WO2022252720A1

WO2022252720A1 - Air conditioner control method and device, and air conditioner

Info

Publication number: WO2022252720A1
Application number: PCT/CN2022/078607
Authority: WO
Inventors: 郭继宾; 李恒元; 李艳春; 路炎; 封荣杰
Original assignee: 青岛海尔空调器有限总公司; 青岛海尔空调电子有限公司; 海尔智家股份有限公司
Priority date: 2021-05-31
Filing date: 2022-03-01
Publication date: 2022-12-08
Also published as: CN113357773A

Abstract

The present application relates to the technical field of smart home appliances. Disclosed is an air conditioner control method, comprising: obtaining the current motion data of a user, and inputting the current motion data into a preset classification model for determining a motion type of the user; determining a target operation parameter of an air conditioner according to a predicted motion type output by the preset classification model and the current physical sign parameter of the user; and controlling the air conditioner to operate under the target operation parameter. In this way, the operation of the air conditioner is more accurately regulated, so that the air conditioner can better meet a real-time motion requirement of a user, thereby improving user experience. Also disclosed in the present application are an air conditioner control device and an air conditioner.

Description

Method, device and air conditioner for air conditioner control

This application is based on a Chinese patent application with application number 202110603490.4 and a filing date of May 31, 2021, and claims the priority of this Chinese patent application. The entire content of this Chinese patent application is hereby incorporated by reference into this application.

technical field

The present application relates to the technical field of smart home appliances, for example, to a method and device for controlling an air conditioner, and an air conditioner.

Background technique

At present, with the advancement of technology and the improvement of people's living standards, more and more people start to pay attention to the development of smart home and pursue a more intelligent home appliance control experience. Taking the air conditioner as an example, the user can send operation instructions to the air conditioner through the air conditioner remote control or a smart terminal, such as a mobile phone, so that the air conditioner operates under the operation information set by the user. This method requires manual operation by the user, which is relatively cumbersome, and the degree of intelligence of the air conditioner is not high.

In order to improve the intelligence of the air conditioner, in the existing air conditioner control scheme, the operating parameters of the air conditioner can be adjusted according to the physical sign information of the user. This scheme of adjusting the operating parameters of the air conditioner only according to the user's signs has a single adjustment method, which may not accurately meet the user's use needs, so the user's use experience is not good.

Contents of the invention

In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is presented below. The summary is not intended to be an extensive overview nor to identify key/important elements or to delineate the scope of these embodiments, but rather serves as a prelude to the detailed description that follows.

Embodiments of the present disclosure provide a method and device for controlling an air conditioner, and an air conditioner, so as to accurately adjust user requirements and improve user experience.

In some embodiments, the method for controlling the air conditioner includes: obtaining the current exercise data of the user, inputting the current exercise data into a preset classification model for determining the user's exercise type; outputting the predicted exercise according to the preset classification model type and the current physical parameters of the user, determine the target operating parameters of the air conditioner; control the air conditioner to operate under the target operating parameters.

In some embodiments, the preset classification model is obtained by obtaining the first sample for training the preset classification model, and dividing the first sample into a training set and a test set, the first sample includes Historical sports data of different sports types; input the historical sports data in the training set to the initial classification model, and use the sports types marked by the historical sports data in the training set as the output of the initial classification model to train the initial classification model; according to the test set, verify the trained initial classification model, and obtain the verification result; when the accuracy rate of the verification result is greater than or equal to the preset accuracy rate, the preset classification model is obtained.

In some embodiments, the historical motion data is obtained in the following manner: Acceleration data and angular velocity data at various moments under different motion types are obtained; feature extraction is performed on the acceleration data and angular velocity data, and multiple sets of feature data obtained are used as historical motion data .

In some embodiments, obtaining the acceleration data and angular velocity data at each moment under different motion types includes: collecting the first acceleration data corresponding to each moment under different motion types, and the second angular velocity corresponding to the previous moment adjacent to each moment data, and the third angular velocity data corresponding to the next moment adjacent to each moment; linear interpolation operation is performed on the second angular velocity data and the third angular velocity data to obtain the first angular velocity data corresponding to each moment.

