CN110553353B - Control method of air conditioner, air conditioner and storage medium - Google Patents
Control method of air conditioner, air conditioner and storage medium Download PDFInfo
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- CN110553353B CN110553353B CN201910878640.5A CN201910878640A CN110553353B CN 110553353 B CN110553353 B CN 110553353B CN 201910878640 A CN201910878640 A CN 201910878640A CN 110553353 B CN110553353 B CN 110553353B
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
- F24F11/46—Improving electric energy efficiency or saving
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/64—Electronic processing using pre-stored data
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/70—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
- F24F11/83—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air by controlling the supply of heat-exchange fluids to heat-exchangers
- F24F11/84—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air by controlling the supply of heat-exchange fluids to heat-exchangers using valves
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Abstract
The invention discloses a control method of an air conditioner, which comprises the following steps: acquiring current working condition parameters of an air conditioner, wherein the current working condition parameters comprise current environmental parameters and current setting parameters of the air conditioner; obtaining target operation parameters according to a plurality of historical operation parameters corresponding to the current working condition parameters and according to first energy efficiency ratios corresponding to the historical operation parameters; and controlling the air conditioner to operate at the target operation parameter. The invention also discloses an air conditioner and a computer readable storage medium. The invention solves the problem of how to optimize the energy efficiency ratio of the air conditioner.
Description
Technical Field
The present invention relates to the field of air conditioners, and in particular, to a method for controlling an air conditioner, and a computer-readable storage medium.
Background
At present, a control table is mainly searched according to setting parameters set by a user to obtain the operating parameters of the air conditioner. The control table is often set by engineers according to work experience, and the energy efficiency of the air conditioner is different when the air conditioner operates under different working conditions, so the setting idea of the control table is often only enough to ensure that the normal operation of the air conditioner and the air conditioning capacity reach the target value, and the consideration of the energy efficiency of the air conditioner during operation is neglected. This makes the air conditioner often unable to operate with more excellent energy efficiency.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a control method of an air conditioner, the air conditioner and a computer readable storage medium, and solves the problem of how to optimize the energy efficiency ratio of the air conditioner.
In order to achieve the above object, the present invention provides a method for controlling an air conditioner, comprising the steps of:
acquiring current working condition parameters of an air conditioner, wherein the current working condition parameters comprise current environmental parameters and current setting parameters of the air conditioner;
obtaining target operation parameters according to a plurality of historical operation parameters corresponding to the current working condition parameters and according to first energy efficiency ratios corresponding to the historical operation parameters;
and controlling the air conditioner to operate at the target operation parameter.
Optionally, the step of obtaining a target operation parameter according to the plurality of historical operation parameters corresponding to the current operating condition parameter and according to the first energy efficiency ratio corresponding to each of the historical operation parameters includes:
and taking the historical operation parameter with the maximum first energy efficiency ratio as the target operation parameter in the plurality of historical operation parameters corresponding to the current working condition parameters.
Optionally, after the step of obtaining the current operating condition parameters of the air conditioner, the method further includes:
detecting the number of historical operating parameters corresponding to the current working condition parameters;
if the number of the historical operating parameters is larger than or equal to the preset number, executing the plurality of historical operating parameters corresponding to the current working condition parameters and acquiring target operating parameters according to the first energy efficiency ratio corresponding to each historical operating parameter;
and acquiring target operation parameters according to the current working condition parameters and the corresponding relation between the preset working condition parameters and the operation parameters.
Optionally, the step of obtaining a target operation parameter according to the plurality of historical operation parameters corresponding to the current operating condition parameter and according to the first energy efficiency ratio corresponding to each of the historical operation parameters includes:
and inputting the current working condition parameters into a neural network model to obtain the target operation parameters, wherein the neural network model is trained or updated according to a plurality of historical operation parameters corresponding to the current working condition parameters and according to first energy efficiency ratios corresponding to the historical operation parameters.
Optionally, the neural network model is trained or updated according to a plurality of training samples, each training sample includes a historical operating condition parameter, the historical operating parameter corresponding to the historical operating condition parameter, and the first energy efficiency ratio, and the neural network model is configured to output, as the target operating parameter, the historical operating parameter with the largest first energy efficiency ratio among the plurality of training samples corresponding to the historical operating condition parameter and the current operating condition parameter.
Optionally, after the step of controlling the air conditioner to operate at the target operation parameter, the method further includes:
determining a second energy efficiency ratio corresponding to the target operation parameter;
generating a sample according to the current working condition parameter, the target operation parameter and the second energy efficiency ratio;
adding the sample as the training sample to the neural network model to update the neural network model.
Optionally, after the step of determining the second energy efficiency ratio corresponding to the target operating parameter, the method further includes:
obtaining refrigerating capacity or heating capacity corresponding to the second energy efficiency ratio;
and if the refrigerating capacity is within a preset refrigerating capacity interval or the heating capacity is within a preset heating capacity interval, executing the step of generating a sample according to the current working condition parameter, the target operation parameter and the second energy efficiency ratio.
Optionally, after the step of controlling the air conditioner to operate at the target operation parameter, the method further includes:
acquiring working condition parameters of a compressor of the air conditioner;
and if the working condition parameter of the compressor is smaller than a preset threshold value, executing the step of generating a sample according to the current working condition parameter, the target operation parameter and the second energy efficiency ratio.
Optionally, after the step of generating a sample according to the current operating condition parameter, the target operating parameter, and the second energy efficiency ratio, the method further includes:
and if the sample meets a preset condition, executing the step of adding the sample serving as the training sample into the neural network model so as to update the neural network model.
Optionally, the preset condition includes any one of:
the ratio of the total number of the samples to a first preset value is greater than a reference value;
the ratio of the sum of the total number of the samples and the total number of the training samples to a second preset value is larger than a reference value;
wherein the step of adding the sample as the training sample to the neural network model to update the neural network model is performed once, the reference value is cumulatively incremented by one.
