Method and device for controlling outdoor fan of air conditioner, air conditioner and storage medium

Abstract

This application relates to the field of air conditioning technology, and discloses a method, apparatus, air conditioner, and storage medium for controlling the outdoor fan of an air conditioner. The method includes: acquiring the current outdoor ambient temperature value of the area where the air conditioner is located and the current operating parameters of the air conditioner; obtaining a current outdoor fan speed value that matches the current outdoor ambient temperature value and the current operating parameters using a machine learning model of the outdoor fan speed; and controlling the operation of the outdoor fan of the air conditioner according to the current outdoor fan speed value. This allows for real-time adjustment of the air conditioner's outdoor fan speed, improving the flexibility of outdoor fan speed adjustment and enhancing the user experience.

Description

Technical Field

[0001] This application relates to the field of air conditioning technology, such as methods, apparatus, air conditioners, and storage media for controlling outdoor fans of air conditioners. Background Technology

[0002] With the popularization of smart technology, smart air conditioners have become an indispensable device in home life. Currently, the control method of the outdoor fan of air conditioners basically adopts a segmented control method based on temperature points and compressor frequency points. For example, taking the air conditioner cooling mode as an example, the outdoor fan has seven fan speed levels, with the fan speed increasing for each level.

[0003] Table 1 shows the correspondence between outdoor ambient temperature, compressor operating frequency, and outdoor fan wind speed rating in related technologies.

[0004]

[0005] Table 1

[0006] In this table, the outdoor ambient temperature value is Tao, the compressor operating frequency value is f, and the wind speed is the wind speed rating of the outdoor fan. Thus, as shown in Table 1, within the set temperature range and the set frequency range, the corresponding wind speed rating of the outdoor fan is the same, meaning the wind speed of the outdoor fan is fixed. This prevents the flexible matching of different outdoor fan wind speeds to different operating conditions. Summary of the Invention

[0007] To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments, but rather as a prelude to the detailed description that follows.

[0008] This disclosure provides a method, apparatus, air conditioner, and storage medium for controlling an outdoor air conditioner fan, in order to address the technical problem that the flexibility of adjusting the wind speed of an outdoor air conditioner fan needs to be improved.

[0009] In some embodiments, the method includes:

[0010] Obtain the current outdoor ambient temperature value of the area where the air conditioner is located and the current operating parameters of the air conditioner;

[0011] The current outdoor fan speed value is obtained by using a machine learning model of the outdoor fan speed, which matches the current outdoor ambient temperature value and the current operating parameters.

[0012] The operation of the outdoor fan of the air conditioner is controlled based on the current outdoor fan speed value.

[0013] In some embodiments, it also includes:

[0014] The optimal speed values ​​of the outdoor fan obtained from tests of different air conditioners under different environmental conditions are used to obtain a training database. The training database includes: the optimal speed values ​​of the outdoor fan, and their corresponding air conditioning equipment parameters, environmental state parameters, and air conditioning operating parameters.

[0015] Based on the training database, a machine learning model for the external fan speed is generated using a deep learning algorithm.

[0016] In some embodiments, after obtaining the current outdoor fan speed value that matches the current outdoor ambient temperature value and the current operating parameters, the method further includes:

[0017] In the training database, the current outdoor ambient temperature value, the current operating parameters, and the current external fan speed value are stored to obtain an updated training database;

[0018] Based on the updated training database, the external fan speed machine learning model is updated using a deep learning algorithm.

[0019] In some embodiments, obtaining the current outdoor fan speed value that matches the current outdoor ambient temperature value and the current operating parameters includes:

[0020] The current outdoor ambient temperature value and the current operating parameters are sent to the platform server. The platform server generates the outdoor fan speed machine learning model based on the stored training database and through a deep learning algorithm. The training database includes: the optimal outdoor fan speed values ​​obtained by testing different air conditioners under different environmental conditions, and their corresponding air conditioning equipment parameters, environmental condition parameters, and air conditioning operating parameters.

[0021] The system receives the current outdoor fan speed value returned by the platform server, which is obtained through the outdoor fan speed machine learning model and matches the current outdoor ambient temperature value and the current operating parameters.

[0022] In some embodiments, the current operating parameters include one or more of the following: current outdoor coil temperature, current exhaust temperature, current compressor operating frequency, etc.

