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WO2022041987A1 - 空调器的智能控制方法与智能控制设备 - Google Patents

空调器的智能控制方法与智能控制设备 Download PDF

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Publication number
WO2022041987A1
WO2022041987A1 PCT/CN2021/102121 CN2021102121W WO2022041987A1 WO 2022041987 A1 WO2022041987 A1 WO 2022041987A1 CN 2021102121 W CN2021102121 W CN 2021102121W WO 2022041987 A1 WO2022041987 A1 WO 2022041987A1
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WO
WIPO (PCT)
Prior art keywords
air conditioner
season
self
operating
intelligent control
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Application number
PCT/CN2021/102121
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English (en)
French (fr)
Inventor
宋世芳
郭丽
Original Assignee
青岛海尔空调器有限总公司
海尔智家股份有限公司
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Application filed by 青岛海尔空调器有限总公司, 海尔智家股份有限公司 filed Critical 青岛海尔空调器有限总公司
Publication of WO2022041987A1 publication Critical patent/WO2022041987A1/zh

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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
    • 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

Definitions

  • the present invention relates to intelligent home appliance control, in particular to an intelligent control method and an intelligent control device of an air conditioner.
  • One object of the present invention is to provide an intelligent control method and an intelligent control device for an air conditioner that at least to a certain extent solve any of the above-mentioned technical problems in the related art.
  • a further object of the present invention is to make the air conditioner intelligently provide a comfortable indoor environment and improve the user experience.
  • the present invention provides an intelligent control method for an air conditioner, the intelligent control method comprising: determining the operating season of the air conditioner, and acquiring a self-adjustment model corresponding to the operation season, and the self-adjustment model utilizes the The operating state of the air conditioner and the corresponding environmental data are obtained through machine learning algorithm training as training samples; the operating state and environmental parameters of the air conditioner are obtained; the operating state and environmental parameters are input into the self-adjusting model; the self-adjustment strategy of the air conditioner; and the air conditioner is controlled according to the self-adjustment strategy.
  • the step of determining the operating season of the air conditioner includes: acquiring information on the installation location of the air conditioner; determining the climate law of the area where the air conditioner is located according to the installation location information; and determining the operating season of the air conditioner according to the climate law.
  • the step of determining the operating season of the air conditioner includes: 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 environmental data.
  • the step of acquiring the air conditioner self-adjustment model corresponding to the operating season includes: acquiring the operation record of the air conditioner; judging whether the air conditioner has invoked the self-adjustment model corresponding to the operating season according to the operation record; The past self-adjusting model corresponding to the operating season.
  • the method further includes: acquiring the initial self-adjustment model of the operation season configured for the area where the air conditioner is located, and using the initial self-adjustment model of the operation season as the self-adjustment model. .
  • the step of controlling the air conditioner according to the self-adjustment strategy further includes: obtaining a manual adjustment record of the air conditioner; judging whether the manual adjustment record exceeds the set number of times threshold; The environmental parameters are used as training samples to iteratively train the self-tuning model.
  • the method further includes: in the case that the determined operating season changes with respect to the determined operating season when the air conditioner was last operated, acquiring the clock information of the air conditioner, and using the The clock information verifies the determined running season; if the running season matches the clock information, the step of obtaining a self-adjusting model corresponding to the running season is performed.
  • the method further includes: in the case where the determined operating season changes relative to the operating season determined by the last operation of the air conditioner, outputting prompt information for changing the operating season; obtaining The response operation to the prompt information of running season replacement fed back by the user, in the case of confirming the running season in response to the operation instruction, execute the step of acquiring the self-adjustment model corresponding to the running season.
  • the operation season includes any one or more of the following: cooling season, heating season, rainy season, and haze removal season.
  • the priority operation mode of the air conditioner is the cooling mode; in the heating season, the priority operation mode of the air conditioner is the heating mode; in the rainy season, the priority operation mode of the air conditioner is the dehumidification mode; in the haze removal season , the priority operation mode of the air conditioner is the purification mode.
  • an intelligent control device for an air conditioner includes: a processor; and a memory, where a machine executable program is stored, and when the machine executable program is executed by the processor, is used to implement any one of the above-mentioned intelligent control methods for an air conditioner.
  • the intelligent control method of the air conditioner of the present invention aiming at that a single air conditioner self-adjustment model cannot meet the adjustment requirements under various operating conditions, selects operation seasons with clear use characteristics, and trains the self-adjustment models separately for each operation season.
  • the self-adjustment model is used to predict and calculate the operating state and environmental parameters of the air conditioner, so as to control the air conditioner according to the obtained self-adjustment strategy.
  • the method of the present invention reduces the difficulty of prediction and calculation of the self-adjustment model because the artificial learning model is trained according to the data of the running season, and the obtained self-adjustment strategy is more in line with the environmental adjustment requirements of the running season, thereby improving the user experience.
  • one or more of the cooling season, the heating season, the rainy season, and the haze removal season can be selected as the operating season according to the installation area of the air conditioner, because these operating seasons generally have If the operating mode is prioritized, then the uncertainty of the prediction calculation through the self-tuning model is greatly reduced.
  • the intelligent control method of the air conditioner of the present invention also optimizes the determination method of the operating season and the iterative training method of the self-adjusting model, etc., so that the control method is more intelligent and efficient, and meets the intelligent requirements of users.
  • the solution of the present invention is more intelligent and efficient, and improves the level of intelligence.
  • FIG. 1 is a schematic diagram 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 device for an air conditioner according to an embodiment of the present invention.
  • FIG. 3 is a schematic diagram of an intelligent control method for an air conditioner according to an embodiment of the present invention.