In order to achieve the above object, the present invention also provides an air conditioner, comprising:
the air conditioner comprises a memory, a processor and a control program of the air conditioner, wherein the control program of the air conditioner is stored on the memory and can run on the processor, and when being executed by the processor, the control program of the air conditioner realizes the steps of the control method of the air conditioner.
To achieve the above object, the present invention also provides a computer-readable storage medium having a control program of an air conditioner stored thereon, which, when executed by a processor, implements the steps of the control method of the air conditioner as described above.
The invention provides a control method of an air conditioner, the air conditioner and a computer readable storage medium, which are used for obtaining current working condition parameters of the air conditioner, wherein the current working condition parameters comprise current environmental parameters and current setting parameters of the air conditioner; obtaining target operation parameters according to a plurality of historical operation parameters corresponding to the current working condition parameters and according to first energy efficiency ratios corresponding to the historical operation parameters; and controlling the air conditioner to operate at the target operation parameter. Therefore, the problem of how to optimize the energy efficiency ratio of the air conditioner is solved by searching the operation parameters of the air conditioner according to the energy efficiency ratio corresponding to the operation parameters and controlling the operation of the air conditioner.
Drawings
Fig. 1 is a schematic diagram of a hardware operating environment of a terminal according to an embodiment of the present invention;
FIG. 2 is a flow chart illustrating a control method of an air conditioner according to a first embodiment of the present invention;
FIG. 3 is a flow chart illustrating a control method of an air conditioner according to a second embodiment of the present invention;
FIG. 4 is a flow chart illustrating a control method of an air conditioner according to a third embodiment of the present invention;
FIG. 5 is a flow chart illustrating a fourth embodiment of a method for controlling an air conditioner according to the present invention;
FIG. 6 is a flow chart illustrating a fifth embodiment of a method for controlling an air conditioner according to the present invention;
FIG. 7 is a flowchart illustrating a sixth embodiment of a method for controlling an air conditioner according to the present invention;
FIG. 8 is a flow chart illustrating a seventh embodiment of a method for controlling an air conditioner according to the present invention;
fig. 9 is a block diagram of a neural network model according to an embodiment of the method for controlling an air conditioner of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention provides a control method of an air conditioner, which solves the problem of how to optimize the energy efficiency ratio of the air conditioner.
As shown in fig. 1, fig. 1 is a schematic diagram of a hardware operating environment of a terminal according to an embodiment of the present invention;
the terminal of the embodiment of the invention can be an air conditioner, and also can be a control terminal or a server for controlling the air conditioner.
As shown in fig. 1, the terminal may include: a processor 1001, such as a Central Processing Unit (CPU), a memory 1002, and a communication bus 1003. The communication bus 1003 is used for implementing connection communication between the components in the terminal. The memory 1002 may be a random-access memory (RAM) or a non-volatile memory (non-volatile memory), such as a disk memory. The memory 1002 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration of the terminal shown in fig. 1 is not intended to be limiting of the terminal of embodiments of the present invention and may include more or less components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, a control program of the air conditioner may be included in the memory 1002 as a kind of computer storage medium.
In the terminal shown in fig. 1, the processor 1001 may be configured to call a control program of the air conditioner stored in the memory 1002, and perform the following operations:
acquiring current working condition parameters of an air conditioner, wherein the current working condition parameters comprise current environmental parameters and current setting parameters of the air conditioner;
obtaining target operation parameters according to a plurality of historical operation parameters corresponding to the current working condition parameters and according to first energy efficiency ratios corresponding to the historical operation parameters;
and controlling the air conditioner to operate at the target operation parameter.
Further, the processor 1001 may call a control program of the air conditioner stored in the memory 1002, and also perform the following operations:
and taking the historical operation parameter with the maximum first energy efficiency ratio as the target operation parameter in the plurality of historical operation parameters corresponding to the current working condition parameters.
Further, the processor 1001 may call a control program of the air conditioner stored in the memory 1002, and also perform the following operations:
detecting the number of historical operating parameters corresponding to the current working condition parameters;
if the number of the historical operating parameters is larger than or equal to the preset number, executing the plurality of historical operating parameters corresponding to the current working condition parameters and acquiring target operating parameters according to the first energy efficiency ratio corresponding to each historical operating parameter;
and acquiring target operation parameters according to the current working condition parameters and the corresponding relation between the preset working condition parameters and the operation parameters.
Further, the processor 1001 may call a control program of the air conditioner stored in the memory 1002, and also perform the following operations:
and inputting the current working condition parameters into a neural network model to obtain the target operation parameters, wherein the neural network model is trained or updated according to a plurality of historical operation parameters corresponding to the current working condition parameters and according to first energy efficiency ratios corresponding to the historical operation parameters.
Further, the processor 1001 may call a control program of the air conditioner stored in the memory 1002, and also perform the following operations:
the neural network model is trained or updated according to a plurality of training samples, each training sample comprises a historical working condition parameter, the historical operating parameter corresponding to the historical working condition parameter and the first energy efficiency ratio, and the neural network model is configured to output the historical operating parameter with the maximum first energy efficiency ratio as the target operating parameter in the plurality of training samples corresponding to the historical working condition parameter and the current working condition parameter.
Further, the processor 1001 may call a control program of the air conditioner stored in the memory 1002, and also perform the following operations:
determining a second energy efficiency ratio corresponding to the target operation parameter;
generating a sample according to the current working condition parameter, the target operation parameter and the second energy efficiency ratio;
adding the sample as the training sample to the neural network model to update the neural network model.
Further, the processor 1001 may call a control program of the air conditioner stored in the memory 1002, and also perform the following operations:
obtaining refrigerating capacity or heating capacity corresponding to the second energy efficiency ratio;
and if the refrigerating capacity is within a preset refrigerating capacity interval or the heating capacity is within a preset heating capacity interval, executing the step of generating a sample according to the current working condition parameter, the target operation parameter and the second energy efficiency ratio.
