CN112128934A - Intelligent control method and intelligent control equipment for air conditioner - Google Patents
Intelligent control method and intelligent control equipment for air conditioner Download PDFInfo
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- CN112128934A CN112128934A CN202010887132.6A CN202010887132A CN112128934A CN 112128934 A CN112128934 A CN 112128934A CN 202010887132 A CN202010887132 A CN 202010887132A CN 112128934 A CN112128934 A CN 112128934A
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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
- F24F2130/00—Control inputs relating to environmental factors not covered by group F24F2110/00
- F24F2130/10—Weather information or forecasts
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Abstract
The invention provides an intelligent control method and intelligent control equipment for an air conditioner. The intelligent control method of the air conditioner comprises the following steps: determining the operating season of the air conditioner, and acquiring a self-adjusting model corresponding to the operating season, wherein the self-adjusting model is obtained by training through a machine learning algorithm by using the operating state of the air conditioner in the operating season and corresponding environmental data as training samples; acquiring the running state and environmental parameters of the air conditioner; inputting the running state and the environmental parameters into a self-adjusting model; carrying out prediction calculation by using a self-adjusting model to obtain a self-adjusting strategy of the air conditioner; and controlling the air conditioner according to the self-adjusting strategy. According to the scheme, the self-adjusting model is obtained by selecting the operating seasons with clear use characteristics and respectively training, the self-adjusting model is used for carrying out prediction calculation, and the intelligent air conditioner is controlled according to the obtained self-adjusting strategy, so that the requirement of a user on the comfort of the environment can be accurately 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 and intelligent control equipment of an 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.
With the rapid development of artificial intelligence, machine learning and other technologies, the use of related intelligent technologies in air conditioners is also becoming a hot point of technical research. However, the use result of the existing intelligent control method of the air conditioner applying the artificial intelligence technology cannot completely meet the use requirement of the user, and part of users even feed back the environment provided by the intelligent air conditioner to be not comfortable enough, which brings more troubles.
Disclosure of Invention
An object of the present invention is to provide an intelligent control method and an intelligent control apparatus for an air conditioner that solve at least some of the above-mentioned problems of the related art.
A further object of the present invention is to provide an air conditioner that can intelligently provide a comfortable indoor environment and improve the user experience.
Particularly, the present invention provides an intelligent control method of an air conditioner, the intelligent control method comprising: determining the operating season of the air conditioner, and acquiring a self-adjusting model corresponding to the operating season, wherein the self-adjusting model is obtained by training through a machine learning algorithm by using the operating state of the air conditioner in the operating season and corresponding environmental data as training samples; acquiring the running state and environmental parameters of the air conditioner; inputting the running state and the environmental parameters into a self-adjusting model; carrying out prediction calculation by using a self-adjusting model to obtain a self-adjusting strategy of the air conditioner; and controlling the air conditioner according to the self-adjusting strategy.
Optionally, the step of determining the operating season in which the air conditioner is located comprises: acquiring installation position information of an air conditioner; determining the climate rule of the area where the air conditioner is located according to the installation position information; and determining the operating season of the air conditioner according to the climate rule.
Optionally, the step of determining the operating season in which the air conditioner is located comprises: acquiring environmental data of the air conditioner in a preset time period; and determining the operating season of the air conditioner according to the environmental data.
Optionally, the step of obtaining an air conditioner self-adjustment model corresponding to the operating season includes: acquiring an operation record of the air conditioner; judging whether the air conditioner calls a self-adjusting model corresponding to the operating season or not according to the operating record; and if so, acquiring the self-adjusting model which is called before and corresponds to the operating season.
Optionally, in a case where the air conditioner has not invoked the self-adjustment model corresponding to the operating season, the method further includes: and acquiring an initial self-adjusting model in the operating season configured for the area where the air conditioner is located, and taking the initial self-adjusting model in the operating season as the self-adjusting model.
Optionally, after the step of controlling the air conditioner according to the self-adjusting strategy, the method further comprises: acquiring a manual adjustment record of the air conditioner; judging whether the manual adjustment record exceeds a set time threshold value or not; and if so, taking the manual adjustment record and the environmental parameters during the manual adjustment as training samples, and performing iterative training on the self-adjustment model.
