CN113375300A - 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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- 230000007613 environmental effect Effects 0.000 claims abstract description 33
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- 238000012549 training Methods 0.000 claims description 27
- 238000011217 control strategy Methods 0.000 claims description 14
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- 230000008569 process Effects 0.000 claims description 6
- 238000012216 screening Methods 0.000 claims description 6
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- 238000004422 calculation algorithm Methods 0.000 abstract description 5
- 238000013528 artificial neural network Methods 0.000 description 8
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/64—Electronic processing using pre-stored data
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/56—Remote control
- F24F11/58—Remote control using Internet communication
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/61—Control or safety arrangements characterised by user interfaces or communication using timers
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/65—Electronic processing for selecting an operating mode
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/10—Temperature
- F24F2110/12—Temperature of the outside air
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/20—Humidity
- F24F2110/22—Humidity of the outside air
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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 environmental data records of an area where a target controlled air conditioner is located within a set time period; predicting the environmental data record by utilizing a pre-trained self-adjusting model to obtain a self-adjusting strategy; and controlling the target controlled air conditioner according to the self-adjusting strategy. According to the scheme of the invention, a self-adjusting model is established in advance by using a machine learning algorithm, and the self-adjusting model is used for predicting the environmental data record to obtain a self-adjusting strategy for controlling the area where the air conditioner is located. The self-adjusting strategy is obtained based on the environment data, so that the requirement of the user on the comfort of the environment can be accurately met, and the use experience of the user is provided.
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 achieve intelligent self-adjustment based on environmental data, thereby improving the 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 environmental data records of an area where a target controlled air conditioner is located within a set time period; predicting the environmental data record by utilizing a pre-trained self-adjusting model to obtain a self-adjusting strategy; and controlling the target controlled air conditioner according to the self-adjusting strategy.
Optionally, the step of obtaining the environmental data record of the area where the target controlled air conditioner is located within the set time period includes: acquiring the position information of a target controlled air conditioner, and acquiring an outdoor environment record in a set time period according to the position information; and acquiring indoor environment records uploaded by the target controlled air conditioner within a set time period.
Optionally, the self-tuning strategy comprises 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.
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, after the step of controlling the air conditioner according to the self-adjusting strategy, the method further comprises: acquiring an operation record uploaded by a target controlled air conditioner; extracting a manual adjustment record from the operation record; and after the manual adjustment record exceeds a set time threshold value, establishing an individual adjustment model of the target controlled air conditioner by using the operation record so as to establish a subsequent control strategy of the target controlled air conditioner by using the individual adjustment model.
Optionally, after the step of controlling the air conditioner according to the self-adjusting strategy, the method further comprises: acquiring an operation record uploaded by a target controlled air conditioner; extracting a manual adjustment record from the operation record; and after the manual adjustment record exceeds a set time threshold value, establishing a user portrait model of the target controlled air conditioner by using the user behavior record of the target controlled air conditioner so as to establish a subsequent control strategy of the target controlled air conditioner by using the user portrait model.
Optionally, the step of creating a user profile model of the target controlled air conditioner using the user behavior record of the target controlled air conditioner comprises: 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; and taking the behavior record and the operation record of the target controlled air conditioner as training samples, and training to obtain the portrait model.
Optionally, the behaviour record further comprises any one or more of: travel records of the user, position records of the user, and physiological characteristic records of the user.
According to another aspect of the present invention, there is also provided an intelligent control apparatus of an air conditioner, including: a processor; and the memory stores a control program, and the control program is used for realizing the intelligent control method of any air conditioner when being executed by the processor.
The intelligent control method of the air conditioner of the invention establishes the self-adjusting model by utilizing the machine learning algorithm in advance, predicts the environmental data record by utilizing the self-adjusting model and obtains the self-adjusting strategy for controlling the area where the air conditioner is located. The self-adjusting strategy is obtained based on the environment data, so that 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 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, an information collection platform 200 for collecting and recording environmental information and/or 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 information collection platform 200 is used to collect and record environmental information, which may include not only indoor environmental information but also outdoor environmental information. Such environmental information may include, but is not limited to: temperature, humidity, time, climate, region, concentration of air pollutants. The information collection platform 200 may interact with various environment detection devices or environment data, etc. to obtain the environment data.