Further, the processor 1001 may call a control program of the air conditioner stored in the memory 1002, and also perform the following operations:
acquiring working condition parameters of a compressor of the air conditioner;
and if the working condition parameter of the compressor is smaller than a preset threshold value, executing the step of generating a sample according to the current working condition parameter, the target operation parameter and the second energy efficiency ratio.
Further, the processor 1001 may call a control program of the air conditioner stored in the memory 1002, and also perform the following operations:
and if the sample meets a preset condition, executing the step of adding the sample serving as the training sample into the neural network model so as to update the neural network model.
Further, the processor 1001 may call a control program of the air conditioner stored in the memory 1002, and also perform the following operations:
the ratio of the total number of the samples to a first preset value is greater than a reference value;
the ratio of the sum of the total number of the samples and the total number of the training samples to a second preset value is larger than a reference value;
wherein the step of adding the sample as the training sample to the neural network model to update the neural network model is performed once, the reference value is cumulatively incremented by one.
Referring to fig. 2, in an embodiment, a control method of an air conditioner includes:
and step S10, obtaining current working condition parameters of the air conditioner, wherein the current working condition parameters comprise current environment parameters and current setting parameters of the air conditioner.
In this embodiment, the terminal in this embodiment may be an air conditioner, or may be a control terminal or a server that controls the air conditioner. The following description will be given taking an example in which the terminal of the embodiment is an air conditioner.
Optionally, the current environmental parameter includes at least one of a current outdoor temperature, a current indoor temperature, a current outdoor humidity, and a current indoor humidity; the current setting parameter includes at least one of a current setting temperature and a current setting humidity.
Optionally, the terminal sets up or communicates there is the data acquisition module, and the data acquisition module has outdoor temperature sensor, indoor humidity transducer and outdoor humidity transducer, outdoor temperature sensor be used for detecting current outdoor temperature indoor temperature transducer be used for detecting current indoor temperature outdoor humidity transducer be used for detecting current outdoor humidity indoor humidity transducer be used for detecting current indoor humidity.
Optionally, when the air conditioner is started, the terminal may obtain a setting parameter of the last startup operation when the air conditioner is shut down as a current setting parameter; after detecting the setting parameter change (0, if the user changes the setting parameter of the air conditioner), the changed setting parameter may be acquired as the current setting parameter.
Optionally, in the process of starting up and running the air conditioner, the terminal may obtain the current working condition parameters of the air conditioner at regular time or in real time.
And step S20, obtaining target operation parameters according to a plurality of historical operation parameters corresponding to the current working condition parameters and according to the first energy efficiency ratios corresponding to the historical operation parameters.
Optionally, in the plurality of historical operating parameters corresponding to the current operating condition parameter, the operating condition parameter corresponding to each historical operating parameter is the same as the current operating condition parameter, or the operating condition parameter corresponding to each historical operating parameter is within the same numerical range as the current operating condition parameter. The first energy efficiency ratio is the energy efficiency ratio achieved by the air conditioner when the air conditioner operates according to the historical operating parameters in the environment of the operating condition parameters corresponding to the historical operating parameters or the current operating condition parameters.
Optionally, the terminal may take a historical operating parameter with the largest first energy efficiency ratio among a plurality of historical operating parameters corresponding to the current operating condition parameters as a target operating parameter.
Optionally, the terminal is provided with or communicated with a neural network model. It should be noted that Neural Networks (NN) are complex network systems formed by a large number of simple processing units (called neurons) widely connected to each other, and a neural network model reflects many basic features of human brain functions and is a highly complex nonlinear dynamical learning system. The neural network model has large-scale parallel, distributed storage and processing, self-organization, self-adaptation and self-learning capabilities, and is particularly suitable for processing inaccurate and fuzzy information processing problems which need to consider many factors and conditions simultaneously.
Optionally, the terminal may input the current operating condition parameter as an input parameter into a neural network model trained in advance based on a plurality of historical operating parameters corresponding to the current operating condition parameter and a first energy efficiency ratio corresponding to the historical operating parameter, and the neural network model may output a group of operating parameters as an output value as a target operating parameter corresponding to the current operating condition parameter.
For example, in the current working condition parameters, the current environment parameters include a current outdoor temperature and a current indoor temperature, and when the current setting parameters include a current setting temperature, the target operation parameters output by the neural network model correspondingly may include a compressor frequency, an opening of the electronic expansion valve, a rotation speed of the inner fan, and a rotation speed of the outer fan; in the current working condition parameters, the current environment parameters include a current outdoor temperature, a current indoor temperature, a current outdoor humidity and a current indoor humidity, and when the current setting parameters include a current setting temperature and a current setting humidity, the target operation parameters correspondingly output by the neural network model may include a compressor frequency, an electronic expansion valve opening degree, an inner fan rotating speed, an outer fan rotating speed and a humidification frequency.
It should be noted that the historical operating parameters in the neural network model may be historical operating parameters in training samples input into the neural network model by an engineer, or historical operating parameters in new training samples obtained by the neural network model through self-learning based on the training samples.
Therefore, the automatic optimization of the energy efficiency ratio of the air conditioner can be realized by utilizing the pre-constructed neural network model, and the optimization of the energy efficiency ratio of the air conditioner can be further realized by continuously iterative training of the neural network model.
And step S30, obtaining target operation parameters according to a plurality of historical operation parameters corresponding to the current working condition parameters and according to the first energy efficiency ratios corresponding to the historical operation parameters.
Optionally, after obtaining the target operation parameter corresponding to the current working condition parameter, the terminal may control the air conditioner to operate according to the target operation parameter.
