Self-cleaning control method and control apparatus for air conditioner, and storage medium and electronic apparatus

Abstract

Provided are a self-cleaning control method and control apparatus for an air conditioner, and a storage medium and an electronic apparatus. The control method comprises: acquiring a real-time operating condition parameter of an air conditioner and a pre-established fouling prediction model; on the basis of the real-time operating condition parameter and the fouling prediction model, obtaining a fouling state of a heat exchanger; and on the basis of the fouling state, controlling the air conditioner to run a corresponding self-cleaning strategy. Whether the heat exchanger has become fouled can be detected in a timely manner, and self-cleaning is performed in a timely manner upon detection of heat exchanger fouling, thereby preventing the cooling capacity and heating capacity of the air conditioner from rapidly decreasing, which is conducive to improving the usage experience of a user.

Description

Control method, control device, storage medium and electronic device for self-cleaning of air conditioner

[0001] The present application claims priority to Chinese Patent Application No. 202410828829.4, filed on June 25, 2024, entitled "Control method, control device, storage medium and electronic device for self-cleaning of air conditioner", the entire contents of which are incorporated herein by reference. TECHNICAL FIELD

[0002] The present application relates to the technical field of household appliances, and specifically provides a control method, a control device, a storage medium and an electronic device for self-cleaning of an air conditioner. BACKGROUND

[0003] At present, with the continuous improvement of people's living standards, air conditioners have become one of the indispensable household appliances in daily life. The outdoor heat exchanger in the outdoor unit of the air conditioner is the core component of the air conditioning system, and its operating state is crucial. However, due to the operating environment and other reasons, the outdoor heat exchanger is prone to dirty and clogging. This dirty and clogging not only affects the cooling and heating capacity of the air conditioner, but also causes the noise to increase during air conditioner operation, increases the operating pressure of the compressor, and thus increases energy consumption and may even damage the air conditioner, seriously affecting the user's comfort experience.

[0004] The related art determines whether the outdoor heat exchanger is dirty and clogged by regular after-sales detection. However, regular after-sales detection cannot timely detect the dirty and clogging of the outdoor heat exchanger, and cannot timely handle the decrease in the cooling and heating capacity of the air conditioner, affecting the user's experience, and regular after-sales detection will cause waste of manpower and increase the cost of after-sales service.

[0005] Accordingly, there is a need in the art for a new technical solution to solve the above problems. SUMMARY

[0006] The present application aims to solve the above technical problems, i.e., to solve the problem that the related art cannot timely detect the dirty and clogging of the outdoor heat exchanger by regular after-sales detection, cannot timely handle the decrease in the cooling and heating capacity of the air conditioner, affects the user's experience, and regular after-sales detection will cause waste of manpower and increase the cost of after-sales service.

[0007] In a first aspect, the present application provides a control method for self-cleaning of an air conditioner, the air conditioner comprising a heat exchanger, the control method comprising: obtaining real-time operating condition parameters of the air conditioner and a pre-established dirty and clogging prediction model; obtaining a dirty and clogging state of the heat exchanger according to the real-time operating condition parameters and the dirty and clogging prediction model; and controlling the air conditioner to run a corresponding self-cleaning strategy according to the dirty and clogging state.

[0008] In some embodiments, the step of "obtaining the dirty-blocked state of the heat exchanger according to the real-time working condition parameter and the dirty-blocked prediction model" specifically comprises: calculating a target working condition parameter according to the real-time working condition parameter; taking the target working condition parameter as the input of the dirty-blocked prediction model, and taking the output of the dirty-blocked prediction model as the dirty-blocked state.

[0009] In some embodiments, the step of "calculating a target working condition parameter according to the real-time working condition parameter" specifically comprises: performing data cleaning on the real-time working condition parameter; and obtaining a target working condition parameter according to the cleaned real-time working condition parameter.

[0010] In some embodiments, the step of "performing data cleaning on the real-time working condition parameter" specifically comprises: obtaining signal data corresponding to the real-time working condition parameter; determining a filter window and calculating the median absolute deviation of signal data in the filter window; judging the validity of the signal data in the filter window according to the median absolute deviation; replacing the invalid signal data with the median of the signal data in the filter window in the case that the signal data is invalid; and sliding the filter window backward to sequentially process the signal data to perform data cleaning on the real-time working condition parameter.