Optionally, after the step of determining the operating season of the air conditioner, the method further comprises: acquiring clock information of the air conditioner under the condition that the determined operating season changes from the operating season determined by the last operation of the air conditioner, and verifying the determined operating season by using the clock information; and under the condition that the operating season is matched with the clock information, executing the step of acquiring the self-adjusting model corresponding to the operating season.
Optionally, after the step of determining the operating season of the air conditioner, the method further comprises: under the condition that the determined operating season changes from the operating season determined by the last operation of the air conditioner, outputting operating season replacement prompt information; and acquiring response operation for the operating season replacement prompt information fed back by the user, and executing the step of acquiring the self-adjusting model corresponding to the operating season when the operating season is confirmed by the response operation instruction.
Optionally, the operating season includes any one or more of: a refrigeration season, a heating season, a plum rain season and a haze removing season. In the refrigeration season, the priority operation mode of the air conditioner is a refrigeration mode; in the heating season, the preferential operation mode of the air conditioner is a heating mode; in the refrigeration season, the prior operation mode of the air conditioner is a dehumidification mode; in the haze removing season, the preferential operation mode of the air conditioner is a purification mode.
According to another aspect of the present invention, there is also provided an intelligent control apparatus of an air conditioner. This intelligent control equipment of air conditioner includes: 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 selects the operating seasons with clear use characteristics aiming at the condition that a single self-adjusting model of the air conditioner cannot meet the adjustment requirements under various operating conditions, trains the self-adjusting model respectively aiming at each operating season, and utilizes the self-adjusting model to carry out predictive calculation on the operating state and the environmental parameters of the air conditioner, thereby controlling the air conditioner according to the obtained self-adjusting strategy. According to the method, the artificial learning model is trained according to the data of the operating season, so that the prediction calculation difficulty of the self-adjusting model is reduced, and the obtained self-adjusting strategy better meets the environmental regulation requirement of the operating season, and the use experience of a user is improved.
Furthermore, the intelligent control method of the air conditioner can select one or more of a refrigerating season, a heating season, a plum rain season and a haze removing season as operation seasons according to the installation area of the air conditioner, and because the operation seasons generally have a priority operation mode, the uncertainty of prediction calculation through the self-adjusting model is greatly reduced.
Furthermore, the intelligent control method of the air conditioner optimizes a determination method of an operating season, a self-adjusting model iterative training mode and the like, so that the control method is more intelligent and efficient, and the intelligent requirements of users are met. Compared with the prior art in the fields of intelligent household appliances (intelligent household appliances) and intelligent air conditioners (intelligent air conditioners), the scheme provided by the invention is more intelligent and efficient, and the intelligent level is improved.
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 data interaction of an air conditioner according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an intelligent control apparatus of an air conditioner according to an embodiment of the present invention;
fig. 3 is a schematic view of an intelligent control method of an air conditioner according to an embodiment of the present invention; and
fig. 4 is a flowchart of an application example of an intelligent control method of an air conditioner according to an embodiment of the present invention.
Detailed Description
Fig. 1 is a data interaction diagram of an air conditioner 10 according to an embodiment of the present invention. An air conditioner control application (App) or other control Client (Client) is installed on the terminal device 20 (including but not limited to various mobile terminals). The user of the air conditioner 10 may configure the functions and application scenarios of the air conditioner 10 through an application program or a client.
The network data platform 30 may be used to collect and record operational data of the air conditioner 10, collect and record user behavior information, and the like. The network data platform 30 may perform machine learning model training on the operation data of the air conditioner 10 and the user behavior information, and perform predictive computation of the air conditioner 10 by using the trained model. The decision conditions of the prediction calculation include indoor and outdoor environments (temperature, humidity, pollution condition, wind power, weather, etc.) of the air conditioner 10, user information (various physiological indexes, positions, dressing indexes, etc.), and the predicted targets include: the on-off state of the air conditioner 10 (including the on-parameter), the operation mode (cooling, heating, cleaning, dehumidifying, etc.), and the setting parameters (wind power, wind direction, temperature, humidity, etc.).
The air conditioner 10 acquires the operation state of the air conditioner 10 and the indoor and outdoor environmental data, and controls the air conditioner 10 according to the self-adjusting strategy predicted by the network data platform 30.
In addition, the network data platform 30 may also send various alert messages to the air conditioner 10 and the terminal device 20, and receive information replied by the user through the air conditioner 10 and the terminal device 20.
