as to achieve rough system control, but the parameter threshold value is determined based on experience, so that the air conditioner can not achieve optimal comfort and energy-saving effect in various complex environments.
Disclosure of Invention
In view of this, to solve the above technical problem of intelligent energy saving of an air conditioner, embodiments of the present invention provide an air conditioner control method, an air conditioner control device, an air conditioner, and a storage medium.
In a first aspect, an embodiment of the present invention provides an air conditioner control method, including
Acquiring environmental information corresponding to an air conditioner and current operating parameters of the air conditioner;
determining a first cooling/heating amount and a first energy consumption of the air conditioner based on the environmental information and the operating parameter;
adjusting the first refrigeration/heat and the first energy consumption according to an energy-saving strategy to obtain second refrigeration/heat and second energy consumption;
generating an adjustment parameter for the air conditioner based on the environmental information, the second cooling/heating amount, and the second energy consumption;
and adjusting the running state of the air conditioner based on the adjusting parameters.
In one possible embodiment, the method further comprises:
inputting the environmental information and the operating parameters into a pre-trained first neural network model, so that the first neural network model outputs a first cooling/heating amount and a first energy consumption of the air conditioner.
In one possible embodiment, the method further comprises:
under the condition that the current operation mode of the air conditioner is refrigeration, if the difference value between the indoor temperature and the set temperature of the air conditioner is smaller than a first threshold value, randomly selecting an adjustment value from a first preset range, and adjusting the first refrigeration capacity and the first energy consumption based on the adjustment value to obtain a second refrigeration capacity and a second energy consumption; if the difference value between the indoor temperature and the set temperature is larger than or equal to the first threshold value, randomly selecting an adjustment value from a second preset range, and adjusting the first refrigerating capacity and the first energy consumption based on the adjustment value to obtain a second refrigerating capacity and a second energy consumption;
alternatively, the first and second electrodes may be,
under the condition that the current operation mode of the air conditioner is heating, if the difference value between the indoor temperature and the set temperature of the air conditioner is smaller than a first threshold value, randomly selecting an adjustment value from a first preset range, and adjusting the first heating quantity and the first energy consumption based on the adjustment value to obtain a second heating quantity and a second energy consumption; and if the difference value between the indoor temperature and the set temperature is greater than or equal to the first threshold value, randomly selecting an adjustment value from a second preset range, and adjusting the first heating capacity and the first energy consumption based on the adjustment value to obtain a second heating capacity and second energy consumption.
In one possible embodiment, the method further comprises:
if the indoor temperature rises after the operation state of the air conditioner is adjusted, adjusting the second refrigerating capacity and the second energy consumption, and updating the adjustment parameters to keep the indoor temperature unchanged;
alternatively, the first and second electrodes may be,
and if the indoor temperature is reduced after the operation state of the air conditioner is adjusted, adjusting the second heating capacity and the second energy consumption, and updating the adjustment parameter to keep the indoor temperature unchanged.
In one possible embodiment, the method further comprises:
inputting the environmental information, the second cooling/heating amount and the second energy consumption into a pre-trained second neural network model, so that the second neural network model outputs adjustment parameters of the air conditioner.
In one possible embodiment, the method further comprises:
sending temperature acquisition instructions to temperature sensors arranged on an inner unit and an outer unit of the air conditioner;
receiving the indoor temperature and the outdoor temperature returned by the temperature sensor in response to the temperature acquisition instruction;
sending a humidity acquisition instruction to a humidity sensor arranged on an inner unit of the air conditioner;
and receiving the indoor humidity returned by the humidity sensor in response to the humidity acquisition instruction.
In a second aspect, an embodiment of the present invention provides an air conditioner control device, including:
the system comprises an acquisition module, a storage module and a control module, wherein the acquisition module is used for acquiring environmental information corresponding to an air conditioner and current operating parameters of the air conditioner;
a determination module for determining a first cooling/heating capacity and a first energy consumption of the air conditioner based on the environmental information and the operation parameter;
the determining module is further configured to generate an adjustment parameter of the air conditioner based on the environmental information, the second cooling/heating amount, and the second energy consumption;
the adjusting module is used for adjusting the first refrigeration/heat and the first energy consumption according to an energy-saving strategy to obtain second refrigeration/heat and second energy consumption;
the adjusting module is further used for adjusting the running state of the air conditioner based on the adjusting parameters.
In a possible embodiment, the adjusting module is specifically configured to, when the current operation mode of the air conditioner is cooling, randomly select an adjustment value from a first preset range if a difference between the indoor temperature and a set temperature of the air conditioner is smaller than a first threshold, and adjust the first cooling capacity and the first energy consumption based on the adjustment value to obtain a second cooling capacity and a second energy consumption; if the difference value between the indoor temperature and the set temperature is larger than or equal to the first threshold value, randomly selecting an adjustment value from a second preset range, and adjusting the first refrigerating capacity and the first energy consumption based on the adjustment value to obtain a second refrigerating capacity and a second energy consumption; or, under the condition that the current operation mode of the air conditioner is heating, if the difference value between the indoor temperature and the set temperature of the air conditioner is smaller than a first threshold value, randomly selecting an adjustment value from a first preset range, and adjusting the first heating quantity and the first energy consumption based on the adjustment value to obtain a second heating quantity and a second energy consumption; if the difference value between the indoor temperature and the set temperature is larger than or equal to the first threshold value, randomly selecting an adjustment value from a second preset range, and adjusting the first heating capacity and the first energy consumption based on the adjustment value to obtain a second heating capacity and a second energy consumption.