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 performs sorting and then inputs the environmental data records of the area where the target controlled air conditioner 110 is located in the set time period from the user terminal 330 and other detection devices, and finally obtains the self-adjusting strategy through data processing of the self-adjusting model. The intelligent air conditioner 110 adjusts the environment of the target user according to a self-adjusting 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 generally include:
step S402, acquiring environmental data records of the area where the target controlled air conditioner is located in a set time period; the environmental data record may include not only indoor environmental data but also outdoor environmental data. These environmental data records characterize the operation of the targeted controlled air conditioner. For example, when the outdoor environment data is acquired, the position information of the target controlled air conditioner may be acquired, and the outdoor environment record in the set time period may be acquired according to the position information. These outdoor environment records may be obtained by acquiring the detection data of the detection device, or by acquiring data collected by other data collection platforms. And for example, when the indoor environment data is acquired, acquiring indoor environment records uploaded by the target controlled air conditioner within a set time period.
The method of the embodiment may select to use the environmental data record within a set period of time, for example, the last month or weeks, in consideration of enabling the air conditioner to update the latest control strategy in time.
And S404, predicting the environmental data record by using a pre-trained self-adjusting model to obtain a self-adjusting strategy. The self-tuning strategy may include 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. The self-adjusting strategy is not only used to output a target parameter (e.g., a target temperature), but may also include an adjusting action of the target controlled air conditioner for different situations. For example, different temperature adjustment modes of the target controlled air conditioner when the actual temperature and the target temperature have different temperature differences; also for example, in the sleep mode, the control curve of the target controlled air conditioner, and the like.
And step S406, controlling the target controlled air conditioner according to the self-adjusting strategy. In the control process, the target controlled 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 utilizing the machine learning algorithm in advance, and predicts the environmental data record by utilizing the self-adjusting model to obtain the self-adjusting strategy for controlling the area where the air conditioner is located. The self-adjusting strategy is obtained based on the environment data, so that 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 self-adjusting strategy obtained by predicting the environmental data record through a self-adjusting model may appear, so that the comfort requirements of the users cannot be met. The embodiment can also establish an individual adjustment model according to the manual adjustment record.
For example, after the step of controlling an air conditioner of an environment where the target user is located according to the self-adjusting strategy, the method may further include: acquiring an operation record uploaded by a target controlled air conditioner; extracting a manual adjustment record from the operation record; and after the manual adjustment record exceeds a set time threshold value, establishing an individual adjustment model of the target controlled air conditioner by using the operation record so as to establish a subsequent control strategy of the target controlled air conditioner by using the individual adjustment model. Namely, the individual adjustment model is established by using the operation record uploaded by the target controlled air conditioner, and the target controlled air conditioner is controlled in a targeted manner. 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.
For another example, after the step of controlling the air conditioner of the environment where the target user is located according to the self-adjusting 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. Namely, the behavior record of the target user and the manual adjustment record are utilized to establish the portrait model.
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.
After the behavior records of the user are obtained, the behavior records can be preprocessed, for example, relevant behaviors are screened from the behavior records according to a predetermined relevance correspondence table, wherein the relevance correspondence table is used for storing the relevance between various user behaviors and the operation state of the air conditioner; the image model is trained using the associated behaviors. 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, screening out records with the correlation reaching a preset threshold value from the behavior records according to the correlation correspondence table, and using the records as training samples of the portrait model of the target user. 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 behavior records can be unified, and the processing of sample data and other processing are facilitated.
The behavioral record further includes any one or more of: travel records of the user, position records of the user, and physiological characteristic records 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 the required related information can be selected through screening of the relevance.
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, obtaining the environmental data record of the area where the target controlled air conditioner is located in the set time period;
step S608, inputting environmental data records into a self-adjusting model, and determining a control strategy;
step S610, controlling the target controlled air conditioner by using a control strategy;
step S612, whether the user manually adjusts the target controlled air conditioner or not is judged, and if the adjustment is not carried out or the adjustment data volume is small, the self-adjustment strategy is continuously used for controlling 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 manually adjusting whether the data meets the sample data requirement, selecting user behavior information according to the correlation with the air conditioner adjustment;
step S618, machine learning model training is carried out on the user behavior information to obtain a user portrait model;
and step S620, utilizing the user portrait model to make a subsequent control strategy of the target controlled air conditioner.