  • FIG. 4 is a flowchart of an application example of an intelligent control method for an air conditioner according to an embodiment of the present invention.
  • FIG. 1 is a schematic diagram of data interaction 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 can configure the functions and application scenarios of the air conditioner 10 through an application program or a client.
  • the network data platform 30 can be used to collect and record the operation data of the air conditioner 10, collect and record user behavior information, and the like.
  • the network data platform 30 can perform machine learning model training on the operation data of the air conditioner 10 and user behavior information, and use the model obtained by training to perform prediction calculation of the air conditioner 10 .
  • the decision conditions for the prediction calculation include the indoor and outdoor environments of the air conditioner 10 (temperature, humidity, pollution, wind, weather, etc.), user information (various physiological indicators, location, clothing index, etc.), and the predicted goals include: air conditioners On/off state (including power-on parameters), operation mode (cooling, heating, purification, dehumidification, etc.) of the appliance 10, setting parameters (wind power, wind direction, temperature, humidity, etc.).
  • the air conditioner 10 acquires its own operating state and indoor and outdoor environment data, and controls the air conditioner 10 according to the self-adjustment strategy predicted by the network data platform 30 .
  • the network data platform 30 can also send various reminder 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-adjusting model) used in this embodiment may be able to learn 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 It is used to perform various tasks, and in this embodiment, it is used to determine the control strategy of the air conditioner 10 .
  • machine learning models include, but are not limited to, various types of deep neural networks (DNNs), support vector machines (SVMs), decision trees, random forest models, and the like.
  • DNNs deep neural networks
  • SVMs support vector machines
  • decision trees random forest models
  • random forest models random forest models
  • the neural network control model can adopt various known network structures suitable for supervised learning, such as perceptron model, classifier model, Hopfield network and other basic neural network structures, and various corresponding mainstream training methods can also be used for Determination of model parameters in this embodiment.
  • Example machine learning models include neural networks or other multi-layer nonlinear models.
  • Example neural networks include feedforward 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 operates according to a client-server relationship ( or application-server) communicates with the terminal device 20 or the air conditioner 10 .
  • a machine learning model may be implemented by a server computing system (network data platform 30) as part of a web service.
  • one or more models may be stored and implemented at the end device 20 and/or one or more models may be stored and implemented at the server computing system (network data platform 30).
  • the server computing system may include or otherwise be implemented by one or more server computing devices. Where a 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 end device 20 or the air conditioner 10 and/or the server computing system may train the model via interaction with a training computing system communicatively coupled through the 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).
  • Interaction between the terminal device 20 or air conditioner 10 and the server computing system network may be through any type of communication network, such as a local area network (eg, an intranet), a wide area network (eg, the Internet), or some combination thereof, and may include any number of wired or wireless link.
  • communication over a network can be via any type of wired and/or wireless connection, using various communication protocols (eg, TCP/IP, HTTP, SMTP, FTP), encoding or formats (eg, HTML, XML) and/or protection schemes (eg, VPN, secure HTTP, SSL).
  • Those skilled in the art can assign data processing and computing functions to the terminal device 20, the air conditioner 10, and the network data platform 30 as required. For example, certain preprocessing is performed on the data 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 device 300 of an air conditioner according to an embodiment of the present invention.
  • the intelligent control device 300 may generally include: a memory 320 and a processor 310, wherein the memory 320 stores a machine-executable program 321, and when the machine-executable program 321 is executed by the processor 310 is used to implement the air conditioner of this embodiment intelligent control method.
  • the processor 310 may be a central processing unit (central processing unit, CPU for short), or 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 .
  • Memory 320 is any medium that can be used to carry or store the desired program code in the form of instructions or data structures and that can be accessed by a computer, and may also be a combination of multiple memories.
  • the above-mentioned machine executable program 321 may be downloaded from a computer-readable storage medium to a corresponding computing/processing device or downloaded and installed to the intelligent control device 300 via a network (eg, the Internet, a local area network, a wide area network, and/or a wireless network).
  • a network eg, 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 above-mentioned network data platform 30 .
  • the functions of the intelligent control device 300 can also be configured, combined, and divided among the network data platform 30 , the terminal 20 , and the air conditioner 10 due to the inherent flexibility of the 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, and the intelligent control method of the air conditioner may include:
  • Step S302 determining the operating season of the air conditioner, and acquiring a self-adjustment model corresponding to the operating season.
  • the above-mentioned operating season is determined according to the operating state of the air conditioner, and can generally be a time period when the air conditioner has a typical application environment.
  • the operating season includes any one or more of the following: cooling season, heating season, rainy season (or called rainy season or wet season), and haze removal season (or called purification season).
  • the climatic characteristics of the region and the needs of users for the environment configure the operating season. For example, for tropical areas, you can configure only cooling season and humid season; for areas with distinct four seasons, you can configure cooling season and heating season; for areas with monsoon climate and harsh environment, you can add haze removal season.
  • the priority operation mode of the air conditioner is the cooling mode; in the heating season, the priority operation mode of the air conditioner is the heating mode; in the rainy season, the priority operation mode of the air conditioner is the dehumidification mode; in the haze removal season , the priority operation mode of the air conditioner is the purification mode.
  • An optional way to determine the operating season of the air conditioner is: obtaining installation location information of the air conditioner; determining the climatic law of the area where the air conditioner is located according to the installation location information, and determining the operating season of the air conditioner according to the climatic law.
