Classifications

• • 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
• • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
  • F24—HEATING; RANGES; VENTILATING
  • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
  • F24F11/00—Control or safety arrangements
  • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
  • F24F11/63—Electronic processing
  • F24F11/65—Electronic processing for selecting an operating mode
• • 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
• • 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
• • 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
• • 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
• • 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
  • F24F2110/30—Velocity
• • 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
  • F24F2110/30—Velocity
  • F24F2110/32—Velocity of the outside air

Definitions

• the present application relates to the technical field of air conditioner control, and in particular to a method for controlling an air conditioner, an air conditioner, and a computer-readable storage medium.
• G Genetic Algorithm
• PID Proportional Integral Derivative
• AI Artificial Intelligence
• the main objective of the present application is to provide a method for controlling an air conditioner, aiming to solve the problem of how to avoid warm air oscillation in the air conditioner.
• the present application provides a method for controlling an air conditioner, including:
• the present application further provides a method for controlling an air conditioner, including:
• the present application further provides a method for controlling an air conditioner, including:
• the present application further provides an air conditioner, including a memory, a processor, and a program for controlling the air conditioner stored in the memory and executable on the processor, the program for controlling the air conditioner, when executed by the processor, implements the steps of the method for controlling air conditioner as described above.
• the present application further provides a computer-readable storage medium.
• the computer-readable storage medium stores a program for controlling an air conditioner, and when executed by a processor, the steps of the method for controlling air conditioner as described above are implemented.
• G Genetic Algorithm
• PID Proportional Integral Derivative
• the present application uses the predicted control parameter combination with the greatest similarity between the historical control parameter combination and the predicted control parameter combination executed by the air conditioner in the previous cycle as the target control parameter combination, so that the parameter changes between different cycles of the air conditioner are relatively small, thereby keeping the warm air relatively stable.
• FIG. 1 is a schematic structural diagram of a hardware operating environment involved in an embodiment of the present application.
• the air conditioner may include: a processor 1001, such as a central processing unit (CPU), a memory 1005, a user interface 1003, a network interface 1004, and a communication bus 1002.
• the communication bus 1002 is used to realize the connection and communication between these components.
• the user interface 1003 may include a display, an input unit such as a keyboard, and the user interface 1003 may also include a standard wired interface and a wireless interface.
• the network interface 1004 may include a standard wired interface and a wireless interface (such as a Wireless-Fidelity interface).
• the memory 1005 may be a high-speed random access memory (RAM), or a stable memory (non-volatile memory), such as a disk memory.
• the memory 1005 may also be a storage device independent of the aforementioned processor 1001.
• the air conditioner structure shown in FIG. 1 does not limit the air conditioner and may include more or fewer components than shown, or a combination of certain components, or a different arrangement of components.
• the memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module and a program for controlling the air conditioner.
• the operating system is a program for managing and controlling the hardware and software resources of the air conditioner, based on the operation of the program for controlling the air conditioner and other software or programs.
• the user interface 1003 is mainly used to connect to the terminal and communicate data with the terminal;
• the network interface 1004 is mainly used for the background server and communicates data with the background server;
• the processor 1001 can be used to call the program for controlling the air conditioner stored in the memory 1005.
• the air conditioner includes: a memory 1005, a processor 1001, and a program for controlling the air conditioner stored in the memory and executable on the processor.
• the processor 1001 calls the program for controlling the air conditioner stored in the memory 1005, the following operations are performed: determining a control parameter combination of the air conditioner that satisfies a preset parameter prediction model, the control parameter combination includes at least two predicted control parameters.
• an embodiment of the method for controlling the air conditioner of the present application is provided.
• the method for controlling the air conditioner includes the following steps: step S10, obtaining at least two predicted control parameter combinations for a current cycle prediction of the air conditioner and obtaining a historical control parameter combination of the air conditioner from a previous cycle operation.
• At least two predicted control parameter combinations output by the main control logic module in the air conditioner in the current cycle are first obtained.
• the predicted control parameter combination is characterized as controllable operating parameters of the air conditioner that satisfy certain air conditioning parameter adjustment strategies, and the historical control parameter combination is characterized as controllable operating parameters executed by the air conditioner in the previous cycle.