In some embodiments, according to the predicted exercise type output by the preset classification model and the user's current physical sign parameters, determining the target operating parameters of the air conditioner includes: obtaining the correlation between the user's physical sign parameters and the air conditioner operating parameters under different predicted exercise types relationship; according to the type of predicted motion, determine the target operating parameter corresponding to the current sign parameter from the association relationship.

In some embodiments, according to the predicted motion type, determining the target operating parameter corresponding to the current physical sign parameter from the association includes: when the current physical sign parameter is greater than or equal to the first threshold, setting the target operating temperature in the target operating parameter to It is determined as the first operating temperature; when the current physical sign parameter is less than the first threshold, the target operating temperature is determined as the second operating temperature; wherein, the first operating temperature is lower than the second operating temperature.

In some embodiments, after controlling the air conditioner to operate under the target operating parameters, it further includes: obtaining new current physical sign parameters when determining that the operating time of the air conditioner at the target operating temperature is greater than or equal to the preset operating time; If the current sign parameter is still greater than the first threshold, the target operating wind speed in the target operating parameters is increased.

In some embodiments, the device for air conditioning control includes an obtaining module, a determining module and a controlling module. The obtaining module is configured to obtain the user's current exercise data, and input the current exercise data to a preset classification model for determining the user's exercise type; the determination module is configured to output the predicted exercise type and the user's current physical signs according to the preset classification model parameters to determine target operating parameters of the air conditioner; the control module is configured to control the air conditioner to operate under the target operating parameters.

In some embodiments, the device for air conditioning control includes a processor and a memory storing program instructions. The processor is configured to execute the above method for air conditioner control when executing the program instructions.

In some embodiments, the air conditioner includes the above-mentioned device for air conditioner control.

The method, device and air conditioner for air conditioner control provided by the embodiments of the present disclosure can achieve the following technical effects:

By obtaining the user's current exercise data and inputting the current exercise data into the preset classification model used to determine the user's exercise type to obtain the predicted exercise type output by the preset classification model, the user's exercise type can be accurately predicted, Improve the intelligence of the air conditioner; determine the target operating parameters of the air conditioner based on the predicted exercise type and the current physical parameters of the user, so as to control the operation of the air conditioner under the target operating parameters, which helps to achieve more precise control of the operation of the air conditioner, so that the air conditioner can Better meet the user's real-time motion needs, thereby improving the user experience.

The foregoing general description and the following description are exemplary and explanatory only and are not intended to limit the application.

Description of drawings

One or more embodiments are exemplified by the corresponding drawings, and these exemplifications and drawings do not constitute a limitation to the embodiments, and elements with the same reference numerals in the drawings are shown as similar elements, The drawings are not limited to scale and in which:

Fig. 1 is a schematic diagram of a method for air conditioning control provided by an embodiment of the present disclosure;

Fig. 2 is a schematic diagram of a device for air conditioning control provided by an embodiment of the present disclosure;

Fig. 3 is a schematic diagram of an apparatus for controlling an air conditioner provided by an embodiment of the present disclosure.

Detailed ways

In order to understand the characteristics and technical content of the embodiments of the present disclosure in more detail, the implementation of the embodiments of the present disclosure will be described in detail below in conjunction with the accompanying drawings. The attached drawings are only for reference and description, and are not intended to limit the embodiments of the present disclosure. In the following technical description, for purposes of explanation, numerous details are set forth in order to provide a thorough understanding of the disclosed embodiments. However, one or more embodiments may be practiced without these details. In other instances, well-known structures and devices may be shown simplified in order to simplify the drawings.

The terms "first", "second" and the like in the description and claims of the embodiments of the present disclosure and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific sequence or sequence. It should be understood that the data so used may be interchanged under appropriate circumstances so as to facilitate the embodiments of the disclosed embodiments described herein. Furthermore, the terms "comprising" and "having", as well as any variations thereof, are intended to cover a non-exclusive inclusion.