  • the installation location information of the air conditioner can be determined through the sales and maintenance records of the air conditioner, or through reporting by the user of the air conditioner, or through the location of the terminal bound to the air conditioner. Since different regions have large differences in their climatic laws, the operating season can be determined by the climatic laws. As described above, different regions can set corresponding operating seasons according to the climate.
  • the heating season can be from mid-November to mid-March of the following year, and the cooling season can be from mid-June to mid-August.
  • mid-June to early July is the rainy season
  • mid-December to the end of February can be the heating season.
  • Another optional way to determine the operating season of the air conditioner is to obtain environmental data of the air conditioner in a previously set time period, and determine the operating season of the air conditioner according to the environmental data. For example, the outdoor temperature and humidity within 5 days to 10 days can be obtained for judging the running season.
  • the outdoor environment data in the set period of the air conditioner (such as 5 days to 10 days) can be matched with the above climatic laws. For example, for the North China region, if the daily average temperature for 5 consecutive days is lower than 10 °C, it can be considered as entering the country. During the heating season, if the daily average temperature is higher than 22°C for 5 consecutive days, it can be regarded as entering the cooling season.
  • the manner of determining the operating season of the air conditioner is not limited to the foregoing manner, and in some embodiments, it may also be determined by acquiring broadcast messages of a network data platform or a manual setting manner.
  • the network data platform can broadcast operating season information to the air conditioners in the designated area according to the forecast of the weather platform; another example, when the season is approaching, it can provide the user with a reminder message, and the user can manually set the operating season.
  • the method may further include: outputting prompt information for the replacement of the operating season when the determined operating season changes with respect to the determined operating season for the last operation of the air conditioner, and obtaining the information fed back by the user.
  • the step of acquiring the self-adjustment model corresponding to the running season is performed. That is to say, when there is a season change, the user can be reminded, and after the user's confirmation, the self-adjustment model can be adjusted according to the characteristics of the operating season.
  • the above self-adjustment model is obtained by using the operating state of the air conditioner in the operating season and the corresponding environmental data as training samples through machine learning algorithm training. That is to say, the method of this embodiment trains corresponding self-adjusting models for typical operating seasons respectively, and the accuracy of the prediction calculation is higher.
  • the method of this embodiment can also provide the user with a manual control option or provide General purpose self-tuning model.
  • Step S304 acquiring the operating state and environmental parameters of the air conditioner.
  • the operating state of the air conditioner may include, but is not limited to: on-off state, operating mode, set temperature, set scene, set wind power, air guide mode, 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 composition, and the like. Further, the environmental parameters may also include the physical state of the user, such as physiological index data, location, and the like.
  • Step S306 input the operating state and environmental parameters into the self-adjusting model.
  • Step S308 using the self-adjustment model to perform prediction and calculation to obtain the self-adjustment strategy of the air conditioner.
  • the self-adjustment strategy is not simply to set the parameter thresholds, but includes the adjustment basis and adjustment methods of various operating parameters of the air conditioner, including but not limited to: various setting parameters, the speed of state change, the type of environmental data that is ignored or adopted, switch conditions, etc.
  • Step S310 the air conditioner is controlled according to the self-adjustment strategy.
  • the air conditioner can be intelligently adjusted according to the self-adjusting measurement to meet the user's comfort requirements.
  • the air conditioner self-adjustment model corresponding to the operation season preferably uses the self-adjustment model used in the same operation season of the previous year. Since the self-adjustment model used in the same operation season of the previous year is generally iteratively trained using the actual operation data of the air conditioner, it is more in line with the actual needs of the user of the air conditioner. Therefore, the step of acquiring the air conditioner self-adjustment model corresponding to the operating season may include: acquiring the operation record of the air conditioner, and judging whether the air conditioner has invoked the self-adjustment model corresponding to the operating season according to the operation record, and if so, acquiring the previously invoked self-adjustment model. The self-adjusting model corresponding to the running season.
  • the above operation records can be used to record various operation data of the air conditioner, including but not limited to: power-on/off records, parameter adjustment records, model usage records, model training records, user manual adjustment records, environmental data, and the like.
  • the operation record of the air conditioner can determine the self-adjustment model used in the same operation season of the previous year, or whether the currently determined self-adjustment model has been invoked by the user.
  • the self-adjustment model used is preferably used, which can better satisfy the user's usage habits.
  • the method further includes: acquiring the initial self-adjustment model of the operation season configured for the area where the air conditioner is located, and using the initial self-adjustment model of the operation season as the self-adjustment model.
  • the initial self-adjustment model in the running season can be obtained by training the operating data of the area where the air conditioner is located.
  • the training of using big data samples preferentially can fully reflect the climate characteristics of the region and the air conditioner usage preferences of users in the area. That is to say, in the case that the controlled air conditioner has not called the self-adjusting model corresponding to the operating season before, using the initial model of the area where it is located for prediction calculation, it can meet the comfort of most users with a high probability sexual requirements.
  • the air conditioner may further include: obtaining a manual adjustment record of the air conditioner, judging whether the manual adjustment record exceeds the set number of times threshold, and if so, using the manual adjustment record and the environmental parameters during the manual adjustment as Training samples to iteratively train the self-tuning model. Iterative training of the self-tuning model with manual tuning records and environmental parameters during manual tuning can better match the user's personalized needs. For example, the manual adjustment records of the last several times (eg, 5 times, 10 times) and the environmental parameters during the manual adjustment period can be used as training samples. The number of times thresholds are set to exclude user misoperations and temporary adjustments in special circumstances.
  • the self-tuning model recorded in the previous same running season for intelligent adjustment, and secondly, the initial self-tuning model trained with big data can be used.
  • the needs of users can be further satisfied, and the changing requirements of user habits can be met.