[0011] In some embodiments, the median absolute deviation of signal data in the filter window is calculated according to the following formula: MAD = Median{|X i -med|}; wherein, MAD is the median absolute deviation, the filter window includes k signal data, Xi is the i-th signal data, k and i are constants, 1≤i≤k, and med is the median of the signal data in the filter window. i

[0012] In some embodiments, the step of "judging the validity of the signal data in the filter window" specifically comprises: determining that the signal data is invalid in the case that |X i -med|>t0*MAD; determining that the signal data is valid in the case that |X i -med|≤t0*MAD; wherein, t0 is a constant.

[0013] In some embodiments, the real-time working condition parameter comprises one or more of the exhaust gas temperature, the suction gas temperature, the defrost sensor temperature, the external environment temperature, the total current, the compressor speed, the outdoor fan speed, the external coil temperature, the auxiliary valve opening degree, the heat exchanger inlet temperature, the heat exchanger outlet temperature, the compressor oil temperature, the compressor low-pressure pressure, the compressor high-pressure pressure, and the compressor pressure ratio.

[0014] In a second aspect, the present application provides an air conditioner comprising a controller configured to perform the above-mentioned control method for air conditioner self-cleaning.​

[0015] In a third aspect, the present application provides a computer readable storage medium, characterized in that the computer readable storage medium stores a computer program, wherein the computer program performs the above-mentioned control method for air conditioner self-cleaning when running.

[0016] In a fourth aspect, the present application provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor can invoke and run the computer program to perform the above-mentioned control method for air conditioner self-cleaning.

[0017] In the technical solution described above, the control method for air conditioner self-cleaning, the control device, the storage medium and the electronic device provided by the present application obtain the dirty block prediction model and the real-time working condition parameters of the air conditioner, analyze the real-time working condition parameters by using the dirty block prediction model to obtain the dirty block state of the heat exchanger, and then control the air conditioner to run the corresponding self-cleaning strategy according to the dirty block state. In this way, the present application can timely find out whether the heat exchanger is dirty and blocked, and timely perform self-cleaning when the heat exchanger is dirty and blocked, thereby avoiding rapid decline of the cooling and heating capacity of the air conditioner, and being conducive to improving the user experience. At the same time, the present application also does not need to be regularly detected by after-sales service, which is conducive to saving labor costs. BRIEF DESCRIPTION OF DRAWINGS

[0018] The preferred embodiments of the present application will be described below with reference to the accompanying drawings, in which:

[0019] Fig. 1 is a main step flow chart of the control method for air conditioner self-cleaning in the present application;

[0020] Fig. 2 is a detailed step flow chart of one step in the present application;

[0021] Fig. 3 is a structural schematic diagram of the dirty block prediction model in the present application;

[0022] Fig. 4 is a structural schematic diagram of the electronic device provided by the present application. DETAILED DESCRIPTION

[0023] The preferred embodiments of the present application will be described below with reference to the accompanying drawings, in which:

[0024] Unless otherwise specified, the term "a plurality of" means two or more.

[0025] The term "corresponding" can refer to a kind of association or binding relationship, A corresponding to B means that there is an association or binding relationship between A and B.

[0026] At present, with the continuous improvement of people's living standards, air conditioners have become one of the indispensable household appliances in daily life. The outdoor heat exchanger in the outdoor unit of the air conditioner is the core component of the air conditioning system, and its operating state is crucial. However, due to the operating environment and other reasons, the outdoor heat exchanger is prone to dirty and clogging. This dirty and clogging not only affects the cooling and heating capacity of the air conditioner, but also causes the noise to increase during the operation of the air conditioner, increases the operating pressure of the compressor, and thus increases the energy consumption and may even cause damage to the air conditioner, seriously affecting the user's comfort experience.

[0027] The related art determines whether the outdoor heat exchanger is dirty and clogged through regular after-sales detection. However, regular after-sales detection cannot timely discover the dirty and clogging condition of the outdoor heat exchanger, and cannot timely handle the decrease in the cooling and heating capacity of the air conditioner, affecting the user's experience, and regular after-sales detection will cause waste of manpower and increase the after-sales cost.

[0028] Therefore, the present application provides an air conditioner self-cleaning control method, control device, storage medium and electronic device, which can timely discover the dirty and clogging condition of the outdoor heat exchanger.

[0029] In a first aspect, the present application provides an air conditioner self-cleaning control method.