If the terminal detects that the setting parameter of the air conditioner is changed, the terminal may execute steps S10 to S30; the terminal may also perform steps S10 to S30 when detecting that the variation of any one of the current environmental parameters exceeds the preset variation range, where the preset variation range corresponding to the temperature value may be 0 to 1 ℃ and the preset variation range corresponding to the humidity value may be 0 to 5%.
In one embodiment, current working condition parameters of an air conditioner are obtained, wherein the current working condition parameters comprise current environmental parameters and current setting parameters of the air conditioner; obtaining target operation parameters according to a plurality of historical operation parameters corresponding to the current working condition parameters and according to first energy efficiency ratios corresponding to the historical operation parameters; and controlling the air conditioner to operate at the target operation parameter. Therefore, the problem of how to optimize the energy efficiency ratio of the air conditioner is solved by searching the operation parameters of the air conditioner according to the energy efficiency ratio corresponding to the operation parameters and controlling the operation of the air conditioner.
In a second embodiment, as shown in fig. 3, based on the embodiment shown in fig. 2, the step of obtaining a target operating parameter according to a plurality of historical operating parameters corresponding to the current operating condition parameter and according to a first energy efficiency ratio corresponding to each of the historical operating parameters includes:
and step S21, inputting the current working condition parameters into a neural network model to obtain the target operation parameters, wherein the neural network model is trained or updated according to a plurality of historical operation parameters corresponding to the current working condition parameters and according to first energy efficiency ratios corresponding to the historical operation parameters.
In this embodiment, the terminal is provided with or communicated with a neural network model. When the current working condition parameters are input into the pre-trained neural network model as input values, the neural network model may output a set of operating parameters as output values as target operating parameters corresponding to the current working condition parameters.
Optionally, the neural network model is trained or updated according to a plurality of training samples, and each training sample includes a historical operating condition parameter, the historical operating parameter corresponding to the historical operating condition parameter, and the first energy efficiency ratio.
Alternatively, referring to fig. 9, the pre-constructed neural network model uses a five-layer fully-connected neural network, the hidden layer activation function is relu, and the output layer uses a linear activation function. And (3) traversing a plurality of data sets of all the working condition parameter numerical combinations (the working condition parameters at the moment are historical working condition parameters), taking the historical operating parameters corresponding to the data sets and the first energy efficiency ratios corresponding to the historical operating parameters as training samples of the neural network model (the historical working condition parameters in each training sample have corresponding historical operating parameters, and each historical operating parameter has a corresponding first energy efficiency ratio), and training the neural network until the loss function does not decline any more and then stopping iteration.
It should be noted that, in all training samples, there is at least one training sample with the same historical working condition parameter; and the first energy efficiency ratio corresponding to the historical operating parameters is the energy efficiency ratio output by the air conditioner when the air conditioner operates according to the historical operating parameters under the historical working condition parameters corresponding to the historical operating parameters.
For example, if all the operating condition parameter value combinations are traversed and 90 data sets exist, the 90 data sets are used as training samples to be added into the neural network model for iterative training. Optionally, another 21 training samples containing the full working condition range are adopted to verify the performance of the neural network model, and if the deviation between the predicted value and the actual value on the 21 training samples is found to be ± 5%, the neural network model is a feasible well-trained neural network model.
It should be understood that the historical operating condition parameters include historical environmental parameters and historical setting parameters, wherein the historical environmental parameters include at least one of historical outdoor temperature, historical indoor temperature, historical outdoor humidity, and historical indoor humidity; the historical set parameter includes at least one of a historical set temperature and a historical set humidity. The historical operating parameters include at least one of compressor frequency, electronic expansion valve opening, inner fan speed, outer fan speed, and humidification frequency.
It should be noted that, in the iterative training process of the neural network model based on the training samples input into the model, a new training sample may also be generated, and each training sample generated by self-learning also has a historical operating condition parameter, a historical operating parameter and a first energy efficiency ratio.
Optionally, after obtaining the current working condition parameters, the terminal inputs the current working condition parameters as input values into the neural network model, the neural network model may query all historical working condition parameters corresponding to the current working condition parameters in the training sample (the historical working condition parameters are the same as the current working condition parameters or within the same numerical range as the current working condition parameters), and output, as output values, the historical operating parameters with the largest corresponding first energy efficiency ratio among the historical working condition parameters as target operating parameters corresponding to the current working condition parameters.
It should be noted that, in all the training samples corresponding to the historical operating condition parameters and the current operating condition parameters, at least one training sample of the same historical operating condition parameter is provided, and at least two training samples corresponding to the historical operating condition parameters and the current operating condition parameters are provided. When there are a plurality of training samples with the same historical operating condition parameter, the historical operating parameters in the training samples with the same historical operating condition parameter may be different, for example, the first training sample has the historical operating condition parameter a and the historical operating parameter B, and the second training sample has the historical operating condition parameter a and the historical operating parameter C, so that compared with the second training sample, the historical operating parameters are the same, the historical operating parameters are different, and certainly, the energy efficiency ratio corresponding to the historical operating parameter B is also different from the historical operating parameter C.
In an embodiment, the current operating condition parameter is input to a neural network model to obtain the target operating parameter, wherein the neural network model is trained or updated according to a plurality of historical operating parameters corresponding to the current operating condition parameter and according to a first energy efficiency ratio corresponding to each historical operating parameter. Therefore, by training the neural network model, the operation parameters of the air conditioner under the optimal energy efficiency ratio are searched based on the current working condition parameters, and the air conditioner is controlled to operate, so that the problem of how to optimize the energy efficiency ratio of the air conditioner is solved.
In a third embodiment, as shown in fig. 4, on the basis of the embodiments of fig. 2 to 3, after the step of obtaining the current operating condition parameters of the air conditioner, the method further includes:
and step S40, detecting the number of historical operating parameters corresponding to the current working condition parameters.
And step S50, the number of the historical operating parameters is less than the preset number, and target operating parameters are obtained according to the corresponding relation between the current operating condition parameters and the preset operating condition parameters and the operating parameters.