[0023] In some embodiments, the device includes:

[0024] The acquisition module is configured to acquire the current outdoor ambient temperature value of the area where the air conditioner is located and the current operating parameters of the air conditioner;

[0025] The learning output module is configured to obtain the current outdoor fan speed value that matches the current outdoor ambient temperature value and the current operating parameters through an outdoor fan speed machine learning model;

[0026] The control module is configured to control the operation of the outdoor fan of the air conditioner based on the current outdoor fan speed value.

[0027] In some embodiments, the apparatus for controlling an outdoor air conditioner fan includes a processor and a memory storing program instructions, the processor being configured to execute the above-described method for controlling an outdoor air conditioner fan when executing the program instructions.

[0028] In some embodiments, the air conditioner includes the aforementioned device for controlling the outdoor fan of the air conditioner.

[0029] In some embodiments, the storage medium stores program instructions that, when executed, perform the method described above for controlling the outdoor fan of an air conditioner.

[0030] The method, apparatus, and air conditioner for controlling the outdoor fan of an air conditioner provided in this disclosure can achieve the following technical effects:

[0031] The air conditioner can obtain the current outdoor ambient temperature value of the area where the air conditioner is located and the current operating parameters of the air conditioner. Based on machine learning, it matches the fan speed value of the outdoor fan of the air conditioner. In this way, the fan speed of the outdoor fan can be adjusted in real time, which improves the flexibility of the fan speed adjustment and also improves the user experience.

[0032] The above general description and the description below are exemplary and illustrative only and are not intended to limit this application. Attached Figure Description

[0033] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations and drawings do not constitute a limitation on the embodiments. Elements having the same reference numerals in the drawings are shown as similar elements. The drawings are not to be scaled. And wherein:

[0034] Figure 1 This is a schematic flowchart of a method for controlling an outdoor fan of an air conditioner, provided in an embodiment of this disclosure.

[0035] Figure 2 This is a flowchart illustrating a machine learning model for configuring the outdoor fan speed of an air conditioner, provided in an embodiment of this disclosure.

[0036] Figure 3 This is a schematic flowchart of a method for controlling an outdoor fan of an air conditioner, provided in an embodiment of this disclosure.

[0037] Figure 4 This is a schematic diagram of a control device for an outdoor air conditioner fan provided in an embodiment of this disclosure;

[0038] Figure 5This is a schematic diagram of a control device for an outdoor air conditioner fan provided in an embodiment of this disclosure;

[0039] Figure 6 This is a schematic diagram of a control device for an outdoor air conditioner fan provided in an embodiment of this disclosure. Detailed Implementation

[0040] To provide a more detailed understanding of the features and technical content of the embodiments of this disclosure, the implementation of the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for illustrative purposes only and are not intended to limit the embodiments of this disclosure. In the following technical description, for ease of explanation, several details are used to provide a full understanding of the disclosed embodiments. However, one or more embodiments may still be implemented without these details. In other cases, well-known structures and devices may be simplified in their depiction to simplify the drawings.

[0041] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this disclosure described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0042] Unless otherwise stated, the term "multiple" means two or more.

[0043] In this embodiment of the disclosure, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0044] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0045] In this embodiment, the optimal speed values ​​of the outdoor fan obtained from numerous tests of different air conditioners under various environmental conditions are used for machine learning and training to obtain a machine learning model for the outdoor fan speed. This allows for the determination of the corresponding outdoor fan speed value by acquiring the current outdoor ambient temperature and the air conditioner's current operating parameters. This enables real-time adjustment of the outdoor fan speed, improving the flexibility of the adjustment. Furthermore, the machine learning and training process yields highly accurate predictions of the outdoor fan speed. Real-time data such as the current outdoor ambient temperature and operating parameters obtained during air conditioner operation can be stored in the training database, allowing the machine learning model to continuously learn, update, and optimize, further improving the accuracy of the outdoor fan speed prediction output.

[0046] Figure 1 This is a schematic flowchart illustrating a method for controlling an outdoor fan of an air conditioner, provided in an embodiment of this disclosure. Figure 1 As shown, the process of controlling the outdoor fan of the air conditioner includes:

[0047] Step 101: Obtain the current outdoor ambient temperature value of the area where the air conditioner is located and the current operating parameters of the air conditioner.

