CN113377018A - Intelligent control method and intelligent control equipment of air conditioner - Google Patents
Intelligent control method and intelligent control equipment of air conditioner Download PDFInfo
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- 238000012549 training Methods 0.000 claims description 28
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- G05B15/00—Systems controlled by a computer
- G05B15/02—Systems controlled by a computer electric
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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Abstract
The invention provides an intelligent control method of an air conditioner and intelligent control equipment of the air conditioner. The intelligent control method of the air conditioner comprises the following steps: acquiring a behavior record of a target user in a set time period, wherein the behavior record at least comprises an air conditioner use record; predicting the behavior record by using a pre-trained self-adjusting model to obtain a recommended adjusting strategy; and controlling the air conditioner of the environment where the target user is located according to the recommended adjustment strategy. The scheme of the invention is to establish a self-adjusting model by using a machine learning algorithm in advance, predict the behavior record of the user by using the self-adjusting model and obtain a recommended adjusting strategy for controlling the air conditioner in the environment where the target user is located. Because the recommended adjustment strategy is obtained based on the user behavior, the comfort requirement of the user on the environment can be accurately met, the use experience of the user is provided, the comfort requirement of the user in the sleeping process is comprehensively met, and the intelligent level of the intelligent household appliance is improved.
Description
Technical Field
The invention relates to intelligent household appliance control, in particular to an intelligent control method of an air conditioner and intelligent control equipment of the air conditioner.
Background
With the increasing living standard, consumers can choose household appliances no longer to pay attention to the quality of products alone, but to the use experience brought by the products.
For an environment conditioning device such as an air conditioner, a user needs to obtain a high comfort environmental experience. In order to meet the requirements of users, the functions of the air conditioner are gradually expanded, and the control is more refined. Therefore, the use of the air conditioner is also more and more complicated. In the prior art, the terminal App is used for control, however, the learning use threshold of the user is higher and higher, and the operation is more and more complicated. This in turn causes inconvenience to the user. The prior art does not provide an effective solution to this problem.
Disclosure of Invention
An object of the present invention is to provide an intelligent control method of an air conditioner and an intelligent control apparatus of an air conditioner, which solve at least some of the above problems in the related art.
A further object of the present invention is to provide an air conditioner that can perform intelligent self-tuning according to user behavior, thereby improving user experience.
It is a further object of the present invention to provide personalized adjustments to users with unique usage habits.
Particularly, the present invention provides an intelligent control method of an air conditioner, which includes: acquiring a behavior record of a target user in a set time period, wherein the behavior record at least comprises an air conditioner use record; predicting the behavior record by using a pre-trained self-adjusting model to obtain a recommended adjusting strategy; and controlling the air conditioner of the environment where the target user is located according to the recommended adjustment strategy.
Optionally, the training process of the self-adjusting model comprises: subscribing batch air conditioner log data from an operation data platform, wherein the operation data platform is used for collecting and recording operation logs of the air conditioner; analyzing the log data into structured data and storing the structured data as a sample database; and performing machine learning model training by using data in the sample database to obtain a self-adjusting model.
Optionally, the step of parsing the log data into structured data comprises: extracting data according to the log data and the type labels; classifying and storing the extracted data to obtain an alternative sample data table; and carrying out statistical screening on the data in the alternative sample data table to obtain structured data.
Optionally, before the step of predicting the behavior record by using the pre-trained self-adjusting model, the method further comprises: screening candidate records from the behavior records according to a predetermined correlation corresponding table, wherein the correlation corresponding table is used for storing the correlation between various user behaviors and the running state of the air conditioner; the candidate records are input into a self-adjusting model.
Optionally, the candidate records include at least any one or more of: the method comprises the following steps of recording the adjustment of the air conditioner by a user, recording the travel of the user, recording the position of the user and recording the physiological characteristics of the user.
Optionally, after the step of controlling the air conditioner in the environment where the target user is located according to the recommended adjustment policy, the method further includes: acquiring a manual adjustment record uploaded by an air conditioner of an environment where a target user is located; judging whether the manual adjustment record of the target user exceeds a set time threshold value or not; and if so, acquiring the behavior record and the manual adjustment record of the target user, and training to obtain the portrait model of the target user by taking the behavior record and the manual adjustment record of the target user as training samples.
