WO2021196483A1 - 空气调节设备及其控制方法、装置、电子设备 - Google Patents

空气调节设备及其控制方法、装置、电子设备 Download PDF

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Publication number
WO2021196483A1
WO2021196483A1 PCT/CN2020/106863 CN2020106863W WO2021196483A1 WO 2021196483 A1 WO2021196483 A1 WO 2021196483A1 CN 2020106863 W CN2020106863 W CN 2020106863W WO 2021196483 A1 WO2021196483 A1 WO 2021196483A1
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Prior art keywords
dimensional
user
adjustment
conditioning equipment
air conditioning
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PCT/CN2020/106863
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English (en)
French (fr)
Inventor
樊其锋
刘景春
翟浩良
吕闯
庞敏
简翱
彭水凤
Original Assignee
广东美的制冷设备有限公司
美的集团股份有限公司
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Publication of WO2021196483A1 publication Critical patent/WO2021196483A1/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
    • F24F11/65—Electronic processing for selecting an operating mode
    • 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

Definitions

  • This application relates to the technical field of electrical appliances, and in particular to a method and device for controlling air conditioning equipment, air conditioning equipment, electronic equipment, and computer-readable storage media.
  • air conditioning equipment is widely used to adjust indoor temperature, humidity and other parameters to improve user comfort.
  • the air-conditioning function of the air-conditioning equipment is relatively single and not flexible enough to meet the needs of users.
  • This application aims to solve one of the technical problems in the related technology at least to a certain extent.
  • the first purpose of this application is to propose a control method for air-conditioning equipment, which screens the most suitable target model for the user through the user’s characteristic information, so as to control the air-conditioning equipment according to the recommended value of the target model so that The recommended value of the monitoring parameter is more in line with the user's usage habits and needs, more humane, and improves the user's comfort.
  • the second purpose of this application is to provide a control device for air conditioning equipment.
  • the third purpose of this application is to propose an air conditioning device.
  • the fourth purpose of this application is to propose an electronic device.
  • the fifth purpose of this application is to provide a computer-readable storage medium.
  • an embodiment of the first aspect of the present application proposes a method for controlling an air-conditioning device, which includes the following steps: responding to a first instruction for turning on the multi-dimensional adjustment mode of the air-conditioning device to enter the multi-dimensional adjustment mode; obtaining According to the characteristic information of the user, at least one candidate model is screened according to the characteristic information to obtain a target model suitable for the user; according to the target model, the recommended value of each dimension of the multi-dimensional monitoring parameter is obtained; according to the target model; The recommended value and the monitoring value of any one-dimensional monitoring parameter in the multi-dimensional monitoring parameters adjust the adjustment component corresponding to the any one-dimensional monitoring parameter.
  • the screening at least one candidate model according to the characteristic information to obtain a target model suitable for the user includes: obtaining the priority of the candidate model; and according to the priority order The feature information is matched with the candidate model one by one; the candidate model that matches the feature information is obtained and used as the target model.
  • the screening at least one candidate model according to the characteristic information to obtain a target model suitable for the user includes: matching the characteristic information with the candidate models one by one; obtaining The number of the candidate models that match the feature information; when the number is greater than a preset threshold, the matched candidate models are sorted according to priority; the candidate with the highest priority The model serves as the target model.
  • the screening at least one candidate model according to the characteristic information to obtain a target model suitable for the user includes: randomly matching the characteristic information with the candidate model; and identifying; There is the candidate model that matches the characteristic information; the candidate model that matches the characteristic information is used as the target model.
  • the candidate model includes at least one of an individual self-learning model, a group self-learning model, and a general self-learning model.
  • the acquiring the recommended value of each dimension of the monitoring parameter in the multi-dimensional monitoring parameter according to the target model includes: acquiring the user Use the historical usage data of the air conditioning equipment, the current environment data of the environment and the current time information as the first data; input the first data into the individual self-learning model to obtain the recommended value.
  • acquiring the recommended value of each dimension of the monitoring parameter in the multi-dimensional monitoring parameter according to the target model includes: acquiring the user The current environmental data and/or current time information of the environment is used as the first data; the first data is input to the group self-learning model to obtain the group attribute of the user; and the group is obtained according to the group attribute User, and obtain the recommended value corresponding to the group of users as the recommended value of the user.
  • the acquiring the recommended value of each dimension of the monitoring parameter in the multi-dimensional monitoring parameter according to the target model includes: acquiring air conditioning equipment The usage data of is used as the first data; the first data is input to the general self-learning model, and the recommended value common to all users is obtained as the recommended value of the user.
  • the user s active adjustment instruction for one of the one-dimensional monitoring parameters among the multi-dimensional monitoring parameters is detected, and the adjustment component corresponding to the one-dimensional monitoring parameter is controlled according to the active adjustment instruction.
  • the adjustment function is locked.
  • a user's closing instruction for one of the one-dimensional monitoring parameters among the multi-dimensional monitoring parameters is detected, and according to the closing instruction, the adjustment component corresponding to the one-dimensional monitoring parameter is controlled to be in a closed state.
  • the method before responding to the first instruction for turning on the multi-dimensional adjustment mode of the air-conditioning equipment, the method further includes: acquiring a selection instruction for selecting at least two-dimensional monitoring parameters from the multi-dimensional monitoring parameters, and The selection instruction generates the first instruction.
  • the adjustment of the adjustment component corresponding to the any one-dimensional monitoring parameter according to the recommended value and the monitoring value of any one-dimensional monitoring parameter in the multi-dimensional monitoring parameters includes: determining and At least one adjustment component corresponding to the any one-dimensional monitoring parameter; according to the recommended value and monitoring value of the any one-dimensional monitoring parameter, an adjustment instruction for the adjustment component is generated, and the adjustment component is performed according to the adjustment instruction adjust.
  • the method before generating the adjustment instruction for the adjustment component according to the recommended value and the monitoring value of the any one-dimensional monitoring parameter, the method further includes: identifying that there are two-dimensional or more than two-dimensional monitoring parameters
  • the corresponding adjustment components include the same adjustment component; and the two-dimensional or more than two-dimensional monitoring parameters need to be adjusted, the priority of each dimension of the two-dimensional or more than two-dimensional monitoring parameters is determined according to the priority
  • the recommended value and the monitoring value of the highest monitoring parameter are adjusted for the same adjustment component.
  • the multi-dimensional monitoring parameters include two or more of humidity, temperature, wind speed, pollutant content in the air, and air quality index.
  • the adjustment component is integrated or independent of the air conditioning device.
  • This application can filter the self-learning model used according to the user's characteristic information to obtain the self-learning model most suitable for the user at present, so that the recommended parameters are more in line with the needs of the user, and multiple monitoring parameters are adjusted at the same time, and each monitoring parameter
  • the adjustment process is independent of each other, which improves the flexibility of the air conditioning equipment. Further, the adjustment component corresponding to the monitoring parameter can be adjusted according to the recommended value and the monitoring value of the monitoring parameter to adjust the monitoring parameter.
  • an embodiment of the second aspect of the present application proposes a control device for air-conditioning equipment, including: a mode activation module for responding to a first instruction for turning on the multi-dimensional adjustment mode of the air-conditioning equipment to enter the multi-dimensional adjustment mode.