[0048] In this embodiment of the disclosure, the outdoor unit of the air conditioner is equipped with a temperature acquisition device, which can collect the outdoor ambient temperature value of the area corresponding to the outdoor unit of the air conditioner in real time or at regular intervals. Each time it is collected, the current outdoor ambient temperature value of the area where the air conditioner is located is obtained.

[0049] During operation, the air conditioner can acquire its operating parameters in real time or at regular intervals. In this embodiment, the operating parameters include one or more of the following: outdoor coil temperature Tp, exhaust temperature Tq, and compressor operating frequency F. Each acquired parameter represents the current operating parameter.

[0050] Step 102: Obtain the current outdoor fan speed value that matches the current outdoor ambient temperature and current operating parameters through the outdoor fan speed machine learning model.

[0051] In this embodiment of the disclosure, the air conditioner may locally store a machine learning model with the output parameter being the outdoor fan speed N, i.e., the outdoor fan speed machine learning model, or the platform server that can communicate with the air conditioner may store the outdoor fan speed machine learning model.

[0052] The process of generating and saving the outdoor fan speed machine learning model for the air conditioner or platform server includes: obtaining the optimal speed values ​​of the outdoor fan obtained from tests of different air conditioners under different environmental conditions, and obtaining a training database, which includes: the optimal speed values ​​of the outdoor fan, and their corresponding air conditioning equipment parameters, environmental state parameters, and air conditioning operating parameters; and generating the outdoor fan speed machine learning model based on the training database using a deep learning algorithm.

[0053] For example, the optimal speed values ​​of the outdoor fan obtained from testing various air conditioners under different conditions are used as training data for the libsvmstrain function. This training database for the Support Vector Machine (SVM) algorithm includes the optimal outdoor fan speed values ​​and their corresponding air conditioning equipment parameters, environmental state parameters, and air conditioning operating parameters. Then, after training with the libsvmstrain function and the SVM algorithm, a machine learning model with the outdoor fan speed N as its output parameter can be generated.

[0054] Thus, in some embodiments, if the air conditioner locally stores a machine learning model of the outdoor fan speed, the current outdoor fan speed value that matches the current outdoor ambient temperature value and the current operating parameters can be obtained through the machine learning model of the outdoor fan speed. The obtained current outdoor ambient temperature value and the current operating parameters of the air conditioner can be input into the machine learning model of the outdoor fan speed to output the corresponding current outdoor fan speed value.

[0055] For example, by inputting the current outdoor ambient temperature value Taod, the current outdoor coil temperature value Tpd, the current exhaust temperature value Tqd, and the current compressor operating frequency Fd into the outdoor fan speed machine learning model, the corresponding current outdoor fan speed value Nd can be output.

[0056] In some embodiments, if the platform server stores a machine learning model of the outdoor fan speed, the current outdoor ambient temperature value and the current operating parameters can be sent to the platform server. The platform server generates the machine learning model of the outdoor fan speed based on the stored training database using a deep learning algorithm. The training database includes: the optimal speed values ​​of the outdoor fan obtained by testing different air conditioners under different environmental conditions, and their corresponding air conditioning equipment parameters, environmental state parameters, and air conditioning operating parameters. The platform server receives the current outdoor fan speed value obtained by the machine learning model of the outdoor fan speed, which matches the current outdoor ambient temperature value and the current operating parameters.

[0057] For example, the current outdoor ambient temperature value Taod, the current outdoor coil temperature value Tpd, the current exhaust temperature value Tqd, and the current compressor operating frequency Fd are all sent to the platform server. Then, the platform server can output the corresponding current outdoor fan speed value Nd through the outdoor fan speed machine learning model and send it to the air conditioner. Thus, the air conditioner obtains the current outdoor fan speed value Nd.

[0058] Of course, the deep learning algorithms in this embodiment are not limited to the SVM algorithm. Other deep learning algorithms, such as BP neural network, random forest, decision tree, K-nearest neighbor (KNN) algorithm, etc., can also be used. The process is similar, so they will not be listed one by one.

[0059] Step 103: Control the operation of the outdoor fan of the air conditioner according to the current outdoor fan speed value.

[0060] Different outdoor ambient temperatures, different compressor operating frequencies, or different outdoor ambient temperatures, different outdoor coil temperatures, and different compressor operating frequencies can all correspond to different outdoor fan speeds. Thus, the outdoor fan speed is not fixed or singular at the time, but can flexibly change to the speed most suitable for the outdoor fan to operate, depending on different operating conditions.