Unless stated otherwise, the term "plurality" means two or more.

In the embodiments of the present disclosure, the character "/" indicates that the preceding and following objects are an "or" relationship. For example, A/B means: A or B.

The term "and/or" is an associative relationship describing objects, indicating that there can be three relationships. For example, A and/or B means: A or B, or, A and B, these three relationships.

Fig. 1 is a schematic diagram of a method for controlling an air conditioner provided by an embodiment of the present disclosure. As shown in FIG. 1 , an embodiment of the present disclosure provides a method for air conditioning control, which may include:

S11, the air conditioner obtains the current exercise data of the user, and inputs the current exercise data into a preset classification model for determining the user's exercise type.

Here, the current motion data of the user may at least include current acceleration data and current angular velocity data during the user's motion. If the user wears a wearable device equipped with an accelerometer and a gyroscope, such as a smart bracelet, the current acceleration data and current angular velocity data can be obtained conveniently and quickly.

As an example, the control terminal associated with the user's smart terminal obtains the acquisition control command and sends it to the client associated with the wearable device; the control terminal performs linear interpolation on the current exercise data submitted by the client and the time stamp information associated with the current exercise data Operation, so that the current angular velocity data in the current motion data and the time stamp information of the current acceleration data are kept in sync. In this way, the current motion data can be obtained conveniently and accurately, and the current motion data can be reconstructed by linear interpolation to improve the smoothness of the current motion data, thereby avoiding distortion problems caused by discontinuous data and uneven time stamps.

Wherein, the time stamp information may refer to the current moment corresponding to the current exercise data.

The user intelligent terminal mentioned above is, for example, a mobile device, a computer, or a vehicle-mounted device built in a floating vehicle, or any combination thereof. In some embodiments, the mobile device may include, for example, a mobile phone, a smart mobile device, etc., or any combination thereof.

The user's smart terminal and the wearable device can establish a connection through wireless communication to realize the judgment of the user's location. The manner of wireless communication may at least include one or more of Wi-Fi connection, Zigbee protocol connection and Bluetooth connection.

Understandably, the control terminal performs linear interpolation on the current exercise data submitted by the client and the timestamp information associated with the current exercise data, which may include:

Among them, t is the current moment, t ₁ is the previous moment adjacent to the current moment t, t ₂ is the next moment adjacent to the current moment t, G(t) is the angular velocity corresponding to the current moment t, G(t ₁ ) is the angular velocity corresponding to the previous time t ₁ adjacent to the current time t, and G(t ₂ ) is the angular velocity corresponding to the next time t ₂ adjacent to the current time t.

In this way, the angular velocity data synchronized with the acceleration data at the current moment after data reconstruction can be accurately obtained.

Correspondingly, the client associated with the wearable device responds to the collection control instruction sent by the control terminal associated with the user's smart terminal, and collects the current motion data according to the preset collection frequency; the client obtains the time stamp information associated with the current motion data, and sends the current The motion data and the time stamp information associated with the current motion data are submitted to the control terminal.

Wherein, the preset collection frequency may be to collect data once every 10 milliseconds.

Optionally, the preset classification model can be obtained in the following manner: the air conditioner obtains a first sample for training the preset classification model, and divides the first sample into a training set and a test set, and the first sample includes a label There are historical sports data of different sports types; the air conditioner inputs the historical sports data in the training set to the initial classification model, and uses the sports types marked by the historical sports data in the training set as the output of the initial classification model to train the initial classification model ; The air conditioner verifies the trained initial classification model according to the test set, and obtains a verification result; when the accuracy rate of the verification result is greater than or equal to the preset accuracy rate, the air conditioner obtains the preset classification model. In this way, the classification model algorithm is introduced into the intelligent control logic of the air conditioner, so that the air conditioner can accurately predict the user's movement type, and the degree of intelligence of the air conditioner is improved.

Wherein, the number ratio of the training set and the testing set in the first sample may be 9:1. The preset accuracy rate may be 90%-95%.