  • an operation season with clear usage characteristics is selected, and the self-adjustment model is obtained by training separately for each operation season. , using the self-adjustment model to predict and calculate the operating state and environmental parameters of the air conditioner, so as to control the air conditioner according to the obtained self-adjustment strategy, which can effectively play the function of the air conditioner.
  • FIG. 4 is a flowchart of an application example of an intelligent control method for an air conditioner according to an embodiment of the present invention. The following steps can be included in this application example:
  • Step S402 determining the operating season of the air conditioner, and the determining method may include passing weather laws, receiving broadcast messages from a climate platform, and judging time.
  • Step S404 judging whether the operating season has changed, that is, judging whether the determined operating season has changed relative to the determined operating season of the last operation of the air conditioner, thereby judging whether there is a change of seasons. For example, if the daily average temperature is higher than 22°C for 5 consecutive days in mid-June, it is determined that the cooling season is entered.
  • Step S406 after determining the change of the operating season, output the prompt information of the replacement of the operating season.
  • Step S408 it is judged whether a confirmation response to the prompt information of running season replacement feedback from the user is obtained.
  • step S410 if a confirmation response from the user is obtained, it is determined whether the air conditioner has invoked the self-adjustment model corresponding to the operating season.
  • Step S412 if not called, obtain the initial self-adjustment model of the operating season configured for the area where the air conditioner is located.
  • Step S414 if it has been called, obtain the self-adjustment model corresponding to the running season that has been called before.
  • Step S416 Obtain the operating state and environmental parameters of the air conditioner, input the operating state and environmental parameters into the self-adjustment model, and use the self-adjustment model to perform prediction and calculation to obtain the self-adjustment strategy of the air conditioner.
  • Step S420 obtaining a manual adjustment instruction of the air conditioner.
  • Step S422 it is determined whether the number of manual adjustment times exceeds the set number of times threshold.
  • Step S424 if it exceeds, the manual adjustment record and the environmental parameters during the manual adjustment period are used as training samples, and the self-adjustment model is iteratively trained.