[0061] As can be seen, in this embodiment, after obtaining the current outdoor ambient temperature value of the area where the air conditioner is located and the current operating parameters of the air conditioner, the corresponding outdoor fan speed value can be obtained through a machine learning model of the outdoor fan speed. This allows for real-time adjustment of the outdoor fan speed, improving the flexibility of the outdoor fan speed adjustment. Furthermore, integrating machine learning with air conditioning aligns with the era of intelligent Internet of Things, and the accuracy of predicting the outdoor fan speed through machine learning and training is high.

[0062] Of course, after obtaining the current outdoor fan speed value that matches the current outdoor ambient temperature value and the current operating parameters, the process also includes: saving the current outdoor ambient temperature value, the current operating parameters, and the current outdoor fan speed value in the training database to obtain an updated training database; and updating the outdoor fan speed machine learning model based on the updated training database using a deep learning algorithm.

[0063] The air conditioner locally stores the outdoor fan speed machine learning model and the corresponding training database. It can save the current outdoor ambient temperature, current operating parameters, and current outdoor fan speed to the training database in a timely manner. This allows for adaptive learning, and the outdoor fan speed machine learning model can be updated through deep learning algorithms.

[0064] If the platform server stores the outdoor fan speed machine learning model and the corresponding training database, the current outdoor ambient temperature, current operating parameters, and current outdoor fan speed should also be stored in the training database. This allows for adaptive learning, and the outdoor fan speed machine learning model can be updated using deep learning algorithms.

[0065] It is evident that the external fan speed machine learning model can adaptively learn, continuously update and optimize, and further improve the accuracy of the predicted output of the external fan speed machine learning model.

[0066] The following describes the operation process in a specific embodiment, illustrating the control process for an outdoor air conditioner fan provided by the embodiments of the present invention.

[0067] In this embodiment, the air conditioner can obtain a machine learning model of the outdoor fan speed using the SVM algorithm. Furthermore, the air conditioner's operating parameters include: outdoor coil temperature Tp, exhaust temperature Tq, and compressor operating frequency F.

[0068] Figure 2 This is a flowchart illustrating a machine learning model for configuring the outdoor fan speed in an air conditioner, provided in an embodiment of this disclosure. Figure 2 As shown, the process of configuring the machine learning model for the external fan speed includes:

[0069] Step 201: Obtain the optimal speed value of the outdoor fan obtained from tests of different air conditioners under different environmental conditions, and obtain the SVM training database.

[0070] The SVM training database includes: the optimal speed of the outdoor fan, and its corresponding air conditioning equipment parameters, environmental condition parameters, and air conditioning operating parameters. The air conditioning operating parameters include: outdoor coil temperature Tp, exhaust temperature Tq, and compressor operating frequency F.

[0071] Step 202: Train the SVM using the libsvmstrain function on the training data in the training database.

[0072] Step 203: Use the SVM algorithm to perform machine learning on the trained data to generate a machine learning model of the outdoor fan speed with the output parameter being the outdoor fan speed N.

[0073] If the air conditioner is equipped with a machine learning model for the outdoor fan speed, the outdoor fan can be controlled during the air conditioner's operation.

[0074] Figure 3 This is a schematic flowchart illustrating a method for controlling an outdoor fan of an air conditioner, provided in an embodiment of this disclosure. Figure 3 As shown, the control process of the outdoor fan of the air conditioner includes:

[0075] Step 301: Obtain the current outdoor ambient temperature value Taod, the current outdoor coil temperature value Tpd, the current exhaust temperature value Tqd, and the current compressor operating frequency Fd of the air conditioner in operation.

[0076] Step 302: Input the current outdoor ambient temperature value Taod, the current outdoor coil temperature value Tpd, the current exhaust temperature value Tqd, and the current compressor operating frequency Fd into the outdoor fan speed machine learning model, and obtain the corresponding current outdoor fan speed value Nd through the SVM algorithm.

[0077] Step 303: Control the operation of the outdoor fan of the air conditioner according to the current outdoor fan speed value Nd.

[0078] Step 304: Save the current outdoor ambient temperature value Taod, the current outdoor coil temperature value Tpd, the current exhaust temperature value Tqd, and the current compressor operating frequency Fd in the SVM training database to obtain the updated SVM training database.

[0079] Step 305: Based on the updated SVM training database, update the external wind turbine speed machine learning model using the SVM algorithm.