In this embodiment, after obtaining the current operating condition parameter, the terminal may query the number of the historical operating parameters corresponding to the current operating condition parameter, or query the number of the training samples of the historical operating condition parameter and the current operating condition parameter in the existing training samples in the neural network model, as the number of the historical operating parameters corresponding to the current operating condition parameter.
Optionally, after acquiring the number of the historical operating parameters corresponding to the current operating condition parameters, the terminal detects whether the number of the historical operating parameters corresponding to the current operating condition parameters is greater than or equal to a preset number, and when detecting that the number of the historical operating parameters corresponding to the current operating condition parameters is less than the preset number, acquires a corresponding relation between the preset operating condition parameters and the operating parameters, and then acquires target operating parameters corresponding to the current operating condition parameters according to the corresponding relation between the current operating condition parameters and the preset operating condition parameters and the operating parameters; in addition, when detecting that the number of the historical operating parameters corresponding to the current operating condition parameters is greater than or equal to the preset number, the terminal executes the steps of obtaining the target operating parameters according to the plurality of historical operating parameters corresponding to the current operating condition parameters and the first energy efficiency ratios corresponding to the historical operating parameters (namely, step 20).
It should be noted that the preset number may be set according to actual needs, such as 10 to 50, and optionally 30.
Optionally, when the number of the historical operating parameters corresponding to the current operating condition parameters is less than the preset number, the characterization is that the number of the training samples corresponding to the current historical operating parameters and the current operating condition parameters of the neural network model is not enough, and the neural network model cannot meet the requirement of automatic optimization of the energy efficiency ratio of the air conditioner, so that the target operating parameters need to be obtained according to the corresponding relationship between the current operating condition parameters, the preset operating condition parameters and the operating parameters.
Optionally, the preset correspondence between the operating condition parameters and the operating parameters may be obtained from an air conditioner control table having correspondence between various operating condition parameters and operating parameters, which is prepared by an engineer according to a work experience. The terminal can query the working condition parameters corresponding to the numerical values and the current working condition parameters (the numerical values are the same as the current working condition parameters or the numerical values and the current working condition parameters are in the same numerical value range) in the air conditioner control table according to the current working condition parameters, and then obtains the operation parameters corresponding to the queried working condition parameters to serve as target operation parameters.
In one embodiment, the number of historical operating parameters corresponding to the current working condition parameters is detected; and acquiring target operation parameters according to the current working condition parameters and the corresponding relation between the preset working condition parameters and the operation parameters. Thus, the stability in acquiring the target operation parameter of the air conditioner is improved.
In a fourth embodiment, as shown in fig. 5, on the basis of the above embodiments of fig. 2 to 4, after the step of controlling the air conditioner to operate at the target operation parameter, the method further includes:
and step S60, determining a second energy efficiency ratio corresponding to the target operation parameter.
And step S70, generating a sample according to the current working condition parameters, the target operation parameters and the second energy efficiency ratio.
And step S80, adding the sample as the training sample into the neural network model to update the neural network model.
In this embodiment, the terminal may input the current operating condition parameter into a neural network model to obtain the target operating parameter; or obtaining the target operation parameter according to the current working condition parameter and the corresponding relation between the preset working condition parameter and the operation parameter. And after the terminal acquires the target operation parameters, controlling the air conditioner to operate according to the target operation parameters.
Optionally, after the air conditioner operates with the target operation parameter, the terminal may detect an energy efficiency parameter of the air conditioner after the indoor environment parameter reaches the target operation parameter, where the energy efficiency parameter includes an air outlet dry bulb temperature, an air outlet wet bulb temperature, an indoor dry bulb temperature, an indoor wet bulb temperature, and a current power of the air conditioner.
Optionally, the terminal further determines a second energy efficiency ratio corresponding to the target operation parameter according to the target operation parameter and the energy efficiency parameter.
Optionally, the terminal may determine the current cooling capacity or heating capacity of the air conditioner according to the target operation parameter and the energy efficiency parameter. And then determining a second energy efficiency ratio corresponding to the target operation parameter according to the cooling capacity or the heating capacity and the current power of the air conditioner. It should be noted that, when the air conditioner operates in the cooling mode, the cooling capacity is determined; when the air conditioner is operated in a heating mode, the heating amount is determined.
Alternatively, the calculation formula of the second energy efficiency ratio is as follows:
and E is a second energy efficiency ratio, Q is refrigerating capacity or heating capacity, and W is the current power of the air conditioner.
Optionally, the calculation formula of the cooling capacity or the heating capacity Q is as follows:
the method comprises the following steps of obtaining an air outlet enthalpy value ha and an air inlet enthalpy value hb by searching an enthalpy diagram according to air outlet dry bulb temperature, air outlet wet bulb temperature, indoor dry bulb temperature and indoor wet bulb temperature; wn is the indoor air humidity and is determined by an air outlet dry ball and an air outlet wet ball; v1 is the air output of the air conditioner; v2 is the specific volume of the air outlet, and V2 can be determined by searching a psychrometric chart.
Optionally, the air output of the air conditioner is calculated according to the following formula:
wherein N is the rotating speed of the inner fan, N is the rotating speed of the outer fan, and V3 is the rated air quantity.
Optionally, after obtaining the second energy efficiency ratio, the terminal may generate a sample according to the current operating condition parameter, the target operating parameter, and the second energy efficiency ratio, where the current operating condition parameter is changed to the historical operating condition parameter, the target operating parameter is changed to the historical operating parameter, and the second energy efficiency ratio is changed to the first energy efficiency ratio.
Optionally, after generating a new sample, the terminal may add the sample as a training sample to the neural network model to update the neural network model.
