WO2022218014A1 - 用于家电控制的方法、装置和家电 - Google Patents

用于家电控制的方法、装置和家电 Download PDF

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Publication number
WO2022218014A1
WO2022218014A1 PCT/CN2022/075195 CN2022075195W WO2022218014A1 WO 2022218014 A1 WO2022218014 A1 WO 2022218014A1 CN 2022075195 W CN2022075195 W CN 2022075195W WO 2022218014 A1 WO2022218014 A1 WO 2022218014A1
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Prior art keywords
target
parameter
air conditioner
parameters
model
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PCT/CN2022/075195
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English (en)
French (fr)
Inventor
谭强
张飞
陈建龙
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青岛海尔空调器有限总公司
青岛海尔空调电子有限公司
海尔智家股份有限公司
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Publication of WO2022218014A1 publication Critical patent/WO2022218014A1/zh

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B15/00—Systems controlled by a computer
    • G05B15/02—Systems controlled by a computer electric
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/26—Pc applications
    • G05B2219/2642—Domotique, domestic, home control, automation, smart house
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B30/00—Energy efficient heating, ventilation or air conditioning [HVAC]
    • Y02B30/70—Efficient control or regulation technologies, e.g. for control of refrigerant flow, motor or heating

Definitions

  • the present application relates to the technical field of smart home appliances, for example, to a method, device and home appliance for controlling home appliances.
  • the embodiments of the present disclosure provide a method, a device, and a home appliance for controlling home appliances, so as to ensure the stability of the indoor environment and improve the life experience of users.
  • the method for controlling home appliances includes: obtaining current environmental parameters, and inputting the current environmental parameters into a preset control model for adjusting the operation information of the air conditioner; controlling the prediction of the output of the air conditioner in the preset control model run under the operating information, and obtain the target environmental parameters and the first real-time environmental parameters during the operation of the air conditioner; according to the first real-time environmental parameters and the target environmental parameters, determine the target household appliances used to meet the target environmental parameters, and the target operation of the target household appliances parameters; control the target appliance to run under the target operating parameters.
  • the preset control model includes a parameter prediction model
  • the parameter prediction model is obtained by: obtaining a first sample for training the parameter prediction model, and randomly dividing the first sample into a training set and a test set
  • the first sample includes the user habit information used to adjust the operation information of the air conditioner under different environmental parameters; the different environmental parameters in the training set are input into the initial prediction model, and the user habit information corresponding to the different environmental parameters in the training set is used as the initial prediction
  • the output of the model is used to train the initial prediction model; the initial prediction model after training is verified according to the test set, and the verification result is obtained; when the verification result indicates that the prediction is accurate, the parameter prediction model is obtained.
  • the mode classification model is obtained by: obtaining the mode classification model for training The second sample of the model, the second sample includes the set operating temperature of the marked operating mode information under different environmental parameters; input the set operating temperature corresponding to the different environmental parameters and the different environmental parameters into the initial classification model, and set the set operating temperature.
  • the operating mode information corresponding to the fixed operating temperature is used as the output of the initial classification model to train the initial classification model to obtain the mode classification model.
  • training the initial classification model to obtain the pattern classification model includes: randomly dividing the second sample into training samples and test samples; training the pattern classification model according to the training samples, and testing the pattern classification model according to the test samples, Obtain the test result; if the test result indicates that the operation mode information does not match the corresponding environmental parameters, continue to train the mode classification model according to the training sample.
  • determining the target household appliance for satisfying the target parameter and the target operating parameter of the target household appliance including: if the first real-time environmental parameter includes at least the indoor real-time humidity, the target The environmental parameters include at least the indoor target humidity, then in the case that the first difference between the indoor target humidity and the indoor real-time humidity is greater than or equal to the preset humidity difference, the humidifier is determined as the target household appliance, and the determination will be made according to the first difference.
  • the humidification volume is set as the target operating parameter of the humidifier.
  • determining a target home appliance for satisfying the target parameter and target operating parameters of the target home appliance according to the first real-time environmental parameter and the target environmental parameter including: if the first real-time environmental parameter at least includes indoor real-time air cleanliness , the target environmental parameters include at least the indoor target air cleanliness, then in the case where the second difference between the indoor target air cleanliness and the indoor real-time air cleanliness is greater than or equal to the preset cleanliness difference, determine the fresh air blower as the target home appliance , and set the fresh air ratio determined according to the second difference as the target operating parameter of the fresh air fan.
  • the method further includes: determining environmental impact information associated with the predicted operating information according to the first real-time environmental information and the current environmental parameters; The influence factor model of the parameters; the influence factor model is modified by using the environmental influence information, the target household appliances and the target operating parameters of the target household appliances.
  • the method further includes: if the user sends a control instruction to the air conditioner, obtaining the set operation information of the air conditioner corresponding to the control instruction; The operation information switches the operation to the set operation information.
  • the apparatus for home appliance control includes an input module, an acquisition module, a determination module and a control module.
  • the input module is configured to obtain the current environmental parameters and input the current environmental parameters to the preset control model for adjusting the operation information of the air conditioner;
  • the obtaining module is configured to control the air conditioner to operate under the predicted operation information output by the preset control model, and Obtaining target environmental parameters and first real-time environmental parameters during the operation of the air conditioner;
  • the determining module is configured to determine, according to the first real-time environmental parameters and the target environmental parameters, target household appliances for satisfying the target environmental parameters, and target operating parameters of the target household appliances ;
  • the control module is configured to control the target appliance to operate under the target operating parameters.
  • the apparatus for controlling a home appliance includes a processor and a memory storing program instructions, and the processor is configured to execute the above-described method for controlling a home appliance when executing the program instructions.
  • the home appliance includes the above-mentioned device for home appliance control.
  • the method, device, and home appliance for controlling home appliances provided by the embodiments of the present disclosure can achieve the following technical effects:
  • the air conditioner By obtaining the current environmental parameters and inputting the current environmental parameters into the preset control model for adjusting the operation information of the air conditioner, the air conditioner can be operated under the predicted operation information output by the preset control model, so that the air conditioner can automatically realize intelligent control, which simplifies the operation of the air conditioner.