[0030] Figure 1 is a main step flow chart of the air conditioner self-cleaning control method in the present application.

[0031] The air conditioner comprises a heat exchanger, and in combination with Figure 1, the air conditioner self-cleaning control method provided by the present application comprises the following steps:

[0032] S101, obtaining real-time working condition parameters of the air conditioner and a pre-established dirty and clogging prediction model. The dirty and clogging prediction model is used to predict the dirty and clogging condition of the heat exchanger, the construction steps of the dirty and clogging prediction model are completed in advance, and the dirty and clogging prediction model is pre-set in the air conditioner.

[0033] Optionally, the real-time working condition parameters include one or more of the following: discharge temperature, suction temperature, defrosting sensor temperature, external environment temperature, total current, compressor speed, outdoor fan speed, outdoor coil temperature, auxiliary valve opening, heat exchanger inlet temperature, heat exchanger outlet temperature, compressor oil temperature, compressor low pressure, compressor high pressure and compressor pressure ratio.

[0034] S102, obtaining the dirty and clogging state of the heat exchanger according to the real-time working condition parameters and the dirty and clogging prediction model. In this step, the real-time working condition parameters are processed and input into the dirty and clogging prediction model, and the dirty and clogging prediction model outputs the dirty and clogging state of the heat exchanger. The dirty and clogging state includes light dirty and clogging, medium dirty and clogging and heavy dirty and clogging.

[0035] S103, controlling the air conditioner to run a corresponding self-cleaning strategy according to the dirty blockage state.

[0036] In the technical solution, the air conditioner self-cleaning control method provided by the application obtains a dirty blockage prediction model and real-time working condition parameters of the air conditioner, analyzes the real-time working condition parameters by using the dirty blockage prediction model to obtain a dirty blockage state of the heat exchanger, and then controls the air conditioner to run a corresponding self-cleaning strategy according to the dirty blockage state. In this way, the application can timely find out whether the heat exchanger is dirty and blocked, and timely perform self-cleaning when the heat exchanger is dirty and blocked, thereby avoiding rapid decline of the air conditioner's refrigeration and heating capacity, and improving the user's experience. At the same time, the application also does not need to be regularly detected by after-sales service, which is beneficial to saving labor costs.

[0037] In some embodiments, the step of "calculating target working condition parameters according to the real-time working condition parameters" specifically includes: performing data cleaning on the real-time working condition parameters; and obtaining the target working condition parameters according to the cleaned real-time working condition parameters. In this embodiment, by performing data cleaning on the real-time working condition parameters, damaged data, abnormal data and error data can be eliminated, and the accuracy of the dirty blockage state prediction result can be improved. The target working condition parameters required by the dirty blockage prediction model can be obtained by simple calculation according to the cleaned real-time working condition parameters.

[0038] In some embodiments, the step of "calculating target working condition parameters according to the real-time working condition parameters" specifically includes: performing data cleaning on the real-time working condition parameters; and obtaining the target working condition parameters according to the cleaned real-time working condition parameters. In this way, damaged data, abnormal data and error data can be eliminated, and the accuracy of the dirty blockage state prediction result can be improved.

[0039] FIG. 2 is a detailed step flowchart of one step in the application. In some embodiments, the step of "performing data cleaning on the real-time working condition parameters" specifically includes:

[0040] S201, obtaining signal data corresponding to the real-time working condition parameters.

[0041] S202, determining a filter window and calculating a median absolute deviation of the signal data in the filter window.

[0042] S203, judging the validity of the signal data in the filter window according to the median absolute deviation.

[0043] S204, in the case that the signal data is invalid, replacing the invalid signal data with the median of the signal data in the filter window.

[0044] S205, sliding the filter window backward to sequentially process the signal data to perform data cleaning on the real-time working condition parameters.

[0045] The application adopts a Hubble filter to remove data, the Hubble filter is a median-based outlier detection method, has strong anti-interference ability, and is widely applied to fields such as signal processing and communication systems. The above technical solution can effectively eliminate outliers in signal data, which may be caused by data damage, data error or real data anomaly, and outliers will have a negative impact on data analysis and modeling. Therefore, the above technical solution can clean the real-time working condition parameters, improve the accuracy of the real-time working condition parameters, and thus improve the accuracy of the subsequent prediction results of the dirty and blocked state.

