WO2020258779A1

WO2020258779A1 - Air conditioner control method and control system

Info

Publication number: WO2020258779A1
Application number: PCT/CN2019/127445
Authority: WO
Inventors: 杜超; 徐鹏飞; 刘朋; 王龙跃; 马肖飞
Original assignee: 青岛海尔空调器有限总公司; 海尔智家股份有限公司
Priority date: 2019-06-27
Filing date: 2019-12-23
Publication date: 2020-12-30
Also published as: CN110410964A; CN110410964B

Abstract

An air conditioner control method and control system. For the purpose of solving the problems of a high recognition error rate and poor user experience in existing smart air conditioners. The control method comprises: acquiring historical operation data of an air conditioner; determining the working scenario and/or user type of the air conditioner on the basis of the historical operation data; determining operating parameters of the air conditioner on the basis of the working scenario and/or the user type; and controlling the air conditioner to operate on the basis of the operating parameters. By means of the described control means, the recognition accuracy rate of the smart air conditioner is increased, and the user experience is greatly improved.

Description

Control method and control system of air conditioner

Technical field

The invention relates to the technical field of air conditioning, in particular to a control method and control system of an air conditioner.

Background technique

With the improvement of living standards, people have higher and higher requirements for the living environment, and air conditioners are becoming more and more popular as a household appliance for indoor environment adjustment. In recent years, the development of smart technology has become increasingly fierce, and the term "smart appliances" has also entered people's field of vision. Consumers' requirements for air conditioners and other appliances are no longer as simple as simply realizing their basic functions. In other words, consumers' choice of home appliances is no longer focusing solely on the quality of the product, but more on the experience that the product can bring.

At present, most of the adjustment methods of intelligent air conditioners appearing on the market are adjusted according to the temperature, or adjusted based on the monitoring of the user's human body characteristic parameters. The intelligent control method is simple and extensive, and cannot meet the user's requirements and comfort experience. . The more intelligent air conditioner can control the air conditioner based on user and scene recognition technology. Most of these air conditioners use image acquisition and recognition technology. However, due to the complexity of the technology, high cost, difficulty in implementation, and high requirements for sensors, this technology results in a high error rate in the recognition process, which is extremely bad for users. Experience.

Correspondingly, a new air conditioner control method is needed in the art to solve the above problems.

Summary of the invention

In order to solve the above-mentioned problems in the prior art, that is, to solve the problems of high recognition error rate and poor user experience in the existing smart air conditioners, the present invention provides a control method of an air conditioner, and the control method includes:

Acquiring historical operating data of the air conditioner;

Based on the historical operating data, determine the working scene and/or user type of the air conditioner;

Determining the operating parameters of the air conditioner based on the working scenario and/or the user type;

Based on the operating parameters, the air conditioner is controlled to operate.

In the preferred technical solution of the control method of the above air conditioner, the control method further includes:

Based on the historical operating data, determine the user's comfort preference type;

Determine the bias coefficient based on the comfort preference type;

Based on the bias coefficient, the operating parameter is adjusted.

In the preferred technical solution of the above air conditioner control method, the step of "determining the user's comfort preference type based on the historical operating data" further includes:

Inputting the historical operating data into a pre-trained classification model to obtain the comfort preference type;

Wherein, the classification model is used to characterize the correspondence between the historical operating data and the comfort preference type.

In the preferred technical solution of the control method of the air conditioner, the step of "determining the working scene and/or user type of the air conditioner based on the historical operating data" further includes:

Inputting the historical operation data into a pre-trained classification model to obtain the working scene of the air conditioner and/or the user type;

Wherein, the classification model is used to characterize the correspondence between the historical operation data and the work scenario and/or the user type.

In the preferred technical solution of the control method of the air conditioner, the classification model is a support vector machine model or a neural network model.

In the preferred technical solution of the above air conditioner control method, the historical operating data includes indoor ambient temperature, outdoor ambient temperature, indoor ambient humidity, outdoor ambient humidity, operating period of the air conditioner, air supply angle, air supply intensity and load One or more of power.