In some embodiments, the different types of exercise marked by the historical exercise data can be embodied as running, cycling, push-ups, rope skipping, yoga, squats, etc.

The initial classification model may be a classification prediction model in a machine learning algorithm, such as a decision tree algorithm, a naive Bayesian algorithm, an artificial neural network algorithm, a K-nearest neighbor algorithm, or a support vector machine algorithm.

Optionally, historical motion data can be obtained in the following manner: the air conditioner obtains acceleration data and angular velocity data at various moments under different motion types; the air conditioner extracts features from the acceleration data and angular velocity data, and obtains multiple sets of feature data as Historical exercise data. In this way, the preprocessing of data dimension reduction is performed on the sample data, which helps to promote the subsequent learning and generalization steps, and improves the prediction accuracy of the preset classification model.

Here, the acceleration data is three-axis acceleration data; the angular velocity data is three-axis angular velocity data. Correspondingly, if the first sample is an m×n matrix, it is denoted as (a _ij ); where m represents the amount of data at each moment; n=6, representing 3 channels of acceleration data and 3 channels of angular velocity data ; i=1,...,m; j=1,...,n; then, the feature matrix corresponding to the first sample is a 18×6 matrix, denoted as (b _ij ); where i=1,...,18, means The extracted 18 features; j=1,...,6, representing 6 channels.

It can be understood that the feature extraction _{of the above acceleration data and angular velocity data can be to extract the absolute mean value b 1j} _, absolute value mean ratio b _2j , variance RMS, kurtosis Kurt, skewness Skew, mean Square root b _6j , mean absolute deviation MAD, zero-crossing rate ZCR, energy Energy, correlation coefficient correlation, model coefficients λ2, λ3 and λ4, interquartile difference Q, wavelet energy WE, fractal dimension d, wavelet peak-mean M and One or more of the number N of small peaks.

Among them, the model coefficients λ ₂ , λ ₃ and λ ₄ can be obtained by the following methods: performing autoregressive modeling on the respective acceleration data or angular velocity data of multiple channels to obtain an autoregressive model; using the Burger algorithm to determine the model of the autoregressive model Coefficients λ ₂ , λ ₃ and λ ₄ .

The wavelet energy WE can be obtained in the following way: using the db5 wavelet as the mother wavelet, decompose the transformed data of the acceleration data or angular velocity data of multiple channels after wavelet transformation, and obtain the number of decomposition layers and the decomposed wavelet detail coefficients; The sum of the squares of the wavelet detail coefficient components of the 4th layer and the 5th layer is regarded as the wavelet energy WE.

The wavelet peak-mean value M and the number of wavelet peaks N can be obtained in the following way: use db4 wavelet to decompose the acceleration amplitude in the acceleration data and the angular velocity amplitude in the angular velocity data respectively to 7 layers, and obtain multiple approximation coefficients of the fourth layer Peak value; according to multiple peak values, determine the mean value M of wavelet peaks and the number N of wavelet peaks.

Optionally, the air conditioner obtains the acceleration data and angular velocity data at each moment under different motion types, which may include: the air conditioner collects the first acceleration data corresponding to each moment under different motion types, and the first acceleration data corresponding to the previous moment adjacent to each moment. The second angular velocity data and the third angular velocity data corresponding to the next moment adjacent to each moment; the air conditioner performs linear interpolation operation on the second angular velocity data and the third angular velocity data to obtain the first angular velocity data corresponding to each moment. The historical motion data is reconstructed by linear interpolation, which improves the smoothness of the historical motion data, thereby avoiding signal distortion problems caused by discontinuous data and uneven time stamps.

Wherein, for a specific implementation process of performing linear interpolation operation on historical motion data, reference may be made to the implementation manner of performing linear interpolation operation on current motion data in the above-mentioned embodiments, which will not be repeated here.

S12. The air conditioner determines its target operating parameters according to the predicted exercise type output by the preset classification model and the current physical sign parameters of the user.