• Controllable operating parameters can be characterized as the controllable operating parameters of the air conditioner itself, such as the internal fan speed, external fan speed, and compressor frequency, etc.
• uncontrollable operating parameters are characterized as the parameters that cannot be controlled by the air conditioner itself, such as indoor and outdoor temperature, indoor and outdoor humidity, exhaust valve temperature, and other environmental parameters that affect the operation of the air conditioner but cannot be set, or user-set parameters such as target temperature and set target wind speed.
• the air conditioning parameter adjustment strategy is characterized as a strategy for adjusting the controllable operating parameters inside the air conditioner according to certain user needs.
• the predicted control parameter combination is not the control parameter combination that the air conditioner is currently operating, but the control parameter combination that the air conditioner will operate in the next cycle, where the next cycle can be understood as the end of the current cycle or the beginning of the next cycle.
• Step S20 determining a target control parameter combination for the air conditioner according to parameter similarities between each of the predicted control parameter combinations and the historical control parameter combination.
• the parameter similarity between each prediction control parameter combination and the historical control parameter combination is determined, and appropriate parameters are selected from each prediction control parameter combination as the target control parameter combination according to the parameter similarity.
• the target control parameter combination may be determined by selecting a predicted control parameter combination having the largest parameter similarity with the historical control parameter combination as the target control parameter combination.
• the predicted control parameter combination and the historical control parameter combination are normalized to obtain the normalized value of each control parameter combination, then the parameter similarity between the normalized values of the predicted control parameter combination and the historical control parameter combination is calculated, and the one with the largest parameter similarity is selected as the target control parameter combination.
• the relative numerical size between the normalized values of the predicted control parameter combination and the historical control parameter combination can be calculated by using similarity calculation strategies such as Manhattan distance calculation, Euclidean distance calculation or cosine distance calculation. Then the parameter similarity is determined based on the relative numerical size, and the larger the relative numerical value, the lower the parameter similarity.
• the prediction control parameter combination is set to include the compressor frequency P and the internal fan speed W.
• the above prediction control parameter combination and historical control parameter combination are normalized respectively.
• operating frequency/maximum operating frequency is the normalized value
• fan speed/maximum fan speed is the normalized value.
• the normalized values of the predicted control parameter combinations in CR1 to CR3 are compared with the normalized values of the historical control parameter combinations in CR0.
• Manhattan distance, Euclidean distance or cosine distance can be used for calculation, where the shorter the distance, the higher the similarity.
• Manhattan distance is used to calculate relative numerical values.
• the Manhattan distance between CR2 and CR0 is the shortest, so CR2 is selected as the target control parameter combination with the highest similarity to the historical control parameter combination of the previous cycle.
• Euclidean distance is used to calculate relative numerical values.
• the Euclidean distance between CR2 and CR0 is the shortest, so CR2 is selected as the target control parameter combination with the highest similarity to the historical control parameter combination of the previous cycle.
• cosine distance is used to calculate the relative numerical size.
• the Euclidean distance between CR2 and CR0 is the shortest, so CR2 is selected as the target control parameter combination with the highest similarity to the historical control parameter combination of the previous cycle.
• Step S30 controlling the air conditioner to operate according to the parameter values in the target control parameter combination.
• the air conditioner is controlled to operate according to the parameter values in the target control parameter combination.
• the compressor frequency of the air conditioner is controlled to operate at 60 Hz, and the internal fan speed of the air conditioner is controlled to operate at 55 rpm.
• the target control parameter combination finally executed by the air conditioner is determined according to the parameter similarity between the prediction control parameter combination and the historical control parameter combination.
• the obtaining the at least two predicted control parameter combinations for the current cycle prediction of the air conditioner includes: step S11, determining a control parameter combination of the air conditioner that satisfies a preset parameter prediction model, the control parameter combination includes at least two predicted control parameters.
• the predicted control parameter combination may be determined by using a parameter prediction model to determine the control parameter combination of the air conditioner, and each control parameter combination includes at least two predicted control parameter combinations.