The machine learning model (self-tuning model) used in the present embodiment may be capable of learning certain knowledge and capabilities from existing data (the operating state of the air conditioner 10 and environmental parameters) for processing new data, and may be designed to perform various tasks, in the present embodiment for determination of the control strategy of the air conditioner 10. 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. In embodiments, the machine learning model may also be referred to as a "learning network". 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 30) that communicates with terminal device 20 or air conditioner 10 according to a client-server relationship (or application-server). For example, the machine learning model may be implemented by a server computing system (network data platform 30) as part of a web service. Accordingly, one or more models may be stored and implemented at terminal device 20 and/or one or more models may be stored and implemented at a server computing system (network data platform 30).
The server computing system (network data platform 30) 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.
The terminal device 20 or the air conditioner 10 and/or the server computing system (network data platform 30) may train the model 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 30) or may be part of the server computing system (network data platform 30).
Those skilled in the art can distribute the processing and computing functions of the data among the terminal device 20, the air conditioner 10, and the network data platform 30 as necessary. For example, the data is subjected to certain preprocessing in the terminal device 20 and the air conditioner 10 to improve the efficiency of data transmission.
Fig. 2 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).
The intelligent control device 300 may be arranged in the network data platform 30 as described above. Furthermore, the functionality of the intelligent control device 300 may also allow for configuration, combination, and division between the network data platform 30, the terminal 20, and the air conditioner 10, with the inherent flexibility of a computer-based system.
Fig. 3 is a schematic diagram of an intelligent control method of an air conditioner according to an embodiment of the present invention, which may include:
and step S302, determining the operating season of the air conditioner, and acquiring a self-adjusting model corresponding to the operating season.
The operating season is determined according to the operating state of the air conditioner, and may be generally a period of time during which the air conditioner has a typical application environment. For example, the operating season includes any one or more of: a cooling season, a heating season, a plum rain season (or called a rain season or a wet season), and a haze removing season (or called a purification season), and those skilled in the art can configure an operating season according to the climate characteristics of the area where the air conditioner is located and the demand of the user on the environment. For example, for a hot zone, only a cooling season and a humid season may be configured; for areas with clear four seasons, a refrigeration season and a heating season can be configured; to the region that has monsoon weather and the environment is comparatively abominable, can increase and set up except that the haze season.
In the refrigeration season, the priority operation mode of the air conditioner is a refrigeration mode; in the heating season, the preferential operation mode of the air conditioner is a heating mode; in the refrigeration season, the prior operation mode of the air conditioner is a dehumidification mode; in the haze removing season, the preferential operation mode of the air conditioner is a purification mode.
One alternative way to determine the operating season in which the air conditioner is located is to: acquiring installation position information of an air conditioner; determining the climate rule of the area where the air conditioner is located according to the installation position information; and determining the operating season of the air conditioner according to the climate rule. The installation position information of the air conditioner can be determined through the sales and maintenance records of the air conditioner, can also be determined through the report of a user of the air conditioner, and can also be determined through the position of a terminal bound with the air conditioner. Due to the fact that different regions have different climate laws, the operating seasons can be determined by the climate laws, and as described above, the different regions can set the corresponding operating seasons according to the climate. For example, for the Beijing area, 11 in the middle of the month to 3 in the next year may be the heating season, and 6 in the middle of the month to 3 in the next year may be the cooling season; for example, in the Shanghai region, the season of plum rain is from 6 to 7 in the middle of the month; the heating season may be from the middle ten days of 12 months to the end of 2 months.
Another alternative way to determine the operating season in which the air conditioner is located is: acquiring environmental data of the air conditioner in a preset time period; and determining the operating season of the air conditioner according to the environmental data. For example, the outdoor temperature and humidity within 5 to 10 days can be obtained for judging the operating season. For example, outdoor environment data in a set period (for example, 5 to 10 days) of the air conditioner may be matched with the climate rule, for example, for north China, the average temperature is lower than 10 ℃ for 5 consecutive days, which means that the air conditioner enters the heating season; the average temperature is higher than 22 ℃ for 5 continuous days, and the season can be considered to enter the refrigeration season.