  • the first acquisition module is used to acquire the characteristic information of the user, and at least one candidate model is screened according to the characteristic information to obtain a target model suitable for the user;
  • the second acquisition module is used to obtain the target model according to the target Model to obtain the recommended value of each dimension of the multi-dimensional monitoring parameter;
  • the adjustment module is used to compare with the any one-dimensional monitoring parameter according to the recommended value and monitoring value of any one-dimensional monitoring parameter in the multi-dimensional monitoring parameter The adjustment component corresponding to the parameter is adjusted.
  • an embodiment of the third aspect of the present application proposes an air conditioning device, including the control device of the air conditioning device.
  • an embodiment of the fourth aspect of the present application proposes an electronic device, including a memory and a processor; wherein the processor reads the executable program code stored in the memory to run the The program corresponding to the program code is executed to realize the control method of the air conditioning equipment.
  • the embodiment of the fifth aspect of the present application proposes a computer-readable storage medium, the computer-readable storage medium stores a computer program, and the program is executed by a processor to realize the control of the air-conditioning equipment. method.
  • Fig. 1 is a flowchart of a control method of an air conditioning device according to an embodiment of the present application
  • Fig. 2 is a flowchart of a control method of an air conditioning device according to an embodiment of the present application
  • Fig. 3 is a flowchart of a control method of an air conditioning device according to an embodiment of the present application
  • Fig. 4 is a flowchart of a control method of an air conditioning device according to an embodiment of the present application.
  • Fig. 5 is a flowchart of a control method of an air conditioning device according to an embodiment of the present application.
  • Fig. 6 is a flowchart of a control method of an air conditioning device according to an embodiment of the present application.
  • Fig. 7 is a flowchart of a control method of an air conditioning device according to an embodiment of the present application.
  • Fig. 8 is a schematic block diagram of a control method of an air conditioning device according to an embodiment of the present application.
  • Fig. 9 is a block diagram of an air conditioning device according to an embodiment of the present application.
  • Fig. 10 is a schematic block diagram of an electronic device according to an embodiment of the present application.
  • Fig. 1 is a flowchart of a control method of an air conditioning device according to an embodiment of the present application.
  • control method of the air conditioning equipment in the embodiment of the present application includes the following steps:
  • S101 Respond to a first instruction for turning on the multi-dimensional adjustment mode of the air-conditioning device to enter the multi-dimensional adjustment mode.
  • the air conditioning device has a multi-dimensional adjustment mode, which can adjust two or more monitoring parameters.
  • the monitoring parameters can be calibrated according to actual conditions and set in the storage space of the air conditioning equipment in advance.
  • the monitoring parameters may include two or more of humidity, temperature, wind speed, pollutant content in the air, Air Quality Index (AQI), and carbon dioxide concentration.
  • the pollutant content in the air can include the concentration of PM2.5.
  • the user can use the remote control, the air-conditioning device APP in the mobile terminal, and the control panel on the air-conditioning device's body to send a non-contact way to the air-conditioning device to activate the multi-dimensional adjustment mode through language, gestures, etc.
  • the first instruction can be used to send a non-contact way to the air-conditioning device to activate the multi-dimensional adjustment mode through language, gestures, etc. The first instruction.
  • the first instruction may include a start-up instruction, so that after the user sends a start-up instruction to the air-conditioning device, the air-conditioning device can enter a multi-dimensional adjustment mode after it is turned on, which avoids the need for air-conditioning in the prior art. After the device is turned on, the user is also required to issue an instruction to turn on the multi-dimensional adjustment mode, which is relatively simple.
  • S102 Acquire characteristic information of the user, and screen at least one candidate model according to the characteristic information to obtain a target model suitable for the user.
  • the candidate model includes at least one of an individual self-learning model, a group self-learning model, and a general self-learning model.
  • the user’s characteristic information may include the user’s basic personal information, such as age, gender, hobbies, etc., or may also include the user’s account for using the air-conditioning equipment, or may also include the user’s historical use data of the air-conditioning equipment, which may include the user’s previous initiative Set data such as temperature information, humidity information, windshield information, sweep mode, fresh air mode, operating mode, cumulative use times, and cumulative use time.
  • this application can screen out the target model that meets the user’s characteristic information from the candidate models according to the acquired characteristic information of various users, so as to use the target model to compare the user’s characteristic information, environment information, and current time information. Perform self-learning with historical usage data and other information to obtain the recommended value of each dimension monitoring parameter used for multi-dimensional control of air conditioning equipment.
  • a numerical value may be recommended for the monitoring parameter, and a value range may also be recommended for the monitoring parameter.
  • the multi-dimensional monitoring parameters include humidity, temperature, wind speed, pollutant content in the air, air quality index, and carbon dioxide concentration
  • a value can be recommended for temperature and wind speed, and humidity, air pollutant content, and air
  • a value range is recommended for quality index and carbon dioxide concentration.
  • the corresponding recommended value when the monitoring parameter is temperature, the corresponding recommended value may be 25°C. When the monitoring parameter is wind speed, the corresponding recommended value can be 2m/s. When the monitored parameter is humidity, the value range of the corresponding recommended value can be (40-70)%. Taking the pollutant content in the air including PM2.5 concentration as an example, when the monitoring parameter is PM2.5 concentration, the corresponding recommended value range can be (0 ⁇ 75) ⁇ g/m 3 . When the monitoring parameter is the air quality index, the value range of the corresponding recommended value can be (0 ⁇ 75) ⁇ g/m 3 . When the monitoring parameter is carbon dioxide concentration, the value range of the corresponding recommended value can be (0 ⁇ 1000)PPM.
  • the target model is an individual self-learning model, as shown in Figure 2
  • the recommended value of each dimension of the multi-dimensional monitoring parameters is obtained, including:
  • S201 Acquire the historical usage data of the air conditioning equipment used by the user, the current environment data of the environment in which the user is in, and current time information as the first data.
  • S202 Input the first data into the value individual self-learning model to obtain a recommended value.
  • the historical usage data of the air conditioning equipment used by the user may include temperature information, humidity information, windshield information, wind sweep mode, fresh air mode, operation mode, cumulative use times, cumulative use time and other data actively set by the user before.
  • the individual self-learning model is a self-learning model for the historical usage data of the air-conditioning equipment of a certain user. Relevance.
  • the individual self-learning model requires users to use the historical usage data of air-conditioning equipment for self-learning
  • the individual self-learning model is obviously suitable for old users who already have historical usage data, that is to say, only for air conditioning Only the old users who have used the equipment can match the individual self-learning model.
  • the target model is a group self-learning model, as shown in Figure 3, according to the target model, the recommended value of each dimension of the multi-dimensional monitoring parameters is obtained, including:
  • S301 Acquire current environment data and/or current time information of the environment where the user is located as the first data.
  • the current environmental data of the environment where the user is located may include data such as the province, city, climate region, indoor temperature, outdoor temperature, indoor humidity, outdoor humidity, PM2.5 concentration, carbon dioxide concentration, and air quality index where the user is located.
  • the current time information may include data such as month, solar terms, specific time period (morning, afternoon, evening), and whether it is on a working day or not.
  • the current environment data of the environment where the user is located may be obtained through a wireless network device query, for example, the province, city, outdoor temperature, and outdoor humidity where the user is located may be obtained through a wireless network device query.
  • the detection device may also be used to obtain current environmental data of the environment where the user is located.
  • a temperature sensor may be installed on the indoor unit of the air conditioning equipment to obtain the indoor temperature of the environment where the user is located.
  • the current time information can be obtained by querying the system time of the air conditioning equipment.