[0046] In the preferred embodiment, a filter window with a length of 2k+1 is determined, and k is a constant. For example, k is 40-60, such as 43, 46, 48, 50, 53, 55, 56 or 58.

[0047] In some embodiments, the median absolute deviation of the signal data in the filter window is calculated according to the following formula: MAD = Median{|X i -med|}.

[0048] In the formula, MAD is the median absolute deviation, the filter window includes k signal data, X i is the i-th signal data, k and i are both constants, and 1≤i≤k, med is the median of the signal data in the filter window. The function Median{} is used to calculate the median. Through the above formula, the median absolute deviation of the signal data in the filter window can be calculated.

[0049] In some embodiments, the step of “judging the validity of the signal data in the filter window” specifically includes:

[0050] In the case of |X i med|>t0*MAD, the signal data is determined to be invalid.

[0051] In the case of |X i med|≤t0*MAD, the signal data is determined to be valid.

[0052] Where t0 is a constant. For example, 2

[0053] In the case of |X i- med|>t0*MAD, the signal data is determined to be invalid, and the median of the signal data in the filter window is used to replace the invalid signal data. In the case of |X iIn the case of med|≤t0*MAD, it is determined that the signal data is valid, and the signal data is output. In this way, the validity of the signal data in the filter window is judged, invalid data is removed, and valid data is retained.

[0054] In some embodiments, the real-time working condition parameters include one or more of exhaust gas temperature, suction gas temperature, defrosting sensor temperature, external environment temperature, total current, compressor rotating speed, outdoor fan rotating speed, external coil temperature, auxiliary valve opening, heat exchanger inlet temperature, heat exchanger outlet temperature, compressor oil temperature, compressor low-pressure pressure, compressor high-pressure pressure, and compressor pressure ratio. By using the above real-time working condition parameters, a plurality of target working condition parameters can be calculated, and a plurality of working condition parameters are comprehensively used to predict the dirty and blocked state, which is beneficial to improve the accuracy of the dirty and blocked state prediction.

[0055] Further, in combination with the above real-time working condition parameters, the calculation of the target working condition parameters according to the real-time working condition parameters is described: for example, the target working condition parameters include suction and exhaust pressure difference and high and low pressure difference, and the real-time working condition parameters include exhaust gas temperature, suction gas temperature, compressor low-pressure pressure and compressor high-pressure pressure. The suction and exhaust pressure difference is obtained by subtracting the exhaust gas temperature from the suction gas temperature, and the high and low pressure difference is obtained by subtracting the compressor low-pressure pressure from the compressor high-pressure pressure.

[0056] Optionally, according to the dirty and blocked state, the control of the air conditioner to run the corresponding self-cleaning strategy includes: in the case of the dirty and blocked state being a light dirty and blocked state, the air conditioner runs a first self-cleaning strategy; in the case of the dirty and blocked state being a moderate dirty and blocked state, the air conditioner runs a second self-cleaning strategy; and in the case of the dirty and blocked state being a heavy dirty and blocked state, the air conditioner alarms. The cleaning effect of the second self-cleaning strategy is greater than that of the first self-cleaning strategy.

[0057] Optionally, in combination with FIG. 3, the present application provides a schematic diagram of a dirty and blocked prediction model, which includes an input layer, a hidden layer and an output layer. The input layer is configured to input the target working condition parameters. The hidden layer is configured to process the data of the input layer and perform layer-by-layer learning to obtain the final output result. The output layer is configured to output the dirty and blocked state. In this way, by inputting the above-mentioned plurality of target working condition parameters into the dirty and blocked prediction model, the dirty and blocked prediction model can analyze the actual running condition of the air conditioner, thereby accurately predicting the actual dirty and blocked state of the heat exchanger.

[0058] The dirty and blocked prediction model adopts a BP neural network with eleven layers, the input layer has 25-dimensional features, the activation layer uses a Relu function for activation, and the output layer identifies the categories of the dirty and blocked state. After outputting the dirty and blocked categories, different self-cleaning strategies are executed according to the level of the dirty and blocked state, and when the dirty and blocked state is too serious to be cleaned by self-cleaning, the air conditioner will issue a dirty and blocked alarm of the heat exchanger, and the customer service personnel will perform targeted dirty and blocked maintenance according to the alarm information.