  • the above process is only an application example, and the execution order of the steps and some steps may be added or deleted based on the introduction of the intelligent control method of the air conditioner in this embodiment.
  • the air conditioner is not in a certain operating season with typical characteristics, or there is no operating season or corresponding self-adjusting model (for example, the weather changes frequently and randomly or the air conditioner has a small operating rate)
  • this implementation may also preferably provide the user with manual control options or configure a general-purpose self-tuning model.

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Abstract

一种空调器的智能控制方法与智能控制设备。其中空调器的智能控制方法包括：确定空调器所处的运行季，并获取与运行季对应的自调整模型，自调整模型利用运行季中的空调器的运行状态及对应的环境数据作为训练样本通过机器学习算法训练得到；获取空调器的运行状态以及环境参数；将运行状态和环境参数输入自调整模型；利用自调整模型进行预测计算得到空调器的自调整策略；以及按照自调整策略对空调器进行控制。本发明的方案，选取具有明确使用特征的运行季分别训练得到自调整模型，利用自调整模型进行预测计算，按照得出的自调整策略对智能空调器进行控制，可以准确地满足用户对环境的舒适性要求，提高了智能家电的智能化水平。

Description

空调器的智能控制方法与智能控制设备 技术领域
本发明涉及智能家电控制，特别是涉及空调器的智能控制方法与智能控制设备。
背景技术
随着生活水平的日益提高，消费者对家电的选择不再是单单注重产品的质量，而是更注重产品能够带来的使用体验。
对于空调器之类的环境调节设备，用户的需求在于获得高舒适性的环境体验。为了满足用户的需求，空调器的功能逐渐扩展，控制也更加精细化。因此，空调器的使用也越来越复杂。现有技术中已经使用终端App来进行控制，然而这也使得用户学习使用的门槛越来越高，操作也越来越复杂。这反而给用户带来的不便。
随着人工智能、机器学习等技术的快速发展，在空调器中使用相关智能技术也逐渐成为技术研究热点。然而现有应用人工智能技术的空调器的智能控制方法的使用结果还不能完全满足用户的使用需求，部分用户甚至反馈智能空调器提供的环境不够舒适，反而带来了更多困扰。
发明内容
本发明的一个目的是要提供一种至少在一定程度上解决上述相关技术中的技术问题任一方面的空调器的智能控制方法与智能控制设备。
本发明一个进一步的目的是要空调器可以智能地提供舒适的室内环境，提高用户的使用体验。
特别地，本发明提供了一种空调器的智能控制方法，该智能控制方法包括：确定空调器所处的运行季，并获取与运行季对应的自调整模型，自调整模型利用运行季中的空调器的运行状态及对应的环境数据作为训练样本通过机器学习算法训练得到；获取空调器的运行状态以及环境参数；将运行状态和环境参数输入自调整模型；利用自调整模型进行预测计算得到空调器的自调整策略；以及按照自调整策略对空调器进行控制。
可选地，确定空调器所处的运行季的步骤包括：获取空调器的安装位置信息；根据安装位置信息确定空调器所在区域的气候规律；按照气候规律确 定空调器所处的运行季。
可选地，确定空调器所处的运行季的步骤包括：获取空调器此前设定时间段内的环境数据；根据环境数据确定空调器所处的运行季。
可选地，获取与运行季对应的空调器自调整模型的步骤包括：获取空调器的运行记录；根据运行记录判断空调器是否调用过与运行季对应的自调整模型；若是，则获取之前调用过的与运行季对应的自调整模型。
可选地，在空调器未调用过与运行季对应的自调整模型的情况下还包括：获取为空调器所在区域配置的运行季初始自调整模型，将运行季初始自调整模型作为自调整模型。
可选地，在按照自调整策略对空调器进行控制的步骤之后还包括：获取空调器的手动调整记录；判断手动调整记录是否超过设定次数阈值；若是，将手动调整记录以及手动调整期间的环境参数作为训练样本，对自调整模型进行迭代训练。
可选地，在确定空调器所处的运行季的步骤之后还包括：在确定出的运行季相对于空调器上次运行确定的运行季出现变化的情况下，获取空调器的时钟信息，利用时钟信息对确定出的运行季进行验证；在运行季与时钟信息相匹配的情况下，执行获取与运行季对应的自调整模型的步骤。
可选地，在确定空调器所处的运行季的步骤之后还包括：在确定出的运行季相对于空调器上次运行确定的运行季出现变化的情况下，输出运行季更替提示信息；获取由用户反馈的针对运行季更替提示信息的响应操作，在响应操作指示确认运行季的情况下，执行获取与运行季对应的自调整模型的步骤。
可选地，运行季包括以下任意一项或多项：制冷季节、采暖季节、梅雨季节、除霾季节。在制冷季节中，空调器的优先运行模式为制冷模式；在采暖季节中，空调器的优先运行模式为制热模式；在梅雨季节中，空调器的优先运行模式为除湿模式；在除霾季节中，空调器的优先运行模式为净化模式。
根据本发明的另一个方面，还提供了一种空调器的智能控制设备。该空调器的智能控制设备包括：处理器；以及存储器，存储器内存储有机器可执行程序，机器可执行程序被处理器执行时用于实现上述任一种的空调器的智能控制方法。
本发明的空调器的智能控制方法，针对单一的空调器自调整模型无法满 足各种运行条件下的调整要求，选取具有明确使用特征的运行季，针对每种运行季分别训练得到自调整模型，利用自调整模型对空调器的运行状态以及环境参数进行预测计算，从而按照得出的自调整策略对空调器进行控制。本发明的方法由于按照运行季的数据进行人工学习模型的训练，减小了自调整模型的预测计算难度，得到的自调整策略更加符合运行季的环境调节需求，从而提高了用户的使用体验。