  • S302 Input the first data into the group self-learning model to obtain the group attribute of the user.
  • the historical usage data of the user’s air-conditioning equipment cannot reflect the user’s air-conditioning equipment
  • the usage habits and needs of the user may not reflect the user's adjustment habits and needs for any one-dimensional monitoring parameter, and the above-mentioned individual self-learning model cannot be used to use historical usage data to obtain the recommended value of each dimensional monitoring parameter.
  • the current environment data and current time information of the environment can be used to obtain the user’s group attributes based on the current environment data and current time information, and then according to groups with the same group attributes
  • the recommended value of the user is used to obtain the recommended value of each dimension of the monitoring parameter of the current user.
  • the adoption of the group self-learning model can comprehensively consider the current environment data, current time information and the influence of group users on the recommended values of monitoring parameters, so that the recommended values of monitoring parameters are more in line with the current environment and time. And the usage habits and needs of group users improve the comfort of users.
  • obtaining group users based on group attributes may include pre-establishing a mapping relationship or a mapping table between group attributes and group users. After obtaining the group attribute of the user, query the mapping relationship or the mapping table to obtain the relationship with the user. Matched groups of users. It should be noted that the recommended value corresponding to the group user may be calibrated according to the actual situation, and may also be the average value of the recommended value of the actual user that conforms to the group user.
  • the current outdoor humidity, indoor humidity, current month, and specific time period of the environment can be used as the first data, and then input the first data into the group self-learning model to obtain The group attribute of the user, assuming that the group attribute of the user is a, and the group user obtained according to the group attribute a is A, the recommended value of the humidity corresponding to the group user A can be obtained as the recommended value of the humidity of the user.
  • the group learning model in the above embodiment is based on the user’s current environment and time. The reason is that the environment and time have a greater impact on the use of air-conditioning equipment, for example, around October.
  • the weather in northern my country is cold.
  • Northern users usually use the heating function when using air-conditioning equipment during this time period, while the weather in southern coastal cities is still mild.
  • Southern users usually use the cooling function when using air-conditioning equipment during this time period.
  • the user's preference characteristics can also be added to the group self-learning model to pass the user's preference characteristics Perform group analysis of users.
  • the group self-learning model can not only include regional group learning about the user’s current environment and time, but also group learning about user preferences, gender and other characteristic information.
  • the user's preference, gender and other characteristic information can be obtained through the user's mobile terminal.
  • the target model is a general self-learning model, as shown in Figure 4, according to the target model, the recommended value of each dimension of the multi-dimensional monitoring parameters is obtained, including:
  • S401 Acquire usage data of the air conditioning equipment as the first data.
  • the usage data of the air-conditioning equipment in the embodiments of the present application is the usage data of the air-conditioning equipment by users of the entire network.
  • S402 Use the universal self-learning model of the first data input value to obtain a universal recommended value for all users as the recommended value of the user.
  • the use data of the air conditioning equipment of the entire network users can be obtained, and then the use data of the entire network users can be analyzed through the general self-learning model without distinguishing the characteristics of the users, and the recommended value applicable to all users can be obtained. , And use the recommended value as the user's recommended value.
  • S104 Adjust the adjustment component corresponding to any one-dimensional monitoring parameter according to the recommended value and monitoring value of any one-dimensional monitoring parameter among the multi-dimensional monitoring parameters.
  • each dimension of monitoring parameters can correspond to one or more adjustment components, and the adjustment components can be controlled independently or linked to control to adjust the monitoring parameters corresponding to the adjustment components, and the adjustment components corresponding to the monitoring parameters of each dimension The adjustments are independent of each other.
  • the present application can filter the self-learning model used according to the user's characteristic information to obtain the self-learning model most suitable for the user at present, so that the recommended parameters are more in line with the needs of the user, and multiple monitoring parameters can be adjusted at the same time.
  • the adjustment process of each monitoring parameter is independent of each other, which improves the flexibility of the air conditioning equipment. Further, the adjustment component corresponding to the monitoring parameter can be adjusted according to the recommended value and the monitoring value of the monitoring parameter to adjust the monitoring parameter.
  • screening at least one candidate model according to feature information to obtain a target model suitable for a user includes:
  • the priority of the individual self-learning model that contains more historical usage records of users is higher than the group self-learning model that only obtains partial user information, and the group self-learning model The priority is higher than the general self-learning model without user information.
  • S502 Match the feature information with the candidate model one by one according to the priority order.
  • S503 Obtain a candidate model that matches the feature information, and use it as a target model.
  • the characteristic information can be matched with the individual self-learning model with the highest priority. If the historical use record of the air-conditioning equipment exists in the characteristic information, the user characteristic information and the individual self-learning can be determined If the model is matched, the individual self-learning model is further used as the target model to obtain the user's recommended value through historical usage data. If there is no historical use record of the air-conditioning equipment in the feature information, it is determined that the user feature information does not match the individual self-learning model , Then the feature information is further matched with the group user self-learning model with the next priority.
  • the group self-learning model is further used as the target model to obtain the user's recommendation value through the user's characteristic information. If the user's environment and time information or user preference, gender and other characteristic information are not included in the characteristic information, the priority is directly set to the lowest
  • the universal self-learning model is used as the user's target model, and the user’s recommended value is obtained through the universal self-learning model.
  • this application can reduce the amount of calculations in the matching process through sequential matching, and matching according to the priority order can obtain the target model that best expresses the user's habits and needs as soon as possible, so that the final target model can be as consistent as possible.
  • the user’s historical usage habits and needs improve the user’s comfort and enhance the user’s experience.
  • screening at least one candidate model according to feature information to obtain a target model suitable for a user includes:
  • S601 Match the feature information with the candidate models one by one.
  • S602 Acquire the number of candidate models that match the feature information.
  • this application can simultaneously match the feature information with the candidate model after acquiring the user's feature information.
  • the feature information contains the user's historical use data of the air conditioning equipment
  • the feature information can be matched at the same time.
  • the feature information when the feature information only contains information such as the user’s environment and time, or the user’s preferences and gender, the feature information can be matched to both the group self-learning model and the general self-learning model.
  • Self-learning model when the feature information does not contain the above data, it is determined that the feature information can only be matched to the general self-learning model.
  • the preset threshold can be 1, that is, when the feature information can only be matched to the general self-learning model, the general self-learning model can be directly used as the user's target model to obtain the recommended value, but when the feature information is matched
  • the obtained model is greater than 1, for example, when the group self-learning model and the general self-learning model are matched at the same time or the individual self-learning model, the group self-learning model and the general self-learning model are matched at the same time, multiple candidate models need to be further screened , To select a unique target model so that the recommended value has a unique certainty. Therefore, the matched candidate models can be further filtered based on the priority of the candidate models.
  • the group self-learning model and the general self-learning model are matched at the same time, since the group self-learning model has a higher priority than the general self-learning model, the group self-learning model is determined as the user's target model, and the individual self-learning model is matched at the same time.
  • the individual self-learning model>group self-learning model>general self-learning model is obtained according to priority. Therefore, the individual self-learning model can be obtained as the user's target model Recommended value.
  • this embodiment can perform matching operations on three candidate models at the same time, effectively saving time for matching candidate models, and improving the efficiency of screening target models.
  • screening at least one candidate model according to feature information to obtain a target model suitable for a user includes:
  • S703 Use the candidate model matching the feature information as the target model.