• the step S11 includes: step S111, determining a control parameter combination formed by each parameter value of each predicted control parameter of the air conditioner based on the temperature model, and predicting an indoor temperature change corresponding to the control parameter combination; determining a control parameter combination that satisfies a target indoor temperature according to the indoor temperature change and using the control parameter combination that satisfies the target indoor temperature as the control parameter combination of the air conditioner that satisfies the temperature model.
• the parameter prediction model may include a temperature model.
• the temperature model is characterized as predicting the future indoor temperature change in the room where the air conditioner is located.
• the air conditioner determines the control parameter combination composed of each parameter value of each predicted control parameter through the temperature model, and predicts the indoor temperature change corresponding to the control parameter combination, and determines the control parameter combination that satisfies the target indoor temperature according to the indoor temperature change, and uses it as the control parameter combination of the air conditioner that satisfies the temperature model.
• controllable operating parameters include the indoor fan speed, the outdoor fan speed and the compressor operating frequency
• the speed value of the indoor fan speed is 750 rpm
• the speed value of the outdoor fan speed is 800 rpm
• the frequency value of the compressor operating frequency is 50 Hz
• the predicted indoor temperature change of the air conditioner is ⁇ T1.
• the predicted indoor temperature change of the air conditioner is ⁇ T2.
• the indoor temperature change corresponding to the parameter combination can be determined through the above mapping relationship, that is, the indoor temperature change corresponding to the operation of the air conditioner according to the parameter values of the controllable operating parameters and the parameter values of the uncontrollable operating parameters can be determined.
• the method for predicting the indoor temperature change corresponding to the control parameter combination can be: recording a controllable operating parameter and an uncontrollable operating parameter in each operating cycle of the air conditioner, and a historical indoor temperature change after each operating cycle of the air conditioner; performing training according to recorded historical operating data of the air conditioner to generate a first mapping relationship between the controllable operating parameters, the uncontrollable operating parameters, and the historical indoor temperature change to predict the indoor temperature change based on the first mapping relationship.
• the temperature change expectation model training process mainly includes the following parts:
• cycle 1 represents the first T after power-on
• cycle i represents the i-th T after power-on.
• cycle i taking the characteristics of cycle i as the independent variable and the indoor temperature change ⁇ Tin between cycle i+1 and cycle i as the dependent variable to train the temperature change expectation model.
• Each cycle, as training data, includes independent variables and dependent variables.
• the independent variables are all the characteristics of the cycle.
• the temperature model i.e., the first mapping relationship between controllable operating parameters, uncontrollable operating parameters and historical indoor temperature changes
• y i ⁇ Tin i f i Tin , Tout , Hin , Hout , Tp . . .
• the step S11 includes: step S112, determining a control parameter combination formed by each parameter value of each predicted control parameter of the air conditioner based on the wind speed model, and predicting a target speed corresponding to the control parameter combination; determining a control parameter combination that satisfies the target speed and using the control parameter combination that satisfies the target speed as the control parameter combination of the air conditioner that satisfies the wind speed model.
• the parameter prediction model may include the wind speed model.
• the wind speed model is characterized as predicting the air outlet wind speed of the air conditioner that the user hopes to achieve.
• controllable operating parameters include the internal fan speed and the compressor operating frequency
• the speed value of the internal fan speed is 750 rpm and the frequency value of the compressor operating frequency is 50 Hz
• the predicted air outlet wind speed of the air conditioner is V1.
• the speed value of the internal fan speed is 800 rpm and the frequency value of the compressor operating frequency is 40 Hz
• the predicted air outlet wind speed of the air conditioner is V2.
• each parameter combination, and the pre-established mapping relationship between each parameter combination and the air outlet wind speed are also determined.
• the air outlet wind speed corresponding to the parameter combination can be determined through the above mapping relationship, that is, the corresponding air outlet wind speed when the air conditioner is operated according to the parameter values of the controllable operating parameters and the parameter values of the uncontrollable operating parameters is determined.
• the parameter combination corresponding to V2 800 rpm, 40 Hz is used as the prediction parameter combination.
• a method for predicting the target speed corresponding to the control parameter combination may be: recording the controllable operating parameter and the uncontrollable operating parameter in each operating cycle of the air conditioner, and a historical internal fan speed after each operating cycle of the air conditioner; performing training according to the recorded historical operating data of the air conditioner to generate a second mapping relationship between the controllable operating parameters, the uncontrollable operating parameters, and the historical internal fan speed to predict the target speed of the internal fan based on the second mapping relationship.
• the difference is that the predicted parameter is changed from the indoor temperature change to the internal fan speed of the air conditioner, which will not be repeated here.