进一步地，本发明的空调器的智能控制方法，可以根据空调器的安装区域选择制冷季节、采暖季节、梅雨季节、除霾季节中的一项或多项作为运行季，由于这些运行季一般具有优先运行模式，那么通过自调整模型的预测计算的不确定性大大降低。
更进一步地，本发明的空调器的智能控制方法，还优化了运行季的确定方法以及自调整模型迭代训练的方式等，使得控制方法更加智能高效，满足了用户的智能要求。相对于智能家电(智慧家电)及智能空调(智慧空调)等领域中的现有技术，本发明的方案更加智能高效，提高了智能化水平。
根据下文结合附图对本发明具体实施例的详细描述，本领域技术人员将会更加明了本发明的上述以及其他目的、优点和特征。
附图说明
后文将参照附图以示例性而非限制性的方式详细描述本发明的一些具体实施例。附图中相同的附图标记标示了相同或类似的部件或部分。本领域技术人员应该理解，这些附图未必是按比例绘制的。附图中：
图1是根据本发明一个实施例的空调器的数据交互示意图；
图2是根据本发明一个实施例的空调器的智能控制设备的示意图；
图3是根据本发明一个实施例的空调器的智能控制方法的示意图；以及
图4是根据本发明一个实施例的空调器的智能控制方法应用实例的流程图。
具体实施方式
图1是根据本发明一个实施例的空调器10的数据交互示意图。终端设备20(包括但不限于各种移动终端)上安装有空调器控制应用程序(App)或者其他控制客户端(Client)。空调器10的用户可以通过应用程序或者客户端来配置空调器10的功能以及应用场景。
网络数据平台30可以用于采集并记录空调器10运行数据、采集并记录用户行为信息等。网络数据平台30可以通过对空调器10运行数据以及用户行为信息进行机器学习模型的训练，并利用训练得到的模型进行空调器10的预测计算。其中预测计算的决策条件包括空调器10的室内外环境(温度、湿度、污染情况、风力、天气等)、用户信息(各项生理指标、位置、穿衣指数等)，预测的目标包括：空调器10的开关状态(包括开机参数)、运行模式(制冷、制热、净化、除湿等)、设定参数(风力、风向、温度、湿度等)。
空调器10获取自身运行状态以及室内外环境数据，并按照网络数据平台30预测得到的自调整策略对空调器10进行控制。
另外，网络数据平台30还可以向空调器10和终端设备20发送各种提醒消息，并接收用户通过空调器10以及终端设备20回复的信息。
本实施例中使用的机器学习模型(自调整模型)可以是能够从已有数据(空调器10的运行状态以及环境参数)中学习到一定的知识和能力用于处理新数据，并可以被设计用于执行各种任务，在本实施例中用于对空调器10控制策略的确定。机器学习模型的示例包括但不限于各类深度神经网络(DNN)、支持向量机(SVM)、决策树、随机森林模型等等。在实施例中，机器学习模型也可以被称为“学习网络”。其中神经网络控制模型可以采用各种已知的适合有监督学习的网络结构，例如感知器模型，分类器模型，Hopfield网络等基本的神经网络结构，各种相应的主流训练方法也都可以用于本实施例的模型参数的确定。示例机器学习模型包括神经网络或其他多层非线性模型。示例神经网络包括前馈神经网络、深度神经网络、递归神经网络和卷积神经网络。
机器学习模型可以包括在服务器计算系统(网络数据平台30)中或以其他方式由服务器计算系统(网络数据平台30)存储和实现，服务器计算系统(网络数据平台30)根据客户端-服务器关系(或者应用程序-服务器)与终端设备20或者空调器10通信。例如，机器学习模型可以由服务器计算系统(网络数据平台30)实现为web服务的一部分。因此，可以在终端设备20处存储和实现一个或多个模型和/或可以在服务器计算系统(网络数据平台30)处存储和实现一个或多个模型。
服务器计算系统(网络数据平台30)可以包括一个或多个服务器计算设 备或以其他方式由一个或多个服务器计算设备实现。在服务器计算系统包括多个服务器计算设备的情况下，这样的服务器计算设备可以根据顺序计算架构、并行计算架构或其一些组合来操作。
终端设备20或者空调器10和/或服务器计算系统(网络数据平台30)可以经由与通过网络通信地耦接的训练计算系统的交互来训练模型。训练计算系统可以与服务器计算系统(网络数据平台30)分离，或者可以是服务器计算系统(网络数据平台30)的一部分。
终端设备20或者空调器10和服务器计算系统网络之间可以通过任何类型的通信网络进行交互，诸如局域网(例如内联网)、广域网(例如因特网)或其一些组合，并且可以包括任何数量的有线或无线链路。通常，通过网络的通信可以经由任何类型的有线和/或无线连接，使用各种通信协议(例如，TCP/IP、HTTP、SMTP、FTP)、编码或格式(例如、HTML、XML)和/或保护方案(例如、VPN、安全HTTP、SSL)来承载。
本领域技术人员可以根据需要在终端设备20、空调器10、网络数据平台30分配数据的处理和运算功能。例如在终端设备20和空调器10中对数据进行一定的预处理，以提高数据传输的效率。
图2是根据本发明一个实施例的空调器的智能控制设备300的示意图。该智能控制设备300一般性地可以包括：存储器320以及处理器310，其中存储器320内存储有机器可执行程序321，机器可执行程序321被处理器310执行时用于实现本实施例的空调器的智能控制方法。处理器310可以是一个中央处理单元(central processing unit，简称CPU)，或者为数字处理单元等等。处理器310通过通信接口收发数据。存储器320用于存储处理器310执行的程序。存储器320是能够用于携带或存储具有指令或数据结构形式的期望的程序代码并能够由计算机存取的任何介质，也可以是多个存储器的组合。上述机器可执行程序321可以从计算机可读存储介质下载到相应计算/处理设备或者经由网络(例如因特网、局域网、广域网和/或无线网络)下载并安装到智能控制设备300。
智能控制设备300可布置于上述网络数据平台30中。此外，智能控制设备300的功能也可以基于计算机的系统的固有灵活性允许在网络数据平台30、终端20、空调器10之间进行配置、组合以及划分。
图3是根据本发明一个实施例的空调器的智能控制方法的示意图，该空 调器的智能控制方法可以包括：
步骤S302，确定空调器所处的运行季，并获取与运行季对应的自调整模型。