  • this application can perform random matching between the feature information and the candidate model.
  • the feature information can be matched with the individual self-learning model first, or the feature information can be matched with the group self-learning model first.
  • the feature information can be matched with the general self-learning model first, and if the matching is successful, the matched candidate model is directly used as the target model.
  • the acquired characteristic information of the user only includes the environment information and time of the user, at this time, if the individual self-learning model is randomly acquired that matches the characteristic information, it is recognized that there is no match with the characteristic information. If the candidate model that matches the feature information is randomly obtained as a group self-learning model or a general self-learning model, it is recognized that there is a candidate model that matches the feature information, and the randomly matched candidate model can be directly used as the target model.
  • the adjustment component is integrated or independent of the air-conditioning equipment, this method can improve the applicability and flexibility of the adjustment component, so that the present application can be more widely applied to the air-conditioning equipment.
  • the return air outlets for temperature adjustment, air quality index adjustment, and air pollutant adjustment can be integrated, that is, only one return air outlet can be set, or multiple return air outlets can be set according to the actual situation. For example, because the mass of pollutants in the air is large, it will cause sinking.
  • the return air outlet for pollutant adjustment during control can be set in the lower part of the air conditioning equipment to make the recovered air contain a higher pollutant content, thereby increasing The efficiency of the adjustment of the pollutant content in the air, and the temperature-regulated return air outlet is arranged in the upper part of the air-conditioning equipment, so that the amount of pollutants in the recovered air is low, and the secondary air pollution caused by the temperature-regulated air supply is reduced.
  • the air outlet for humidity adjustment can be independently set.
  • the air conditioning equipment can monitor the monitoring parameters of each dimension to obtain the monitoring values of the monitoring parameters of each dimension.
  • the temperature monitoring value can be obtained by installing a temperature sensor on the indoor unit of the air conditioning equipment.
  • the wind speed sensor can be installed at the air outlet of the indoor unit of the air conditioning equipment to obtain the monitoring value of the wind speed.
  • the monitoring parameters and their corresponding adjustment components can be calibrated according to actual conditions and set in the storage space of the air conditioning equipment in advance.
  • the corresponding adjustment component may include a fan.
  • the corresponding adjustment components may include compressors and fans.
  • a mapping relationship or a mapping table between the monitoring parameter and the adjustment component can be established in advance. After the monitoring parameter is obtained, the mapping relationship or the mapping table can be queried to determine the adjustment component corresponding to the monitoring parameter, and then the adjustment component Make adjustments.
  • adjusting the adjustment component corresponding to any one-dimensional monitoring parameter according to the recommended value and monitoring value of any one-dimensional monitoring parameter among the multi-dimensional monitoring parameters may include determining at least one adjustment component corresponding to any one-dimensional monitoring parameter, Then, according to the recommended value and monitoring value of any one-dimensional monitoring parameter, an adjustment instruction for the adjustment component is generated, and the adjustment component is adjusted according to the adjustment instruction.
  • the corresponding adjustment component may include a fan.
  • an adjustment command for the fan can be generated, and the fan can be adjusted according to the adjustment command.
  • the monitored value of wind speed is greater than the recommended value of wind speed, it means that the wind speed is too high at this time and the wind speed needs to be reduced.
  • An adjustment command to reduce the fan speed can be generated, and the fan speed can be reduced according to the adjustment command to reduce the wind speed.
  • the monitored value of wind speed is less than the recommended value of wind speed, it means that the wind speed is too low at this time and the wind speed needs to be increased.
  • An adjustment command to increase the fan speed can be generated, and the fan speed can be increased according to the adjustment command to increase the wind speed. If the monitored value of the wind speed is equal to the recommended value of the wind speed, no adjustment command for the fan may be generated, so that the fan continues to run at the current speed.
  • the method can adjust the wind speed by adjusting the rotation speed of the fan, and can make the monitoring value of the wind speed approach the recommended value of the wind speed, and improve the comfort of the user.
  • the corresponding adjustment components may include compressors and fans.
  • the adjustment instructions for the compressors and fans can be generated respectively, and the compressors and fans can be adjusted according to the adjustment instructions. adjust.
  • the temperature monitoring value is greater than the recommended temperature value, it means that the temperature is too high at this time and the temperature needs to be lowered, which can respectively reduce the operating frequency of the compressor and reduce the speed of the fan.
  • the temperature monitoring value is less than the recommended temperature value, it means that the temperature is too low at this time and the temperature needs to be increased.
  • the adjustment commands to increase the operating frequency of the compressor and the fan speed can be generated respectively, and the operating frequency and operating frequency of the compressor can be increased according to the adjustment instructions.
  • the speed of the fan to increase the temperature. If the monitoring value of the temperature is equal to the recommended value of the temperature, no adjustment commands for the compressor and fan may be generated, so that the compressor continues to run at the current operating frequency and the fan continues to run at the current speed.
  • This method can adjust the temperature by adjusting the operating frequency of the compressor and the rotation speed of the fan, so that the monitoring value of the temperature can be close to the recommended value of the temperature, and the comfort of the user is improved.
  • the method before responding to the first instruction for turning on the multi-dimensional adjustment mode of the air-conditioning equipment, the method further includes obtaining a selection instruction for selecting at least two-dimensional monitoring parameters from the multi-dimensional monitoring parameters, indicating that the user is The monitoring parameters are willing to adjust, and the air-conditioning equipment needs to open the multi-dimensional adjustment mode.
  • the first instruction for turning on the multi-dimensional adjustment mode of the air-conditioning equipment can be generated according to the selected instruction, so that the air-conditioning equipment enters the multi-dimensional adjustment mode according to the first instruction. To adjust the selected at least two-dimensional monitoring parameters.
  • This method allows the user to select at least two-dimensional monitoring parameters from the multi-dimensional monitoring parameters according to personal wishes, as the monitoring parameters to be adjusted by the air conditioning equipment, and has high flexibility.
  • the multi-dimensional monitoring parameters include humidity, temperature, and wind speed
  • a selection instruction for selecting humidity and temperature it indicates that the user has a willingness to adjust the humidity and temperature, and the air conditioning equipment needs to open the multi-dimensional adjustment mode.
  • the user can be generated according to the selected instruction.
  • the first instruction for turning on the multi-dimensional adjustment mode of the air-conditioning device causes the air-conditioning device to enter the multi-dimensional adjustment mode according to the first instruction to adjust humidity and temperature.
  • the user can use the remote control, the air conditioning device APP in the mobile terminal, and the control panel on the air conditioning device's body to select monitoring parameters from multi-dimensional monitoring parameters through non-contact methods such as language and gestures, and generate Select the command.
  • non-contact methods such as language and gestures
  • the selection menu for the user to select monitoring parameters can be preset on the control panel, and the menu selection operation of the user in the selection interface can be monitored. After the menu selection operation, the selection menu is displayed, and the user's operating position on the selection menu is obtained, and then the monitoring parameters selected by the user are identified according to the operating position. When the number of monitoring parameters selected by the identified user is greater than or equal to two , A first instruction for turning on the multi-dimensional adjustment mode of the air-conditioning device may be generated, so that the air-conditioning device responds to the first instruction to enter the multi-dimensional adjustment mode.
  • the air-conditioning device after the air-conditioning device enters the multi-dimensional adjustment mode, it can also identify the selected monitoring parameters according to the selection instruction for selecting at least two-dimensional monitoring parameters from the multi-dimensional monitoring parameters, and then obtain the selected multi-dimensional monitoring parameters Recommended values of monitoring parameters for each dimension.