In the preferred technical solution of the control method of the above air conditioner, the work scene includes one or more of bedroom, living room, dining room, study room and activity room; and/or the user type includes children, adults, elderly One or more of people; and/or the comfort preference type includes one or more of hot, cold, wet, and dry.

In the preferred technical solution of the control method of the air conditioner, after the step of "controlling the operation of the air conditioner based on the operating parameters", the control method further includes:

Receiving a control instruction for artificially adjusting operating parameters, and adjusting the operating parameters of the air conditioner based on the control instruction;

Based on the adjusted operating parameters, re-determine the working scene and/or user type of the air conditioner.

The present invention also provides a control system of an air conditioner, the control system comprising:

An acquisition module, which is used to acquire historical operating data of the air conditioner;

A classification module, which is used to determine the working scene and/or user type corresponding to the air conditioner based on the historical operating data;

A control module, which determines the operating parameters of the air conditioner based on the work scene and/or user type, and controls the operation of the air conditioner based on the operating parameters.

In the above-mentioned preferred technical solution of the control system of the air conditioner, the classification module is further configured to determine the user's comfort preference type based on the historical operating data;

The control module is further configured to determine an offset coefficient based on the comfort preference type, and adjust the operating parameter based on the offset coefficient.

Those skilled in the art can understand that in the preferred technical solution of the present invention, the control method of the air conditioner includes: acquiring historical operating data of the air conditioner; determining the working scene and/or user type of the air conditioner based on the historical operating data; Determine the operating parameters of the air conditioner based on the work scenario and/or user type; control the operation of the air conditioner based on the operating parameters.

Through the above control method, the control method of the air conditioner of the present application improves the recognition accuracy of the smart air conditioner and greatly improves the user experience. Specifically, by classifying the working scenes and/or user types of air conditioners based on historical operating data, and determining the operating parameters of the air conditioners based on the classification results, the control method of this application only relies on the collection and analysis of historical operating data. It can effectively identify the working scene and/or user type of the air conditioner. This method reduces the technical complexity and difficulty of implementation, improves the accuracy of the air conditioner’s judgment and recognition, and avoids the uncertainty of image recognition technology. The problem of high recognition error rate brings an excellent user experience to users. And using the historical operating data of the air conditioner as the basic data for classification, the control system is simple in design, no need to add a large number of sensors, and it is also helpful to greatly reduce the technical cost.

Further, by determining the user's comfort preference type based on historical operating data, and determining the bias coefficient based on the preference type to adjust the operating parameters of the air conditioner, the control method of the present application can also be used to adjust the work scene and/or user type. On the basis of classification, the user’s comfort preference for using air conditioners is further classified based on historical operating data, so that the operating parameters of the air conditioner are adjusted based on the work scene and/or user type, and the air conditioner is adjusted based on the comfort preference. The operating parameters of the air conditioner are adjusted twice to meet the different preferences of users for using the air conditioner, making the air conditioner more intelligent and personalized, and further improving the user experience. In addition, the user's comfort preference is determined by directly searching for rules from historical operating data, which also solves the problem of directly digitizing personal comfort preference.

Further, when the air conditioner is operating with operating parameters, it receives a control instruction for artificially adjusting operating parameters, adjusts the operating parameters of the air conditioner based on the instructions, and re-determines the working scene and/or user of the air conditioner based on the adjusted operating parameters Type, the air conditioner control method of the present application can also make timely adjustments to the work scene and/or user type when the work scene changes, so as to improve the applicability of the air conditioner.

Description of the drawings

The control method and control system of the air conditioner of the present invention will be described below with reference to the drawings. In the attached picture:

Figure 1 is a flow chart of the control method of the air conditioner of the present invention;

2 is a schematic diagram of the implementation process of the control method of the air conditioner of the present invention.