  • User operation at the same time, the target environment parameters and the first real-time environment parameters during the operation of the air conditioner are obtained, and according to the first real-time environment parameters and the target environment parameters, the target home appliances for satisfying the target environment parameters and the target operation of the target home appliances are determined. parameters, intelligently control the target home appliances to operate under the target operating parameters, ensure that the indoor temperature is always in a suitable range, avoid the environmental imbalance caused by the long-term opening of the air conditioner, and affect the user experience.
  • FIG. 1 is a flowchart of a method for controlling a home appliance provided by an embodiment of the present disclosure
  • FIG. 2 is a schematic diagram of an apparatus for controlling a home appliance provided by an embodiment of the present disclosure
  • FIG. 3 is a schematic diagram of an apparatus for controlling a home appliance provided by an embodiment of the present disclosure.
  • A/B means: A or B.
  • a and/or B means: A or B, or, A and B three relationships.
  • the method for controlling a home appliance is applied to an indoor environment with at least an air conditioning system.
  • the air conditioning system can include home appliances such as air conditioners, humidifiers, and fresh fans, so as to achieve precise regulation of indoor air.
  • FIG. 1 is a flowchart of a method for controlling a home appliance provided by an embodiment of the present disclosure.
  • an embodiment of the present disclosure provides a method for controlling a home appliance, so as to realize the control of the above-mentioned indoor environment, and the method may include:
  • the current environmental parameters may at least include parameters such as the current indoor temperature, the current outdoor temperature, the current humidity, and the current air cleanliness.
  • the current environmental parameters can be obtained quickly and accurately.
  • the parameter prediction model can be obtained by obtaining a first sample for training the parameter prediction model, and randomly dividing the first sample into a training set and a test set.
  • the first sample includes the user habit information used to adjust the operation information of the air conditioner under different environmental parameters; the different environmental parameters in the training set are input into the initial prediction model, and the user habit information corresponding to the different environmental parameters in the training set is used as the initial prediction
  • the output of the model is used to train the initial prediction model; the initial prediction model after training is verified according to the test set, and the verification result is obtained; when the verification result indicates that the prediction is accurate, the parameter prediction model is obtained.
  • the machine learning algorithm is introduced into the intelligent control logic of the air conditioner, which helps the air conditioner to run accurately and automatically under the operating information that the user is accustomed to, frees the user from the complicated operation method of manually setting the operating information of the air conditioner, and improves the use of the user. experience.
  • the initial prediction model may be a prediction model determined according to a machine learning algorithm, and specifically may be an artificial neural network algorithm, a random forest algorithm, a decision tree algorithm, a support vector machine algorithm, or the like.
  • SVM Small Vector Machine
  • the first sample is randomly divided into a training set and a test set, different environmental parameters in the training set are used as training input, and user habit information corresponding to different environmental parameters is used as training.
  • Output call the svmtrain function to train the initial prediction model; use the different environmental parameters in the test set as the prediction input, call the svmprediect function to output the respective predicted user habit information under different environmental parameters; predict the user habit information and the training set.
  • User habit information Fit into a curve to validate the initial predictive model after training, and get the validation result. In this way, the model obtained according to the SVM algorithm has a higher prediction accuracy, thereby improving the intelligence of the air conditioner.
  • different environmental parameters may be embodied as different values corresponding to various environmental parameters.
  • the user habit information used to adjust the operation information of the air conditioner under different environmental parameters may specifically include the user's habitual set operating temperature, set operating mode, set wind speed, set wind mode, and the like.
  • the user habit information can be obtained in various implementation manners, which are described below with examples.
  • the air conditioner operation information set by the user under different environmental parameters may be collected respectively within a preset time period, so as to determine the user habit information according to the collected data.
  • the preset duration may be 30 days to 90 days. It is preferably 90 days, so that complete air-conditioning operation information can be collected on a quarterly basis as much as possible, so as to accurately obtain user habit information on a seasonal basis.
  • the user habit information can be obtained by means of a user inputting information.
  • the information input module can be embodied as a keyboard, so that the user can manually input user habit information through the keyboard; or, the information input module can be embodied as a voice acquisition module, the user can input user habit information through voice, and the air conditioner or smart terminal can perform voice identification to obtain user habit information.
  • the user habit information can be obtained very conveniently, quickly and at a low cost through the information input module.
  • the above-mentioned intelligent terminal is, for example, a mobile device, a computer, or a vehicle-mounted device built in a floating car, or any combination thereof.
  • the mobile device may include, for example, a cell phone, a wearable device, a smart mobile device, etc., or any combination thereof.
  • the mode classification model can be obtained by the following method:
  • the second sample includes the set operating temperature of the marked operating mode information under different environmental parameters; input the set operating temperature corresponding to the different environmental parameters and different environmental parameters into the initial classification model, and set the set operating temperature
  • the operating mode information corresponding to the operating temperature is used as the output of the initial classification model to train the initial classification model to obtain the mode classification model.
  • the operation mode information may be a cooling mode or a heating mode.
  • the cooling mode may refer to the working state of the air conditioner when the indoor heat exchanger acts as an evaporator to participate in the air conditioning process, which may at least include the normal cooling mode, the dehumidification mode, and the frosting of the indoor heat exchanger during the self-cleaning process or the outdoor The working mode of heat exchanger defrosting.
  • the heating mode can refer to the working state of the air conditioner when the indoor heat exchanger is used as a condenser to participate in the air conditioning process, which can at least include the normal heating mode, the defrosting mode of the indoor heat exchanger during the self-cleaning process, and the self-cleaning mode. High temperature sterilization mode of the indoor heat exchanger during the process.
  • the operation mode information may be marked with different identification information, for example, the cooling mode is marked as "-1", and the heating mode is marked as "1", which may not be specifically limited in this embodiment of the present disclosure.
  • the initial classification model may be a classification model determined according to a machine learning algorithm, and may specifically be a K-nearest neighbor algorithm, a naive Bayesian algorithm, an SVM algorithm, or the like.
  • training the initial classification model to obtain the pattern classification model may include: randomly dividing the second sample into training samples and test samples; training the pattern classification model according to the training samples, and testing the pattern classification model according to the test samples, and obtaining the test Result; if the test result indicates that the operating mode information does not match the corresponding environmental parameters, continue to train the mode classification model according to the training samples. In this way, it is convenient to build a model and subsequently test the accuracy of the pattern classification model.