[0080] As can be seen, in this embodiment, the optimal speed values ​​of the outdoor fan obtained from testing a large number of different air conditioners under various environmental conditions are used for SVM machine learning and training to obtain an outdoor fan speed machine learning model. Thus, after obtaining the current outdoor ambient temperature and the current operating parameters of the air conditioner in its operating area, the corresponding outdoor fan speed value can be obtained through the outdoor fan speed machine learning model. This allows for real-time adjustment of the outdoor fan speed, improving the flexibility of outdoor fan speed adjustment. Furthermore, the accuracy of predicting the outdoor fan speed is high through machine learning and training. Real-time data such as the current outdoor ambient temperature and current operating parameters obtained during air conditioner operation can also be stored in the training database, enabling continuous learning, updating, and optimization of the machine learning model, further improving the accuracy of the outdoor fan speed machine learning model's predictive output.

[0081] Based on the above process for controlling the outdoor fan of an air conditioner, a device for controlling the outdoor fan of an air conditioner can be constructed.

[0082] Figure 4 This is a schematic diagram of a control device for an outdoor air conditioner fan provided in an embodiment of this disclosure. Figure 4 As shown, the control device for the outdoor fan of an air conditioner includes: an acquisition module 410, a learning output module 420, and a control module 430.

[0083] The acquisition module 410 is configured to acquire the current outdoor ambient temperature value of the area where the air conditioner is located and the current operating parameters of the air conditioner.

[0084] The learning output module 420 is configured to obtain the current outdoor fan speed value that matches the current outdoor ambient temperature value and the current operating parameters through the outdoor fan speed machine learning model.

[0085] The control module 430 is configured to control the operation of the outdoor fan of the air conditioner based on the current outdoor fan speed value.

[0086] In some embodiments, it further includes:

[0087] The data storage module is configured to obtain the optimal speed values ​​of the outdoor fan obtained from tests of different air conditioners under different environmental conditions, and to obtain a training database. The training database includes: the optimal speed values ​​of the outdoor fan, and their corresponding air conditioning equipment parameters, environmental condition parameters, and air conditioning operating parameters.

[0088] The learning generation module is configured to generate a machine learning model of the external fan speed based on the training database using a deep learning algorithm.

[0089] In some embodiments, the data storage module is further configured to save the current outdoor ambient temperature value, current operating parameters, and current external fan speed value in the training database to obtain an updated training database.

[0090] The learning generation module is also configured to update the external wind turbine speed machine learning model based on the updated training database using a deep learning algorithm.

[0091] In some embodiments, the learning output module is further configured to send the current outdoor ambient temperature value and the current operating parameters to the platform server. The platform server generates a machine learning model of the outdoor fan speed based on a stored training database using a deep learning algorithm. The training database includes: the optimal speed values ​​of the outdoor fan obtained from tests of different air conditioners under different environmental conditions, and their corresponding air conditioning equipment parameters, environmental state parameters, and air conditioning operating parameters. The module receives the current outdoor fan speed value returned by the platform server, which matches the current outdoor ambient temperature value and the current operating parameters, obtained through the machine learning model of the outdoor fan speed.

[0092] The following describes the control process of the outdoor fan of an air conditioner for an outdoor fan control device in conjunction with embodiments.

[0093] In this embodiment of the disclosure, the operating parameters of the air conditioner include: compressor operating frequency F.

[0094] Figure 5 This is a schematic diagram of a control device for an outdoor air conditioner fan provided in an embodiment of this disclosure. Figure 5 As shown, the air conditioner outdoor fan control device includes: an acquisition module 410, a learning output module 420, and a control module 430. It also includes: a data storage module 440 and a learning generation module 450.

[0095] In this way, the data storage module 440 obtains the optimal speed values ​​of the outdoor fan obtained from tests of different air conditioners under different environmental conditions, and obtains the SVM training database. The operating parameters of the air conditioner include the compressor operating frequency F. The learning and generation module 450 can then train the training data in the SVM training database through the libsvmstrain function, and perform machine learning on the trained data through the SVM algorithm to generate an outdoor fan speed machine learning model with the outdoor fan speed N as the output parameter.