In a third aspect, an embodiment of the present invention provides an air conditioner, including: the processor is used for executing the air conditioner control program stored in the memory so as to realize the air conditioner control method in any one of the first aspect.
In a fourth aspect, an embodiment of the present invention provides a storage medium, where the storage medium stores one or more programs, and the one or more programs are executable by one or more processors to implement the air conditioner control method according to any one of the first aspects.
According to the air conditioner control method provided by the embodiment of the invention, the environmental information corresponding to the air conditioner and the current operating parameters of the air conditioner are obtained; determining a first cooling/heating capacity and a first energy consumption of the air conditioner based on the environmental information and the operating parameters; adjusting the first refrigeration/heat and the first energy consumption according to an energy-saving strategy to obtain second refrigeration/heat and second energy consumption; generating an adjustment parameter for the air conditioner based on the environmental information, the second cooling/heating amount, and the second energy consumption; the operation state of the air conditioner is adjusted based on the adjustment parameters, so that the air conditioner can determine optimal operation parameters more intelligently and adjust according to the optimal operation parameters, and the energy consumption of the air conditioner is saved on the premise of not influencing the comfort.
Drawings
Fig. 1 is a schematic flowchart of an air conditioner control method according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of another air conditioner control method according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an air conditioner control device according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an air conditioner according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
For the convenience of understanding of the embodiments of the present invention, the following description will be further explained with reference to specific embodiments, which are not to be construed as limiting the embodiments of the present invention.
The invention relates to the application of artificial intelligence in the field of intelligent air conditioners, and the intelligent air conditioner can more intelligently find out the optimal operating parameters under different operating environments through the method, thereby adjusting the operating state and realizing that the air conditioner reduces the energy consumption on the premise of not influencing the comfort.
It should be noted that the environment information at least includes: indoor temperature, indoor humidity, and/or outdoor temperature;
the operating parameters include at least: compressor frequency and speed;
the first neural network model is a pre-trained model for predicting air conditioner refrigeration/heat and energy consumption according to environmental information and operation parameters;
the second neural network model is a model for calculating the operation parameters of the air conditioner according to the refrigeration/heat quantity and the energy consumption of the air conditioner.
Fig. 1 is a schematic flow diagram of an air conditioner control method according to an embodiment of the present invention, and as shown in fig. 1, the method specifically includes:
s11, obtaining environment information corresponding to the air conditioner and current operation parameters of the air conditioner.
In the embodiment of the invention, the temperature sensor and the humidity sensor are arranged on the air conditioner internal unit, the temperature sensor is arranged on the external unit, and the sensors can be used for acquiring the environment information such as indoor temperature, indoor humidity, outdoor temperature or season and the like corresponding to the air conditioner; and further acquiring the compression frequency, the rotating speed, the preset temperature or the operation mode and other operation parameters of the compressor of the air conditioner.
For example, the environment information corresponding to the air conditioner is obtained in summer, the indoor temperature is 27 ℃, the indoor humidity is 40% and the outdoor temperature is 38 ℃; the obtained compressor frequency of the air conditioner is 50Hz, the compressor rotating speed is 3000r/min, the preset temperature is 26 ℃, and the operation mode is a refrigeration mode.
For another example, the environment information corresponding to the air conditioner is obtained in winter, the indoor temperature is 24 ℃, the indoor humidity is 30% and the outdoor temperature is 4 ℃; the obtained compressor frequency of the air conditioner is 70Hz, the compressor rotating speed is 4000r/min, the preset temperature is 26 ℃, and the operation mode is a heating mode.
And S12, determining a first cooling/heating amount and a first energy consumption of the air conditioner based on the environmental information and the operation parameters.
And inputting the acquired environment information and the acquired operation parameters corresponding to the air conditioner into the first-layer neural network model as a multi-dimensional vector, so that the model predicts the current refrigeration/heat and the corresponding energy consumption of the air conditioner according to the environment information and the operation parameters corresponding to the air conditioner.
And S13, adjusting the first refrigeration/heat quantity and the first energy consumption according to an energy-saving strategy to obtain a second refrigeration/heat quantity and a second energy consumption.
In the embodiment of the invention, an energy-saving strategy is preset, and the predicted refrigeration/heat quantity and the corresponding energy consumption of the air conditioner are adjusted according to the energy-saving strategy.
Further, determining a predicted current refrigeration/heat quantity of the air conditioner and an adjustment strategy of the corresponding energy consumption, further performing downward-probing adjustment on the current refrigeration/heat quantity of the air conditioner and the corresponding energy consumption, and adjusting the current refrigeration/heat quantity of the air conditioner and the corresponding energy consumption under the condition of not influencing the indoor temperature to obtain the optimal second refrigeration/heat quantity and second energy consumption.
In an alternative of the embodiment of the present invention, the current cooling/heating capacity of the air conditioner and the adjustment strategy of the corresponding energy consumption correspond to different conditions, and the conditions may be determined according to a difference between the current indoor temperature and the air conditioner temperature set by the user.
For example, when the air conditioner model is cooling, when the difference between the indoor temperature (e.g., 26 degrees celsius) and the air conditioner temperature (e.g., 24 degrees celsius) set by the user is greater than a first threshold value (e.g., 1 degree celsius), and the air conditioner belongs to a cooling state, it is determined that the current cooling capacity and the corresponding energy consumption of the air conditioner are adjusted in a downward manner according to a formula Q' = (1-a correction coefficient) × Q, where the initialization value of the a correction coefficient is 0% and the value range is [0% -10% ].