The mode of determining the operating season of the air conditioner is not limited to the above mode, and in some embodiments, the mode may also be determined by acquiring a broadcast message of the network data platform or manually setting the mode. For example, the network data platform can broadcast the operating season message to the air conditioners in the designated area according to the forecast of the weather platform; for another example, when a season change time is close, a reminding message can be provided for the user, and the operating season is manually set by the user.
After determining the operating season of the air conditioner, the method further comprises the following steps: under the condition that the determined operating season changes from the operating season determined by the last operation of the air conditioner, outputting operating season replacement prompt information; and acquiring response operation for the operating season replacement prompt information fed back by the user, and executing the step of acquiring the self-adjusting model corresponding to the operating season when the operating season is confirmed by the response operation instruction. That is to say, in the case of season change, the user can be reminded, and after the user confirms, the self-adjusting model is adjusted according to the characteristics of the operating season.
The self-adjusting model is obtained by training through a machine learning algorithm by taking the running state of the air conditioner in the running season and corresponding environmental data as training samples. That is to say, the method of the present embodiment trains the corresponding self-adjusting models respectively for typical operating seasons, and the accuracy of the prediction calculation is higher.
The existing intelligent air conditioner can realize more and more functions, only uses air supply as an example, and adds various air supply modes such as natural wind, circulating wind, fresh air, no wind sense and the like besides wind power and wind direction, and the comfort feeling of users is different in different seasons. For example, in spring and autumn, although the temperature is not very different, the demands of users are obviously different. Under the condition that the same environmental data appears in different seasons, the same self-adjusting model is used for prediction calculation, and obviously, the comfort requirement of a user cannot be met. Based on the problem, the intelligent control method of the air conditioner of the embodiment respectively carries out model training for the operating seasons with typical use characteristics, so that the self-adjusting model corresponds to the operating seasons, the obtained control strategy basically conforms to the characteristics of the operating seasons, and the use experience of users is improved.
For the time period which is not in the set operating season, namely the time period when the air conditioner is in the time period when the weather changes frequently and randomly or the use frequency of the air conditioner is low, the method of the embodiment can also provide manual control options for the user or provide a universal self-adjusting model.
Step S304, acquiring the running state and the environmental parameters of the air conditioner. The operation state of the air conditioner may include, but is not limited to: the system comprises a startup and shutdown state, an operation mode, a set temperature, a set scene, a set wind power, a wind guide mode, a compressor frequency and the like. Environmental parameters may include, but are not limited to: indoor and outdoor temperature, indoor and outdoor humidity, weather, air particle data, air components and the like. Further, the environmental parameter may also include a physical state of the user, such as physiological index data, location, etc.
Step S306, inputting the running state and the environmental parameters into the self-adjusting model.
And step S308, carrying out prediction calculation by using the self-adjusting model to obtain a self-adjusting strategy of the air conditioner. The self-adjusting strategy is not a simple setting of parameter threshold values, but includes adjusting bases and adjusting modes of various operating parameters of the air conditioner, including but not limited to: various setting parameters, speed of change of state, type of environmental data ignored or employed, power on and off conditions, and the like.
And step S304, controlling the air conditioner according to the self-adjusting strategy. The air conditioner can be intelligently adjusted according to self-adjustment measurement, and the comfort requirement of a user is met.
The self-adjustment model of the air conditioner corresponding to the operating season preferably uses the self-adjustment model used in the same operating season of the last year. Since the self-adjusting model used in the same operating season of the last year is generally subjected to iterative training by using actual operating data of the air conditioner, the actual requirements of the user of the air conditioner are better met. Thus, the step of obtaining an air conditioner self-adjustment model corresponding to the operating season may include: acquiring an operation record of the air conditioner; judging whether the air conditioner calls a self-adjusting model corresponding to the operating season or not according to the operating record; and if so, acquiring the self-adjusting model which is called before and corresponds to the operating season. The operation records may be used to record various operation data of the air conditioner, including but not limited to: startup and shutdown records, parameter adjustment records, model use records, model training records, user manual adjustment records, environmental data and the like. The operating record of the air conditioner may determine whether the self-adjusting model used in the same operating season of the previous year, or the currently determined self-adjusting model was invoked by the user. The self-adjusting model is preferably used, so that the use habit of a user can be better met.