  • the air conditioner is controlled to operate under the predicted operation information output by the preset control model, and the target environment parameter and the first real-time environment parameter during the operation of the air conditioner are obtained.
  • the air conditioner after controlling the air conditioner to operate under the predicted operation information output by the preset control model, it may further include: if the user sends a control command to the air conditioner, obtaining the set operation information of the air conditioner corresponding to the control command; switching the air conditioner from the predicted operation information Run to set running information. In this way, the control instruction output by the user is preferentially responded to, which can provide the user with a better use experience.
  • new environmental parameters for the operation of the air conditioner under the set operation information can be obtained; according to the set operation information and the new environment parameters, new user habit information for adjusting the operation information of the air conditioner under the new environment parameters is determined to update first sample.
  • new user habit information in this way can optimize the training sample data of the predictive control model, thereby helping the air conditioner to operate under the most satisfactory operating information for the user and improving the user experience.
  • S13 Determine, according to the first real-time environment parameter and the target environment parameter, a target home appliance for satisfying the target environment parameter, and a target operation parameter of the target home appliance.
  • determining the target household appliance for satisfying the target parameter and the target operating parameter of the target household appliance may include: if the first real-time environmental parameter includes at least the indoor real-time humidity, the target environment The parameters include at least the indoor target humidity, then in the case that the first difference between the indoor target humidity and the indoor real-time humidity is greater than or equal to the preset humidity difference, the humidifier is determined as the target household appliance, and the first difference is determined.
  • the humidification amount is set as the target operating parameter of the humidifier. In this way, it can be ensured that when the air conditioner is running, the indoor humidity is always in a suitable range, avoiding environmental imbalance caused by the long-term opening of the air conditioner, and improving the intelligent linkage control of home appliances and the user experience.
  • the value range of the indoor target humidity may be 40% to 60%.
  • the preset humidity difference value can be 20%. Setting the preset humidity difference in this way can avoid adjusting the humidity when the room is too dry, which affects the user experience, and also avoid adjusting the humidity when the room is not dry, causing energy waste.
  • determining the target household appliance for satisfying the target parameter and the target operating parameter of the target household appliance may include: if the first real-time environmental parameter includes at least the indoor real-time air cleanliness, The target environment parameters include at least the indoor target air cleanliness, then when the second difference between the indoor target air cleanliness and the indoor real-time air cleanliness is greater than or equal to the preset cleanliness difference, determine the fresh air blower as the target home appliance, The fresh air ratio determined according to the second difference is set as the target operating parameter of the fresh air fan.
  • air cleanliness can be expressed as air quality, that is, the concentration of pollutants in the air, such as formaldehyde concentration, PM2.5 (fine particulate matter) concentration, and the like.
  • air quality that is, the concentration of pollutants in the air, such as formaldehyde concentration, PM2.5 (fine particulate matter) concentration, and the like.
  • PM2.5 the indoor target air cleanliness, that is, the indoor target PM2.5 concentration can be 50 mg.
  • the value of the preset cleanliness difference may be 10-20 mg. Setting the preset cleanliness difference in this way can avoid purifying when the indoor air quality is too poor, which affects the user experience, and can avoid purifying when the indoor air quality is good, resulting in energy waste.
  • the target household appliance may further include: determining the environmental impact information associated with the predicted operating information according to the first real-time environmental information and the current environmental parameters; obtaining information for determining the target household appliance and the target operating parameters.
  • Influence factor model Use environmental influence information, target household appliances and target operating parameters of target household appliances to modify the influence factor model. In this way, the intelligent effect of the linkage control of home appliances can be further improved. By coordinating and linking multiple home appliances, the indoor environment can always be in a suitable range, and the user experience can be improved.
  • the first real-time environmental parameter includes the first real-time humidity
  • the predicted operation information of the air conditioner includes that the air conditioner is in the cooling mode
  • the first real-time humidity will drop when the air conditioner operates in the cooling mode, so the predicted operation information is associated with
  • the environmental impact information can at least be reflected in the impact on humidity when the air conditioner is running.
  • the air conditioner can be controlled in the preset control model.
  • the air conditioner automatically realizes intelligent control and simplifies the user operation; at the same time, the target environment parameters and the first real-time environment parameters during the operation of the air conditioner are obtained, and according to the first real-time environment parameters and the target environment parameters, Determine the target household appliances that meet the target environmental parameters and the target operating parameters of the target household appliances, intelligently control the target household appliances to operate under the target operating parameters, ensure that the indoor temperature is always in the appropriate range, and avoid the environmental imbalance caused by the long-term opening of the air conditioner, affect the user experience.
  • FIG. 2 is a schematic diagram of an apparatus for controlling a home appliance provided by an embodiment of the present disclosure.
  • an embodiment of the present disclosure provides an apparatus for controlling a home appliance, including an input module 21 , an acquisition module 22 , a determination module 23 and a control module 24 .
  • the input module 21 is configured to obtain the current environmental parameters and input the current environmental parameters to the preset control model for adjusting the operation information of the air conditioner;
  • the obtaining module 22 is configured to control the air conditioner to operate under the predicted operation information output by the preset control model , and obtain the target environmental parameters and the first real-time environmental parameters during the operation of the air conditioner;
  • the determining module 23 is configured to determine, according to the first real-time environmental parameters and the target environmental parameters, the target household appliances for satisfying the target environmental parameters, and the target household appliances Target operating parameters;
  • the control module 24 is configured to control the target appliance to operate under the target operating parameters.
  • the air conditioner is operated under the predicted operation information output by the preset control model, and the air conditioner is automatically operated. It realizes intelligent control and simplifies user operations; at the same time, intelligently controls the target home appliances to operate under the target operating parameters to ensure that the indoor temperature is always within a suitable range, avoiding environmental imbalance caused by long-term air conditioning, and affecting user experience.
  • FIG. 3 is a schematic diagram of an apparatus for controlling a home appliance provided by an embodiment of the present disclosure.
  • an embodiment of the present disclosure provides an apparatus for controlling a home appliance, including a processor (processor) 100 and a memory (memory) 101 .