Detailed ways

Hereinafter, preferred embodiments of the present invention will be described with reference to the drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention. For example, although the various steps are described in a sequential manner in the following embodiments, those skilled in the art can understand that in order to achieve the effects of this embodiment, different steps need not be executed in such an order, and they can be performed at the same time. (Parallel) execution or execution in reverse order, these simple changes are within the protection scope of the present invention.

First, referring to FIG. 1, the XLR estimation method of the air conditioner of the present invention will be described. Among them, FIG. 1 is a flowchart of the control method of the air conditioner of the present invention.

As shown in Figure 1, in order to solve the problems of high recognition error rate and poor user experience existing in existing smart air conditioners, the control method of the air conditioner of the present application mainly includes:

S100. Obtain historical operating data of the air conditioner; for example, the historical operating data in this embodiment includes indoor ambient temperature, outdoor ambient temperature, indoor ambient humidity, outdoor ambient humidity, operating period of the air conditioner, air supply angle, and air supply intensity And load power, etc., during the operation of the air conditioner, the air conditioner obtains and stores the above historical operating data, and calls the above data when this step is executed. Of course, the selection range of historical operating data is not fixed, and those skilled in the art can appropriately increase or decrease based on this embodiment, so that it can be applied to more specific application scenarios, for example, historical operating data also includes operating modes.

S200. Determine the working scene and/or user type of the air conditioner based on historical operating data; for example, the working scene in this embodiment may include bedroom, living room, dining room, study room, and activity room, etc., and user types may include children, adults , The elderly, etc., after obtaining the historical operating data of the air conditioner, the air conditioner analyzes and judges based on the historical operating data, and determines the working scene and user type of the air conditioner. For example, the air conditioner only determines that the working scene of the air conditioner is the living room based on historical operating parameters, or the air conditioner simultaneously determines that the working scene of the air conditioner is the bedroom and the user type is the elderly based on the historical operating parameters. Similarly, the selection range of working scenarios and user types can be adjusted by those skilled in the art based on actual conditions, and the adjustment does not deviate from the protection scope of the present application. For example, the work scene can also include storage rooms or cold storage rooms, and the user types can be further subdivided into young people.

S300. Determine the operating parameters of the air conditioner based on the work scene and/or user type; for example, for each different work scene and user type, set the corresponding work scene and/or user type and operating parameter comparison table in advance And stored in the air conditioner, when the work scene and/or user type are determined, the corresponding operating parameters are directly selected from the comparison table. Those skilled in the art can understand that, although the above-mentioned method of determining the operating parameters is given in the form of a comparison table, the method of determining is not limited to this, and any reasonable method of determining without departing from the principles of this application All are within the protection scope of this application. For example, the method of determining the operating parameters can also be obtained based on empirical formulas, etc. At this time, the operating parameters can be obtained by simply assigning values to the work scene and user type and substituting the assignments into the formula.

S400. Control the operation of the air conditioner based on the operating parameters; for example, after the operating parameters of the air conditioner are determined, the controller of the air conditioner controls the operation of the air conditioner using the operating parameters, thereby realizing different control modes. For example, for an air conditioner working in a bedroom, the controller will gradually reduce its load power at night and increase the temperature parameters of the air conditioner to prevent the user from rising cold due to the decline in sleep metabolism and ensure the comfort of the user in the sleep state; Another example is that the air conditioner in the living room will adjust the set temperature and fan speed according to the change of the outdoor environment; the air conditioner in the restaurant will increase the load power and reduce the temperature during meal time; the air conditioner in the room of children or the elderly will appropriately increase the temperature and reduce the indoor parameters. Sudden changes, and reduce the intensity of the air supply, using the upward air supply method to minimize the additional impact of the air conditioner on the room and increase its comfort experience.