The method also comprises the following steps under the condition that the air conditioner does not call the self-adjusting model corresponding to the operating season: and acquiring an initial self-adjusting model in the operating season configured for the area where the air conditioner is located, and taking the initial self-adjusting model in the operating season as the self-adjusting model. The initial self-adjusting model in the operating season can be obtained by training the operating data of the area where the air conditioner is located, and the climate characteristics of the area and the use preference of the air conditioner of users in the area can be fully reflected by preferentially utilizing big data samples for training. That is, in the case where the self-adjusting model corresponding to the operating season has not been called before by the controlled air conditioner, the initial model of the area where the controlled air conditioner is located is used to perform the prediction calculation, which can satisfy the comfort requirements of most users with a high probability.
After the step S304 of controlling the air conditioner according to the self-adjusting strategy, the method may further include: acquiring a manual adjustment record of the air conditioner; judging whether the manual adjustment record exceeds a set time threshold value or not; and if so, taking the manual adjustment record and the environmental parameters during the manual adjustment as training samples, and performing iterative training on the self-adjustment model. The personalized needs of the user can be better matched by iteratively training the self-adjusting model by utilizing the manual adjustment records and the environmental parameters during the manual adjustment. For example, the last several times (e.g., 5 times, 10 times) of manual adjustment records and the environmental parameters during the manual adjustment can be used as training samples. The threshold value of the number of times is set to exclude a user's misoperation and temporary adjustment of a special case.
When a new operating season comes, the self-adjusting model of the operating record of the same operating season is preferably used for intelligent adjustment, and then an initial self-adjusting model trained by adopting big data can be adopted. Through the iterative training of the self-adjusting model, the requirements of the user can be further met, and the requirement that the habit of the user changes constantly can be met.
According to the intelligent control method of the air conditioner, aiming at the condition that a single self-adjusting model of the air conditioner cannot meet the adjustment requirements under various operating conditions, operating seasons with clear use characteristics are selected, the self-adjusting model is obtained by training for each operating season, and the self-adjusting model is used for carrying out prediction calculation on the operating state and the environmental parameters of the air conditioner, so that the air conditioner is controlled according to the obtained self-adjusting strategy, and the functions of the air conditioner can be effectively exerted.
Fig. 4 is a flowchart of an application example of an intelligent control method of an air conditioner according to an embodiment of the present invention. The application example can comprise the following steps:
step S402, determining the operating season of the air conditioner, wherein the determining mode can comprise weather rule, receiving broadcast information of a weather platform, time judgment and the like.
Step S404, judging whether the operating season changes, namely judging whether the determined operating season changes with the operating season determined by the last operation of the air conditioner, thereby judging whether the season change occurs. For example, in the middle of 6 months, the average temperature is higher than 22 ℃ for 5 consecutive days, and it is judged that the season enters the cooling season.
Step S406, outputting operating season replacement prompt information after determining that the operating season changes;
step S408, whether a confirmation response for the replacement prompt information of the operating season fed back by the user is acquired or not is judged;
step S410, judging whether the air conditioner calls a self-adjusting model corresponding to the operating season;
step S412, if the model is not called, obtaining an initial self-adjusting model of the operating season configured for the area where the air conditioner is located;
step S414, if the model is called, obtaining the self-adjusting model which is called before and corresponds to the operating season;
step S416, acquiring the running state and the environmental parameters of the air conditioner, inputting the running state and the environmental parameters into a self-adjusting model, and performing predictive calculation by using the self-adjusting model to obtain a self-adjusting strategy of the air conditioner;
step S418, controlling the air conditioner according to a self-adjusting strategy;
step S420, acquiring a manual adjustment instruction of the air conditioner;
step S422, judging whether the manual adjustment times exceed a set time threshold value;
step S424, using the manual adjustment record and the environmental parameters during the manual adjustment as training samples, and performing iterative training on the self-adjustment model.
It should be understood by those skilled in the art that the above-mentioned flow is only an application example, and the execution order of the steps and the addition and deletion of some steps may be adjusted based on the description of the intelligent control method for the air conditioner in this embodiment. For the case that the air conditioner is not in a typical operating season or there is no operating season or corresponding self-adjusting model (for example, the weather changes frequently and randomly or the on-time rate of the air conditioner is small), the method of this embodiment may preferably provide the user with a manual control option or configure a general self-adjusting model.