When the target operation parameters are obtained according to the current working condition parameters and the corresponding relation between the preset working condition parameters and the operation parameters, generating samples corresponding to the target operation parameters, and iterating along with the continuous updating of the training samples of the neural network model, so that the number of the training samples corresponding to the current working condition parameters is larger than or equal to the preset number, and thus, the training of the neural network model is completed, and the trained neural network model meets the requirement of automatic optimization of the energy efficiency ratio of the air conditioner; when the target operation parameter is obtained according to the plurality of historical operation parameters corresponding to the current working condition parameter and the first energy efficiency ratio corresponding to each historical operation parameter, a sample corresponding to the target operation parameter is generated and is used as a training sample to be added into the neural network model for training, and continuous optimization of the neural network model can be achieved.
In a fifth embodiment, as shown in fig. 6, based on the above embodiments of fig. 2 to 5, after the step of determining the second energy efficiency ratio corresponding to the target operating parameter, the method further includes:
and step S61, obtaining the refrigerating capacity or the heating capacity corresponding to the second energy efficiency ratio.
And S71, if the refrigerating capacity is within a preset refrigerating capacity interval or the heating capacity is within a preset heating capacity interval, executing the step of generating a sample according to the current working condition parameter, the target operation parameter and the second energy efficiency ratio.
In this embodiment, the terminal may determine the current cooling capacity or heating capacity of the air conditioner according to the target operation parameter and the energy efficiency parameter. It should be noted that, when the air conditioner is currently operating in the cooling mode, the current cooling capacity of the air conditioner is determined; when the air conditioner is currently operated in the heating mode, the current heating quantity of the air conditioner is determined.
It should be noted that, since the second energy efficiency ratio corresponds to the target operation parameter, the cooling capacity or the heating capacity determined according to the target operation parameter and the energy efficiency parameter is obtained, that is, the cooling capacity or the heating capacity corresponding to the second energy efficiency ratio is obtained.
Optionally, after the terminal obtains the current working condition parameter, the terminal may query a working condition parameter corresponding to the numerical value and the current working condition parameter in the air conditioner control table (the numerical value is the same as the current working condition parameter, or the numerical value is in the same numerical value range as the current working condition parameter), then obtain an operation parameter corresponding to the queried obtained working condition parameter, and then determine the cooling capacity or the heating capacity corresponding to the working condition parameter according to the working condition parameter and the operation parameter. Adding or subtracting preset values before and after the refrigerating capacity to obtain two end values of a preset refrigerating capacity interval, so as to obtain a preset refrigerating capacity interval; and adding or subtracting preset values before and after the heating capacity to obtain two end values of the preset heating capacity interval, thus obtaining the preset heating capacity interval.
It should be noted that the value range of the preset value may be 50W to 200W, and may be 100W.
Optionally, when the air conditioner is currently operated in the cooling mode, when the cooling capacity of the second energy efficiency ratio is within a preset cooling capacity interval, it is determined that a generation condition of a sample is currently satisfied, and the step of generating the sample according to the current working condition parameter, the target operation parameter, and the second energy efficiency ratio is performed (i.e., step 70). And when the refrigerating capacity of the second energy efficiency ratio is not in the preset refrigerating capacity interval, discarding the group of data, and generating a sample by using the current working condition parameter, the target operation parameter and the second energy efficiency ratio.
Optionally, when the air conditioner is currently operated in the heating mode, when the heating capacity of the second energy efficiency ratio is within a preset heating capacity interval, it is determined that a generation condition of a sample is currently satisfied, and the step of generating the sample according to the current working condition parameter, the target operation parameter, and the second energy efficiency ratio is performed (i.e., step 70). And when the heating capacity of the second energy efficiency ratio is not in the preset heating capacity interval, discarding the group of data, and generating a sample by using the current working condition parameter, the target operation parameter and the second energy efficiency ratio.
Optionally, based on the preset cooling capacity interval and the preset heating capacity interval, the training process of the neural network model may also be: and after a group of historical working condition parameters are input into the neural network model, traversing all possible values of all possible control parameters of the air conditioner according to the numerical range and step length of the parameters to obtain all possible control parameter combinations as historical operating parameters corresponding to the historical working condition parameters. And sequentially inputting the combination of each group of possible control parameters of the historical operating parameters into a neural network model, calculating the refrigerating capacity or the heating capacity obtained after the operation according to the group of parameters to be Q, and when the refrigerating capacity is in a preset refrigerating capacity interval or the heating capacity is in a preset heating capacity interval, forming a qualified training sample by the historical operating parameters, the refrigerating capacity corresponding to the historical operating parameters, the historical operating parameters of which the heating capacity is in the preset refrigerating capacity interval or the historical operating parameters corresponding to the historical operating parameters and the first energy efficiency value corresponding to the historical operating parameters, and recording the training sample and the output value of the neural network together as a piece of data in a list.
In an embodiment, the refrigerating capacity or the heating capacity corresponding to the second energy efficiency ratio is obtained; and if the refrigerating capacity is within a preset refrigerating capacity interval or the heating capacity is within a preset heating capacity interval, executing the step of generating a sample according to the current working condition parameter, the target operation parameter and the second energy efficiency ratio. Therefore, the refrigerating capacity or the heating capacity which can be achieved by the target operation parameters output by the neural network model can be ensured to meet the normal refrigerating or heating requirements of the air conditioner.
In a sixth embodiment, as shown in fig. 7, on the basis of the above embodiments of fig. 2 to 6, after the step of controlling the air conditioner to operate at the target operation parameter, the method further includes:
and step S62, obtaining the working condition parameters of the compressor of the air conditioner.
And S72, if the compressor working condition parameter is smaller than a preset threshold value, executing the step of generating a sample according to the current working condition parameter, the target operation parameter and the second energy efficiency ratio.
In this embodiment, after the air conditioner operates with the target operation parameter, the terminal may detect and acquire a compressor operating condition parameter of the air conditioner after the indoor environment parameter reaches the target operation parameter; the terminal can also obtain the compressor working condition parameters of the compressor of the air conditioner when the air conditioner runs under the target operation parameters in the environment corresponding to the current working condition parameters, wherein the target operation parameters are obtained through prediction of the neural network model.