• the step S11 includes: step S113, determining a control parameter combination formed by each parameter value of each predicted control parameter of the air conditioner based on the heating and cooling rate model, and predicting a temperature change rate corresponding to the control parameter combination; determining a control parameter combination that satisfies the temperature change rate and using the control parameter combination that satisfies the temperature change rate as the control parameter combination of the air conditioner that satisfies the heating and cooling rate model.
• the parameter prediction model may include a heating and cooling rate model.
• the heating and cooling rate model is characterized as predicting the heating speed/cooling speed of the air conditioner desired by the user.
• controllable operating parameters include the internal fan speed and the compressor operating frequency
• speed value of the internal fan speed is 750 rpm and the frequency value of the compressor operating frequency is 50 Hz
• the predicted heating rate/cooling rate of the air conditioner is ⁇ V1.
• speed value of the internal fan speed is 800 rpm and the frequency value of the compressor operating frequency is 40 Hz
• the predicted air outlet speed of the air conditioner is ⁇ V2.
• each parameter combination, and the pre-established mapping relationship between each parameter combination and the heating rate/cooling rate are also determined.
• the heating rate/cooling rate corresponding to the parameter combination can be determined through the above mapping relationship, that is, the corresponding heating rate/cooling rate when the air conditioner is operated according to the parameter values of the controllable operating parameters and the parameter values of the uncontrollable operating parameters is determined.
• the parameter combination corresponding to ⁇ V2 800 rpm, 40 Hz is used as the prediction parameter combination.
• predicting the temperature change rate corresponding to the control parameter combination can be: recording the controllable operating parameters and uncontrollable operating parameters in each operating cycle of the air conditioner, and a historical compressor frequency and the historical internal fan speed after each operating cycle of the air conditioner; performing training according to the recorded historical operating data of the air conditioner to generate a third mapping relationship between the controllable operating parameters, the uncontrollable operating parameters, the historical compressor frequency, and the historical internal fan speed to predict the temperature change rate based on the third mapping relationship.
• the specific determination process please refer to the above-mentioned temperature model determination embodiment.
• the difference is that the predicted parameters are changed from the indoor temperature change to the internal fan speed and compressor frequency of the air conditioner, which will not be repeated here.
• a parameter prediction model is used to predict multiple predicted control parameter combinations that will be executed by the air conditioner in the future, which provides a prerequisite for how to select one of the predicted control parameter combinations as the target control parameter combination in an embodiment.
• the method before step S10, the method further includes:
• the input side in the main control logic module of the air conditioner may be smoothed, and the parameters of the input side include environmental state parameters and operating state parameters.
• the environmental state parameters may include: indoor temperature, outdoor temperature, indoor humidity, outdoor humidity, and PM2.5.
• the operating state parameters may include: compressor operating frequency, operating power, operating current, and operating voltage.
• optimization is performed based on the collected environmental state parameters, and the environmental state optimization parameters of the current cycle of the air conditioner are determined according to the current environmental state parameters collected in the current cycle of the air conditioner and the historical environmental state parameters determined in multiple historical cycles, so as to determine multiple prediction control parameter combinations according to the operating state parameters of the current cycle and the environmental state optimization parameters.
• the current environment state parameter and the historical environment state parameter may be averaged.
• C0_y is used as the input side data of the main control logic of the air conditioner, so that the main control logic of the air conditioner outputs the predicted control parameter combination.
• the method for controlling an air conditioner further includes:
• the predicted control parameter combination with the greatest similarity determined can be further optimized and smoothed with the historical control parameter combination executed in the historical cycles of multiple air conditioners, and the optimized and smoothed control parameter combination can be used as the target control parameter combination to enable the air conditioner to operate according to the target control parameter combination.
• the optimization smoothing method may be to average the prediction control parameter combination with the greatest similarity (hereinafter referred to as the most similar prediction control parameter combination) and the historical control parameter combinations of multiple historical cycles.
• the predicted control parameter combination includes the compressor frequency P and the internal fan speed W.
• the predicted control parameter combination with the greatest similarity is further optimized and smoothed with the historical control parameter combination executed in the historical cycles of multiple air conditioners, and the optimized and smoothed control parameter combination is used as the target control parameter combination, so that the air conditioner operates according to the target control parameter combination, thereby achieving the effect of avoiding the occurrence of warm air oscillation.