  • the selection instruction for the aforementioned monitoring parameters may occur before responding to the first instruction for turning on the multi-dimensional adjustment mode of the air-conditioning device, or may occur in response to the first instruction for turning on the multi-dimensional adjustment mode of the air-conditioning device.
  • the user can first select the monitoring parameters, and then generate the first instruction according to the selected monitoring parameters to turn on the multi-dimensional adjustment mode according to the first instruction to adjust the monitoring parameters selected by the user, or according to the first Command to turn on the multi-dimensional adjustment mode, and then wait for the user to select the monitoring parameter that needs to be adjusted, and then adjust the monitoring parameter according to the recommended value after the user has determined the monitoring parameter that needs to be adjusted.
  • This method only needs to adjust the adjustment components corresponding to the selected monitoring parameters, so that only the selected monitoring parameters need to be adjusted, which can better respond to the actual needs of users, and does not need to adjust the monitoring parameters of each dimension, which can save energy. Consumption.
  • the process of adjusting the adjustment component further includes detecting the user's active adjustment instruction for one of the multi-dimensional monitoring parameters, indicating that the user has the willingness to actively adjust the monitoring parameter. There is no need for air conditioning equipment to adjust the monitoring parameter, and the adjustment function of the adjustment component corresponding to the monitoring parameter can be controlled to be in a locked state according to the active adjustment instruction.
  • the user can select the monitoring parameter by selecting the method of retaining the monitoring parameter, or by locking some of the monitoring parameters.
  • the multi-dimensional monitoring parameters include humidity, temperature, and wind speed
  • a selection instruction for selecting humidity and temperature is obtained, the user can directly select humidity and temperature as the monitoring parameters that need to be adjusted, or can choose to lock the wind speed monitoring parameters, that is, The wind speed monitoring parameters are not adjusted.
  • the method can control the adjustment function of the adjustment component corresponding to the monitoring parameter to be in a locked state according to the user's active adjustment instruction for the monitoring parameter, and can better respond to the actual needs of the user with high flexibility.
  • the user can actively adjust the monitoring parameters through the remote control, the air-conditioning device APP in the mobile terminal, and the control panel on the air-conditioning device's body through non-contact methods such as language and gestures, and send active adjustments. instruction.
  • the process of adjusting the adjustment component further includes detecting the user's shutdown instruction for one of the one-dimensional monitoring parameters among the multi-dimensional monitoring parameters, indicating that the user has the willingness to close the adjustment function of the monitoring parameter, that is, At this time, there is no need for air conditioning equipment to adjust the monitoring parameter, and the adjustment component corresponding to the monitoring parameter can be controlled to be in the closed state according to the shutdown instruction.
  • the monitoring function of the monitoring parameter can be controlled to close according to the shutdown instruction, that is, the monitoring value of the monitoring parameter is not obtained, so as to save energy consumption.
  • the method can control the adjustment component corresponding to the monitoring parameter to be in the closed state according to the user's closing instruction for the monitoring parameter, and can better respond to the actual needs of the user, has high flexibility, and is also conducive to energy saving.
  • the user can use the remote control, the air-conditioning device APP in the mobile terminal, and the control panel on the air-conditioning device's body to turn off the monitoring parameters through non-contact methods such as language and gestures, and issue a shutdown instruction.
  • the present application can adjust the adjustment component corresponding to any one-dimensional monitoring parameter according to the recommended value and monitoring value of any one-dimensional monitoring parameter in the selected multi-dimensional monitoring parameters, so that only the selected monitoring parameters need to be adjusted. It can better respond to the actual needs of users, without adjusting the monitoring parameters of each dimension, which can save energy.
  • the difference between the monitored value of the monitored parameter and the recommended value is within the preset allowable range, it means that the difference between the monitored value of the monitored parameter and the recommended value is small, and there is no need to The monitoring parameters are adjusted. If the difference between the monitored value of the monitored parameter and the recommended value is not within the preset allowable range, it indicates that the difference between the monitored value of the monitored parameter and the recommended value is large, and the monitoring parameter needs to be adjusted.
  • the method can identify that the difference is not within the preset allowable range, and then compare at least one corresponding to the monitoring parameter.
  • the adjustment of the adjustment components reduces the number of times the air-conditioning equipment adjusts the monitoring parameters, which is beneficial to improve the adjustment efficiency.
  • preset allowable range can be calibrated according to actual conditions, and different monitoring parameters can correspond to different allowable ranges, and are preset in the storage space of the air conditioning equipment.
  • the corresponding allowable range when the monitoring parameter is temperature, the corresponding allowable range may be 2°C. When the monitoring parameter is humidity, the corresponding allowable range can be 5%. When the monitoring parameter is wind speed, the corresponding allowable range can be 0.5m/s. When the monitoring parameter is PM2.5 concentration, the corresponding allowable range can be 10 ⁇ g/m 3 . When the monitoring parameter is the air quality index, the corresponding allowable range can be 15 ⁇ g/m 3 . When the monitoring parameter is carbon dioxide concentration, the corresponding allowable range can be 50PPM.
  • a mapping relationship or a mapping table between the monitoring parameter and the preset allowable range can be established in advance. After the monitoring parameter is obtained, the mapping relationship or the mapping table can be queried to determine the preset allowable corresponding to the monitoring parameter. The range is then used to compare with the difference between the monitored value of the monitored parameter and the recommended value to identify whether the difference between the monitored value of the monitored parameter and the recommended value is within the preset allowable range.
  • the adjustment components corresponding to the monitoring parameters of two or more dimensions include the same adjustment component, and the monitoring parameters of two or more dimensions need to be adjusted, that is, two-dimensional or two-dimensional
  • the difference between the monitoring value of the above monitoring parameters and the recommended value is not within the preset allowable range, indicating that two-dimensional or more than two-dimensional monitoring parameters need to be adjusted.
  • it can be based on the two-dimensional or more than two-dimensional monitoring
  • the priority of each dimension of the monitoring parameter in the parameters is determined, the monitoring parameter with the highest priority among them is determined, and then the same adjustment component is adjusted according to the recommended value and monitoring value of the monitoring parameter with the highest priority.
  • the method can be based on two or more The recommended value and monitoring value of the monitoring parameter with the highest priority among the monitoring parameters that need to be adjusted are adjusted to the same adjustment component.
  • the priority of each dimension monitoring parameter can be calibrated according to the actual situation and set in the storage space of the air conditioning equipment in advance.
  • the priority of each dimension monitoring parameter can be pre-set when the air conditioning equipment leaves the factory, or can be defined by the user, which has high flexibility.
  • the adjustment components corresponding to the identified temperature and wind speed include fans, and the difference between the monitored values of temperature and wind speed and the recommended value is not within the preset allowable range, it means that the temperature and wind speed need to be adjusted.
  • the priority of temperature and wind speed can be recalled from its own storage space. Taking the priority of temperature higher than the priority of wind speed as an example, the fan can be adjusted according to the recommended value and monitoring value of temperature.
  • this application can filter the self-learning model used according to the user's characteristic information to obtain the self-learning model most suitable for the user at present, so that the recommended parameters are more in line with the needs of the user, and multiple monitoring parameters can be adjusted at the same time , And the adjustment process of each monitoring parameter is independent of each other, which improves the flexibility of the air conditioning equipment. Further, the adjustment component corresponding to the monitoring parameter can be adjusted according to the recommended value and the monitoring value of the monitoring parameter to adjust the monitoring parameter.