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:
determining the operating season of the air conditioner, and acquiring a self-adjusting model corresponding to the operating season, wherein the self-adjusting model is obtained by training through a machine learning algorithm by using the operating state of the air conditioner in the operating season and corresponding environmental data as training samples;
acquiring the running state and the environmental parameters of the air conditioner;
inputting the operating state and the environmental parameter into the self-adjusting model;
carrying out predictive calculation by using the self-adjusting model to obtain a self-adjusting strategy of the air conditioner; and
and controlling the air conditioner according to the self-adjusting strategy.
2. The intelligent control method of an air conditioner according to claim 1, wherein the step of determining an operating season in which the air conditioner is located comprises:
acquiring installation position information of the air conditioner;
determining the climate rule of the area where the air conditioner is located according to the installation position information;
and determining the operating season of the air conditioner according to the climate rule.
3. The intelligent control method of an air conditioner according to claim 1, wherein the step of determining an operating season in which the air conditioner is located comprises:
acquiring environmental data of the air conditioner in a previously set time period;
and determining the operating season of the air conditioner according to the environment data.
4. An intelligent control method of an air conditioner according to claim 1, wherein said step of obtaining an air conditioner self-adjustment model corresponding to the operating season includes:
acquiring an operation record of the air conditioner;
judging whether the air conditioner calls a self-adjusting model corresponding to the operating season or not according to the operating record;
and if so, acquiring a self-adjusting model which is called before and corresponds to the operating season.
5. The intelligent control method of an air conditioner according to claim 4, wherein,
further comprising, in the event that the air conditioner has not invoked a self-tuning model corresponding to the operating season: and acquiring an initial self-adjusting model in the operating season configured for the area where the air conditioner is located, and taking the initial self-adjusting model in the operating season as the self-adjusting model.
6. The intelligent control method of an air conditioner according to claim 1, further comprising, after the step of controlling the air conditioner in accordance with the self-adjusting strategy:
acquiring a manual adjustment record of the air conditioner;
judging whether the manual adjustment record exceeds a set time threshold value;
and if so, taking the manual adjustment record and the environmental parameters during the manual adjustment as training samples, and performing iterative training on the self-adjusting model.
7. The intelligent control method of an air conditioner according to claim 1, further comprising, after the step of determining an operating season in which the air conditioner is located:
acquiring clock information of the air conditioner under the condition that the determined operating season changes from the operating season determined by the last operation of the air conditioner, and verifying the determined operating season by using the clock information;
and under the condition that the operating season is matched with the clock information, executing a step of acquiring a self-adjusting model corresponding to the operating season.
8. The intelligent control method of an air conditioner according to claim 1, further comprising, after the step of determining an operating season in which the air conditioner is located:
under the condition that the determined operating season changes from the operating season determined by the last operation of the air conditioner, outputting operating season replacement prompt information;
and acquiring response operation for the operating season replacement prompt information fed back by a user, and executing the step of acquiring the self-adjusting model corresponding to the operating season under the condition that the response operation indicates that the operating season is confirmed.
9. The intelligent control method of an air conditioner according to claim 1, wherein,
the operating season includes any one or more of: a cooling season, a heating season, a plum rain season, and a haze removing season, wherein
In the refrigerating season, the priority operation mode of the air conditioner is a refrigerating mode;
in the heating season, the preferential operation mode of the air conditioner is a heating mode;
in the refrigerating season, the priority operation mode of the air conditioner is a dehumidification mode;
and in the haze removing season, the priority operation mode of the air conditioner is a purification mode.
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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| CN202010887132.6A CN112128934A (en) | 2020-08-28 | 2020-08-28 | Intelligent control method and intelligent control equipment for air conditioner |
| PCT/CN2021/102121 WO2022041987A1 (en) | 2020-08-28 | 2021-06-24 | Intelligent control method and intelligent control device for air conditioner |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113465141A (en) * | 2021-06-11 | 2021-10-01 | 珠海格力电器股份有限公司 | Fresh air system control method and device, electronic equipment and storage medium |
| WO2022041987A1 (en) * | 2020-08-28 | 2022-03-03 | 青岛海尔空调器有限总公司 | Intelligent control method and intelligent control device for air conditioner |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117469782A (en) * | 2022-07-20 | 2024-01-30 | 青岛海尔空调器有限总公司 | Method and device for controlling air conditioner and air conditioner |
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| WO2022041987A1 (en) | 2022-03-03 |
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