  • the apparatus may further include a communication interface (Communication Interface) 102 and a bus 103 .
  • the processor 100 , the communication interface 102 , and the memory 101 can communicate with each other through the bus 103 .
  • Communication interface 102 may be used for information transfer.
  • the processor 100 can call the logic instructions in the memory 101 to execute the method for controlling a home appliance of the above-mentioned embodiments.
  • logic instructions in the memory 101 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.
  • the memory 101 can be used to store software programs and computer-executable programs, such as program instructions/modules corresponding to the methods in the embodiments of the present disclosure.
  • the processor 100 executes the function application and data processing by executing the program instructions/modules stored in the memory 101, that is, the method for controlling the home appliance in the above-mentioned embodiment is implemented.
  • the memory 101 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal device, and the like.
  • the memory 101 may include high-speed random access memory, and may also include non-volatile memory.
  • An embodiment of the present disclosure provides a home appliance, including the above-mentioned device for controlling the home appliance.
  • Embodiments of the present disclosure provide a computer-readable storage medium storing computer-executable instructions, where the computer-executable instructions are configured to execute the above method for controlling a home appliance.
  • An embodiment of the present disclosure provides a computer program product, where the computer program product includes a computer program stored on a computer-readable storage medium, and the computer program includes program instructions that, when executed by a computer, cause all The computer executes the above-described method for home appliance control.
  • the above-mentioned computer-readable storage medium may be a transient computer-readable storage medium, and may also be a non-transitory computer-readable storage medium.
  • the technical solutions of the embodiments of the present disclosure may be embodied in the form of software products, and the computer software products are stored in a storage medium and include one or more instructions to enable a computer device (which may be a personal computer, a server, or a network equipment, etc.) to execute all or part of the steps of the methods described in the embodiments of the present disclosure.
  • the aforementioned storage medium can be a non-transitory storage medium, including: U disk, removable hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, etc.
  • the term “and/or” as used in this application is meant to include any and all possible combinations of one or more of the associated listings.
  • the term “comprise” and its variations “comprises” and/or including and/or the like refer to stated features, integers, steps, operations, elements, and/or The presence of a component does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groupings of these.
  • an element qualified by the phrase “comprising a" does not preclude the presence of additional identical elements in the process, method, or device that includes the element.