[0059] Optionally, the dirty block prediction model is established by the following method: a neural network model is constructed; a plurality of groups of known working condition parameters when the heat exchanger is in a dirty block state are obtained; a training data set is constructed according to the known working condition parameters; and the neural network model is trained according to the training data set to obtain the dirty block prediction model.

[0060] In this embodiment, a neural network model needs to be designed and constructed. The neural network model is a computational model that simulates the neural system of the human brain, and realizes the nonlinear mapping and prediction of input data through the connection and weight adjustment between multiple neurons. The neural network model includes a convolutional neural network, a recurrent neural network or a fully connected neural network, etc. After obtaining the known working condition parameters, these parameters need to be sorted and organized to form a data set suitable for training the neural network model. After the neural network model is constructed, the training data set is used to train the neural network model. During the training process, the neural network model learns and adjusts the internal weights and parameters according to the input feature data, so as to minimize the error between the predicted output and the actual output. Through multiple iterations and optimization, the model can gradually learn the mapping relationship from the known working condition parameters to the dirty block state, and finally obtain a model that can accurately predict the dirty block state.

[0061] Optionally, the known working condition parameters include one or more of the exhaust temperature, the suction temperature, the defrost sensor temperature, the external environment temperature, the total current, the compressor speed, the outdoor fan speed, the external coil temperature, the auxiliary valve opening, the heat exchanger inlet temperature, the heat exchanger outlet temperature, the compressor oil temperature, the compressor low-pressure pressure, the compressor high-pressure pressure and the compressor pressure ratio. The known working condition parameters and the real-time working condition parameters include the same types of parameters.

[0062] In some embodiments, the step of "constructing a training data set according to the known working condition parameters" specifically includes: data cleaning of the known working condition parameters; obtaining target working condition parameters according to the cleaned real-time working condition parameters; and constructing a training data set according to the target working condition parameters.

[0063] The error data may have a negative impact on the training of the model, and the abnormal values in the data can be eliminated through data cleaning. The training data set is usually composed of input features and corresponding output features. In the dirty block prediction model, the input features are the target working condition parameters, and the output features are the corresponding dirty block states. By data cleaning of the dirty block working condition parameters, non-steady state data in the dirty block working condition parameters can be eliminated, and then the data features are extracted to construct the data set, which is conducive to further improving the accuracy of the dirty block condition prediction.

[0064] In a second aspect, the present application provides an air conditioner, which comprises a controller configured to execute the air conditioner self-cleaning control method described above. In the case of adopting the technical solution described above, the air conditioner provided by the present application obtains a dirty blockage prediction model and real-time working condition parameters of the air conditioner, and analyzes the real-time working condition parameters by using the dirty blockage prediction model to obtain a dirty blockage state of the heat exchanger, and then controls the air conditioner to run a corresponding self-cleaning strategy according to the dirty blockage state. In this way, the air conditioner provided by the present application can timely find out whether the heat exchanger is dirty and blocked, and timely performs self-cleaning when the heat exchanger is dirty and blocked, thereby avoiding rapid decline of the cooling and heating capacity of the air conditioner, and being beneficial to improving the user experience. At the same time, the present application also does not need to be regularly detected by after-sales service, which is beneficial to saving labor costs.

[0065] The present application provides a computer-readable storage medium, which stores a computer program, wherein the computer program executes the air conditioner self-cleaning control method described above.

[0066] The computer-readable storage medium described above can be a transitory computer-readable storage medium or a non-transitory computer-readable storage medium.

[0067] In combination with FIG. 4, the present application provides an electronic device, which comprises a memory 901 and a processor 900, the memory stores a computer program, and the processor can call and run the computer program to execute the air conditioner self-cleaning control method described above. Optionally, the device can further comprise a communication interface 902 and a bus 903. The processor 900, the communication interface 902 and the memory 901 can complete mutual communication through the bus 903. The communication interface 902 can be used for information transmission. The processor 900 can call the logical instructions in the memory 901 to execute the control method for long-term energy saving of the air conditioner in the above-described embodiments.

[0068] In addition, the logical instructions in the memory 901 described above can be implemented in the form of a software functional unit and sold or used as an independent product, which can be stored in a computer-readable storage medium.

[0069] The memory 901 as a computer-readable storage medium can be used to store software programs, computer executable programs, such as program instructions / modules corresponding to the method in the embodiments of the present application. The processor executes the program instructions / modules stored in the memory to perform functional applications and data processing, that is, to implement the control method for long-term energy saving of the air conditioner in the above-described embodiments.