Taking the determination of the starting parameters of the air conditioner as an example for introduction, the environmental data records including the indoor and outdoor temperature, the indoor and outdoor humidity, the region, the time, the day of the week, whether the working day is or not and the like can be input into the self-adjusting model for prediction, so as to obtain the self-adjusting strategy. The output parameters from the adjustment model may include: each boot parameter and its corresponding impact weight. For example, the final output parameters are: starting time, season, month, working day, setting mode, indoor temperature, adjusting time interval and indoor and outdoor proper temperature difference.
When the personalized portrait model is used for control, for example, the user's arrival time, the user's living location, the user's vehicle usage, the user's air conditioner usage, and the information such as season, working day information, and regional climate can be combined to be used as a training sample of the user portrait model. The record can be selected from the data of the previous week or several weeks, and when the control is performed, the behavior information of the recent user is input into the user portrait model to obtain the subsequent control strategy.
The intelligent control method of the air conditioner of the embodiment establishes the self-adjusting model by utilizing the machine learning algorithm in advance, and predicts the environmental data record by utilizing the self-adjusting model to obtain the self-adjusting strategy for controlling the area where the air conditioner is located. The self-adjusting strategy is obtained based on the environment data, so that the requirement of the user on the comfort of the environment can be accurately met, and the use experience of the user is provided. The method of the embodiment can also establish a personalized portrait model for users with unique use records, and meet the intelligent requirements of the users.
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 environmental data records of an area where a target controlled air conditioner is located within a set time period;
predicting the environmental data record by utilizing a pre-trained self-adjusting model to obtain a self-adjusting strategy; and
and controlling the target controlled air conditioner according to the self-adjusting strategy.
2. The method of claim 1, wherein the step of obtaining the environmental data record of the area where the target controlled air conditioner is located within a set time period comprises:
acquiring the position information of the target controlled air conditioner, and acquiring the outdoor environment record in the set time period according to the position information; and
and acquiring the indoor environment record uploaded by the target controlled air conditioner within the set time period.
3. The method of claim 1, wherein,
the self-tuning strategy comprises 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.
4. 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.
5. The method of claim 4, 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.
6. The method of claim 1, wherein the step of controlling the air conditioner in accordance with the self-adjusting strategy is further followed by:
acquiring an operation record uploaded by the target controlled air conditioner;
extracting a manual adjustment record from the operation record;
and after the manual adjustment record exceeds a set time threshold value, establishing an individual adjustment model of the target controlled air conditioner by using the operation record so as to establish a subsequent control strategy of the target controlled air conditioner by using the individual adjustment model.
7. The method of claim 1, wherein the step of controlling the air conditioner in accordance with the self-adjusting strategy is further followed by:
acquiring an operation record uploaded by the target controlled air conditioner;
extracting a manual adjustment record from the operation record;
and after the manual adjustment record exceeds a set time threshold value, establishing a user portrait model of the target controlled air conditioner by using the user behavior record of the target controlled air conditioner so as to establish a subsequent control strategy of the target controlled air conditioner by using the user portrait model.
8. The method of claim 6, wherein the step of building a user profile model of the target controlled air conditioner using the user behavior record of the target controlled air conditioner comprises:
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;
and taking the behavior record and the operation record of the target controlled air conditioner as training samples, and training to obtain the portrait model.
9. The method of claim 8, wherein,
the behavioral record further includes any one or more of: travel records of the user, position records of the user, and physiological characteristic records of the user.
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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| CN202010158824.7A CN113375300A (en) | 2020-03-09 | 2020-03-09 | Intelligent control method and intelligent control equipment of air conditioner |
| PCT/CN2021/078687 WO2021179958A1 (en) | 2020-03-09 | 2021-03-02 | Intelligent control method for air conditioner, and intelligent control device for air conditioner |
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| CN114838470B (en) * | 2022-03-04 | 2024-08-13 | 五邑大学 | Control method and system of heating ventilation air conditioner |
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