[0096] Thus, after the air conditioner is turned on, the acquisition module 410 can obtain the current outdoor ambient temperature value Taod and the current compressor operating frequency Fd. The learning output module 420 can then input the current outdoor ambient temperature value Taod and the current compressor operating frequency Fd into the outdoor fan speed machine learning model, and obtain the corresponding current outdoor fan speed value Nd through the SVM algorithm. Therefore, the control module 430 controls the operation of the air conditioner's outdoor fan based on the current outdoor fan speed value Nd.

[0097] Of course, the data storage module 440 can also save the current outdoor ambient temperature value Taod and the current compressor operating frequency Fd in the SVM training database to obtain an updated SVM training database. Then, the learning generation module 450 updates the outdoor fan speed machine learning model based on the updated SVM training database using the SVM algorithm.

[0098] As can be seen, in this embodiment, the device for controlling the outdoor fan of the air conditioner uses the optimal speed values ​​of the outdoor fan obtained from testing a large number of different air conditioners under different environmental conditions to perform machine learning and training, resulting in a machine learning model for the outdoor fan speed. Therefore, after the air conditioner is turned on, the device obtains the current outdoor ambient temperature value and the current operating parameters of the air conditioner in the area where the air conditioner is located. Through the outdoor fan speed machine learning model, the corresponding outdoor fan speed value can be obtained, thus allowing for real-time adjustment of the outdoor fan speed and improving the flexibility of the outdoor fan speed adjustment. Furthermore, the accuracy of predicting the outdoor fan speed is high through machine learning and training. Real-time data such as the current outdoor ambient temperature value and current operating parameters obtained during air conditioner operation can also be stored in the training database, allowing the machine learning to continuously learn, update, and optimize, further improving the accuracy of the outdoor fan speed machine learning model's predictive output.

[0099] This disclosure provides a device for controlling an outdoor fan of an air conditioner, the structure of which is as follows: Figure 6 As shown, it includes:

[0100] The processor 1000 and memory 1001 may further include a communication interface 1002 and a bus 1003. The processor 1000, communication interface 1002, and memory 1001 can communicate with each other via the bus 1003. The communication interface 1002 can be used for information transmission. The processor 1000 can call logical instructions stored in the memory 1001 to execute the method for controlling the outdoor fan of an air conditioner according to the above embodiment.

[0101] Furthermore, the logic instructions in the aforementioned memory 1001 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0102] The memory 1001, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of this disclosure. The processor 1000 executes functional applications and data processing by running the program instructions / modules stored in the memory 1001, that is, it implements the method for controlling the outdoor fan of an air conditioner in the above method embodiments.

[0103] The memory 1001 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 1001 may include high-speed random access memory and may also include non-volatile memory.

[0104] This disclosure provides an outdoor fan control device for an air conditioner, including: a processor and a memory storing program instructions, wherein the processor is configured to execute a method for controlling an outdoor fan of an air conditioner when executing the program instructions.

[0105] This disclosure provides an air conditioner, including the above-described control device for the outdoor fan of the air conditioner.

[0106] This disclosure provides a storage medium storing program instructions that, when executed, perform the method described above for controlling an outdoor fan of an air conditioner.

[0107] This disclosure provides a computer program product, which includes a computer program stored on a storage medium. The computer program includes program instructions, which, when executed by a computer, cause the computer to perform the above-described method for controlling an outdoor fan of an air conditioner.

[0108] The aforementioned storage medium can be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.

[0109] The technical solutions of this disclosure can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes one or more instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in this disclosure. The aforementioned storage medium can be a non-transitory storage medium, including: a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and other media capable of storing program code; it can also be a transient storage medium.

[0110] The foregoing description and accompanying drawings fully illustrate embodiments of the present disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operation may vary. Parts and features of some embodiments may be included or replace parts and features of other embodiments. The scope of the embodiments of this disclosure includes the entire scope of the claims and all available equivalents of the claims. While the terms “first,” “second,” etc., may be used in this application to describe elements, these elements should not be limited by these terms. These terms are used only to distinguish one element from another. For example, a first element may be called a second element without changing the meaning of the description, and similarly, a second element may be called a first element, provided that all occurrences of “first element” are consistently renamed and all occurrences of “second element” are consistently renamed. First and second elements are both elements, but may not be the same element. Moreover, the terminology used in this application is only for describing embodiments and is not intended to limit the claims. As used in the description of the embodiments and claims, unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “the” are intended to also include the plural forms. Similarly, the term “and / or” as used herein means including one or more of the associated listed elements and all possible combinations thereof. Additionally, when used herein, the terms “comprise” and its variations “comprises” and / or “comprising” refer to the presence of stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Without further limitations, an element defined by the phrase “comprising an…” does not exclude the presence of additional identical elements in the process, method, or apparatus that includes said element. In this document, each embodiment may focus on the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, the relevant parts can be referred to the description of the method section.