Optionally, the compressor operating condition parameter includes at least one of a compressor discharge temperature, a compressor current, and a compressor refrigerant pressure. It should be noted that the preset thresholds corresponding to different types of compressor operating condition parameters are different, and when the terminal detects that the compressor operating condition parameter is smaller than the preset threshold corresponding to the compressor operating condition parameter, the step of generating a sample according to the current operating condition parameter, the target operating parameter and the second energy efficiency ratio is executed. The following description will take the compressor operating condition parameters as the compressor discharge temperature as an example.
Optionally, when the terminal detects that the acquired compressor discharge temperature is less than a preset threshold (i.e., a preset temperature) corresponding to the compressor discharge temperature, it is determined that the load of the air conditioner is within a reasonable operation range when the air conditioner operates at the current target operation temperature, that is, the compressor is in a stable operation state, and at this time, the step of generating the sample according to the current working condition parameter, the target operation parameter, and the second energy efficiency ratio may be performed (i.e., step 70); when the terminal detects that the acquired compressor exhaust temperature is greater than or equal to a preset threshold corresponding to the compressor exhaust temperature, it is determined that the load of the air conditioner is not in a reasonable operation range when the air conditioner operates at the current target operation temperature, namely, the compressor is in an unstable operation state, at this time, the set of data can be discarded, and the current working condition parameter, the target operation parameter and the second energy efficiency ratio are not used for generating samples.
Optionally, the value range of the preset threshold corresponding to the discharge temperature of the compressor may be 90 ℃ to 100 ℃, and may be 95 ℃.
Optionally, based on a preset cooling capacity interval or a preset heating capacity interval, and a compressor operating condition parameter and a corresponding preset threshold, the training process of the neural network model may also be: and after a group of historical working condition parameters are input into the neural network model, traversing all possible values of all possible control parameters of the air conditioner according to the numerical range and step length of the parameters to obtain all possible control parameter combinations as historical operating parameters corresponding to the historical working condition parameters. For the combination of each group of possible control parameters of the historical operation parameters, sequentially inputting the combination into a neural network model, calculating the refrigerating capacity or the heating capacity obtained after the operation according to the group of parameters to be Q, when the refrigerating capacity is in a preset refrigerating capacity interval, or when the heating quantity is in the preset heating quantity interval, further judging whether the compressor working condition parameter corresponding to the historical operation parameter in the group of data is smaller than the preset threshold value or not, and when the compressor working condition parameter is smaller than the preset threshold value, then the historical working condition parameters and the refrigerating output corresponding to the historical working condition parameters are positioned in a preset refrigerating output interval, or forming a qualified training sample by the historical operating parameter of which the heating capacity is in the preset heating capacity interval and the first energy efficiency value corresponding to the historical operating parameter, and recording the training sample and the output value of the neural network together as a piece of data in the list.
In one embodiment, the working condition parameters of the compressor of the air conditioner are obtained; and if the working condition parameter of the compressor is smaller than a preset threshold value, executing the step of generating a sample according to the current working condition parameter, the target operation parameter and the second energy efficiency ratio. Therefore, the compressor of the air conditioner can be ensured to stably operate when the air conditioner operates according to the target operation parameters output by the neural network model.
In a seventh embodiment, as shown in fig. 8, based on the above embodiments of fig. 2 to 7, after the step of generating samples according to the current operating condition parameter, the target operating parameter, and the second energy efficiency ratio, the method further includes:
and step S81, if the sample meets the preset condition, executing the step of adding the sample as the training sample into the neural network model to update the neural network model.
In this embodiment, a new sample may be generated, that is, the sample is added to the neural network model as a training sample, the neural network model is updated, and the neural network model is further trained together with the original training sample according to the new training sample; or when the samples meet the preset conditions, adding all samples which are not added into the neural network model as training samples, updating the neural network model, and further training the neural network model together with the original training samples according to the new training samples.
The preset condition includes any one of: the ratio of the total number of the samples to a first preset value is greater than a reference value; and the ratio of the sum of the total number of the samples and the total number of the training samples to a second preset value is larger than a reference value.
Optionally, when the preset condition is that a ratio between the total number of the samples and the first preset value is greater than a reference value, the condition formula is as follows:
wherein N1 is the total number of samples not added to the neural network model, X is a first preset value, N is a reference value, and the reference value is added by one every time the neural network model is updated.
Optionally, when the preset condition is that the ratio of the sum of the total number of the samples and the total number of the training samples to the second preset value is greater than the reference value, the condition formula is as follows:
wherein N1 is the total number of samples not added to the neural network model, N2 is the total number of training samples existing in the neural network model, Y is a first preset value, N is a reference value, and the reference value is added by one every time the neural network model is updated.
It should be noted that, each time the step of adding the sample as the training sample to the neural network model to update the neural network model (i.e. step 80) is executed, the reference value is added by one; the initial value of the required reference value can be selected as 0 value; the value range of the first preset value can be selected to be 1-1000, and the value can be 100; the value range of the second preset value can be selected to be 1-1000, and the value can be 100.
Alternatively, the preset condition may be characterized by the number of samples not added to the neural network model reaching a certain number.
Alternatively, the terminal may store a new sample into the database after each new sample is generated, and update the neural network once after the number of samples in the database reaches a certain number (for example, the samples in the database satisfy (N1/100) > N, N is accumulated with the neural network module once again, N is N +1, and the initial value may be set to 0), so as to replace the neural network model of the previous time with the latest neural network model.
In an embodiment, if the sample meets a preset condition, the step of adding the sample as the training sample to the neural network model to update the neural network model is performed. Therefore, frequent updating of the neural network model can be avoided, and the effect of saving system resources can be achieved.