  • each embodiment may focus on the differences from other embodiments, and the same and similar parts between the various embodiments may refer to each other.
  • the methods, products, etc. disclosed in the embodiments if they correspond to the method section disclosed in the embodiments, reference may be made to the description of the method section for relevant parts.
  • the disclosed methods and products may be implemented in other ways.
  • the apparatus embodiments described above are only illustrative.
  • the division of the units may only be a logical function division.
  • there may be other division methods for example, multiple units or components may be combined Either it can be integrated into another system, or some features can be omitted, or not implemented.
  • the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.
  • each functional unit in the embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.
  • each block in the flowchart or block diagrams may represent a module, segment, or portion of code that contains one or more functions for implementing the specified logical function(s) executable instructions.
  • the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.

Abstract

提供了用于家电控制的方法，装置和家电，包括：获得当前环境参数，并将当前环境参数输入至用于调节空调运行信息的预设控制模型(S11)；控制空调在预设控制模型输出的预测运行信息下运行，并获得目标环境参数和空调运行过程中的第一实时环境参数(S12)；根据第一实时环境参数和目标环境参数，确定用于满足目标环境参数的目标家电，以及目标家电的目标运行参数(S13)；控制目标家电在目标运行参数下运行(S14)，从而保证室内温度始终处于合适范围，避免空调长时间开启导致的环境失衡。

Description

用于家电控制的方法、装置和家电
本申请基于申请号为202110405226.X、申请日为2021年4月15日的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此引入本申请作为参考。
技术领域
本申请涉及智能家电技术领域，例如涉及一种用于家电控制的方法、装置和家电。
背景技术
随着科技的进步和人们生活水平的提高，越来越多的人开始关注智能家居的发展，追求更智能化的家电控制体验。以空调为例，在空调开机后，一般都是通过用户遥控的方式来调节空调的运行模式、运行温度以及运行风速等，这样操作比较繁琐。此外，如果用户长时间不调节空调的运行参数，可能会导致室内环境变差，如温湿度失衡，影响用户的使用体验。
发明内容
为了对披露的实施例的一些方面有基本的理解，下面给出了简单的概括。所述概括不是泛泛评述，也不是要确定关键/重要组成元素或描绘这些实施例的保护范围，而是作为后面的详细说明的序言。
本公开实施例提供了一种用于家电控制的方法、装置和家电，以保证室内环境的稳定，提高用户的生活体验。
在一些实施例中，所述用于家电控制的方法包括：获得当前环境参数，并将当前环境参数输入至用于调节空调运行信息的预设控制模型；控制空调在预设控制模型输出的预测运行信息下运行，并获得目标环境参数和空调运行过程中的第一实时环境参数；根据第一实时环境参数和目标环境参数，确定用于满足目标环境参数的目标家电，以及目标家电的目标运行参数；控制目标家电在目标运行参数下运行。
在一些实施例中，预设控制模型包括参数预测模型，则参数预测模型通过如下方式获得：获得用于训练参数预测模型的第一样本，并将第一样本随机分为训练集和测试集，第一样本包括不同环境参数下用于调节空调运行信息的用户习惯信息；将训练集中 的不同环境参数输入至初始预测模型，并将训练集中不同环境参数对应的用户习惯信息作为初始预测模型的输出，以对初始预测模型进行训练；根据测试集验证训练后的初始预测模型，得到验证结果；在验证结果表示预测准确的情况下，得到参数预测模型。
在一些实施例中，如果用户习惯信息包括空调的设定运行温度，预设控制模型还包括与设定运行温度对应的模式分类模型，则模式分类模型通过如下方式获得：获得用于训练模式分类模型的第二样本，第二样本包括不同环境参数下，已标注运行模式信息的设定运行温度；将不同环境参数和不同环境参数对应的设定运行温度输入至初始分类模型中，并将设定运行温度对应的运行模式信息作为初始分类模型的输出，以对初始分类模型进行训练，得到模式分类模型。
在一些实施例中，对初始分类模型进行训练，得到模式分类模型，包括：将第二样本随机分为训练样本和测试样本；根据训练样本训练模式分类模型，并根据测试样本测试模式分类模型，得到测试结果；如果测试结果表示运行模式信息和对应的环境参数不匹配，则继续根据训练样本训练模式分类模型。
在一些实施例中，根据第一实时环境参数和目标环境参数，确定用于满足目标参数的目标家电，以及目标家电的目标运行参数，包括：如果第一实时环境参数至少包括室内实时湿度，目标环境参数至少包括室内目标湿度，则在室内目标湿度和室内实时湿度的第一差值大于或等于预设湿度差值的情况下，将加湿器确定为目标家电，并将根据第一差值确定的加湿量设置为加湿器的目标运行参数。
在一些实施例中，根据第一实时环境参数和目标环境参数，确定用于满足目标参数的目标家电，以及目标家电的目标运行参数，包括：如果第一实时环境参数至少包括室内实时空气洁净度，目标环境参数至少包括室内目标空气洁净度，则在室内目标空气洁净度和室内实时空气洁净度的第二差值大于或等于预设洁净度差值的情况下，将新风机确定为目标家电，并将根据第二差值确定的新风比设置为新风机的目标运行参数。
在一些实施例中，控制目标家电在目标运行参数下运行后，还包括：根据第一实时环境信息和当前环境参数，确定预测运行信息关联的环境影响信息；获得用于确定目标家电和目标运行参数的影响因子模型；利用环境影响信息、目标家电以及目标家电的目标运行参数，修正影响因子模型。
在一些实施例中，控制空调在预设控制模型输出的预测运行信息下运行后，还包括：如果用户向空调发送控制指令，则获得控制指令对应的空调的设定运行信息；将空调从预测运行信息切换运行至设定运行信息。
在一些实施例中，所述用于家电控制的装置包括输入模块、获取模块、确定模块和控制模块。输入模块被配置为获得当前环境参数，并将当前环境参数输入至用于调节空调运行信息的预设控制模型；获取模块被配置为控制空调在预设控制模型输出的预测运行信息下运行，并获得目标环境参数和空调运行过程中的第一实时环境参数；确定模块被配置为根据第一实时环境参数和目标环境参数，确定用于满足目标环境参数的目标家电，以及目标家电的目标运行参数；控制模块被配置为控制目标家电在目标运行参数下运行。