For another example, when the air conditioner model is heating, and when the difference between the indoor temperature (e.g., 25.5 degrees celsius) and the air conditioner temperature (e.g., 26 degrees celsius) set by the user is less than the first threshold (e.g., 1 degree celsius), the air conditioner is in a constant temperature state, it is determined that the current heating capacity and the corresponding energy consumption of the air conditioner are adjusted in a downward-type manner according to a formula Q' = (1-B correction coefficient) × Q, where the initialization value of the B correction coefficient is 0% and the value range is [0% -20% ].
And S14, generating an adjusting parameter of the air conditioner based on the environment information, the second cooling/heating amount and the second energy consumption.
And S15, adjusting the running state of the air conditioner based on the adjusting parameters.
And inputting the adjusted optimal second refrigeration/heat, second energy consumption and environment information corresponding to the air conditioner into a second-layer neural network model as a multi-dimensional vector, so that the model obtains the optimal adjustment parameters of the air conditioner according to the second refrigeration/heat, the second energy consumption and the environment information corresponding to the air conditioner as parameters.
And further, generating an adjustment control instruction for the air conditioner based on the obtained optimal adjustment parameter of the air conditioner, so that the air conditioner can adjust the current operation parameter and state in response to the adjustment control instruction.
For example, the second-layer neural network model calculates the optimal adjustment parameters of the air conditioner to be the cooling mode, 27 ℃ and 2-level wind speed according to the second cooling capacity, the second energy consumption and the environment information corresponding to the air conditioner, and then adjusts the air conditioner according to the adjustment parameters.
According to the air conditioner control method provided by the embodiment of the invention, the environmental information corresponding to the air conditioner and the current operating parameters of the air conditioner are obtained; determining a first cooling/heating amount and a first energy consumption of the air conditioner based on the environmental information and the operating parameter; adjusting the first refrigeration/heat quantity and the first energy consumption according to an energy-saving strategy to obtain a second refrigeration/heat quantity and a second energy consumption; generating an adjustment parameter for the air conditioner based on the environmental information, the second cooling/heating amount, and the second energy consumption; the running state of the air conditioner is adjusted based on the adjustment parameters, so that the air conditioner can determine the optimal running parameters in the current environment more intelligently, and the air conditioner can be adjusted according to the optimal running parameters, thereby reducing the energy consumption of the air conditioner on the premise of not influencing the comfort.
Fig. 2 is a schematic flowchart of another air conditioner control method according to an embodiment of the present invention, and as shown in fig. 2, the method specifically includes:
and S21, sending temperature acquisition instructions to temperature sensors arranged on an internal unit and an external unit of the air conditioner.
And S22, receiving the indoor temperature and the outdoor temperature returned by the temperature sensor in response to the temperature acquisition instruction.
In the embodiment of the invention, a microprocessor arranged in an air conditioner generates and sends temperature acquisition instructions to temperature sensors arranged on an inner unit and an outer unit of the air conditioner at regular time according to a preset time period, then the temperature sensors on the inner unit and the outer unit respectively respond to the temperature acquisition instructions sent by the processor to return indoor temperature and outdoor temperature to the processor, and the processor receives and records the indoor temperature and the outdoor temperature respectively sent by the temperature sensors on the inner unit and the outer unit of the air conditioner.
And S23, sending a humidity acquisition instruction to a humidity sensor arranged on an internal unit of the air conditioner.
And S24, receiving the indoor humidity returned by the humidity sensor in response to the humidity acquisition instruction.
In the embodiment of the invention, a micro processor arranged in an air conditioner regularly generates a humidity acquisition instruction to be sent to a humidity sensor arranged on an indoor unit of the air conditioner according to a preset time period, then the humidity sensor on the indoor unit of the air conditioner returns indoor humidity to the processor in response to the humidity acquisition instruction sent by the processor, and the processor receives and records the indoor humidity sent by the humidity sensor on the indoor unit of the air conditioner.
And S25, inputting the environmental information and the operating parameters into a pre-trained first neural network model so that the first neural network model outputs first refrigeration/heat and first energy consumption of the air conditioner.
In the embodiment of the invention, a processor arranged in the air conditioner inputs the acquired environment information and the operating parameters corresponding to the air conditioner into a pre-trained first-layer neural network model as a multi-dimensional vector, the first-layer neural network model performs data fitting on the received multi-dimensional vector, and the predicted refrigeration/heat and energy consumption of the air conditioner at the moment are calculated through self operation parameters.
For example, the acquired environment information corresponding to the air conditioner is summer, the indoor temperature is 26 ℃, the indoor humidity is 37% and the outdoor temperature is 37 ℃; the method comprises the steps of obtaining that the frequency of a compressor of the air conditioner is 50Hz, the rotating speed of the compressor is 3000r/min, the preset temperature is 24 ℃ and the operation mode is a refrigeration mode, inputting the data into a pre-trained first-layer neural network model as a multi-dimensional vector, and predicting that the refrigeration capacity of the air conditioner is 2500w/h and the energy consumption is 1.5 ℃ per hour by the model.