Here, if the user wears a wearable device equipped with a physical sign sensor, such as a smart bracelet, the user's current physical sign parameters can be obtained conveniently and quickly.

Optionally, the air conditioner determines the target operating parameters of the air conditioner according to the predicted exercise type output by the preset classification model and the user's current physical sign parameters, which may include: the air conditioner obtains the difference between the user's physical sign parameters and its operating parameters under different predicted exercise types. The air conditioner determines the target operating parameters corresponding to the current physical sign parameters from the correlation relationship according to the predicted motion type. In this way, the target operating parameters of the air conditioner can be determined according to the predicted exercise type and the user's current physical sign parameters, so as to achieve more precise regulation of the operation of the air conditioner, so that the air conditioner can better meet the user's real-time exercise needs, thereby improving the user experience .

Optionally, the air conditioner determines the target operating parameters corresponding to the current physical sign parameters from the association relationship according to the predicted motion type, which may include: when the current physical sign parameters are greater than or equal to the first threshold, the air conditioner converts the target operating parameters to The target operating temperature is determined as the first operating temperature; when the current physical sign parameter is smaller than the first threshold, the air conditioner determines the target operating temperature as the second operating temperature; wherein the first operating temperature is lower than the second operating temperature. In this way, the operation of the air conditioner can be regulated more precisely, so that the air conditioner can better meet the user's real-time exercise needs, thereby improving the user experience.

Because the human body is exercising, the heart rate, body temperature and blood pressure generally increase gradually with the increase of exercise intensity until they become stable. Accordingly, the current sign parameters may include one or more of current heart rate, current body temperature, and current blood pressure. Here, if any parameter in the current physical sign parameters is greater than or equal to its corresponding first threshold value, then the target operating temperature in the target operating parameters is determined as the first operating temperature; , then the target operating temperature is determined as the second operating temperature.

Correspondingly, the first threshold corresponding to the current heart rate in the current sign parameter is 100 beats/minute. The first threshold corresponding to the current body temperature in the current sign parameter is 37°C. The first threshold corresponding to the current blood pressure in the current sign parameters is 130/85 mmHg, that is, the systolic blood pressure is 130 mmHg and the diastolic blood pressure is 85 mmHg.

S13, the air conditioner controls the air conditioner to operate under the target operating parameters.

Optionally, after the air conditioner controls it to run under the target operating parameters, it may also include: when the air conditioner determines that the operating time of the air conditioner at the target operating temperature is greater than or equal to the preset operating time, obtaining new current sign parameters ; If the new current sign parameter is still greater than the first threshold, the air conditioner increases the target operating wind speed in the target operating parameters.

Here, the value range of the preset running time may be 10 minutes to 60 minutes. Preferably it is 30 minutes. In this way, excessive adjustment or ineffective adjustment of the operating parameters of the air conditioner can be avoided, thereby improving the intelligence of the air conditioner and the use experience of the user.

To sum up, using the method for air-conditioning control provided by the embodiments of the present disclosure, the preset classification can be obtained by obtaining the user's current exercise data and inputting the current exercise data into the preset classification model used to determine the user's exercise type. The predicted exercise type output by the model can accurately predict the user's exercise type and improve the intelligence of the air conditioner; determine the target operating parameters of the air conditioner according to the predicted exercise type and the user's current physical parameters, so as to control the air conditioner under the target operating parameters It helps to more precisely regulate the operation of the air conditioner, so that the air conditioner can better meet the user's real-time exercise needs, thereby improving the user's experience.

As shown in FIG. 2 , an embodiment of the present disclosure provides an apparatus for controlling an air conditioner, including an obtaining module 21 , a determining module 22 and a control module 23 . The obtaining module 21 is configured to obtain the user's current exercise data, and input the current exercise data to a preset classification model for determining the user's exercise type; the determination module 22 is configured to output the predicted exercise type according to the preset classification model and the user's The current sign parameters determine the target operating parameters of the air conditioner; the control module 23 is configured to control the air conditioner to operate under the target operating parameters.