It can be seen from the above description that the control method of the air conditioner of the present application can improve the recognition accuracy of the smart air conditioner and improve the user experience. Specifically, image recognition technology is greatly affected by application scenarios, and errors are likely to occur in the recognition of work scenarios and user types. For example, there may be activities of adults such as parents in the children’s room, and children will also come and go in the parent’s room. Image recognition technology cannot identify and judge complex issues, and the scene division is incorrect due to the variety of room layouts and decorations. The probability of division will be greater. However, the present invention uses the method of judging the work scene and user type based on historical operating data, and does not need to use complex technical methods such as image recognition or infrared sensors, but only relying on the collection and analysis of historical operating data to effectively identify the air conditioner Because the user has a strong habit and regularity in the parameter setting of a specific air conditioner, this method can effectively reduce the complexity and difficulty of recognition, thereby improving the accuracy of the air conditioner’s judgment and recognition It avoids the problem of high recognition error rate caused by the uncertainty of image recognition technology, and brings excellent user experience to users. In addition, the historical operating data of the air conditioner is used as the basic data for classification, the control system is simple in design, and there is no need to set a large number of high-precision sensors, and it is also beneficial to reduce the technical cost of the air conditioner.

Hereinafter, the control method of the air conditioner of the present application will be described in detail with reference to FIGS. 1 and 2. 2 is a schematic diagram of the implementation process of the control method of the air conditioner of the present invention.

As shown in Figure 2, in a more preferred embodiment, the control method of the air conditioner further includes: determining the user's comfort preference type based on historical operating data; determining the bias coefficient based on the comfort preference type; and Offset coefficient, adjust operating parameters. in particular:

Based on historical operating data, determine the user's comfort preference type; for example, the comfort preference type includes hot, cold, dry, and wet, etc., when determining the working scene and/or user type of the air conditioner (step S200) At the same time, the air conditioner determines the user’s comfort preference type based on historical operating data. If it is determined that the preference type is dry and hot. Of course, the execution timing of this step is not static. In addition to being executed simultaneously with step S200, it can also be executed before or after step S200, and the change of the execution timing does not deviate from the principle of this application. In addition, in addition to the several types of comfort preference introduced in this embodiment, those skilled in the art can also increase or decrease them appropriately, such as wind preference and noise preference.

Determine the bias coefficient based on the comfort preference type; for example, when it is determined that the comfort preference type is heat-loving, the temperature bias coefficient is determined to be 1.02 based on the comfort preference type, or the temperature bias coefficient is determined to be 1. ℃; For example, it is determined that the bias coefficient of the wind speed of the fan is 0.8, or the bias coefficient of the wind speed of the fan is 100r/min.

Adjust the operating parameters based on the offset coefficient; for example, after the offset coefficient is determined, adjust the operating parameters determined by the work scenario and/or user classification, such as multiplying the current set temperature by the temperature offset coefficient 1.02 or Increase the current setting temperature by 1℃ as the adjusted setting temperature; for example, multiply the fan wind speed by the wind speed offset coefficient 0.8 or lower the fan speed by 100r/min as the adjusted fan wind speed.

By determining the user’s comfort preference type based on historical operating data, and determining the bias coefficient based on the preference type to adjust the operating parameters of the air conditioner, the control method of the present application can also be used on the basis of classifying work scenarios and/or user types. In the above, the user’s comfort preference for using the air conditioner is further classified based on historical operating data, so that the operating parameters of the air conditioner are adjusted based on the work scene and/or user type, and the operation of the air conditioner is based on the comfort preference. The parameters are adjusted twice to meet the different preferences of users for using the air conditioner, making the air conditioner more intelligent and personalized, and further improving the user experience. In addition, the user's comfort preference is determined by directly searching for rules from historical operating data, which also solves the problem of directly digitizing personal comfort preference.

With further reference to Fig. 2, in a more preferred embodiment, based on historical operating data, the following methods can be used to determine the working scene and/or user type of the air conditioner, and the user’s comfort preference type:

Input historical operating data into a pre-trained classification model, and the classification model directly derives the work scene and/or user type, as well as the user's comfort preference.