S26, under the condition that the current operation mode of the air conditioner is refrigeration, if the difference value between the indoor temperature and the set temperature of the air conditioner is smaller than a first threshold value, randomly selecting an adjustment value from a first preset range, and adjusting the first refrigeration capacity and the first energy consumption based on the adjustment value to obtain a second refrigeration capacity and second energy consumption; and if the difference value between the indoor temperature and the set temperature is greater than or equal to the first threshold value, randomly selecting an adjustment value from a second preset range, and adjusting the first refrigerating capacity and the first energy consumption based on the adjustment value to obtain a second refrigerating capacity and a second energy consumption.
In the embodiment of the invention, when the operation mode of the air conditioner is refrigeration, the air conditioner can be divided into a constant temperature mode and a cooling mode, and the adjustment strategies of the refrigeration capacity and the energy consumption of the air conditioner under the two modes are respectively set.
If the difference value between the indoor temperature and the set temperature of the air conditioner is smaller than a first threshold value (for example, 2 ℃), determining that the operation mode of the air conditioner is a constant temperature mode, randomly generating an adjustment value from a first preset range (for example, 0% -20%), detecting a first refrigerating capacity and a first energy consumption at the position predicted by a first layer of neural network model according to the generated adjustment value, respectively reducing by 10%, then acquiring whether the indoor temperature is changed, if so, adjusting the reduction white percentage to 7%, then acquiring the indoor temperature again, and if not, determining that the refrigerating capacity and the energy consumption which are adjusted at the moment are optimal data to obtain the optimal refrigerating capacity and energy consumption.
Optionally, if the difference between the indoor temperature and the set temperature of the air conditioner is greater than or equal to a first threshold (e.g., 2 degrees celsius), determining that the operation mode of the air conditioner is a cooling mode, randomly generating an adjustment value from a second preset range (e.g., 0% to 10%), detecting the first cooling capacity and the first energy consumption predicted by the first-layer neural network model according to the generated adjustment value, respectively reducing the first cooling capacity and the first energy consumption by 5%, then obtaining whether the indoor temperature changes, if so, adjusting the reduction percentage to 2%, then obtaining the indoor temperature again, and if not, determining that the cooling capacity and the energy consumption adjusted at this time are optimal data, so as to obtain the optimal cooling capacity and energy consumption.
S27, under the condition that the current operation mode of the air conditioner is heating, if the difference value between the indoor temperature and the set temperature of the air conditioner is smaller than a first threshold value, randomly selecting an adjustment value from a first preset range, and adjusting the first heating quantity and the first energy consumption based on the adjustment value to obtain a second heating quantity and second energy consumption; if the difference value between the indoor temperature and the set temperature is larger than or equal to the first threshold value, randomly selecting an adjustment value from a second preset range, and adjusting the first heating capacity and the first energy consumption based on the adjustment value to obtain a second heating capacity and a second energy consumption.
In the embodiment of the invention, when the operation mode of the air conditioner is heating, the air conditioner can be divided into a constant temperature mode and a heating mode, and the heating amount and the energy consumption of the air conditioner in the two modes are respectively set.
If the difference value between the indoor temperature and the set temperature of the air conditioner is smaller than a first threshold value (for example, 1 ℃), determining that the operation mode of the air conditioner is a constant temperature mode, randomly generating an adjustment value from a first preset range (for example, 0% -20%), detecting a first heating capacity and a first energy consumption at the predicted position of a first layer of neural network model according to the generated adjustment value, respectively reducing the heating capacity and the energy consumption by 15%, then acquiring whether the indoor temperature changes, if so, adjusting the reduction percentage to 10%, then acquiring the indoor temperature again, and if not, determining that the adjusted heating capacity and energy consumption are optimal data to obtain the optimal heating capacity and energy consumption.
Optionally, if the difference between the indoor temperature and the set temperature of the air conditioner is greater than or equal to a first threshold (e.g., 1 degree celsius), determining that the operation mode of the air conditioner is a heating mode, randomly generating an adjustment value from a second preset range (e.g., 0% to 10%), detecting the first heating capacity and the first energy consumption predicted by the first-layer neural network model according to the generated adjustment value, respectively reducing by 7%, then obtaining whether the indoor temperature changes, if so, adjusting the reduction white fraction to 5%, then re-obtaining the indoor temperature, and if not, determining that the cooling capacity and the energy consumption adjusted at this time are optimal data, so as to obtain the optimal heating capacity and energy consumption.
And S28, if the indoor temperature is increased after the operation state of the air conditioner is adjusted, adjusting the second refrigerating capacity and the second energy consumption, and updating the adjustment parameters to keep the indoor temperature unchanged.
In the embodiment of the invention, the air conditioner operation mode is a refrigeration mode, the indoor temperature is higher than the set temperature of the air conditioner according to experience, if the refrigeration capacity and the energy consumption of the air conditioner are reduced, the indoor temperature can be increased, so that the difference between the indoor temperature and the set temperature of the air conditioner is increased, and if the indoor temperature is increased, the adjustment percentage of the refrigeration capacity and the energy consumption is reduced, so that the adjusted refrigeration capacity and energy consumption can not cause the change of the indoor temperature.
And S29, if the indoor temperature is reduced after the operation state of the air conditioner is adjusted, adjusting the second heating capacity and the second energy consumption, and updating the adjustment parameters to keep the indoor temperature unchanged.
In the embodiment of the invention, the air conditioner operation mode is a heating mode, the indoor temperature is lower than the set temperature of the air conditioner according to experience, if the heating capacity and the energy consumption of the air conditioner are reduced, the indoor temperature is possibly reduced, so that the difference between the indoor temperature and the set temperature of the air conditioner is increased, and if the indoor temperature is reduced, the adjustment percentage of the heating capacity and the energy consumption is reduced, so that the indoor temperature cannot be changed due to the adjusted heating capacity and the adjusted energy consumption.