Optionally, after the training of the portrait model of the target user, the method further includes: determining a control strategy of a target user by using the portrait model; and controlling the air conditioner of the environment where the target user is located according to the control strategy.
Optionally, the step of obtaining the behavior record of the target user within the set time period includes: and acquiring a terminal use record of the target user, and generating a behavior record according to the terminal use record.
Optionally, the recommended adjustment policy includes any one or more of: the method comprises the steps of starting and shutting down the air conditioner at the starting and shutting down time of the air conditioner, the starting initial mode of the air conditioner, starting initial parameters of the air conditioner and response actions of the air conditioner to environment detection data changes.
According to another aspect of the present invention, there is also provided an intelligent control apparatus of an air conditioner, including: a processor; and a memory in which a control program is stored, the control program being executed by the processor to implement any one of the above-described intelligent control methods for an air conditioner.
The intelligent control method of the air conditioner provided by the invention is characterized in that a self-adjusting model is established in advance by utilizing a machine learning algorithm, and the behavior record of a user is predicted by utilizing the self-adjusting model to obtain a recommended adjusting strategy for controlling the air conditioner in the environment where a target user is located. Because the recommended adjustment strategy is obtained based on the user behavior, the requirement of the user on the comfort of the environment can be accurately met, and the use experience of the user is provided.
Further, the self-adjusting model used in the intelligent control method of the air conditioner is obtained by training big data collected by the operation data platform. Due to the adoption of enough samples, the self-adjusting model is accurate and effective, and can meet the recommendation requirement of the control strategy of the air conditioner.
Furthermore, the intelligent control method of the air conditioner can also comprehensively collect the behavior records of the user, screen the behavior records with higher correlation degree due to environmental adjustment or environmental preference according to the correlation calculation result, and input the screened candidate records into the self-adjusting model. Therefore, the required input parameters can be adjusted according to the requirements, and the flexibility of the method application is improved.
Furthermore, the intelligent control method of the air conditioner can also establish a personalized portrait model for users with unique use records, and meet the intelligent requirements of the users.
The above and other objects, advantages and features of the present invention will become more apparent to those skilled in the art from the following detailed description of specific embodiments thereof, taken in conjunction with the accompanying drawings.
Drawings
Some specific embodiments of the invention will be described in detail hereinafter, by way of illustration and not limitation, with reference to the accompanying drawings. The same reference numbers in the drawings identify the same or similar elements or components. Those skilled in the art will appreciate that the drawings are not necessarily drawn to scale. In the drawings:
FIG. 1 is a schematic view of an environment in which an air conditioner according to one embodiment of the present invention is located;
FIG. 2 is a schematic diagram of data interaction of an intelligent control system of an air conditioner according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an intelligent control apparatus of an air conditioner according to an embodiment of the present invention;
fig. 4 is a schematic view of an intelligent control method of an air conditioner according to an embodiment of the present invention;
fig. 5 is a schematic diagram of training a self-adjusting model in an intelligent control method of an air conditioner according to an embodiment of the present invention; and
fig. 6 is a flowchart illustrating an intelligent control method of an air conditioner according to an embodiment of the present invention.
Detailed Description
Fig. 1 is a schematic view of an environment in which an air conditioner according to an embodiment of the present invention is located. The intelligent control system of the air conditioner of the embodiment is provided with a plurality of network data platforms. The plurality of network data platforms may include: the system comprises an operation data platform 100 for collecting and recording an air conditioner operation log, a user information collection platform 200 for collecting and recording user behavior information and the like.
The operation data platform 100 may perform data interaction with the air conditioner 110, and collect operation records of the air conditioner 110, thereby obtaining a large amount of air conditioner log data. These air conditioner log data may be used to provide sample data needed for machine learning, in addition to after-market service, status tracking, and fault diagnosis.