上述运行季根据空调器的运行状态进行确定，一般可为空调器具有典型应用环境的时间段。例如运行季包括以下任意一项或多项：制冷季节、采暖季节、梅雨季节(或称为雨季或潮湿季节)、除霾季节(或称为净化季)，本领域技术人员可以根据空调器所在区域的气候特点以及用户对环境的需求，配置运行季。例如对于热带区域，可以仅配置制冷季节以及潮湿季节；对于四季分明的区域，可以配置制冷季节、采暖季节；对于具有季风气候且环境较为恶劣的区域，可以增加设置除霾季节。
在制冷季节中，空调器的优先运行模式为制冷模式；在采暖季节中，空调器的优先运行模式为制热模式；在梅雨季节中，空调器的优先运行模式为除湿模式；在除霾季节中，空调器的优先运行模式为净化模式。
确定空调器所处的运行季的一种可选方式为：获取空调器的安装位置信息；根据安装位置信息确定空调器所在区域的气候规律，按照气候规律确定空调器所处的运行季。空调器的安装位置信息可以通过空调器的销售及维护记录确定，也可以通过空调器的用户上报确定，还可以通过与空调器绑定的终端的位置确定。由于不同的区域，其气候规律存在较大的差别，可以通过气候规律来确定运行季，如上文所介绍的，不同的区域可以根据气候设置相应运行季。例如对于北京区域，11月中旬至次年3月中旬可以为采暖季节，而6月中旬至8月中旬可以为制冷季节。又例如对于上海区域，6月中旬至7月上旬是梅雨季节，而12月中旬至2月底可以为采暖季节。
确定空调器所处的运行季的另一种可选方式为：获取空调器此前设定时间段内的环境数据，根据环境数据确定空调器所处的运行季。例如可以获取5天至10天内的室外温湿度，用于运行季的判断。例如可以将空调器设定周期内(例如5天至10天)的室外环境数据与上述气候规律进行匹配，例如对于中国华北区域，若连续5天日平均气温低于10℃，即可认为进入采暖季节，若连续5天日平均气温高于22℃，即可认为进入制冷季节。
确定空调器所处的运行季的方式并不局限于上述方式，在一些实施例中还可以通过获取网络数据平台的广播消息或者人工设定方式来确定。例如网络数据平台可以根据气象平台的预报向指定区域的空调器广播运行季消息； 又例如在临近换季时间，可以向用户提供提醒消息，由用户人工设定运行季。
在确定出空调器所处的运行季之后还可以包括：在确定出的运行季相对于空调器上次运行确定的运行季出现变化的情况下，输出运行季更替提示信息，获取由用户反馈的针对运行季更替提示信息的响应操作，在响应操作指示确认运行季的情况下，执行获取与运行季对应的自调整模型的步骤。也就是说在出现换季的情况下，可以向用户提醒，在得到用户的确认后，按照该运行季的特点调整自调整模型。
上述自调整模型利用运行季中的空调器的运行状态及对应的环境数据作为训练样本通过机器学习算法训练得到。也就是说本实施例的方法分别针对典型型性的运行季分别训练相应的自调整模型，其预测计算的准确性更高。
现有智能空调器的可实现的功能越来越多，仅仅以送风为例，除了风力、风向之外，还增加了自然风、循环风、新风、无风感等多种送风模式，而针对不同季节，用户的舒适度感受也存在差别。例如对于春季和秋季，虽然温度相差不大，但是用户的需求确明显不同。不同季节中出现同一环境数据的情况下，使用同一自调整模型进行预测计算，显然无法满足用户的舒适性要求。基于这一问题，本实施例的空调器的智能控制方法对于具有典型使用特征的运行季分别进行模型训练，从而使得自调整模型与运行季相对应，得出的控制策略基本符合运行季的特点，提高了用户的使用体验。
对于并不处于设置的运行季的时段，也即空调器处于天气变化较为频繁且随机或者空调器使用频率较小的时段的情况下，本实施例的方法还可以为用户提供手动控制选项或者提供通用型的自调整模型。
步骤S304，获取空调器的运行状态以及环境参数。空调器的运行状态可以包括但不限于：开关机状态、运行模式、设定温度、设定场景、设定风力、导风方式、压缩机频率等。环境参数可以包括但不限于：室内外温度、室内外湿度、天气、空气颗粒数据、空气成分等。进一步地，环境参数还可以包括用户的身体状态，例如生理指标数据、所在位置等。
步骤S306，将运行状态和环境参数输入自调整模型。
步骤S308，利用自调整模型进行预测计算得到空调器的自调整策略。自调整策略并非单纯的设定参数阈值，而包括空调器各种运行参数的调整依据以及调整方式，包括但不限于：各种设定参数、状态的变化速度、忽略或 者采用的环境数据类型、开关机条件等等。
步骤S310，按照自调整策略对空调器进行控制。空调器可以根据自调整测量智能调整，满足用户的舒适性要求。
与运行季对应的空调器自调整模型优选使用在上一年度的相同的运行季中使用过的自调整模型。由于上一年度的相同的运行季中使用过的自调整模型一般利用空调器的实际运行数据进行了迭代训练，更加符合该空调器的用户的实际需求。因此获取与运行季对应的空调器自调整模型的步骤可以包括：获取空调器的运行记录，根据运行记录判断空调器是否调用过与运行季对应的自调整模型，若是，则获取之前调用过的与运行季对应的自调整模型。上述运行记录可以用于记录空调器的各项运行数据，包括但不限于：开关机记录、参数调整记录、模型使用记录、模型训练记录、用户手动调整记录、环境数据等。空调器的运行记录可以确定出上一年度的相同的运行季中使用过的自调整模型，或者当前确定出的自调整模型是否被该用户调用过。优选采用使用的自调整模型，可以更加满足用户的使用习惯。
在空调器未调用过与运行季对应的自调整模型的情况下还包括：获取为空调器所在区域配置的运行季初始自调整模型，将运行季初始自调整模型作为自调整模型。运行季初始自调整模型可以利用空调器所在区域的运行数据进行训练得到，优先利用大数据样本训练可以充分反映地域的气候特点以及该区域内用户的空调器使用偏好。也就是说，在被控的空调器之前未调用过与运行季对应的自调整模型的情况下，使用其所在区域的初始模型进行预测计算，其在很大概率上可以满足大多数用户的舒适性要求。
在步骤S310按照自调整策略对空调器进行控制之后还可以包括：获取空调器的手动调整记录，判断手动调整记录是否超过设定次数阈值，若是，将手动调整记录以及手动调整期间的环境参数作为训练样本，对自调整模型进行迭代训练。通过利用手动调整记录以及手动调整期间的环境参数对自调整模型进行迭代训练可以更好地匹配用户的个性化需求。例如可以将最近若干次(例如5次、10次)的手动调整记录以及手动调整期间的环境参数作为训练样本。设置次数阈值是为了排除用户的误操作以及特殊情况的临时调整。
在新的运行季到来时，优选使用此前相同的运行季的运行记录的自调整模型进行智能调整，其次可以采用大数据训练的初始自调整模型。通过对自 调整模型的迭代训练，可以进一步满足用户的需求，并且可以满足用户习惯不断变化的要求。