S210, inputting the environmental information, the second refrigeration/heat quantity and the second energy consumption into a pre-trained second neural network model so that the second neural network model outputs the adjusting parameters of the air conditioner.
And inputting the adjusted optimal second refrigeration/heat, the second energy consumption and the current corresponding environment information of the air conditioner into a second-layer neural network model as a multi-dimensional vector, so that the second-layer neural network model performs data fitting calculation according to the second refrigeration/heat, the second energy consumption and the corresponding environment information of the air conditioner as parameters to obtain the optimal adjustment parameters of the air conditioner.
For example, the second-layer neural network model calculates the optimal adjustment parameters of the air conditioner to be the cooling mode, 26 ℃ and 1-level wind speed according to the second cooling capacity, the second energy consumption and the environment information corresponding to the air conditioner, and then adjusts the air conditioner according to the adjustment parameters.
For another example, the second-layer neural network model calculates the optimal adjustment parameters of the air conditioner to be a heating mode, 20 degrees centigrade and a wind speed level 1 according to the second heating quantity, the second energy consumption and the environment information corresponding to the air conditioner, and then adjusts the air conditioner according to the adjustment parameters.
If the optimal adjustment parameters of the air conditioner are the air supply mode, 20 ℃ and 2-level wind speed, which are calculated by the second layer neural network model according to the second refrigerating capacity, the second energy consumption and the environment information corresponding to the air conditioner, the air conditioner is adjusted according to the adjustment parameters.
According to the air conditioner control method provided by the embodiment of the invention, the environmental information corresponding to the air conditioner and the current operating parameters of the air conditioner are obtained; inputting the environmental information and the operation parameters into a pre-trained neural network model, and predicting first refrigeration/heat and first energy consumption of the air conditioner; performing cyclic downward detection adjustment on the first refrigeration/heat and the first energy consumption according to an energy-saving strategy to obtain optimal second refrigeration/heat and second energy consumption; then inputting the environmental information, the second refrigeration/heat and the second energy consumption into an optimization depth network model to generate an optimal adjustment parameter of the air conditioner; the operation state of the air conditioner is adjusted based on the optimal adjustment parameter, so that the air conditioner can determine the optimal operation parameter in the current environment more intelligently, and the adjustment is performed according to the optimal operation parameter, thereby reducing the energy consumption.
Fig. 3 is a schematic structural diagram of an air conditioner control device according to an embodiment of the present invention, which specifically includes:
an obtaining module 301, configured to obtain environment information corresponding to an air conditioner and current operating parameters of the air conditioner;
a determining module 302, configured to determine a first cooling/heating capacity and a first energy consumption of the air conditioner based on the environmental information and the operating parameter;
the determining module 302 is further configured to generate an adjustment parameter of the air conditioner based on the environmental information, the second cooling/heating amount, and the second energy consumption;
an adjusting module 303, configured to adjust the first refrigeration/heat and the first energy consumption according to an energy saving policy to obtain a second refrigeration/heat and a second energy consumption;
the adjusting module 303 is further configured to adjust the operation state of the air conditioner based on the adjustment parameter.
In a possible embodiment, the obtaining module is specifically configured to send a temperature obtaining instruction to temperature sensors disposed on an internal unit and an external unit of the air conditioner; receiving indoor temperature and outdoor temperature returned by the temperature sensor in response to the temperature acquisition instruction; sending a humidity acquisition instruction to a humidity sensor arranged on an internal unit of the air conditioner; and receiving the indoor humidity returned by the humidity sensor in response to the humidity acquisition instruction.
In a possible embodiment, the determining module is specifically configured to input the environmental information and the operating parameter into a pre-trained first neural network model, so that the first neural network model outputs a first cooling/heating amount and a first energy consumption of the air conditioner.
In a possible embodiment, the determining module is further configured to input the environmental information, the second cooling/heating amount, and the second energy consumption into a pre-trained second neural network model, so that the second neural network model outputs the adjustment parameter of the air conditioner.
In a possible embodiment, the adjusting module is specifically configured to, when the current operation mode of the air conditioner is cooling, randomly select an adjustment value from a first preset range if a difference between the indoor temperature and a set temperature of the air conditioner is smaller than a first threshold, and adjust the first cooling capacity and the first energy consumption based on the adjustment value to obtain a second cooling capacity and a second energy consumption; if the difference value between the indoor temperature and the set temperature is larger than or equal to the first threshold value, randomly selecting an adjustment value from a second preset range, and adjusting the first refrigerating capacity and the first energy consumption based on the adjustment value to obtain a second refrigerating capacity and a second energy consumption; or, under the condition that the current operation mode of the air conditioner is heating, if the difference value between the indoor temperature and the set temperature of the air conditioner is smaller than a first threshold value, randomly selecting an adjustment value from a first preset range, and adjusting the first heating quantity and the first energy consumption based on the adjustment value to obtain a second heating quantity and a second energy consumption; and if the difference value between the indoor temperature and the set temperature is greater than or equal to the first threshold value, randomly selecting an adjustment value from a second preset range, and adjusting the first heating capacity and the first energy consumption based on the adjustment value to obtain a second heating capacity and second energy consumption.