Using the device for air conditioner control provided by the embodiments of the present disclosure, through the cooperation of the acquisition module, the determination module and the control module, it is helpful to realize more precise regulation and control of the operation of the air conditioner, so that the air conditioner can better meet the real-time needs of users. Exercise needs, thereby improving the user experience.

As shown in FIG. 3 , an embodiment of the present disclosure provides an apparatus for controlling an air conditioner, including a processor (processor) 100 and a memory (memory) 101 . Optionally, the device may also include a communication interface (Communication Interface) 102 and a bus 103. Wherein, the processor 100 , the communication interface 102 , and the memory 101 can communicate with each other through the bus 103 . Communication interface 102 may be used for information transfer. The processor 100 can call the logic instructions in the memory 101 to execute the method for air conditioner control in the above embodiments.

In addition, the above logic instructions in the memory 101 may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as an independent product.

As a computer-readable storage medium, the memory 101 can be used to store software programs and computer-executable programs, such as program instructions/modules corresponding to the methods in the embodiments of the present disclosure. The processor 100 executes the program instructions/modules stored in the memory 101 to execute functional applications and data processing, that is, to implement the method for air-conditioning control in the above-mentioned embodiments.

The memory 101 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application program required by at least one function; the data storage area may store data created according to the use of the terminal device, and the like. In addition, the memory 101 may include a high-speed random access memory, and may also include a non-volatile memory.

An embodiment of the present disclosure provides an air conditioner, including the above-mentioned device for controlling an air conditioner.

An embodiment of the present disclosure provides a computer-readable storage medium, which stores computer-executable instructions, and the computer-executable instructions are configured to execute the above method for controlling an air conditioner.

An embodiment of the present disclosure provides a computer program product, the computer program product includes a computer program stored on a computer-readable storage medium, the computer program includes program instructions, and when the program instructions are executed by a computer, the The computer executes the above method for air conditioner control.

The above-mentioned computer-readable storage medium may be a transitory computer-readable storage medium, or a non-transitory computer-readable storage medium.

The technical solutions of the embodiments of the present disclosure can be embodied in the form of software products, which are stored in a storage medium and include one or more instructions to make a computer device (which can be a personal computer, a server, or a network equipment, etc.) to perform 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: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, etc. A medium that can store program code, or a transitory storage medium.

The above description and drawings sufficiently illustrate the embodiments of the present disclosure to enable those skilled in the art to practice them. Other embodiments may incorporate structural, logical, electrical, procedural, and other changes. The examples merely represent possible variations. Individual components and functions are optional unless explicitly required, and the order of operations may vary. Portions and features of some embodiments may be included in or substituted for those of other embodiments. Also, the terms used in the present application are used to describe the embodiments only and are not used to limit the claims. As used in the examples and description of the claims, the singular forms "a", "an" and "the" are intended to include the plural forms as well unless the context clearly indicates otherwise . Similarly, the term "and/or" as used in this application is meant to include any and all possible combinations of one or more of the associated listed ones. Additionally, when used in this application, the term "comprise" and its variants "comprises" and/or comprising (comprising) etc. refer to stated features, integers, steps, operations, elements, and/or The presence of a component does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groupings of these. Without further limitations, an element defined by the statement "comprising a ..." does not exclude the presence of additional identical elements in the process, method or apparatus comprising said element. Herein, what each embodiment focuses on may be the difference from other embodiments, and the same and similar parts of the various embodiments may refer to each other. For the method, product, etc. disclosed in the embodiment, if it corresponds to the method part disclosed in the embodiment, then the relevant part can refer to the description of the method part.

Those skilled in the art can appreciate that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software may depend on the specific application and design constraints of the technical solution. Said artisans may implement the described functions using different methods for each particular application, but such implementation should not be regarded as exceeding the scope of the disclosed embodiments. The skilled person can clearly understand that for the convenience and brevity of the description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the foregoing method embodiment, which will not be repeated here.