The user information gathering platform 200 is used to gather various behaviors of the user, which may include but are not limited to: records of the user's use of various home devices (e.g., records of adjusting an air conditioner), records of the user's travel (e.g., driving records), records of the user's location, records of the user's physiological characteristics (e.g., body temperature, heart rate, blood pressure), records of the user's movements, records of the user's work and rest, and so forth. The user information collection platform 200 may perform data interaction with various devices of a user, for example, interaction with a mobile terminal, a vehicle, an intelligent appliance, a wearable device, and the like. The behavior records collected in the user information collection platform 200 provide a basis for various data analysis for the user.
Since the data collection and storage technology of the network data platform is known to those skilled in the information technology field, the detailed description of the data collection and storage process is omitted here.
Fig. 2 is a schematic diagram of data interaction of an intelligent control system of an air conditioner according to an embodiment of the present invention. The air conditioner 110 uploads the operation record to the operation data platform 100. The operation data platform 100 forms a big data platform of the air conditioner 110 by collecting operation records of the air conditioner 110. The intelligent control device 300 of the air conditioner subscribes to a batch of air conditioner log data from the operation data platform 100, and trains to obtain a self-adjusting model by adopting a machine learning algorithm.
In the intelligent operation state of the air conditioner, the intelligent control device 300 of the air conditioner obtains the behavior record of the target user in a set time period from the user terminal 330 or other devices, inputs the behavior record into the self-adjusting model after the behavior record is collated, and finally obtains the recommended adjustment strategy through the data processing of the self-adjusting model. The intelligent air conditioner 110 adjusts the environment of the target user according to the recommended adjustment strategy. Under the intelligent running state, the user can obtain a comfortable environment meeting the self requirement without any adjusting operation.
Fig. 3 is a schematic diagram of an intelligent control apparatus 300 of an air conditioner according to an embodiment of the present invention. The intelligent control device 300 may include, in general: a memory 320 and a processor 310, wherein the memory 320 stores a control program 321, and the control program 321 is used for implementing the intelligent control method of the air conditioner of the present embodiment when being executed by the processor 310. The processor 310 may be a Central Processing Unit (CPU), a digital processing unit, or the like. The processor 310 transmits and receives data through the communication interface. The memory 320 is used to store programs executed by the processor 310. The memory 320 is any medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, or a combination of memories. The control program 321 may be downloaded from a computer-readable storage medium to a corresponding computing/processing device or downloaded and installed to the smart control device 300 via a network (e.g., the internet, a local area network, a wide area network, and/or a wireless network).
Fig. 4 is a schematic diagram of an intelligent control method of an air conditioner according to an embodiment of the present invention, which may include:
step S402, acquiring the behavior record of the target user in a set time period, wherein the behavior record at least comprises the air conditioner use record. The behavior record may be a record of various behaviors of the user, which may include, but are not limited to: records of the user's use of various home devices (e.g., records of adjusting an air conditioner), records of the user's travel (e.g., driving records), records of the user's location, records of the user's physiological characteristics (e.g., body temperature, heart rate, blood pressure), records of the user's movements, records of the user's work and rest, and so forth.
In this embodiment, the behavior record may be obtained by performing data interaction with various devices of the user, for example, interacting with a mobile terminal, a vehicle, a smart home appliance, a wearable device, and the like. In addition, related data can be directly obtained from a data platform for recording user behaviors.
The method of the embodiment selects and uses the behavior record of the target user in a set time period, for example, the behavior record of the last month or weeks, in consideration of the possible change of the behavior habit of the user and the change of the climate and the position to the behavior of the user.
After the behavior records of the user are obtained, preprocessing the behavior records, for example, screening candidate records from the behavior records according to a predetermined correlation correspondence table, where the correlation correspondence table is used to store correlations between various user behaviors and the operating state of the air conditioner; the candidate records are input into a self-adjusting model.
Due to the fact that the types of the acquired behavior records are more, the data which can be acquired by different users are different. These behavior log data may have a correlation with the use of the air conditioner, and some have a low correlation with the use of the air conditioner. Therefore, the embodiment calculates the data correlation between each behavior of the user and the air conditioner adjustment and environmental comfort requirements in advance to obtain the correlation corresponding table. And after the behavior records of the user are obtained, the records with the correlation reaching a preset threshold value are screened out from the behavior records according to the correlation correspondence table and are used as input data of the self-adjusting model. The above-mentioned data correlation calculation can be performed by using the prior art of statistical calculation in the prior art. Through screening, the data formats of the candidate records can be unified, so that the subsequent data characteristics can be conveniently extracted, calculated and processed.