  • this application also proposes a control device for air conditioning equipment.
  • Fig. 8 is a schematic block diagram of a control method of an air conditioning device according to an embodiment of the present application.
  • the control device 100 of the air conditioning equipment includes: a mode activation module 10, a first acquisition module 20, a second acquisition module 30, and an adjustment module 40.
  • the mode activation module 10 is used to respond to a first instruction for turning on the multi-dimensional adjustment mode of the air-conditioning equipment to enter the multi-dimensional adjustment mode; the first obtaining module 20 is used to obtain the characteristic information of the user, and according to the characteristic information, at least A candidate model is screened to obtain a target model suitable for the user; the second acquisition module 30 is used to obtain the recommended value of each dimension of the multi-dimensional monitoring parameters according to the target model; the adjustment module 40 is used to The recommended value and the monitoring value of any one-dimensional monitoring parameter in the multi-dimensional monitoring parameters adjust the adjustment component corresponding to the any one-dimensional monitoring parameter.
  • the first obtaining module 20 is further configured to: obtain the priority of the candidate model; successively match the characteristic information with the candidate model according to the priority order; obtain the matching with the characteristic information And use it as the target model.
  • the first obtaining module 20 is further configured to: match the feature information with the candidate models one by one; obtain the number of the candidate models that match the feature information; when the number is greater than When the threshold is preset, the matched candidate models are sorted according to priority; the candidate model with the highest priority is used as the target model.
  • the first acquiring module 20 is further configured to: randomly match the feature information with the candidate model; identify that there is the candidate model that matches the feature information; and match the feature information with the feature information
  • the candidate model of is used as the target model.
  • the candidate model includes at least one of an individual self-learning model, a group self-learning model, and a general self-learning model.
  • the second acquisition module 30 is further configured to: acquire the historical usage data of the air-conditioning equipment used by the user, the current environmental data of the environment in which the user is located, and the current Time information is used as the first data; the first data is input to the individual self-learning model to obtain the recommended value.
  • the second acquisition module 30 is further configured to: acquire current environment data and/or current time information of the environment where the user is located as the first data; The first data is input to the group self-learning model to obtain the group attribute of the user; according to the group attribute, the group user is obtained, and the recommendation value corresponding to the group user is obtained as the user’s The recommended value.
  • the second acquisition module 30 is also used to: acquire usage data of the air-conditioning equipment as the first data; and input the first data into the universal self-learning The model obtains the recommended value common to all users as the recommended value of the user.
  • detecting the user's active adjustment instruction for one of the one-dimensional monitoring parameters of the multi-dimensional monitoring parameters and controlling the adjustment function of the adjustment component corresponding to the one of the one-dimensional monitoring parameters to be in a locked state according to the active adjustment instruction .
  • detecting a user's closing instruction for one of the one-dimensional monitoring parameters of the multi-dimensional monitoring parameters and controlling the adjustment component corresponding to the one of the one-dimensional monitoring parameters to be in a closed state according to the closing instruction.
  • a selection instruction for selecting at least two-dimensional monitoring parameters from the multi-dimensional monitoring parameters is acquired, and the first instruction is generated according to the selection instruction.
  • At least one adjustment component corresponding to the any one-dimensional monitoring parameter is determined; according to the recommended value and monitoring value of the any one-dimensional monitoring parameter, an adjustment instruction for the adjustment component is generated, and an adjustment instruction is generated according to the adjustment instruction The adjustment component is adjusted.
  • the adjustment component corresponding to the monitoring parameter of two or more dimensions includes the same adjustment Component; and the two-dimensional or more than two-dimensional monitoring parameters need to be adjusted, determine the priority of each dimension of the two-dimensional or more than two-dimensional monitoring parameters, according to the recommended value of the monitoring parameter with the highest priority And the monitored value, the same adjustment component is adjusted.
  • the multi-dimensional monitoring parameters include two or more of humidity, temperature, wind speed, pollutant content in the air, and air quality index.
  • the adjustment component is integrated or independent of the air conditioning equipment.
  • the present application also proposes an air-conditioning device 200, as shown in FIG. 9, which includes the above-mentioned control device 100 of the air-conditioning device.
  • the air conditioning equipment of the embodiment of the present application can adjust multiple monitoring parameters at the same time, and the adjustment process of each monitoring parameter is independent of each other, which improves the flexibility of the air conditioning equipment. Further, the adjustment component corresponding to the monitoring parameter can be adjusted according to the recommended value and the monitoring value of the monitoring parameter to adjust the monitoring parameter.
  • the present application also proposes an electronic device 300.
  • the electronic device 300 includes a memory 31 and a processor 32.
  • the processor 32 runs a program corresponding to the executable program code by reading the executable program code stored in the memory 31, so as to implement the above-mentioned control method of the air conditioning device.
  • first and second are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined with “first” and “second” may explicitly or implicitly include at least one of the features. In the description of the present application, "a plurality of” means at least two, such as two, three, etc., unless specifically defined otherwise.
  • a "computer-readable medium” can be any device that can contain, store, communicate, propagate, or transmit a program for use by an instruction execution system, device, or device or in combination with these instruction execution systems, devices, or devices.
  • computer readable media include the following: electrical connections (electronic devices) with one or more wiring, portable computer disk cases (magnetic devices), random access memory (RAM), Read only memory (ROM), erasable and editable read only memory (EPROM or flash memory), fiber optic devices, and portable compact disk read only memory (CDROM).
  • the computer-readable medium may even be paper or other suitable medium on which the program can be printed, because it can be used, for example, by optically scanning the paper or other medium, followed by editing, interpretation, or other suitable media if necessary. The program is processed in a manner to obtain the program electronically, and then stored in the computer memory.
  • each part of this application can be implemented by hardware, software, firmware, or a combination thereof.
  • multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system.
  • Discrete logic gate circuits with logic functions for data signals Logic circuits, application specific integrated circuits with suitable combinational logic gates, programmable gate array (PGA), field programmable gate array (FPGA), etc.
  • a person of ordinary skill in the art can understand that all or part of the steps carried in the method of the foregoing embodiments can be implemented by a program instructing relevant hardware to complete.
  • the program can be stored in a computer-readable storage medium. When executed, it includes one of the steps of the method embodiment or a combination thereof.
  • the functional units in the various embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units may be integrated into one module.
  • the above-mentioned integrated modules can be implemented in the form of hardware or software functional modules. If the integrated module is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer readable storage medium.
  • the aforementioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.