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

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to embodiments of the disclosure. In this regard, each block in a flowchart or block diagram may represent a module, program segment, or part of code that includes one or more Executable instructions. In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks in succession may, in fact, be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality 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 that disclosed in the description, and sometimes there is no specific agreement between different operations or steps. order. For example, two consecutive operations or steps may, in fact, be performed substantially concurrently, or they may sometimes be performed in the reverse order, depending upon the functionality involved. Each block in the block diagrams and/or flowcharts, and combinations of blocks in the block diagrams and/or flowcharts, can be implemented by a dedicated hardware-based system that performs the specified function or action, or can be implemented by dedicated hardware implemented in combination with computer instructions.

Claims

A method for controlling an air conditioner, comprising:

obtaining the current exercise data of the user, and inputting the current exercise data into a preset classification model for determining the user's exercise type;

Determine the target operating parameters of the air conditioner according to the predicted exercise type output by the preset classification model and the current physical sign parameters of the user;

The air conditioner is controlled to operate under the target operating parameters.
The method according to claim 1, wherein the preset classification model is obtained in the following manner:

Obtaining a first sample for training the preset classification model, and dividing the first sample into a training set and a test set, the first sample including historical motion data marked with different motion types;

The historical motion data in the training set is input to the initial classification model, and the sports types marked by the historical motion data in the training set are used as the output of the initial classification model to train the initial classification model;

According to the test set, verifying the trained initial classification model to obtain a verification result;

When the accuracy rate of the verification result is greater than or equal to the preset accuracy rate, the preset classification model is obtained.
The method according to claim 2, wherein the historical motion data is obtained in the following manner:

Obtain acceleration data and angular velocity data at each moment under different motion types;

Multiple sets of feature data obtained by performing feature extraction on the acceleration data and the angular velocity data are used as the historical motion data.
The method according to claim 3, wherein said obtaining acceleration data and angular velocity data at each moment under different motion types comprises:

Collect the first acceleration data corresponding to each moment under different motion types, the second angular velocity data corresponding to the previous moment adjacent to the respective moments, and the third angular velocity data corresponding to the next moment adjacent to the respective moments ;

A linear interpolation operation is performed on the second angular velocity data and the third angular velocity data to obtain the first angular velocity data corresponding to each moment.
The method according to any one of claims 1 to 4, wherein the determining the target operating parameters of the air conditioner according to the predicted motion type output by the preset classification model and the current physical sign parameters of the user includes:

Obtain the correlation between user sign parameters and air conditioner operating parameters under different predicted exercise types;

According to the predicted motion type, the target operating parameter corresponding to the current sign parameter is determined from the association relationship.
The method according to claim 5, wherein, according to the predicted motion type, determining the target operating parameter corresponding to the current physical sign parameter from the association relationship includes:

When the current physical sign parameter is greater than or equal to a first threshold, determining a target operating temperature among the target operating parameters as the first operating temperature;

If the current physical sign parameter is less than the first threshold, determining the target operating temperature as a second operating temperature;

Wherein, the first operating temperature is lower than the second operating temperature.
The method according to claim 6, characterized in that, after controlling the air conditioner to operate under the target operating parameters, further comprising:

When it is determined that the operating time of the air conditioner at the target operating temperature is greater than or equal to the preset operating time, obtain new current physical sign parameters;

If the new current sign parameter is still greater than the first threshold, increase the target operating wind speed in the target operating parameters.
A device for air conditioning control, characterized in that it comprises:

An obtaining module configured to obtain the current exercise data of the user, and input the current exercise data into a preset classification model for determining the user's exercise type;

The determination module is configured to determine the target operating parameters of the air conditioner according to the predicted exercise type output by the preset classification model and the current physical sign parameters of the user;

A control module configured to control the air conditioner to operate under the target operating parameters.
A device for air-conditioning control, comprising a processor and a memory storing program instructions, wherein the processor is configured to execute the program described in any one of claims 1 to 7 when executing the program instructions. The method for air conditioning control described above.
An air conditioner, characterized by comprising the device for air conditioner control according to claim 8 or 9.