• the present application also performs smoothing between at least one predicted control parameter determined and historical control parameters executed in multiple historical cycles of the air conditioners, and then uses the smoothed control parameters as target control parameters so that the air conditioner operates according to the target control parameters, thereby achieving the effect of avoiding the occurrence of warm air oscillation.
• the air conditioner includes: a memory 1005, a processor 1001, and a program for controlling the air conditioner stored in the memory and executable on the processor.
• the processor 1001 calls the program for controlling the air conditioner stored in the memory 1005, the following operations are performed: determining, based on a preset smoothing strategy, a target control parameter of the air conditioner according to the historical control parameters and the predicted control parameters of one or more historical cycles.
• the processor 1001 calls the program for controlling the air conditioner stored in the memory 1005, the following operations are performed: calculating an average value of one or more historical control parameters and each predicted control parameter as the target control parameter corresponding to the each predicted control parameter.
• the method for controlling the air conditioner includes the following steps: step S210, obtaining a control scheme predicted by the air conditioner in a current cycle and historical control parameters executed by the air conditioner in historical cycles, the control scheme includes at least one predicted control parameter.
• a control scheme output by a main control logic module in an air conditioner in a current cycle is first obtained, and the control scheme includes at least one predicted control parameter.
• the predicted control parameter is characterized as an optimal predicted control parameter selected based on a preset selection strategy from the controllable operating parameters predicted by multiple air conditioners in the next cycle.
• the historical control parameter is characterized as the controllable operating parameter executed by the air conditioner in the previous cycle.
• Controllable operating parameters are characterized as the controllable operating parameters of the air conditioner itself, such as the internal fan speed, external fan speed, and compressor frequency, etc.
• uncontrollable operating parameters are characterized as the parameters that the air conditioner itself cannot control, such as indoor and outdoor temperature, indoor and outdoor humidity, exhaust valve temperature, and other environmental parameters that affect the operation of the air conditioner but cannot be set, or user-set parameters such as target temperature and set target wind speed.
• Step S220 determining a target control parameter of the air conditioner according to the historical control parameter and the predicted control parameter, the target control parameter is obtained by smoothing the predicted control parameter according to the historical control parameter.
• the smoothing method of the target control parameter can be based on a preset smoothing strategy, according to the historical control parameters and the predicted control parameters of multiple historical cycles, to determine the parameter optimization value corresponding to each of the predicted control parameters in the current cycle as the target control parameter of the air conditioner.
• the smoothing strategy may be arithmetic average optimization, which averages the prediction control function and historical control parameters of multiple historical cycles, and uses the average value as the parameter optimization value corresponding to each prediction control parameter.
• the predicted control parameters include the compressor frequency P and the internal fan speed W.
• the above arithmetic mean optimization process is the same as the parameter averaging process in the optimization smoothing method of one embodiment, and will not be repeated here.
• the smoothing strategy can be weighted average optimization, which obtains the parameter value corresponding to the predicted control parameter in the historical control data, then determines the weight value of the obtained parameter value, and performs weighted calculation on the obtained parameter value based on the determined weight value, so as to obtain the parameter optimization value corresponding to the predicted control parameter.
• the predicted control parameters include the compressor frequency P and the internal fan speed W.
• the weight value corresponding to the C1 cycle is 0.3
• the weight value corresponding to the C2 cycle is 0.2
• the weight value corresponding to the C3 cycle is 0.1
• the weight value corresponding to the current cycle is 0.4.
• Step S230 controlling the air conditioner to operate according to the target control parameter.
• the air conditioner is controlled to operate according to the target control parameter.
• At least one acquired predicted control parameter is smoothed with historical control parameters in multiple cycles, and the smoothed control parameter is used as the target control parameter, thereby avoiding the phenomenon of warm air oscillation in the air conditioner due to excessive difference in control parameter values between adjacent cycles when the air conditioner is executed according to the predicted control parameter.
• the step of obtaining at least one prediction control parameter of the current cycle prediction of the air conditioner includes the following steps:
• control scheme can be determined by determining a control parameter combination of the air conditioner through a parameter prediction model, each control parameter combination includes at least two control parameters. Furthermore, when at least two control parameter combinations that satisfy the parameter prediction model are determined, a control parameter combination that satisfies the strategy is selected through a combination selection strategy as the control scheme.