In some alternative embodiments, the candidate records may include at least any one or more of: the method comprises the following steps of recording the adjustment of the air conditioner by a user, recording the travel of the user, recording the position of the user and recording the physiological characteristics of the user. The record of the user for adjusting the air conditioner directly reflects the adjusting and controlling habits of the user; the travel record of the user can determine the time when the user reaches a home place or a working place, so that the starting time of the corresponding air conditioner can be determined; the user's location record may be used to determine environmental data, climate, etc.; the physiological characteristic record of the user reflects the physical state of the user, which also directly affects the comfort requirements.
In the method of the embodiment, the expansibility of various records of the user is fully considered, and various behavior information of the user can be fully collected. Different behavior information can be collected by aiming at different data collection devices of users, and required candidate information is selected through screening of correlation. Due to the difference of the input candidate information, the data output from the adjustment model may have difference.
And S404, predicting the behavior record by using a pre-trained self-adjusting model to obtain a recommended adjusting strategy. And processing the behavior record by the self-adjusting model to obtain a recommended adjusting strategy. The recommended adjustment strategy includes any one or more of: the air conditioner control method comprises the steps of starting and shutting down the air conditioner at the starting and shutting down time, the starting initial mode of the air conditioner, starting initial parameters of the air conditioner, and responding actions of the air conditioner to the change of environment detection data (for example, how to respond to different deviations of target parameters and actual detection data). The recommended adjustment strategy is not only used to output a target parameter (e.g., a target temperature), but may also include adjustment actions for different conditions of the air conditioner. For example, different temperature adjusting modes of the air conditioner exist under the condition that different temperature differences exist between the actual temperature and the target temperature; also for example in sleep mode, control curves of air conditioners, etc.
And step S406, controlling the air conditioner of the environment where the target user is located according to the recommended adjustment strategy. In the control process, the air conditioner can be started by itself, the target parameters are determined, response adjustment is carried out, user intervention is not needed, and the comfort and the use experience of the user are greatly improved.
The intelligent control method of the air conditioner of the embodiment establishes the self-adjusting model by using the machine learning algorithm in advance, predicts the behavior record of the user by using the self-adjusting model, and obtains the recommended adjusting strategy for controlling the air conditioner of the environment where the target user is located. Because the recommended adjustment strategy is obtained based on the user behavior, the requirement of the user on the comfort of the environment can be accurately met, and the use experience of the user is provided.
Fig. 5 is a schematic diagram of training an auto-adjustment model in an intelligent control method of an air conditioner according to an embodiment of the present invention, where the training process of the auto-adjustment model may include:
step S502, subscribing batch air conditioner log data from an operation data platform, wherein the operation data platform is used for collecting and recording the operation log of the air conditioner.
Step S504, the log data is analyzed into structured data and stored as a sample database. The parsing process of the structured data may include: extracting data according to the log data and the type labels; classifying and storing the extracted data to obtain an alternative sample data table; and carrying out statistical screening on the data in the alternative sample data table to obtain structured data. For example, the start-up time, the shut-down time, the initial temperature, the target temperature, the wind speed, the wind direction, the operation mode, the compressor frequency, the adaptation scene, the target parameter adjustment time, and other classifications of the air conditioner may be stored for use in subsequent training.
Step S506, performing machine learning model training by using the data in the sample database to obtain a self-adjusting model.
The machine learning model may be capable of learning certain knowledge and capabilities from existing data for processing new data and may be designed to perform various tasks, in this embodiment for the determination of air conditioner control strategies. Examples of machine learning models include, but are not limited to, deep neural networks of various types (DNNs), Support Vector Machines (SVMs), decision trees, random forest models, and so forth. Machine learning models may also be referred to in the art as "learning networks". The neural network control model may adopt various known network structures suitable for supervised learning, such as a perceptron model, a classifier model, a Hopfield network and other basic neural network structures, and various corresponding mainstream training methods may also be used for determining the model parameters of the embodiment. Example machine learning models include neural networks or other multi-layered nonlinear models. Example neural networks include feed-forward neural networks, deep neural networks, recurrent neural networks, and convolutional neural networks.