在一些实施例中，所述用于家电控制的装置包括处理器和存储有程序指令的存储器，处理器被配置为在执行程序指令时，执行上述的用于家电控制的方法。
在一些实施例中，所述家电包括上述的用于家电控制的装置。
本公开实施例提供的用于家电控制的方法、装置和家电，可以实现以下技术效果：
通过获得当前环境参数，并将当前环境参数输入至用于调节空调运行信息的预设控制模型，可以使空调在预设控制模型输出的预测运行信息下运行，使空调自动实现智能控制，简化了用户操作；同时，获得目标环境参数和空调运行过程中的第一实时环境参数，并根据第一实时环境参数和目标环境参数，确定用于满足目标环境参数的目标家电，以及目标家电的目标运行参数，智能化地控制目标家电在目标运行参数下运行，保证室内温度始终处于合适范围，避免空调长时间开启导致的环境失衡，影响用户的使用体验。
以上的总体描述和下文中的描述仅是示例性和解释性的，不用于限制本申请。
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一个或多个实施例通过与之对应的附图进行示例性说明，这些示例性说明和附图并不构成对实施例的限定，附图中具有相同参考数字标号的元件示为类似的元件，附图不构成比例限制，并且其中：
图1是本公开实施例提供的一个用于家电控制的方法的流程图；
图2是本公开实施例提供的一个用于家电控制的装置的示意图；
图3是本公开实施例提供的一个用于家电控制的装置的示意图。
具体实施方式
为了能够更加详尽地了解本公开实施例的特点与技术内容，下面结合附图对本公 开实施例的实现进行详细阐述，所附附图仅供参考说明之用，并非用来限定本公开实施例。在以下的技术描述中，为方便解释起见，通过多个细节以提供对所披露实施例的充分理解。然而，在没有这些细节的情况下，一个或多个实施例仍然可以实施。在其它情况下，为简化附图，熟知的结构和装置可以简化展示。
本公开实施例的说明书和权利要求书及上述附图中的术语“第一”、“第二”等是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换，以便这里描述的本公开实施例的实施例。此外，术语“包括”和“具有”以及他们的任何变形，意图在于覆盖不排他的包含。
除非另有说明，术语“多个”表示两个或两个以上。
本公开实施例中，字符“/”表示前后对象是一种“或”的关系。例如，A/B表示：A或B。
术语“和/或”是一种描述对象的关联关系，表示可以存在三种关系。例如，A和/或B，表示：A或B，或，A和B这三种关系。
本公开实施例提供的用于家电控制的方法，应用于至少具有空气调节系统的室内环境中。这里，空气调节系统可以包括空调、加湿器以及新风机等家电，以便实现对室内空气的精准调控。
图1是本公开实施例提供的一个用于家电控制的方法的流程图。结合图1所示，本公开实施例提供一种用于家电控制的方法，以实现对上述室内环境的控制，该方法可以包括：
S1，获得当前环境参数，并将当前环境参数输入至用于调节空调运行信息的预设控制模型。
其中，当前环境参数至少可以包括当前室内温度、当前室外温度、当前湿度、当前空气洁净度等参数。对应地，通过在室内设置温湿度传感器和空气质量传感器，以及在室外设置温度传感器的方式，可以快速、准确地获得当前环境参数。
可选地，如果预设控制模型包括参数预测模型，则参数预测模型可以通过如下方式获得：获得用于训练参数预测模型的第一样本，并将第一样本随机分为训练集和测试集，第一样本包括不同环境参数下用于调节空调运行信息的用户习惯信息；将训练集中的不同环境参数输入至初始预测模型，并将训练集中不同环境参数对应的用户习惯信息作为初始预测模型的输出，以对初始预测模型进行训练；根据测试集验证训练后的初始预测模型，得到验证结果；在验证结果表示预测准确的情况下，得到参数预测模型。这 样将机器学习算法引入空调的智能控制逻辑中，有助于空调能够准确、自动地在用户所习惯的运行信息下运行，使用户摆脱手动设置空调运行信息的复杂操作方式，提高了用户的使用体验。
其中，初始预测模型可以是根据机器学习算法确定的预测模型，具体可以是人工神经网络算法、随机森林算法、决策树算法、支持向量机算法等。以SVM(Support Vector Machine，支持向量机)算法为例，将第一样本随机分为训练集和测试集，以训练集中的不同环境参数作为训练输入，不同环境参数对应的用户习惯信息作为训练输出，调用svmtrain函数以训练初始预测模型；以测试集中的不同环境参数作为预测输入，调用svmprediect函数以输出不同环境参数下各自的预测用户习惯信息；将预测用户习惯信息和训练集中的用户习惯信息拟合成曲线，以验证训练后的初始预测模型，得到验证结果。这样根据SVM算法得到的模型，预测准确度较高，从而提高了空调的智能化。
此外，不同环境参数，可以体现为多种环境参数各自对应的不同数值。不同环境参数下用于调节空调运行信息的用户习惯信息，具体可以包括用户习惯的设定运行温度、设定运行模式、设定风速以及设定摆风方式等。
对应地，用户习惯信息可以通过多种实现方式获得，下面举例说明。
作为一种示例，可以在预设时长内，分别采集不同环境参数下用户设定的空调运行信息，以便根据采集到的数据确定用户习惯信息。具体地，预设时长可以为30天～90天。优选为90天，这样可以尽可能地按季度采集完整的空调运行信息，以便按季节准确地获得用户习惯信息。
作为另一种示例，如果空调，或者与空调关联的智能终端配置有信息录入模块，则可通过用户输入信息的方式，获得用户习惯信息。例如，信息录入模块可以体现为键盘，这样用户可以通过键盘手动输入用户习惯信息；或者，信息录入模块可以体现为语音采集模块，用户可以通过语音方式输入用户习惯信息，由空调或智能终端进行语音识别，以获得用户习惯信息。通过信息录入模块可以非常方便、快捷、低成本地获取用户习惯信息。
上述智能终端，例如为移动设备、电脑、或浮动车中内置的车载设备等，或其任意组合。在一些实施例中，移动设备例如可以包括手机、可穿戴设备、智能移动设备等，或其任意组合。
可选地，如果用户习惯信息包括空调的设定运行温度，预设控制模型还包括与设定运行温度对应的模式分类模型，则模式分类模型可以通过如下方式获得：获得用于训 练模式分类模型的第二样本，第二样本包括不同环境参数下，已标注运行模式信息的设定运行温度；将不同环境参数和不同环境参数对应的设定运行温度输入至初始分类模型中，并将设定运行温度对应的运行模式信息作为初始分类模型的输出，以对初始分类模型进行训练，得到模式分类模型。这样，可以更准确地获得空调的运行模式信息，减少仅根据环境参数判断空调运行模式导致的误判，有助于提高空调的控制运行精度和智能化程度。
其中，运行模式信息可以是制冷模式或制热模式。这里，制冷模式可以指，在室内换热器作为蒸发器参与空气调节过程的情况下，空调的工作状态，至少可以包括普通制冷模式、除湿模式以及自清洁过程中室内换热器凝霜或室外换热器化霜的工作模式。制热模式可以指，在室内换热器作为冷凝器参与空气调节过程的情况下，空调的工作状态，至少可以包括普通制热模式、自清洁过程中室内换热器的除霜模式以及自清洁过程中室内换热器的高温灭菌模式。这里，运行模式信息可以通过不同的标识信息标注，例如，制冷模式标注为“-1”，制热模式标注为“1”，对此本公开实施例可不做具体限定。
初始分类模型可以是根据机器学习算法确定的分类模型，具体可以是K近邻算法、朴素贝叶斯算法、SVM算法等。
具体地，对初始分类模型进行训练，得到模式分类模型，可以包括：将第二样本随机分为训练样本和测试样本；根据训练样本训练模式分类模型，并根据测试样本测试模式分类模型，得到测试结果；如果测试结果表示运行模式信息和对应的环境参数不匹配，则继续根据训练样本训练模式分类模型。这样，便于构建模型，以及后续检验模式分类模型的准确率。