[0111] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this disclosure. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0112] The methods and products (including but not limited to devices and equipment) disclosed in the embodiments herein can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units may be merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed units may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected to implement this embodiment according to actual needs. Furthermore, the functional units in the embodiments of this disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0113] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

Claims

1. A method for controlling an outdoor fan of an air conditioner, characterized in that, include: Obtain the current outdoor ambient temperature value of the area where the air conditioner is located and the current operating parameters of the air conditioner; The current outdoor fan speed value is obtained by using a machine learning model of the outdoor fan speed, which matches the current outdoor ambient temperature value and the current operating parameters. The operation of the outdoor fan of the air conditioner is controlled according to the current outdoor fan speed value; The process of generating the outdoor fan speed machine learning model includes: obtaining the optimal speed values ​​of the outdoor fans obtained from tests of different air conditioners under different environmental conditions, and obtaining a training database, wherein the training database includes: the optimal speed values ​​of the outdoor fans, and their corresponding air conditioning equipment parameters, environmental state parameters, and air conditioning operating parameters; and generating the outdoor fan speed machine learning model based on the training database using a deep learning algorithm. The current operating parameters include one or more of the following: current outdoor coil temperature, current exhaust temperature, and current compressor operating frequency.

2. The method according to claim 1, characterized in that, After obtaining the current outdoor fan speed value that matches the current outdoor ambient temperature value and the current operating parameters, the method further includes: In the training database, the current outdoor ambient temperature value, the current operating parameters, and the current external fan speed value are stored to obtain an updated training database; Based on the updated training database, the external fan speed machine learning model is updated using a deep learning algorithm.

3. The method according to claim 1, characterized in that, The obtained current outdoor fan speed value, which matches the current outdoor ambient temperature value and the current operating parameters, includes: The current outdoor ambient temperature value and the current operating parameters are sent to the platform server. The platform server generates the outdoor fan speed machine learning model based on the stored training database and through a deep learning algorithm. The training database includes: the optimal outdoor fan speed values ​​obtained by testing different air conditioners under different environmental conditions, and their corresponding air conditioning equipment parameters, environmental condition parameters, and air conditioning operating parameters. The system receives the current outdoor fan speed value returned by the platform server, which is obtained through the outdoor fan speed machine learning model and matches the current outdoor ambient temperature value and the current operating parameters.

4. A device for controlling an outdoor fan of an air conditioner, characterized in that, include: The acquisition module is configured to acquire the current outdoor ambient temperature value of the area where the air conditioner is located and the current operating parameters of the air conditioner; The learning output module is configured to obtain the current outdoor fan speed value that matches the current outdoor ambient temperature value and the current operating parameters through an outdoor fan speed machine learning model; The control module is configured to control the operation of the outdoor fan of the air conditioner based on the current outdoor fan speed value; Also includes: The data storage module is configured to acquire the optimal speed value of the outdoor fan obtained by testing different air conditioners under different environmental conditions, and to obtain a training database. The training database includes: the optimal speed value of the outdoor fan, and its corresponding air conditioning equipment parameters, environmental state parameters, and air conditioning operating parameters. The learning generation module is configured to generate a machine learning model of the external fan speed based on the training database using a deep learning algorithm.

5. The apparatus according to claim 4, characterized in that, The data storage module is also configured to save the current outdoor ambient temperature value, the current operating parameters, and the current external fan speed value in the training database to obtain an updated training database; The learning generation module is also configured to update the external fan speed machine learning model based on the updated training database using a deep learning algorithm.

6. A device for controlling an outdoor fan of an air conditioner, the device comprising a processor and a memory storing program instructions, characterized in that, The processor is configured to perform, when executing the program instructions, the method for controlling an outdoor air conditioning fan as described in any one of claims 1 to 3.

7. An air conditioner, characterized in that, include: The device for controlling the outdoor fan of an air conditioner as described in claim 4 or 6.

8. A storage medium storing program instructions, characterized in that, When the program instructions are executed, they perform the method for controlling the outdoor fan of an air conditioner as described in any one of claims 1 to 3.

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