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Abstract

一种空气调节设备及其控制方法、装置、电子设备，方法包括：响应用于开启空气调节设备的多维调节模式的第一指令，以进入多维调节模式；获取用户的特征信息，根据特征信息对候选模型进行筛选，得到适用于用户的目标模型；根据目标模型，获取多维监控参数中每维监控参数的推荐值；根据多维监控参数中的任意一维监控参数的推荐值和监控值，对与任意一维监控参数对应的调节组件进行调节。

Description

空气调节设备及其控制方法、装置、电子设备
相关申请的交叉引用
本申请基于申请号为202010238937.8、申请日为2020年03月30日的中国专利申请提出，并要求上述中国专利申请的优先权，上述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
本申请涉及电器技术领域，特别涉及一种空气调节设备的控制方法、装置、空气调节设备、电子设备和计算机可读存储介质。
背景技术
目前，空气调节设备广泛应用于调节室内温度、湿度等参数，提高了用户的舒适度。然而，相关技术中，空气调节设备的空气调节功能较为单一，不够灵活，无法满足用户需求。
发明内容
本申请旨在至少在一定程度上解决相关技术中的技术问题之一。
为此，本申请的第一个目的在于提出一种空气调节设备的控制方法，通过用户的特征信息筛选最适宜用户的目标模型，从而根据目标模型的推荐值对空气调节设备进行控制，以使监控参数的推荐值更符合用户的使用习惯和需求，更加人性化，提高了用户的舒适度。
本申请的第二个目的在于提出一种空气调节设备的控制装置。
本申请的第三个目的在于提出一种空气调节设备。
本申请的第四个目的在于提出一种电子设备。
本申请的第五个目的在于提出一种计算机可读存储介质。
为达上述目的，本申请第一方面实施例提出了一种空气调节设备的控制方法，包括以下步骤：响应用于开启空气调节设备的多维调节模式的第一指令，以进入多维调节模式；获取用户的特征信息，根据所述特征信息对至少一个候选模型进行筛选，得到适用于所述用户的目标模型；根据所述目标模型，获取所述多维监控参数中每维监控参数的推荐值；根据所述多维监控参数中的任意一维监控参数的推荐值和监控值，对与所述任意一维监控参数对应的调节组件进行调节。
根据本申请的一个实施例，所述根据所述特征信息对至少一个候选模型进行筛选，得到适用于所述用户的目标模型，包括：获取所述候选模型的优先级；按照所述优先级顺序将所述特征信息逐次与所述候选模型进行匹配；获取与所述特征信息相匹配的所述候选模型，并将其作为所述目标模型。
根据本申请的一个实施例，所述根据所述特征信息对至少一个候选模型进行筛选，得到适用于所述用户的目标模型，包括：将所述特征信息逐个与所述候选模型进行匹配；获取与所述特征信息相匹配的所述候选模型的个数；在所述个数大于预设阈值时，将匹配的所述候选模型按照优先级进行排序；将所述优先级最高的所述候选模型作为所述目标模型。
根据本申请的一个实施例，所述根据所述特征信息对至少一个候选模型进行筛选，得到适用于所述用户的目标模型，包括：将所述特征信息随机与所述候选模型进行匹配；识别存在与所述特征信息相匹配的所述候选模型；将与所述特征信息相匹配的所述候选模型作为所述目标模型。
根据本申请的一个实施例，所述候选模型包括个体自学习模型、群体自学习模型和通用自学习模型中的至少一个。
根据本申请的一个实施例，所述目标模型为所述个体自学习模型时，所述根据所述目标模型，获取所述多维监控参数中每维监控参数的推荐值，包括：获取所述用户使用所述空气调节设备的历史使用数据、所处环境的当前环境数据和当前时间信息作为所述第一数据；将所述第一数据输入至所述个体自学习模型，得到所述推荐值。
根据本申请的一个实施例，所述目标模型为所述群体自学习模型时，所述根据所述目标模型，获取所述多维监控参数中每维监控参数的推荐值，包括：获取所述用户所处环境的当前环境数据和/或当前时间信息作为所述第一数据；所述第一数据输入至所述群体自学习模型，获取所述用户的群体属性；根据所述群体属性，获取群体用户，并获取所述群体用户对应的所述推荐值，作为所述用户的所述推荐值。
根据本申请的一个实施例，所述目标模型为所述通用自学习模型时，所述根据所述目标模型，获取所述多维监控参数中每维监控参数的推荐值，包括：获取空气调节设备的使用数据，作为第一数据；所述第一数据输入至所述通用自学习模型，获取全用户通用的所述推荐值，作为所述用户的所述推荐值。
根据本申请的一个实施例，检测所述用户针对所述多维监控参数中其中一维监控参数的主动调节指令，根据所述主动调节指令，控制与所述其中一维监控参数对应的调节组件的调节功能处于锁定状态。
根据本申请的一个实施例，检测用户针对所述多维监控参数中其中一维监控参数的关闭 指令，根据所述关闭指令，控制与所述其中一维监控参数对应的调节组件处于关闭状态。
根据本申请的一个实施例，所述响应用于开启空气调节设备的多维调节模式的第一指令之前，还包括：获取从所述多维监控参数中选取至少两维监控参数的选取指令，根据所述选取指令生成所述第一指令。
根据本申请的一个实施例，所述根据所述多维监控参数中任意一维监控参数的推荐值和监控值，对与所述任意一维监控参数对应的调节组件进行调节，包括：确定与所述任意一维监控参数对应的至少一个调节组件；根据所述任意一维监控参数的推荐值和监控值，生成针对所述调节组件的调节指令，并按照所述调节指令对所述调节组件进行调节。
根据本申请的一个实施例，所述根据所述任意一维监控参数的推荐值和监控值，生成针对所述调节组件的调节指令之前，还包括：识别有两维或者两维以上的监控参数对应的调节组件包括同一调节组件；且所述两维或者两维以上的监控参数均需要调节，则确定所述两维或者两维以上的监控参数中每维监控参数的优先级，根据优先级最高的监控参数的推荐值和监控值，对所述同一调节组件进行调节。
根据本申请的一个实施例，所述多维监控参数包括：湿度、温度、风速、空气中污染物含量和空气质量指数中的两个及两个以上。
根据本申请的一个实施例，所述调节组件集成或者独立于所述空气调节设备。
本申请能够根据用户的特征信息对使用的自学习模型进行筛选，以获取当前最适合用户的自学习模型，使得推荐参数更符合用户的需求，同时对多个监控参数进行调节，而且各个监控参数的调节过程是相互独立的，提高了空气调节设备的灵活性。进一步地，能够根据监控参数的推荐值和监控值，对与监控参数对应的调节组件进行调节，以对监控参数进行调节。
为达上述目的，本申请第二方面实施例提出了一种空气调节设备的控制装置，包括：模式启动模块，用于响应用于开启空气调节设备的多维调节模式的第一指令，以进入多维调节模式；第一获取模块，用于获取用户的特征信息，根据所述特征信息对至少一个候选模型进行筛选，得到适用于所述用户的目标模型；第二获取模块，用于根据所述目标模型，获取所述多维监控参数中每维监控参数的推荐值；调节模块，用于根据所述多维监控参数中的任意一维监控参数的推荐值和监控值，对与所述任意一维监控参数对应的调节组件进行调节。
为达上述目的，本申请第三方面实施例提出了一种空气调节设备，包括所述的空气调节设备的控制装置。
为了实现上述目的，本申请第四方面实施例提出了一种电子设备，包括存储器、处理器； 其中，所述处理器通过读取所述存储器中存储的可执行程序代码来运行与所述可执行程序代码对应的程序，以用于实现所述的空气调节设备的控制方法。