The machine learning model may be included in or otherwise stored and implemented by a server computing system (network data platform) that communicates with the terminal device according to a client-server relationship. For example, the machine learning model may be implemented by a server computing system (network data platform) as part of a web service. Thus, one or more models can be stored and implemented at a terminal device and/or one or more models can be stored and implemented at a server computing system (network data platform).
The server computing system (network data platform) may include or otherwise be implemented by one or more server computing devices. Where the server computing system includes multiple server computing devices, such server computing devices may operate according to a sequential computing architecture, a parallel computing architecture, or some combination thereof.
Terminal devices (air conditioners and other user devices) and/or server computing systems (network data platforms) may train the models via interaction with a training computing system communicatively coupled over a network. The training computing system may be separate from the server computing system (network data platform) or may be part of the server computing system (network data platform).
The interaction between the terminal devices (air conditioners and other user devices) and the server computing system network may be through any type of communications network, such as a local area network (e.g., an intranet), a wide area network (e.g., the internet), or some combination thereof, and may include any number of wired or wireless links. In general, communications over a network may be carried using various communication protocols (e.g., TCP/IP, HTTP, SMTP, FTP), encodings or formats (e.g., HTML, XML), and/or protection schemes (e.g., VPN, secure HTTP, SSL) via any type of wired and/or wireless connection.
The method of the embodiment also considers that the use habits of some users are unique, and a recommended adjustment strategy obtained by predicting the behavior record through a self-adjustment model may appear, so that the comfort requirements of the users cannot be met. Therefore, after the step of controlling the air conditioner in the environment where the target user is located according to the recommended adjustment strategy, the method may further include: acquiring a manual adjustment record uploaded by an air conditioner of an environment where a target user is located; judging whether the manual adjustment record of the target user exceeds a set time threshold value or not; and if so, acquiring the behavior record and the manual adjustment record of the target user, and training to obtain the portrait model of the target user by taking the behavior record and the manual adjustment record of the target user as training samples. After the portrait model of the target user is obtained through training, the method further comprises the following steps: determining a control strategy of a target user by using the portrait model; and controlling the air conditioner of the environment where the target user is located according to the control strategy. The set number threshold is used for judging whether the manual adjustment records of the target user can meet the quantity requirement as sample data.
The portrait model of the target user can be used as a special model of the user to meet the intelligent requirements of the users. The present mainstream training method can also be adopted for the portrait model training method.
Fig. 6 is a flowchart illustrating an intelligent control method of an air conditioner according to an embodiment of the present invention, and the flowchart may include:
step S602, operating a data platform to count the log data of the air conditioner, and training to obtain a self-adjusting model;
step S604, taking the log data of the air conditioner as a sample, and training to obtain a self-adjusting model;
step S606, selecting user behavior information according to the correlation with the air conditioner adjustment;
step S608, inputting user behavior information into a self-adjusting model, and determining a control strategy;
step S610, controlling the air conditioner by using a control strategy;
step S612, whether the user manually adjusts the air conditioner or not is judged, and if the user does not adjust the air conditioner or the adjustment data volume is small, the user continuously uses the control strategy to control the air conditioner;
step S614, if the user manually adjusts the air conditioner, judging whether the manually adjusted data meets the sample data requirement;
step S616, after the manual adjustment data meets the sample data requirement, performing machine learning model training on the manual adjustment data to obtain a user portrait model;
in step S618, a control strategy is determined using the user profile model.
For example, to determine the turn-on parameters of the air conditioner, the turn-on parameters are determined for A, B, C three users, and then behavior records of A, B, C three users may be input into the self-adjusting model. For example, the user arrival time, the user living position, the user vehicle use record and the user air conditioner use record are input into the self-adjusting model by combining with the information of seasons, working day information, regional climate and the like. The recording may select the previous week or weeks of data.
The output parameters from the adjustment model may include: each boot parameter and its corresponding impact weight. For example, the final output parameters are:
the user A: starting time, season, month, working day, setting mode and indoor temperature;
and a user B: starting time, season, month, working day, setting mode and adjusting time interval;
and a user C: starting time, season, month, whether working day, indoor or outdoor proper temperature difference.