• the parameter prediction model may include a temperature model, a wind speed model, and a heating and cooling rate model, which have been described in detail in an embodiment and will not be repeated here.
• the combined selection strategy can be an energy-saving strategy, a comfort strategy, and a health strategy.
• the energy-saving strategy is characterized as selecting a combination with the highest energy efficiency ratio among various control parameter combinations as a control scheme.
• the comfort strategy is characterized as selecting a control parameter combination whose comfort value corresponding to each control parameter combination is closest to a preset comfort value as a control scheme.
• the target operating parameter combination in order to ensure the comfort level of a human body when the air conditioner is operating, can be determined by a comfort value, which is a quantitative value representing the comfort level of a human body in an environment under the air conditioner.
• the preset comfort value can be the theoretically most comfortable value of the temperature, relative humidity, and wind speed in the environment for a human body.
• the comfort value determination parameters may be determined by temperature, relative humidity and/or the air outlet speed of the air conditioner.
• the predicted control parameters include the internal fan speed, the external fan speed and the compressor frequency.
• the temperature T0 when the human body is in a relatively comfortable state is set to 24°C
• the relative humidity RH0 is set to 30%
• the air outlet speed V0 is set to 0.2m/S.
• the preset comfort value is 0, that is, the optimal comfort value. If the comfort value determined in the subsequent parameter combination is greater than 0, it means that under this parameter combination, the user will feel hotter, and if it is less than 0, it means that under this parameter combination, the user will feel colder.
• the corresponding comfort values of the three are -1, 2, and -1.5 respectively, so H1, which is closest to the preset comfort value of 0, is selected as the control scheme.
• the health strategy is characterized as selecting the control parameter combination closest to the preset comfort value and determining it as the control scheme. Since the air-conditioned room is tightly sealed and the indoor and outdoor air cannot be exchanged, harmful microorganisms such as bacteria and viruses in the indoor air are easy to breed and multiply, which can easily cause low air quality. Therefore, this embodiment further provides a method for determining the most suitable target parameter combination from multiple parameter combinations based on the health value.
• the health value is characterized as a quantitative value of the degree of health impact on the human body caused by being under the air conditioner for a long time.
• the preset health value may be determined by the indoor and outdoor temperature difference and the relative humidity.
• the indoor and outdoor temperature difference ⁇ T 5°C when the human body is in a state with a relatively small impact on health is set.
• the relative humidity RH 40%, and based on the preset mapping relationship, the preset health value determined is 100.
• H6 is selected as the control scheme.
• a parameter prediction model is used to predict the control parameter combination to be executed by the air conditioner in the future, and when there are multiple control parameter combinations, a parameter combination that satisfies the selection strategy is selected as a control scheme based on the combination selection strategy. This provides a prerequisite for how to select one of the predicted control parameters as the target control parameter in an embodiment. On the basis of one embodiment and in combination with the technical solution in this embodiment, the effect of avoiding the phenomenon of warm air oscillation in the air conditioner due to the large difference in the control parameter values between adjacent cycles when the air conditioner is executed according to the predicted control parameters is achieved.
• the steps include:
• C0_y is used as the input side data of the main control logic of the air conditioner, so that the main control logic of the air conditioner outputs the predicted control parameter.
• the present application optimizes the environmental state parameters on the input side of the main control logic of the air conditioner. On the one hand, it avoids oscillation of the output control parameter combination due to errors in the air conditioner's sensors. On the other hand, it also avoids the occurrence of temperature and wind shock caused by a large difference between the control parameter combination output by the air conditioner and the control parameters between the air conditioners when the temperature of the environment in which the air conditioner is located changes suddenly.
• the air conditioner includes: a memory 1005, a processor 1001, and a program for controlling the air conditioner stored in the memory and executable on the processor.
• the processor 1001 calls the program for controlling the air conditioner stored in the memory 1005, the following operations are performed: determining an optimized value of the environmental parameter corresponding to the air conditioner in the current cycle as the environmental state optimization parameter based on a preset environmental parameter optimization strategy according to the historical environmental state parameters of the plurality of historical cycles and the current environmental state parameters.
• the processor 1001 calls the program for controlling the air conditioner stored in the memory 1005, the following operations are performed: calculating an average value of the historical environmental state parameters and the current environmental state parameters as the environmental parameter optimization value corresponding to the air conditioner in the current cycle.
• an embodiment of the method for controlling an air conditioner of the present application is provided.