Specifically, the training model may be a support vector machine model or a neural network model, etc. The methods for establishing the above two models are conventional technical means in the field, and will not be repeated here. After the classification model is established, the training method for working scenes and/or user types in the classification model can be as follows: Collect operating data of different types of users and working scenes of air conditioners as the training set and input the classification model to obtain the operating data of the air conditioner and Correspondence between work scenarios and/or user types. After training, input any historical data into the model to output the work scenario and/or user type corresponding to the historical data. The training method for the user's comfort preference type in the classification model can be: collect the operating data of different air conditioners and the comfort preference type corresponding to the operating data and input the training model to obtain the correspondence between the historical operating data and the comfort preference type relationship. After training, input any historical data into the model to get the comfort preference type corresponding to the historical data.

For example, indoor and outdoor temperature and humidity data can better reflect the user's comfort preference; the operating period of an air conditioner is closely related to its working scene. If an air conditioner only runs at night for a long time, it is likely to be installed in the bedroom. Air conditioner; if the air conditioner always blows upwards or avoids blowing directly on the human body during operation, and always runs silently or runs at a lower wind speed, then it may be installed in a children's room or an elderly room If the air conditioner's operating load is mainly affected by the outdoor environment, then it is likely to be installed in the living room; if the air-conditioning load is greatly affected by indoor factors, it is likely to be located in the dining room or activity room.

By adopting the support vector machine model or neural network model as the classification model, and classifying the working scene, user type and comfort preference of the air conditioner according to the user's historical operating data, the control method of this application can realize the parameter adjustment of the air conditioner. Adaptation changes are made to work scenarios, user types and user preferences to improve the accuracy of scene recognition and enhance user experience.

Further, in a more preferred embodiment, the user's comfort preference type in the classification model can be trained based on the PMV index. Specifically, the PMV index is an evaluation index used to characterize the thermal response of the human body. It was proposed by Professor Vangel in Denmark. This index represents the average of the hot and cold sensations of most people in the same environment. There are 7 levels of sensation: cold ( -3), cool (-2), slightly cooler (-1), moderate (0), slightly warmer (1), warm (2), hot (3); PMV index and temperature, humidity, wind speed, average radiation temperature , Clothing thermal resistance is related to human metabolism and other factors. After inputting historical operating data, you can first calculate the corresponding PMV=0 standard based on the environmental data before turning on the air conditioner in the historical operating data (such as indoor and outdoor ambient temperature before turning on the air conditioner, indoor and outdoor environmental humidity, etc.) The operating parameters are then compared with the standard operating parameters based on the environmental data after the air conditioner is turned on in the historical operating data (such as indoor and outdoor ambient temperature after the air conditioner is turned on, indoor and outdoor environmental humidity, etc.) to determine the user's comfort preference Types of.

Since the PMV index represents the heat sensation of most people in the same environment, but there are physiological differences between people, the PMV index may not necessarily represent the feeling of everyone. Therefore, Professor Van Geer proposed the predictive dissatisfaction percentage PPD indicator to indicate the percentage of people who are dissatisfied with the thermal environment. Professor Fanger found that at PMV=0, PDD is about 5%. This means that even if the indoor environment is in the best thermal comfort state, 5% of people are not satisfied with the environmental comfort due to people's physiological differences. Therefore, only adjusting the operating parameters of the air conditioner based on the temperature or the human body characteristic parameters cannot fully meet the user's comfort requirements. The control system proposed by the present invention can classify the difference between the user’s comfort preference and the PMV index by analyzing the user’s historical operating data, determine the user’s comfort preference type, and then generate different bias coefficients for the air conditioner. The operating parameters are adjusted twice to make the operation of the air conditioner more in line with the user's personal comfort experience.

It should be noted that although the above embodiments are classified and determined by the same classification model in combination with work scenarios, user types and comfort preferences, those skilled in the art should understand that the present invention contributes to the prior art. It is embodied in determining the work scene, user type and comfort preference through historical operating data, rather than determining which model or models the process is performed by. Therefore, when the inventive concept of the present invention is adopted, other models or devices are used The /module determines the work scene, user type and comfort preference, or the scheme in which the work scene, user type and comfort preference are respectively determined by different models does not deviate from the principle of the present invention. For example, the classification model is only used to determine the work scene and the user type, and the comfort preference is completed by the controller of the air conditioner based on the comparison table of the comfort model and the operating data stored in the air conditioner.