本实施例的空调器的智能控制方法，针对单一的空调器自调整模型无法满足各种运行条件下的调整要求，选取具有明确使用特征的运行季，针对每种运行季分别训练得到自调整模型，利用自调整模型对空调器的运行状态以及环境参数进行预测计算，从而按照得出的自调整策略对空调器进行控制，可以有效地发挥空调器的功能。
图4是根据本发明一个实施例的空调器的智能控制方法应用实例的流程图。在该应用实例中可以包括以下步骤：
步骤S402，确定空调器所处的运行季，确定方式可以包括通过气候规律、接收气候平台的广播消息、时间判断等。
步骤S404，判断运行季是否出现变化，也即判断确定出的运行季相对于空调器上次运行确定的运行季是否出现变化，从而判断是否出现换季的情况。例如若在6月中旬出现连续5天日平均气温高于22℃的情况，则判断进入制冷季节。
步骤S406，在确定运行季变化后，输出运行季更替提示信息。
步骤S408，判断是否获取到用户反馈的针对运行季更替提示信息的确认响应。
步骤S410，若得到用户的确认响应，判断空调器是否调用过与运行季对应的自调整模型。
步骤S412，若未调用过，获取为空调器所在区域配置的运行季初始自调整模型。
步骤S414，若调用过，获取之前调用过的与运行季对应的自调整模型。
步骤S416，获取空调器的运行状态以及环境参数，将运行状态和环境参数输入自调整模型，利用自调整模型进行预测计算得到空调器的自调整策略。
步骤S418，按照自调整策略对空调器进行控制。
步骤S420，获取空调器的手动调整指令。
步骤S422，判断手动调整次数是否超过设定次数阈值。
步骤S424，若超过，将手动调整记录以及手动调整期间的环境参数作为训练样本，对自调整模型进行迭代训练。
本领域技术人员应该了解上述流程仅为一个应用实例，可以在本实施例对空调器的智能控制方法的介绍的基础上调整步骤的执行顺序以及增删部分步骤。对于空调器并不处于某一具有典型特点的运行季中或者不存在运行季或对应的自调整模型的情况下(例如天气变化较为频繁且随机或者空调器开机率较小的情况)，本实施例的方法还可以优选为用户提供手动控制选项或者配置通用型的自调整模型。
至此，本领域技术人员应认识到，虽然本文已详尽示出和描述了本发明的多个示例性实施例，但是，在不脱离本发明精神和范围的情况下，仍可根据本发明公开的内容直接确定或推导出符合本发明原理的许多其他变型或修改。因此，本发明的范围应被理解和认定为覆盖了所有这些其他变型或修改。

Claims (10)

  1. 一种空调器的智能控制方法，包括：
    确定所述空调器所处的运行季，并获取与所述运行季对应的自调整模型，所述自调整模型利用所述运行季中的空调器的运行状态及对应的环境数据作为训练样本通过机器学习算法训练得到；
    获取所述空调器的运行状态以及环境参数；
    将所述运行状态和所述环境参数输入所述自调整模型；
    利用所述自调整模型进行预测计算得到所述空调器的自调整策略；以及
    按照所述自调整策略对所述空调器进行控制。
  2. 根据权利要求1所述的空调器的智能控制方法，其中，所述确定所述空调器所处的运行季的步骤包括：
    获取所述空调器的安装位置信息；
    根据所述安装位置信息确定所述空调器所在区域的气候规律；
    按照所述气候规律确定所述空调器所处的运行季。
  3. 根据权利要求1所述的空调器的智能控制方法，其中，所述确定所述空调器所处的运行季的步骤包括：
    获取所述空调器此前设定时间段内的环境数据；
    根据所述环境数据确定所述空调器所处的运行季。
  4. 根据权利要求1所述的空调器的智能控制方法，其中，所述获取与所述运行季对应的空调器自调整模型的步骤包括：
    获取所述空调器的运行记录；
    根据所述运行记录判断所述空调器是否调用过与所述运行季对应的自调整模型；
    若是，则获取之前调用过的与所述运行季对应的自调整模型。
  5. 根据权利要求4所述的空调器的智能控制方法，在所述空调器未调用过与所述运行季对应的自调整模型的情况下，还包括：
    获取为所述空调器所在区域配置的运行季初始自调整模型，将所述运行 季初始自调整模型作为所述自调整模型。
  6. 根据权利要求1所述的空调器的智能控制方法，在所述按照所述自调整策略对所述空调器进行控制的步骤之后，还包括：
    获取所述空调器的手动调整记录；
    判断所述手动调整记录是否超过设定次数阈值；
    若是，将所述手动调整记录以及手动调整期间的环境参数作为训练样本，对所述自调整模型进行迭代训练。
  7. 根据权利要求1所述的空调器的智能控制方法，在确定所述空调器所处的运行季的步骤之后，还包括：
    在确定出的所述运行季相对于所述空调器上次运行确定的运行季出现变化的情况下，获取所述空调器的时钟信息，利用所述时钟信息对确定出的所述运行季进行验证；
    在所述运行季与所述时钟信息相匹配的情况下，执行获取与所述运行季对应的自调整模型的步骤。
  8. 根据权利要求1所述的空调器的智能控制方法，在确定所述空调器所处的运行季的步骤之后，还包括：
    在确定出的所述运行季相对于所述空调器上次运行确定的运行季出现变化的情况下，输出运行季更替提示信息；
    获取由用户反馈的针对所述运行季更替提示信息的响应操作，在所述响应操作指示确认所述运行季的情况下，执行获取与所述运行季对应的自调整模型的步骤。
  9. 根据权利要求1所述的空调器的智能控制方法，其中，
    所述运行季包括以下任意一项或多项：制冷季节、采暖季节、梅雨季节、除霾季节；且
    在所述制冷季节中，所述空调器的优先运行模式为制冷模式；
    在所述采暖季节中，所述空调器的优先运行模式为制热模式；
    在所述梅雨季节中，所述空调器的优先运行模式为除湿模式；
    在所述除霾季节中，所述空调器的优先运行模式为净化模式。
  10. 一种空调器的智能控制设备，包括：
    处理器；以及
    存储器，所述存储器内存储有机器可执行程序，所述机器可执行程序被所述处理器执行时用于实现根据权利要求1至9中任一项所述的空调器的智能控制方法。
PCT/CN2021/102121 2020-08-28 2021-06-24 空调器的智能控制方法与智能控制设备 WO2022041987A1 (zh)

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