• the method for controlling an air conditioner includes the following steps: step S310, obtaining current environmental state parameters collected by the air conditioner in a current cycle and obtaining historical environmental state parameters determined in a plurality of historical cycles of the air conditioner.
• optimization is performed based on collected environmental state parameters.
• Step S320 determining environmental state optimization parameters of the current cycle of the air conditioner according to the historical environmental state parameters and the current environmental state parameters.
• the process of determining the environmental state optimization parameters of the current cycle of the air conditioner is the same as in the first embodiment, and will not be repeated here.
• a weighted average optimization can be performed between the current environmental state parameters and the historical environmental state parameters, and the parameter values corresponding to the environmental state parameters in the historical environmental state parameters are obtained, and then the weight values of the obtained parameter values are determined. Based on the determined weight values, the obtained parameter values are weighted averaged to obtain the optimized values of the environmental parameters corresponding to the air conditioner in the current cycle.
• the environmental state parameters include indoor temperature T1 and outdoor temperature T4.
• the weight value corresponding to the C1 cycle is 0.3
• the weight value corresponding to the C2 cycle is 0.2
• the weight value corresponding to the C3 cycle is 0.1
• the weight value corresponding to the current cycle is 0.4.
• Step S330 controlling the air conditioner to operate according to the environmental state optimization parameters.
• the environmental state optimization parameters are used as input side data of the main control logic of the air conditioner, so that the main control logic of the air conditioner outputs a control parameter combination.
• C0_y is input into the main control logic
• the air conditioner is controlled to operate according to the inner fan 1000 rpm, the outer fan 800 rpm, and the compressor frequency 35Hz.
• the step S330 includes:
• the determined environmental state optimization parameters and the operating state parameters of the air conditioner in the current cycle are input into the parameter prediction model to determine the control parameter combination of the air conditioner. Furthermore, when the determined control parameter combinations that satisfy the parameter prediction model are at least two, the control parameter combination that satisfies the strategy is selected through the combination selection strategy.
• the parameter prediction model may include an operating power model, which is characterized as a prediction based on the operating power of the air conditioner expected by the user.
• the air conditioner determines the control parameter combination composed of the parameter values of each control parameter combination through the operating power model, and predicts the operating power corresponding to the control parameter combination, and determines the control parameter combination that satisfies the target operating power based on the operating power, and uses it as the control parameter combination of the air conditioner that satisfies the operating power model.
• controllable operating parameters include the internal fan speed, the external fan speed and the compressor operating frequency
• the internal fan speed is 750 rpm
• the external fan speed is 800 rpm
• the compressor operating frequency is 50 Hz
• the predicted operating power of the air conditioner is P1.
• the speed value of the internal fan speed is 800 rpm
• the speed value of the external fan speed is 900 rpm
• the frequency value of the compressor operating frequency is 40 Hz.
• the predicted operating power of the air conditioner is P2.
• the operating power corresponding to the parameter combination can be determined through the above mapping relationship, that is, the target operating power corresponding to the air conditioner when the air conditioner is operated according to the parameter values of the controllable operating parameters and the parameter values of the uncontrollable operating parameters is determined.
• the parameter combination corresponding to P2 800 rpm, 900 rpm, 40 Hz is used as the target control parameter combination.
• the parameter prediction model may also include a capacity energy efficiency model, which is characterized as a prediction based on the operating energy efficiency and/or output capacity of the air conditioner expected by the user.
• the air conditioner inputs the operating state parameters of the current cycle and the environmental state optimization parameters into the capacity energy efficiency model to predict the operating energy efficiency and/or output capacity corresponding to each control parameter combination of the operating state parameters of the next cycle of the air conditioner; and determines the control parameter combination that satisfies the target operating energy efficiency and/or target output capacity based on the operating energy efficiency and/or the output capacity.
• controllable operation parameters include the internal fan speed, the external fan speed and the compressor operating frequency
• the speed value of the internal fan speed is 750 rpm
• the speed value of the external fan speed is 800 rpm
• the frequency value of the compressor operating frequency is 50 Hz
• the predicted operation energy efficiency of the air conditioner is Q1.
• the speed value of the internal fan speed is 800 rpm
• the speed value of the external fan speed is 900 rpm
• the frequency value of the compressor operating frequency is 40 Hz.
• the predicted operating energy efficiency of the air conditioner is Q2.