Further referring to Fig. 2, in a more preferred embodiment, the control method of the air conditioner further includes:

Receive control instructions for artificially adjusting operating parameters, and adjust operating parameters of the air conditioner based on the control instructions; based on the adjusted operating parameters, re-determine the working scene and/or user type of the air conditioner. For example, during the operation of the air conditioner based on the operating conditions, user type, and comfort preference, the user manually adjusted the operating parameters of the air conditioner. It is probably due to the current air conditioner’s operating conditions, user type and The comfort preference is changed. At this time, the air conditioner records the adjusted operating parameters in time, and based on the adjusted operating parameters, re-determines the working scene, user type, and comfort preference type of the air conditioner.

When the air conditioner is operating with operating parameters, it receives control instructions for artificially adjusting operating parameters, adjusts the operating parameters of the air conditioner based on the instructions, and re-determines the working scene and/or user type of the air conditioner based on the adjusted operating parameters. The applied control method of the air conditioner can also make timely adjustments to the work scene and/or user type when the work scene changes, so as to improve the applicability of the air conditioner and further enhance the user experience.

It should be noted that although the detailed steps of the method of the present invention are described in detail above, those skilled in the art can combine, split and exchange the order of the above steps without departing from the basic principles of the present invention. The modified technical solution does not change the basic concept of the present invention, and therefore also falls within the protection scope of the present invention.

Hereinafter, referring to Fig. 2, the control system of the air conditioner of the present application will be briefly described. Among them, FIG. 2 is a system schematic diagram of the control system of the air conditioner of the present invention.

As shown in Fig. 2, corresponding to the control method of the air conditioner, the present application also provides a control system of the air conditioner. The control system mainly includes a collection module, a classification module and a control module, wherein:

The acquisition module is used to acquire and store the historical operating data of the air conditioner. For example, the acquisition module acquires and stores the indoor ambient temperature, outdoor ambient temperature, indoor ambient humidity, outdoor ambient humidity, operating period of the air conditioner, and air supply angle during operation of the air conditioner. , Air supply intensity and load power and other operating data, and call the above operating data when the air conditioner control method is executed.

The classification module is used to determine the working scenario and/or user type corresponding to the air conditioner based on historical operating data. For example, the classification module is a trained support vector machine model or neural network model. After inputting historical operating data into the model, it can be obtained The working scene and/or user type corresponding to the air conditioner.

The control module determines the operating parameters of the air conditioner based on the work scenario and/or user type, and controls the operation of the air conditioner based on the operating parameters; for example, the control module is a controller of the air conditioner, which can be based on the determined work scenario and/or user Select the appropriate operating parameters for the type, and control the air conditioner to run with the operating parameters.

Further, the classification module is also used to determine the user's comfort preference type based on historical operating data; for example, after inputting historical operating data into the classification model, the user's preference type when using the air conditioner, such as hot, favorite Cold, dry and wet, etc.

The control module is also used to determine the bias coefficient based on the type of comfort preference, and adjust the operating parameters based on the bias coefficient; for example, the controller of an air conditioner determines the bias of parameters such as temperature and wind speed based on the determined type of comfort preference And adjust the current operating parameters of the air conditioner based on the offset coefficient, so that the operating parameters of the air conditioner are more in line with the user’s preference.

Through the above setting method, the control system of the air conditioner of the present application can classify the working scene, user type and comfort preference of the air conditioner according to the user's historical operating data, and realize that the parameter adjustment of the air conditioner varies with the working scene, user type and comfort Make adaptive changes to degree preferences, improve the recognition accuracy of work scenarios, user types and comfort preferences, and improve user experience.

It can be understood by those skilled in the art that the above-mentioned modules may be physically installed in the air conditioner specifically for executing the method of the present invention, or may be a functional module or functional unit in the controller of the existing air conditioner .