In a possible embodiment, the adjusting module is further configured to adjust the second cooling capacity and the second energy consumption and update the adjusting parameter to keep the indoor temperature unchanged if the indoor temperature rises after the operating state of the air conditioner is adjusted; or if the indoor temperature is reduced after the operation state of the air conditioner is adjusted, adjusting the second heating capacity and the second energy consumption, and updating the adjustment parameter so as to keep the indoor temperature unchanged.
The air conditioning control device provided in this embodiment may be the air conditioning control device shown in fig. 3, and may perform all the steps of the air conditioning control method shown in fig. 1-2, so as to achieve the technical effects of the air conditioning control method shown in fig. 1-2, and for brevity, it is described with reference to fig. 1-2, which is not described herein again.
Fig. 4 is a schematic structural diagram of an air conditioner according to an embodiment of the present invention, and the air conditioner 400 shown in fig. 4 includes: at least one processor 401, memory 402, at least one network interface 404, and other user interfaces 403. The various components in the air conditioner 400 are coupled together by a bus system 405. It is understood that the bus system 405 is used to enable connection communication between these components. The bus system 505 includes a power bus, a control bus, and a status signal bus in addition to a data bus. For clarity of illustration, however, the various buses are labeled as bus system 405 in fig. 4.
The user interface 403 may include, among other things, a display, a keyboard, or a pointing device (e.g., a mouse, trackball, touch pad, or touch screen, among others.
It will be appreciated that memory 402 in embodiments of the invention may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of example, and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), synchronous Dynamic Random Access Memory (SDRAM), double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), enhanced Synchronous SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memory 402 described herein is intended to comprise, without being limited to, these and any other suitable types of memory.
In some embodiments, memory 402 stores the following elements, executable units or data structures, or a subset thereof, or an expanded set thereof: an operating system 4021 and application programs 4022.
The operating system 4021 includes various system programs, such as a framework layer, a core library layer, a driver layer, and the like, and is configured to implement various basic services and process hardware-based tasks. The application programs 4022 include various application programs, such as a Media Player (Media Player), a Browser (Browser), and the like, for implementing various application services. A program for implementing the method according to the embodiment of the present invention may be included in the application 4022.
In this embodiment of the present invention, by calling a program or an instruction stored in the memory 402, specifically, a program or an instruction stored in the application 4022, the processor 401 is configured to execute the method steps provided by the method embodiments, for example, including:
acquiring environmental information corresponding to an air conditioner and current operating parameters of the air conditioner; determining a first cooling/heating amount and a first energy consumption of the air conditioner based on the environmental information and the operating parameter; adjusting the first refrigeration/heat quantity and the first energy consumption according to an energy-saving strategy to obtain a second refrigeration/heat quantity and a second energy consumption; generating an adjustment parameter for the air conditioner based on the environmental information, the second cooling/heating amount, and the second energy consumption; and adjusting the running state of the air conditioner based on the adjusting parameters.
In one possible embodiment, the environmental information and the operating parameters are input into a first neural network model that is pre-trained, so that the first neural network model outputs a first cooling/heating capacity and a first energy consumption of the air conditioner.
In a possible embodiment, when the current operation mode of the air conditioner is refrigeration, if a difference between the indoor temperature and a set temperature of the air conditioner is smaller than a first threshold, an adjustment value is randomly selected from a first preset range, and the first refrigeration capacity and the first energy consumption are adjusted based on the adjustment value to obtain a second refrigeration capacity and a second energy consumption; if the difference value between the indoor temperature and the set temperature is larger than or equal to the first threshold value, randomly selecting an adjustment value from a second preset range, and adjusting the first refrigerating capacity and the first energy consumption based on the adjustment value to obtain a second refrigerating capacity and a second energy consumption; or, under the condition that the current operation mode of the air conditioner is heating, if the difference value between the indoor temperature and the set temperature of the air conditioner is smaller than a first threshold value, randomly selecting an adjustment value from a first preset range, and adjusting the first heating quantity and the first energy consumption based on the adjustment value to obtain a second heating quantity and a second energy consumption; if the difference value between the indoor temperature and the set temperature is larger than or equal to the first threshold value, randomly selecting an adjustment value from a second preset range, and adjusting the first heating capacity and the first energy consumption based on the adjustment value to obtain a second heating capacity and a second energy consumption.
In a possible embodiment, if the indoor temperature rises after the operation state of the air conditioner is adjusted, the second cooling capacity and the second energy consumption are adjusted, and the adjustment parameter is updated so that the indoor temperature is not changed; or if the indoor temperature is reduced after the operation state of the air conditioner is adjusted, adjusting the second heating capacity and the second energy consumption, and updating the adjustment parameter to keep the indoor temperature unchanged.
In one possible embodiment, the environmental information, the second cooling/heating amount, and the second energy consumption are input into a pre-trained second neural network model, so that the second neural network model outputs the adjustment parameters of the air conditioner.
In one possible embodiment, temperature acquisition instructions are sent to temperature sensors arranged on an inner unit and an outer unit of the air conditioner; receiving indoor temperature and outdoor temperature returned by the temperature sensor in response to the temperature acquisition instruction; sending a humidity acquisition instruction to a humidity sensor arranged on an internal unit of the air conditioner; and receiving the indoor humidity returned by the humidity sensor in response to the humidity acquisition instruction.