S12，控制空调在预设控制模型输出的预测运行信息下运行，并获得目标环境参数和空调运行过程中的第一实时环境参数。
这里，控制空调在预设控制模型输出的预测运行信息下运行后，还可以包括：如果用户向空调发送控制指令，则获得控制指令对应的空调的设定运行信息；将空调从预测运行信息切换运行至设定运行信息。这样，优先响应用户输出的控制指令，可以给用户提供更好的使用体验。
此外，可以获得空调在设定运行信息下运行的新的环境参数；根据设定运行信息和新的环境参数，确定新的环境参数下用于调节空调运行信息的新的用户习惯信息，以更新第一样本。这样采用新的用户习惯信息，可以优化预测控制模型的训练样本数据， 从而有助于空调在用户最满意的运行信息下运行，提高用户的使用体验。
S13，根据第一实时环境参数和目标环境参数，确定用于满足目标环境参数的目标家电，以及目标家电的目标运行参数。
可选地，根据第一实时环境参数和目标环境参数，确定用于满足目标参数的目标家电，以及目标家电的目标运行参数，可以包括：如果第一实时环境参数至少包括室内实时湿度，目标环境参数至少包括室内目标湿度，则在室内目标湿度和室内实时湿度的第一差值大于或等于预设湿度差值的情况下，将加湿器确定为目标家电，并将根据第一差值确定的加湿量设置为加湿器的目标运行参数。这样，可以保证在空调运行时，室内的湿度也始终处于合适的范围，避免空调长时间开启导致的环境失衡，提高了家电联动控制的智能化，以及用户的使用体验。
其中，室内目标湿度的取值范围可以为40％～60％。预设湿度差值的取值可以为20％。这样设置预设湿度差值，可以避免在室内过于干燥时才调节湿度，影响用户的体验，也可以避免在室内并不干燥时就调节湿度，造成能源浪费。
可选地，根据第一实时环境参数和目标环境参数，确定用于满足目标参数的目标家电，以及目标家电的目标运行参数，可以包括：如果第一实时环境参数至少包括室内实时空气洁净度，目标环境参数至少包括室内目标空气洁净度，则在室内目标空气洁净度和室内实时空气洁净度的第二差值大于或等于预设洁净度差值的情况下，将新风机确定为目标家电，并将根据第二差值确定的新风比设置为新风机的目标运行参数。这样，可以保证在空调运行时，室内的空气洁净度也始终处于合适的范围，避免空调长时间开启导致的环境失衡，提高了家电联动控制的智能化，以及用户的使用体验。
这里，空气洁净度可以体现为空气质量，即空气中污染物的浓度，例如甲醛浓度、PM2.5(细颗粒物)浓度等。以PM2.5为例，室内目标空气洁净度，即室内目标PM2.5浓度可以为50毫克。预设洁净度差值的取值可以为10～20毫克。这样设置预设洁净度差值，可以避免在室内空气质量过差时才进行净化，影响用户的体验，也可以避免在室内空气质量良好时就进行净化，造成能源浪费。
S14，控制目标家电在目标运行参数下运行。
进一步地，控制目标家电在目标运行参数下运行后，还可以包括：根据第一实时环境信息和当前环境参数，确定预测运行信息关联的环境影响信息；获得用于确定目标家电和目标运行参数的影响因子模型；利用环境影响信息、目标家电以及目标家电的目标运行参数，修正影响因子模型。这样，可以进一步地提高家电家电联动控制的智能化 效果，通过协调联动多个家电，保证室内环境能够始终处于合适的范围，提高用户的使用体验。
在实际应用中，如果第一实时环境参数包括第一实时湿度，且空调的预测运行信息包括空调处于制冷模式，则当空调在制冷模式下运行时第一实时湿度会下降，因此预测运行信息关联的环境影响信息至少可以体现为空调运行时对湿度的影响。
综上，采用本公开实施例提供的用于家电控制的方法，通过获得当前环境参数，并将当前环境参数输入至用于调节空调运行信息的预设控制模型，可以使空调在预设控制模型输出的预测运行信息下运行，使空调自动实现智能控制，简化了用户操作；同时，获得目标环境参数和空调运行过程中的第一实时环境参数，并根据第一实时环境参数和目标环境参数，确定用于满足目标环境参数的目标家电，以及目标家电的目标运行参数，智能化地控制目标家电在目标运行参数下运行，保证室内温度始终处于合适范围，避免空调长时间开启导致的环境失衡，影响用户的使用体验。
图2是本公开实施例提供的一个用于家电控制的装置的示意图。结合图2所示，本公开实施例提供一种用于家电控制的装置，包括输入模块21、获取模块22、确定模块23和控制模块24。输入模块21被配置为获得当前环境参数，并将当前环境参数输入至用于调节空调运行信息的预设控制模型；获取模块22被配置为控制空调在预设控制模型输出的预测运行信息下运行，并获得目标环境参数和空调运行过程中的第一实时环境参数；确定模块23被配置为根据第一实时环境参数和目标环境参数，确定用于满足目标环境参数的目标家电，以及目标家电的目标运行参数；控制模块24被配置为控制目标家电在目标运行参数下运行。
采用本公开实施例提供的用于家电控制的装置，通过输入模块、获取模块、确定模块和控制模块之间的联动配合，使空调在预设控制模型输出的预测运行信息下运行，使空调自动实现智能控制，简化了用户操作；同时，智能化地控制目标家电在目标运行参数下运行，保证室内温度始终处于合适范围，避免空调长时间开启导致的环境失衡，影响用户的使用体验。
图3是本公开实施例提供的一个用于家电控制的装置的示意图。结合图3所示，本公开实施例提供一种用于家电控制的装置，包括处理器(processor)100和存储器(memory)101。可选地，该装置还可以包括通信接口(Communication Interface)102和总线103。其中，处理器100、通信接口102、存储器101可以通过总线103完成相互间的通信。通信接口102可以用于信息传输。处理器100可以调用存储器101中的逻 辑指令，以执行上述实施例的用于家电控制的方法。
此外，上述的存储器101中的逻辑指令可以通过软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。
存储器101作为一种计算机可读存储介质，可用于存储软件程序、计算机可执行程序，如本公开实施例中的方法对应的程序指令/模块。处理器100通过运行存储在存储器101中的程序指令/模块，从而执行功能应用以及数据处理，即实现上述实施例中用于家电控制的方法。
存储器101可包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需的应用程序；存储数据区可存储根据终端设备的使用所创建的数据等。此外，存储器101可以包括高速随机存取存储器，还可以包括非易失性存储器。
本公开实施例提供了一种家电，包含上述的用于家电控制的装置。
本公开实施例提供了一种计算机可读存储介质，存储有计算机可执行指令，所述计算机可执行指令设置为执行上述用于家电控制的方法。
本公开实施例提供了一种计算机程序产品，所述计算机程序产品包括存储在计算机可读存储介质上的计算机程序，所述计算机程序包括程序指令，当所述程序指令被计算机执行时，使所述计算机执行上述用于家电控制的方法。
上述的计算机可读存储介质可以是暂态计算机可读存储介质，也可以是非暂态计算机可读存储介质。
本公开实施例的技术方案可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括一个或多个指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本公开实施例所述方法的全部或部分步骤。而前述的存储介质可以是非暂态存储介质，包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等多种可以存储程序代码的介质，也可以是暂态存储介质。