• the operating power corresponding to the parameter combination can be determined through the above mapping relationship. That is, the target operating energy efficiency corresponding to the air conditioner when it is operated according to the parameter values of the controllable operating parameters and the parameter values of the uncontrollable operating parameters.
• the parameter combination corresponding to Q2 (800 rpm, 900 rpm, 40 Hz) is used as the target control parameter combination.
• the parameter prediction model may include a temperature model, which has been described in detail in the above embodiment and will not be described again here.
• the combined selection strategy may be an energy-saving strategy, a comfort strategy, a health strategy, and a similarity strategy.
• the energy-saving strategy, the comfort strategy, and the health strategy have been described in detail in the above embodiment and will not be repeated here.
• the similarity strategy is characterized as selecting the one with the largest parameter similarity with the historical control parameters among the control parameter combinations as the target control parameter combination.
• a parameter prediction model is used to predict the control parameter combination to be executed by the air conditioner in the future, and when there are multiple control parameter combinations, a parameter combination that satisfies the selection strategy is selected as the control parameter combination based on the combination selection strategy, which provides a prerequisite for how to select one of the control parameter combinations as the target control parameter in an embodiment.
• the step S330 further includes:
• this embodiment in order to avoid the temperature oscillation phenomenon of the air conditioner due to the determined control parameter combination changing too much compared with the historical control parameter combination when the air conditioner is operating, therefore, this embodiment optimizes and smoothes the determined control parameter combination and the historical control parameter combination executed in the historical cycles of multiple air conditioners, and then uses the optimized and smoothed control parameters as the target control parameters.
• the optimization smoothing method of the control parameter combination can be based on a preset parameter optimization strategy, according to the historical control parameter combinations and the control parameter combinations of multiple historical cycles, to determine the parameter optimization value corresponding to each control parameter combination in the current cycle as the target control parameter of the air conditioner.
• the parameter optimization strategy may be arithmetic mean optimization, and the arithmetic mean optimization process has been described in detail in the above embodiment, which will not be repeated here.
• the parameter optimization strategy may be weighted average optimization, and the weighted average optimization process has been described in detail in the above embodiment and will not be repeated here.
• At least one acquired control parameter combination is optimized and smoothed with historical control parameter combinations in multiple cycles, and the control parameters in the optimized and smoothed control parameter combination are used as target control parameters, thereby avoiding the phenomenon of warm air oscillation in the air conditioner due to excessive difference in control parameter values between adjacent cycles when the air conditioner is executed according to the control parameter combination.
• the present application further provides a computer-readable storage medium, which stores a program for controlling an air conditioner.
• a program for controlling an air conditioner When the program for controlling the air conditioner is executed by a processor, the steps of the method for controlling the air conditioner described in the above embodiment are implemented.
• the computer-readable storage medium may be a universal serial bus (USB) flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk, etc., which are computer-readable storage medium that can store program codes.
• USB universal serial bus
• ROM read-only memory
• magnetic disk magnetic disk
• optical disk etc.
• the storage medium provided in the embodiment of the present application is the storage medium used to implement the method of the embodiment of the present application, based on the method introduced in the embodiment of the present application, those skilled in the art can understand the specific structure and deformation of the storage medium, so it is not repeated here. All storage medium used in the method of the embodiment of the present application belong to the scope of protection of the present application.
• These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.
• These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.
• any reference signs placed between brackets shall not be construed as limiting the claims.
• the word “comprising” does not exclude the presence of components or steps not listed in the claims.
• the word “a” or “an” preceding a component does not exclude the presence of a plurality of such components.
• the present application may be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware.
• the use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names.

Landscapes

• Engineering & Computer Science (AREA)
• Signal Processing (AREA)
• Chemical & Material Sciences (AREA)
• Combustion & Propulsion (AREA)
• Mechanical Engineering (AREA)
• General Engineering & Computer Science (AREA)
• Physics & Mathematics (AREA)
• Fuzzy Systems (AREA)
• Mathematical Physics (AREA)
• Air Conditioning Control Device (AREA)

Applications Claiming Priority (4)

Publications (2)

Family

ID=90929623

Family Applications (1)

Country Status (3)

Families Citing this family (1)

Family Cites Families (13)

• 2023
  • 2023-08-03 EP EP23884350.2A patent/EP4614082A4/de active Pending
  • 2023-08-03 WO PCT/CN2023/111096 patent/WO2024093411A1/zh not_active Ceased
• 2025
  • 2025-04-25 US US19/189,990 patent/US20250257895A1/en active Pending

Also Published As

Similar Documents

Legal Events