The method disclosed in the above embodiments of the present invention may be applied to the processor 401, or implemented by the processor 401. The processor 401 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 401. The Processor 401 may be a general-purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, or discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software elements in the decoding processor. The software elements may be located in ram, flash, rom, prom, or eprom, registers, etc. as is well known in the art. The storage medium is located in the memory 402, and the processor 401 reads the information in the memory 402 and completes the steps of the method in combination with the hardware.
It is to be understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or any combination thereof. For a hardware implementation, the Processing units may be implemented within one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), general purpose processors, controllers, micro-controllers, microprocessors, other electronic units configured to perform the functions described herein, or a combination thereof.
For a software implementation, the techniques described herein may be implemented by means of units performing the functions described herein. The software codes may be stored in a memory and executed by a processor. The memory may be implemented within the processor or external to the processor.
The air conditioner provided in this embodiment may be the air conditioner shown in fig. 4, and may perform all steps of the air conditioner control method shown in fig. 1-2, so as to achieve the technical effects of the air conditioner control method shown in fig. 1-2, and for brevity, the related description is specifically referred to fig. 1-2, and is not repeated herein.
The embodiment of the invention also provides a storage medium (computer readable storage medium). The storage medium herein stores one or more programs. Among others, storage media may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, a hard disk, or a solid state disk; the memory may also comprise a combination of memories of the kind described above.
When one or more programs in the storage medium are executable by one or more processors, the above-described air-conditioning control method performed on the air-conditioning side is implemented.
The processor is used for executing the air conditioner control program stored in the memory so as to realize the following steps of the air conditioner control method executed on the air conditioner side:
acquiring environmental information corresponding to an air conditioner and current operating parameters of the air conditioner; determining a first cooling/heating capacity and a first energy consumption of the air conditioner based on the environmental information and the operating parameters; adjusting the first refrigeration/heat and the first energy consumption according to an energy-saving strategy to obtain second refrigeration/heat and second energy consumption; generating an adjustment parameter for the air conditioner based on the environmental information, the second cooling/heating amount, and the second energy consumption; and adjusting the running state of the air conditioner based on the adjusting parameters.
In one possible embodiment, the environmental information and the operating parameter are input into a first neural network model that is pre-trained, so that the first neural network model outputs a first cooling/heating amount and a first energy consumption of the air conditioner.
In a possible embodiment, under the condition that the current operation mode of the air conditioner is refrigeration, if a difference value between the indoor temperature and a set temperature of the air conditioner is smaller than a first threshold value, an adjustment value is randomly selected from a first preset range, and the first refrigeration capacity and the first energy consumption are adjusted based on the adjustment value to obtain a second refrigeration capacity and a second energy consumption; if the difference value between the indoor temperature and the set temperature is larger than or equal to the first threshold value, randomly selecting an adjustment value from a second preset range, and adjusting the first refrigerating capacity and the first energy consumption based on the adjustment value to obtain a second refrigerating capacity and a second energy consumption; or, under the condition that the current operation mode of the air conditioner is heating, if the difference value between the indoor temperature and the set temperature of the air conditioner is smaller than a first threshold value, randomly selecting an adjustment value from a first preset range, and adjusting the first heating quantity and the first energy consumption based on the adjustment value to obtain a second heating quantity and a second energy consumption; and if the difference value between the indoor temperature and the set temperature is greater than or equal to the first threshold value, randomly selecting an adjustment value from a second preset range, and adjusting the first heating capacity and the first energy consumption based on the adjustment value to obtain a second heating capacity and second energy consumption.
In a possible embodiment, if the indoor temperature rises after the operation state of the air conditioner is adjusted, the second cooling capacity and the second energy consumption are adjusted, and the adjustment parameter is updated so that the indoor temperature is not changed; or if the indoor temperature is reduced after the operation state of the air conditioner is adjusted, adjusting the second heating capacity and the second energy consumption, and updating the adjustment parameter so as to keep the indoor temperature unchanged.
In one possible embodiment, the environmental information, the second cooling/heating amount, and the second energy consumption are input into a pre-trained second neural network model, so that the second neural network model outputs the adjustment parameters of the air conditioner.
In one possible embodiment, temperature acquisition instructions are sent to temperature sensors arranged on an inner unit and an outer unit of the air conditioner; receiving indoor temperature and outdoor temperature returned by the temperature sensor in response to the temperature acquisition instruction; sending a humidity acquisition instruction to a humidity sensor arranged on an internal unit of the air conditioner; and receiving the indoor humidity returned by the humidity sensor in response to the humidity acquisition instruction.
Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, a software module executed by a processor, or a combination of the two. A software module may reside in Random Access Memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (8)
1. An air conditioner control method, comprising:
acquiring environmental information corresponding to an air conditioner and current operating parameters of the air conditioner;
determining a first cooling/heating capacity and a first energy consumption of the air conditioner based on the environmental information and the operating parameters;
adjusting the first refrigeration/heat quantity and the first energy consumption according to an energy-saving strategy to obtain a second refrigeration/heat quantity and a second energy consumption;
under the condition that the current operation mode of the air conditioner is refrigeration, if the difference value between the indoor temperature and the set temperature of the air conditioner is smaller than a first threshold value, randomly selecting an adjustment value from a first preset range, and performing downward probing type adjustment on the first refrigeration capacity and the first energy consumption based on the adjustment value to obtain a second refrigeration capacity and second energy consumption; if the difference value between the indoor temperature and the set temperature is larger than or equal to the first threshold value, randomly selecting an adjustment value from a second preset range, and performing downward-probing type adjustment on the first refrigerating capacity and the first energy consumption based on the adjustment value to obtain a second refrigerating capacity and a second energy consumption, wherein the first preset range comprises: 0% -20%, the second preset range includes: 0% -10%;
under the condition that the current operation mode of the air conditioner is heating, if the difference value between the indoor temperature and the set temperature of the air conditioner is smaller than a first threshold value, randomly selecting an adjustment value from a first preset range, and performing downward probing adjustment on the first heating quantity and the first energy consumption based on the adjustment value to obtain a second heating quantity and second energy consumption; if the difference value between the indoor temperature and the set temperature is larger than or equal to the first threshold value, randomly selecting an adjustment value from a second preset range, and performing downward probing type adjustment on the first heating quantity and the first energy consumption based on the adjustment value to obtain a second heating quantity and second energy consumption;
if the indoor temperature rises after the operation state of the air conditioner is adjusted, adjusting the second refrigerating capacity and the second energy consumption, and updating the adjustment parameters so that the adjusted indoor temperature does not change any more;
if the indoor temperature is reduced after the operation state of the air conditioner is adjusted, adjusting the second heating capacity and the second energy consumption, and updating the adjustment parameters so that the adjusted indoor temperature is not changed any more;
generating an adjustment parameter for the air conditioner based on the environmental information, the second cooling/heating amount, and the second energy consumption;
and adjusting the running state of the air conditioner based on the adjusting parameters.
2. The method of claim 1, wherein determining a first cooling/heating amount and a first energy consumption of the air conditioner based on the environmental information and the operating parameter comprises:
inputting the environmental information and the operating parameters into a pre-trained first neural network model, so that the first neural network model outputs a first cooling/heating amount and a first energy consumption of the air conditioner.
3. The method of claim 1, wherein the context information comprises: indoor temperature, indoor humidity, and/or outdoor temperature.
4. The method of claim 1, wherein generating the adjustment parameter for the air conditioner based on the environmental information, the second cooling/heating capacity, and the second energy consumption comprises:
inputting the environmental information, the second cooling/heating amount and the second energy consumption into a pre-trained second neural network model, so that the second neural network model outputs adjustment parameters of the air conditioner.
5. The method of claim 1, wherein the obtaining environmental information corresponding to the air conditioner comprises:
sending temperature acquisition instructions to temperature sensors arranged on an internal unit and an external unit of the air conditioner;
receiving indoor temperature and outdoor temperature returned by the temperature sensor in response to the temperature acquisition instruction;
sending a humidity acquisition instruction to a humidity sensor arranged on an inner unit of the air conditioner;
and receiving the indoor humidity returned by the humidity sensor in response to the humidity acquisition instruction.
6. An air conditioning control device, characterized by comprising:
the system comprises an acquisition module, a storage module and a control module, wherein the acquisition module is used for acquiring environmental information corresponding to an air conditioner and current operating parameters of the air conditioner;
a determination module for determining a first cooling/heating capacity and a first energy consumption of the air conditioner based on the environmental information and the operation parameter;
the adjusting module is used for adjusting the first refrigeration/heat and the first energy consumption according to an energy-saving strategy to obtain second refrigeration/heat and second energy consumption;
the adjusting module is further configured to, in a case that the current operation mode of the air conditioner is refrigeration, randomly select an adjusting value from a first preset range if a difference between an indoor temperature and a set temperature of the air conditioner is smaller than a first threshold, and perform downward detection type adjustment on the first refrigeration capacity and the first energy consumption based on the adjusting value to obtain a second refrigeration capacity and a second energy consumption; if the difference value between the indoor temperature and the set temperature is larger than or equal to the first threshold value, randomly selecting an adjustment value from a second preset range, and performing downward-probing type adjustment on the first refrigerating capacity and the first energy consumption based on the adjustment value to obtain a second refrigerating capacity and a second energy consumption, wherein the first preset range comprises: 0% -20%, the second preset range includes: 0% -10%;
the adjusting module is further configured to, when the current operation mode of the air conditioner is heating, randomly select an adjusting value from a first preset range if a difference between the indoor temperature and a set temperature of the air conditioner is smaller than a first threshold, and perform downward probing adjustment on the first heating capacity and the first energy consumption based on the adjusting value to obtain a second heating capacity and a second energy consumption; if the difference value between the indoor temperature and the set temperature is larger than or equal to the first threshold value, randomly selecting an adjustment value from a second preset range, and performing downward probing type adjustment on the first heating quantity and the first energy consumption based on the adjustment value to obtain a second heating quantity and second energy consumption;
the adjusting module is further configured to adjust the second cooling capacity and the second energy consumption and update the adjusting parameter if the indoor temperature rises after the operating state of the air conditioner is adjusted, so that the adjusted indoor temperature does not change any more;
the adjusting module is further configured to adjust the second heating capacity and the second energy consumption and update the adjusting parameter if the indoor temperature is reduced after the operating state of the air conditioner is adjusted, so that the adjusted indoor temperature does not change any more;
the determining module is further configured to generate an adjustment parameter of the air conditioner based on the environmental information, the second cooling/heating amount, and the second energy consumption;
the adjusting module is further used for adjusting the running state of the air conditioner based on the adjusting parameters.
7. An air conditioner, comprising: a processor and a memory, wherein the processor is used for executing the air conditioner control program stored in the memory so as to realize the air conditioner control method of any one of claims 1 to 5.
8. A storage medium storing one or more programs executable by one or more processors to implement the air conditioning control method according to any one of claims 1 to 5.
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