Hereinafter, a possible operation process of the air conditioner of the present application will be described with reference to FIGS. 1 and 2.

As shown in Figure 1 and Figure 2, in a possible implementation process, after the user selects the "smart adjustment" function through the remote control, the air conditioner first retrieves the historical operating data of the air conditioner, and determines this based on the historical operating data. The air conditioner is set in the bedroom of the elderly, and the user’s preference for comfort is hot when using the air conditioner; then, based on the above judgment results, the air conditioner controller first determines that the standard operating parameters of the air conditioner are the set temperature of 29°C and the guide The fan speed is 300r/min and the fan speed is 300r/min. Secondly, based on the user's comfort preference type, the controller determines that the bias coefficient of the set temperature is 1.05, and the bias coefficient of the fan speed is 0.8, thereby adjusting the operation of the air conditioner The parameters are: set temperature=29×1.05≈30.5℃; fan speed=300×0.8=240r/min; wind deflector rises. Finally, the controller controls the air conditioner to run at a set temperature of 30.5°C, a fan speed of 240r/min, and the air deflector to run upward.

So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the drawings. However, those skilled in the art will readily understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

A control method of an air conditioner, characterized in that the control method includes:

Acquiring historical operating data of the air conditioner;

Based on the historical operating data, determine the working scene and/or user type of the air conditioner;

Determining the operating parameters of the air conditioner based on the working scenario and/or the user type;

Based on the operating parameters, the air conditioner is controlled to operate.
The control method of an air conditioner according to claim 1, wherein the control method further comprises:

Based on the historical operating data, determine the user's comfort preference type;

Determine the bias coefficient based on the comfort preference type;

Based on the bias coefficient, the operating parameter is adjusted.
The air conditioner control method of claim 2, wherein the step of "determining the user's comfort preference type based on the historical operating data" further comprises:

Inputting the historical operating data into a pre-trained classification model to obtain the comfort preference type;

Wherein, the classification model is used to characterize the correspondence between the historical operating data and the comfort preference type.
The air conditioner control method according to claim 1, wherein the step of "determining the working scene and/or user type of the air conditioner based on the historical operating data" further comprises:

Inputting the historical operation data into a pre-trained classification model to obtain the working scene of the air conditioner and/or the user type;

Wherein, the classification model is used to characterize the correspondence between the historical operation data and the work scenario and/or the user type.
The air conditioner control method according to claim 3 or 4, wherein the classification model is a support vector machine model or a neural network model.
The air conditioner control method according to claim 1, wherein the historical operating data includes indoor environmental temperature, outdoor environmental temperature, indoor environmental humidity, outdoor environmental humidity, operating period of the air conditioner, air supply angle, air supply One or more of wind intensity and load power.
The control method of an air conditioner according to claim 2, wherein the work scene includes one or more of a bedroom, a living room, a dining room, a study room, and an activity room; and/or

The user type includes one or more of children, adults, and elderly; and/or

The comfort preference type includes one or more of hot, cold, wet and dry.
The air conditioner control method according to claim 1, wherein after the step of "controlling the operation of the air conditioner based on the operating parameters", the control method further comprises:

Receiving a control instruction for artificially adjusting operating parameters, and adjusting the operating parameters of the air conditioner based on the control instruction;

Based on the adjusted operating parameters, re-determine the working scene and/or user type of the air conditioner.
An air conditioner control system, characterized in that, the control system includes:

An acquisition module, which is used to acquire historical operating data of the air conditioner;

A classification module, which is used to determine a working scene and/or user type corresponding to the air conditioner based on the historical operating data;

A control module, which determines the operating parameters of the air conditioner based on the work scene and/or user type, and controls the operation of the air conditioner based on the operating parameters.
The air conditioner control system according to claim 9, wherein the classification module is further configured to determine the user's comfort preference type based on the historical operating data;

The control module is further configured to determine an offset coefficient based on the comfort preference type, and adjust the operating parameter based on the offset coefficient.