[0070] The memory can include a program storage area and a data storage area, wherein the program storage area can store an operating system, application programs required by at least one function; the data storage area can store data created according to the use of the terminal device, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory.

[0071] The technical solutions of the embodiments of the present disclosure can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes one or more instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiments of the present disclosure. The aforementioned storage medium can be a non-transitory storage medium, including: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, etc. various media that can store program codes, or can be a transitory storage medium.

[0072] Although the above embodiments describe each step in the above-mentioned order, those skilled in the art can understand that, in order to achieve the effect of the embodiments, the different steps do not have to be executed in such an order, and they can be executed simultaneously (in parallel) or in a reversed order, and these simple changes are within the protection scope of the present application.

[0073] So far, the technical solutions of the present application have been described in combination with the preferred embodiments shown in the drawings, but those skilled in the art can easily understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to the related technical features without departing from the principles of the present application, and the technical solutions after these changes or replacements will fall within the protection scope of the present application.

Claims

1. A control method of self-cleaning of an air conditioner including a heat exchanger, the control method comprising: determining whether a predetermined condition for self-cleaning is satisfied; and performing self-cleaning of the heat exchanger when the predetermined condition for self-cleaning is satisfied. The control method comprises: obtaining real-time working condition parameters of the air conditioner and a pre-established dirty blockage prediction model; obtaining a dirty blockage state of the heat exchanger according to the real-time working condition parameters and the dirty blockage prediction model; controlling the air conditioner to run a corresponding self-cleaning strategy according to the dirty blockage state.

2. The control method according to claim 1, characterized by, The step of "obtaining a dirty blockage state of the heat exchanger according to the real-time working condition parameters and the dirty blockage prediction model" specifically comprises: calculating target working condition parameters according to the real-time working condition parameters; taking the target working condition parameters as inputs of the dirty blockage prediction model and taking outputs of the dirty blockage prediction model as the dirty blockage state.

3. The control method according to claim 2, characterized by, The step of "calculating target working condition parameters according to the real-time working condition parameters" specifically comprises: performing data cleaning on the real-time working condition parameters; obtaining target working condition parameters according to the cleaned real-time working condition parameters.

4. The control method according to claim 3, characterized by The step of "performing data cleaning on the real-time working condition parameters" specifically comprises: obtaining signal data corresponding to the real-time working condition parameters; determining a filter window and calculating a median absolute deviation of signal data in the filter window; judging the validity of the signal data in the filter window according to the median absolute deviation; in the case that the signal data is invalid, replacing the invalid signal data with a median of the signal data in the filter window; sliding the filter window backward and sequentially processing the signal data to clean the real-time working condition parameters.

5. The control method according to claim 4, characterized by The median absolute deviation of the signal data in the filter window is calculated according to the following equation: MAD = Median{|X i -med|}; wherein MAD is the median absolute deviation of the signal data, the filter window comprises k signal data, X i is the i-th signal data, k and i are constants, and 1≤i≤k, and med is the median of the signal data in the filter window.

6. The control method according to claim 5, characterized by The step of "judging the validity of the signal data in the filter window" specifically comprises: In |X i In case of |med| > t0*MAD, the signal data is determined to be invalid; In |X i In case of |med|≤t0*MAD, the signal data is determined to be valid; wherein t0 is a constant.

7. The control method according to any one of claims 1 to 6, characterized by, The real-time working condition parameters comprise one or more of exhaust temperature, suction temperature, defrost sensor temperature, external environment temperature, total current, compressor speed, outdoor fan speed, external coil temperature, auxiliary valve opening, heat exchanger inlet temperature, heat exchanger outlet temperature, compressor oil temperature, compressor low-pressure pressure, compressor high-pressure pressure, and compressor pressure ratio.

8. An air conditioner comprising a controller, characterized by The controller is configured to perform the control method of air conditioner self-cleaning according to any one of claims 1 to 7.

9. A computer readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program performs the control method of air conditioner self-cleaning according to any one of claims 1 to 7 when running. 10.An electronic device comprising a memory and a processor, the electronic device characterized by, The memory stores a computer program, and the processor can invoke and run the computer program to perform the control method of air conditioner self-cleaning according to any one of claims 1 to 7.

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