There may also be differences in the types of control strategies that are output from the adjustment model for different inputs by the user.
The intelligent control method of the air conditioner of the embodiment establishes the self-adjusting model by using the machine learning algorithm in advance, predicts the behavior record of the user by using the self-adjusting model, and obtains the recommended adjusting strategy for controlling the air conditioner of the environment where the target user is located. Because the recommended adjustment strategy is obtained based on the user behavior, the requirement of the user on the comfort of the environment can be accurately met, and the use experience of the user is provided. Further, the self-adjusting model is obtained through big data training collected by the operation data platform. Due to the adoption of enough samples, the self-adjusting model is accurate and effective, and can meet the recommendation requirement of the control strategy of the air conditioner.
Thus, it should be appreciated by those skilled in the art that while a number of exemplary embodiments of the invention have been illustrated and described in detail herein, many other variations or modifications consistent with the principles of the invention may be directly determined or derived from the disclosure of the present invention without departing from the spirit and scope of the invention. Accordingly, the scope of the invention should be understood and interpreted to cover all such other variations or modifications.
Claims (10)
1. An intelligent control method of an air conditioner comprises the following steps:
acquiring a behavior record of a target user in a set time period, wherein the behavior record at least comprises an air conditioner use record;
predicting the behavior record by using a pre-trained self-adjusting model to obtain a recommended adjusting strategy; and
and controlling the air conditioner of the environment where the target user is located according to the recommended adjustment strategy.
2. The method of claim 1, wherein the training process of the self-adjusting model comprises:
subscribing batch air conditioner log data from an operation data platform, wherein the operation data platform is used for collecting and recording operation logs of an air conditioner;
analyzing the log data into structured data and storing the structured data into a sample database;
and performing machine learning model training by using the data in the sample database to obtain the self-adjusting model.
3. The method of claim 2, wherein the parsing the log data into structured data comprises:
extracting data according to the log data and the type label;
classifying and storing the extracted data to obtain an alternative sample data table;
and carrying out statistical screening on the data in the alternative sample data table to obtain the structured data.
4. The method of claim 2, wherein prior to the step of predicting the behavioral record using a pre-trained self-tuning model, further comprising:
screening candidate records from the behavior records according to a predetermined correlation corresponding table, wherein the correlation corresponding table is used for storing the correlation between various user behaviors and the running state of the air conditioner;
inputting the candidate record into the self-adjusting model.
5. The method of claim 4, wherein,
the candidate records include at least any one or more of: the method comprises the following steps of recording the adjustment of the air conditioner by a user, recording the travel of the user, recording the position of the user and recording the physiological characteristics of the user.
6. The method of claim 1, wherein after the step of controlling the air conditioners in the environment of the target user according to the recommended adjustment strategy, further comprising:
acquiring a manual adjustment record uploaded by an air conditioner of the environment where the target user is located;
judging whether the manual adjustment record of the target user exceeds a set time threshold value or not;
and if so, acquiring the behavior record of the target user and the manual adjustment record, and training to obtain the portrait model of the target user by taking the behavior record of the target user and the manual adjustment record as training samples.
7. The method of claim 6, wherein after said training results in a representation model of the target user, further comprising:
determining a control strategy for the target user using the representation model; and
and controlling the air conditioner of the environment where the target user is located according to the control strategy.
8. The method of claim 1, wherein the step of obtaining a record of the target user's behavior over a set period of time comprises:
and acquiring a terminal use record of the target user, and generating the behavior record according to the terminal use record.
9. The method of claim 1, wherein
The recommendation adjustment policy includes any one or more of: the method comprises the steps of starting and shutting down the air conditioner at the starting and shutting down time of the air conditioner, the starting initial mode of the air conditioner, starting initial parameters of the air conditioner and response actions of the air conditioner to environment detection data changes.
10. An intelligent control apparatus of an air conditioner, comprising:
a processor; and
a memory in which a control program is stored, the control program being executed by the processor to implement the intelligent control method of the air conditioner according to any one of claims 1 to 9.
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