In addition, the present invention further provides an air conditioner, which includes a memory, a processor and a control program of the air conditioner stored in the memory and operable on the processor, wherein the processor implements the steps of the control method of the air conditioner according to the above embodiment when executing the control program of the air conditioner.
Furthermore, the present invention also proposes a computer-readable storage medium including a control program of an air conditioner, which implements the steps of the control method of the air conditioner as described in the above embodiments when executed by a processor.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is an alternative embodiment. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a television, a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only an alternative embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (11)
1. A control method of an air conditioner is characterized by comprising the following steps:
acquiring current working condition parameters of an air conditioner, wherein the current working condition parameters comprise current environmental parameters and current setting parameters of the air conditioner;
obtaining target operation parameters according to a plurality of historical operation parameters corresponding to the current working condition parameters and according to first energy efficiency ratios corresponding to the historical operation parameters;
controlling the air conditioner to operate at the target operation parameter;
the step of obtaining target operation parameters according to the plurality of historical operation parameters corresponding to the current working condition parameters and according to the first energy efficiency ratios corresponding to the historical operation parameters comprises the following steps: and taking the historical operation parameter with the maximum first energy efficiency ratio as the target operation parameter in the plurality of historical operation parameters corresponding to the current working condition parameters.
2. The method for controlling an air conditioner according to claim 1, wherein the step of obtaining the current operating condition parameter of the air conditioner is followed by further comprising:
detecting the number of historical operating parameters corresponding to the current working condition parameters;
if the number of the historical operating parameters is larger than or equal to the preset number, executing the plurality of historical operating parameters corresponding to the current working condition parameters and acquiring target operating parameters according to the first energy efficiency ratio corresponding to each historical operating parameter;
and acquiring target operation parameters according to the current working condition parameters and the corresponding relation between the preset working condition parameters and the operation parameters.
3. The method for controlling an air conditioner according to any one of claims 1-2, wherein the step of obtaining a target operation parameter according to a plurality of historical operation parameters corresponding to the current operating condition parameter and according to the first energy efficiency ratio corresponding to each of the historical operation parameters comprises:
and inputting the current working condition parameters into a neural network model to obtain the target operation parameters, wherein the neural network model is trained or updated according to a plurality of historical operation parameters corresponding to the current working condition parameters and according to first energy efficiency ratios corresponding to the historical operation parameters.
4. The method of claim 3, wherein the neural network model is trained or updated according to a plurality of training samples, each training sample comprising a historical operating condition parameter, the historical operating parameter corresponding to the historical operating condition parameter, and the first energy efficiency ratio, and wherein the neural network model is configured to output, as the target operating parameter, a historical operating parameter with a largest first energy efficiency ratio among the plurality of training samples corresponding to the historical operating condition parameter and the current operating condition parameter.
5. The method of controlling an air conditioner according to claim 4, further comprising, after the step of controlling the air conditioner to operate at the target operation parameter:
determining a second energy efficiency ratio corresponding to the target operation parameter;
generating a sample according to the current working condition parameter, the target operation parameter and the second energy efficiency ratio;
adding the sample as the training sample to the neural network model to update the neural network model.
6. The method of controlling an air conditioner according to claim 5, further comprising, after the step of determining the second energy efficiency ratio corresponding to the target operation parameter:
obtaining refrigerating capacity or heating capacity corresponding to the second energy efficiency ratio;
and if the refrigerating capacity is within a preset refrigerating capacity interval or the heating capacity is within a preset heating capacity interval, executing the step of generating a sample according to the current working condition parameter, the target operation parameter and the second energy efficiency ratio.
7. The method of controlling an air conditioner according to claim 5, further comprising, after the step of controlling the air conditioner to operate at the target operation parameter:
acquiring working condition parameters of a compressor of the air conditioner;
and if the working condition parameter of the compressor is smaller than a preset threshold value, executing the step of generating a sample according to the current working condition parameter, the target operation parameter and the second energy efficiency ratio.
8. The control method of an air conditioner according to any one of claims 5-7, characterized in that, after the step of generating samples according to the current operating condition parameter, the target operating parameter, and the second energy efficiency ratio, further comprising:
and if the sample meets a preset condition, executing the step of adding the sample serving as the training sample into the neural network model so as to update the neural network model.
9. The control method of an air conditioner according to claim 8, wherein the preset condition includes any one of:
the ratio of the total number of the samples to a first preset value is greater than a reference value;
the ratio of the sum of the total number of the samples and the total number of the training samples to a second preset value is larger than a reference value;
wherein the step of adding the sample as the training sample to the neural network model to update the neural network model is performed once, the reference value is cumulatively incremented by one.
10. An air conditioner, characterized in that the air conditioner comprises a memory, a processor, and a control program of the air conditioner stored on the memory and executable on the processor, the control program of the air conditioner realizing the steps of the control method of the air conditioner according to any one of claims 1 to 9 when executed by the processor.
11. A computer-readable storage medium, characterized in that a control program of an air conditioner is stored thereon, which when executed by a processor implements the steps of the control method of the air conditioner according to any one of claims 1 to 9.
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| CN111537252B (en) * | 2020-04-28 | 2022-05-13 | 宁波奥克斯电气股份有限公司 | Air conditioner testing method and device, computer equipment and storage medium |
| CN111750501B (en) * | 2020-05-15 | 2022-02-01 | 海信（山东）空调有限公司 | Air conditioner and control method |
| CN111928423B (en) * | 2020-06-30 | 2023-05-26 | 青岛海尔空调电子有限公司 | Defrosting control method for air conditioning unit |
| CN111928430A (en) * | 2020-07-27 | 2020-11-13 | 珠海格力电器股份有限公司 | Air conditioner energy efficiency grade determining method and device, computer equipment and storage medium |
| US20230349577A1 (en) | 2020-08-04 | 2023-11-02 | Belim Holding Ag | Device and method for controlling an orifice of a valve in an hvac system |
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