以上描述和附图充分地示出了本公开的实施例，以使本领域的技术人员能够实践它们。其他实施例可以包括结构的、逻辑的、电气的、过程的以及其他的改变。实施例仅代表可能的变化。除非明确要求，否则单独的部件和功能是可选的，并且操作的顺序可以变化。一些实施例的部分和特征可以被包括在或替换其他实施例的部分和特征。而且，本申请中使用的用词仅用于描述实施例并且不用于限制权利要求。如在实施例以及权利要求的描述中使用的，除非上下文清楚地表明，否则单数形式的“一个”(a)、 “一个”(an)和“所述”(the)旨在同样包括复数形式。类似地，如在本申请中所使用的术语“和/或”是指包含一个或一个以上相关联的列出的任何以及所有可能的组合。另外，当用于本申请中时，术语“包括”(comprise)及其变型“包括”(comprises)和/或包括(comprising)等指陈述的特征、整体、步骤、操作、元素，和/或组件的存在，但不排除一个或一个以上其它特征、整体、步骤、操作、元素、组件和/或这些的分组的存在或添加。在没有更多限制的情况下，由语句“包括一个…”限定的要素，并不排除在包括所述要素的过程、方法或者设备中还存在另外的相同要素。本文中，每个实施例重点说明的可以是与其他实施例的不同之处，各个实施例之间相同相似部分可以互相参见。对于实施例公开的方法、产品等而言，如果其与实施例公开的方法部分相对应，那么相关之处可以参见方法部分的描述。
本领域技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，可以取决于技术方案的特定应用和设计约束条件。所述技术人员可以对每个特定的应用来使用不同方法以实现所描述的功能，但是这种实现不应认为超出本公开实施例的范围。所述技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的系统、装置和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
本文所披露的实施例中，所揭露的方法、产品(包括但不限于装置、设备等)，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述单元的划分，可以仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另外，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例。另外，在本公开实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。
附图中的流程图和框图显示了根据本公开实施例的系统、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表 一个模块、程序段或代码的一部分，所述模块、程序段或代码的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个连续的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这可以依所涉及的功能而定。在附图中的流程图和框图所对应的描述中，不同的方框所对应的操作或步骤也可以以不同于描述中所披露的顺序发生，有时不同的操作或步骤之间不存在特定的顺序。例如，两个连续的操作或步骤实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这可以依所涉及的功能而定。框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或动作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。

Claims (10)

  1. 一种用于家电控制的方法，其特征在于，包括：
    获得当前环境参数，并将所述当前环境参数输入至用于调节空调运行信息的预设控制模型；
    控制空调在所述预设控制模型输出的预测运行信息下运行，并获得目标环境参数和所述空调运行过程中的第一实时环境参数；
    根据所述第一实时环境参数和所述目标环境参数，确定用于满足所述目标环境参数的目标家电，以及所述目标家电的目标运行参数；
    控制所述目标家电在所述目标运行参数下运行。
  2. 根据权利要求1所述的方法，其特征在于，如果所述预设控制模型包括参数预测模型，则所述参数预测模型通过如下方式获得：
    获得用于训练所述参数预测模型的第一样本，并将所述第一样本随机分为训练集和测试集，所述第一样本包括不同环境参数下用于调节空调运行信息的用户习惯信息；
    将所述训练集中的不同环境参数输入至初始预测模型，并将所述训练集中不同环境参数对应的用户习惯信息作为所述初始预测模型的输出，以对所述初始预测模型进行训练；
    根据所述测试集验证训练后的初始预测模型，得到验证结果；
    在所述验证结果表示预测准确的情况下，得到所述参数预测模型。
  3. 根据权利要求2所述的方法，其特征在于，如果用户习惯信息包括所述空调的设定运行温度，所述预设控制模型还包括与所述设定运行温度对应的模式分类模型，则所述模式分类模型通过如下方式获得：
    获得用于训练所述模式分类模型的第二样本，所述第二样本包括不同环境参数下，已标注运行模式信息的设定运行温度；
    将所述不同环境参数和所述不同环境参数对应的设定运行温度输入至初始分类模型中，并将所述设定运行温度对应的运行模式信息作为所述初始分类模型的输出，以对所述初始分类模型进行训练，得到所述模式分类模型。
  4. 根据权利要求3所述的方法，其特征在于，所述对所述初始分类模型进行训练，得到所述模式分类模型，包括：
    将所述第二样本随机分为训练样本和测试样本；
    根据所述训练样本训练所述模式分类模型，并根据所述测试样本测试所述模式分类模型，得到测试结果；
    如果所述测试结果表示运行模式信息和对应的环境参数不匹配，则继续根据所述训练样本训练所述模式分类模型。
  5. 根据权利要求1所述的方法，其特征在于，所述根据所述第一实时环境参数和所述目标环境参数，确定用于满足所述目标参数的目标家电，以及所述目标家电的目标运行参数，包括：
    如果所述第一实时环境参数至少包括室内实时湿度，所述目标环境参数至少包括室内目标湿度，则在所述室内目标湿度和所述室内实时湿度的第一差值大于或等于预设湿度差值的情况下，将加湿器确定为所述目标家电，并将根据所述第一差值确定的加湿量设置为所述加湿器的目标运行参数。
  6. 根据权利要求1所述的方法，其特征在于，所述根据所述第一实时环境参数和所述目标环境参数，确定用于满足所述目标参数的目标家电，以及所述目标家电的目标运行参数，包括：
    如果所述第一实时环境参数至少包括室内实时空气洁净度，所述目标环境参数至少包括室内目标空气洁净度，则在所述室内目标空气洁净度和所述室内实时空气洁净度的第二差值大于或等于预设洁净度差值的情况下，将新风机确定为所述目标家电，并将根据所述第二差值确定的新风比设置为所述新风机的目标运行参数。
  7. 根据权利要求1至6任一项所述的方法，其特征在于，所述控制所述目标家电在所述目标运行参数下运行后，还包括：
    根据所述第一实时环境信息和所述当前环境参数，确定所述预测运行信息关联的环境影响信息；
    获得用于确定目标家电和目标运行参数的影响因子模型；
    利用所述环境影响信息、所述目标家电以及所述目标家电的目标运行参数，修正所述影响因子模型。
  8. 根据权利要求3所述的方法，其特征在于，所述控制空调在所述预设控制模型输出的预测运行信息下运行后，还包括：
    如果用户向所述空调发送控制指令，则获得所述控制指令对应的所述空调的设定运行信息；
    将所述空调从所述预测运行信息切换运行至所述设定运行信息。
  9. 一种用于家电控制的装置，包括处理器和存储有程序指令的存储器，其特征在于，所述处理器被配置为在执行所述程序指令时，执行如权利要求1至8任一项所述的用于家电控制的方法。
  10. 一种家电，其特征在于，包括如权利要求9所述的用于家电控制的装置。
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