为了实现上述目的，本申请第五方面实施例提出了一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，该程序被处理器执行时实现所述的空气调节设备的控制方法。
本申请附加的方面和优点将在下面的描述中部分给出，部分将从下面的描述中变得明显，或通过本申请的实践了解到。
附图说明
本申请的上述和/或附加的方面和优点从结合下面附图对实施例的描述中将变得明显和容易理解，其中：
图1为根据本申请一个实施例的空气调节设备的控制方法的流程图；
图2为根据本申请一个实施例的空气调节设备的控制方法的流程图；
图3为根据本申请一个实施例的空气调节设备的控制方法的流程图；
图4为根据本申请一个实施例的空气调节设备的控制方法的流程图；
图5为根据本申请一个实施例的空气调节设备的控制方法的流程图；
图6为根据本申请一个实施例的空气调节设备的控制方法的流程图；
图7为根据本申请一个实施例的空气调节设备的控制方法的流程图；
图8为根据本申请一个实施例的空气调节设备的控制方法的方框示意图；
图9为根据本申请一个实施例的空气调节设备的方框示意图；以及
图10为根据本申请一个实施例的电子设备的方框示意图。
具体实施方式
下面详细描述本申请的实施例，所述实施例的示例在附图中示出，其中自始至终相同或类似的标号表示相同或类似的元件或具有相同或类似功能的元件。下面通过参考附图描述的实施例是示例性的，旨在用于解释本申请，而不能理解为对本申请的限制。
下面结合附图来描述本申请实施例的空气调节设备的控制方法、装置、空气调节设备、电子设备和计算机可读存储介质。
图1为根据本申请一个实施例的空气调节设备的控制方法的流程图。
如图1所示，本申请实施例的空气调节设备的控制方法，包括以下步骤：
S101，响应用于开启空气调节设备的多维调节模式的第一指令，以进入多维调节模 式。
需要说明的是，在本申请的实施例中，空气调节设备具有多维调节模式，可对两个或者两个以上的监控参数进行调节。
其中，监控参数可根据实际情况进行标定，并预先设置在空气调节设备的存储空间中。例如，监控参数可包括湿度、温度、风速、空气中污染物含量、空气质量指数(Air Quality Index，简称“AQI”)、二氧化碳浓度中的两个及两个以上。其中，空气中污染物含量可包括PM2.5的浓度。
可选地，用户可通过遥控器、移动终端中的空气调节设备APP、空气调节设备的机身上的操控面板，通过语言、手势等非接触类方式向空气调节设备发出用于开启多维调节模式的第一指令。
在本申请的一个实施例中，第一指令可包括开机指令，从而在用户向空气调节设备发出开机指令后，空气调节设备可在开机后进入多维调节模式，避免了现有技术中在空气调节设备开机后，还需要用户再发出开启多维调节模式的指令，比较简便。
S102：获取用户的特征信息，根据特征信息对至少一个候选模型进行筛选，得到适用于用户的目标模型。
其中，候选模型包括个体自学习模型、群体自学习模型和通用自学习模型中的至少一个。用户的特征信息可包括用户基本个人信息，例如年龄、性别、爱好等，或者还可包括用户使用空气调节设备的账号，又或者还可包括用户使用空气调节设备的历史使用数据可包括用户之前主动设置的温度信息、湿度信息、风挡信息、扫风模式、新风模式、运行模式、累计使用次数、累计使用时长等数据。
S103：根据目标模型，获取多维监控参数中每维监控参数的推荐值。
也就是说，本申请能够根据获取到的多种用户的特征信息，从候选模型中筛选出符合用户特征信息的目标模型，以通过目标模型对用户的特征信息、所处环境信息、当前时间信息和历史使用数据等信息进行自学习，得到用于对空气调节设备进行多维控制的每维监控参数的推荐值。
可选地，可为监控参数推荐一个数值，可也为监控参数推荐一个取值范围。
举例而言，多维监控参数包括湿度、温度、风速、空气中污染物含量、空气质量指数、二氧化碳浓度时，可分别对温度、风速推荐一个数值，以及分别对湿度、空气中污染物含量、空气质量指数、二氧化碳浓度推荐一个取值范围。
举例而言，监控参数为温度时，对应的推荐值可为25℃。监控参数为风速时，对应的推荐值可为2m/s。监控参数为湿度时，对应的推荐值的取值范围可为(40～70)％。 以空气中污染物含量包括PM2.5浓度为例，监控参数为PM2.5浓度时，对应的推荐值的取值范围可为(0～75)μg/m 3。监控参数为空气质量指数时，对应的推荐值的取值范围可为(0～75)μg/m 3。监控参数为二氧化碳浓度时，对应的推荐值的取值范围可为(0～1000)PPM。
当目标模型为个体自学习模型时，如图2所示，根据目标模型，获取多维监控参数中每维监控参数的推荐值，包括：
S201：获取用户使用空气调节设备的历史使用数据、所处环境的当前环境数据和当前时间信息作为第一数据。
S202：将第一数据输入值个体自学习模型，得到推荐值。
需要说明的是，用户使用空气调节设备的历史使用数据可包括用户之前主动设置的温度信息、湿度信息、风挡信息、扫风模式、新风模式、运行模式、累计使用次数、累计使用时长等数据。
也就是说，个体自学习模型是针对某一用户个体的对空气调节设备的历史使用数据的自学习模型，即，个体自学习模型具有个性化的特定，与用户特征信息之间具有较高的关联性。
应当理解的是，由于个体自学习模型需要用户使用空气调节设备的历史使用数据进行自学习，因此，个体自学习模型明显适用于已经拥有历史使用数据的老用户，也就是说，只有对空气调节设备进行使用过的老用户才能够匹配到个体自学习模型。
当目标模型为群体自学习模型时，如图3所示，根据目标模型，获取多维监控参数中每维监控参数的推荐值，包括：
S301：获取用户所处环境的当前环境数据和/或当前时间信息作为第一数据。
其中，所处环境的当前环境数据可包括用户所处的省份、城市、气候区域、室内温度、室外温度、室内湿度、室外湿度、PM2.5浓度、二氧化碳浓度、空气质量指数等数据。
其中，当前时间信息可包括月份、节气、具体时间段(上午、下午、晚上)、是否处于工作日等数据。
可选地，可通过无线网络装置查询来获取用户所处环境的当前环境数据，例如，可通过无线网络装置查询来获取用户所处的省份、城市、室外温度、室外湿度。还可以通过检测装置来获取用户所处环境的当前环境数据，例如，可在空气调节设备的室内机上安装温度传感器来获取用户所处环境的室内温度。
可选地，可通过查询空气调节设备的系统时间来获取当前时间信息。
S302：将第一数据输入至群体自学习模型，获取用户的群体属性。
S303：根据群体属性，获取群体用户，并获取群体用户对应的推荐值，作为用户的推荐值。
需要说明的是，当用户为新用户或者用户对任意一维监控参数进行主动调节的次数较少或者使用时间较短，此时用户使用空气调节设备的历史使用数据不能映出用户对空气调节设备的使用习惯和需求，或者不能反映出用户对任意一维监控参数的调节习惯和需求，无法采用上述个体自学习模型将历史使用数据用于获取每维监控参数的推荐值。但由于为了使推荐值更符合当前的环境和时间，可通过所处环境的当前环境数据和当前时间信息获取该用户基于当前环境数据和当前时间信息的群体属性，然后根据具有相同群体属性的群体用户的推荐值来获取当前用户每维监控参数的推荐值。
由此，采用群体自学习模型能够综合考虑到所处环境的当前环境数据、当前时间信息和群体用户对监控参数的推荐值的影响，使得到的监控参数的推荐值更符合当前的环境、时间和群体用户的使用习惯和需求，提高了用户的舒适度。
可选地，根据群体属性获取群体用户，可包括预先建立群体属性与群体用户之间的映射关系或者映射表，获取到用户的群体属性后，查询映射关系或者映射表，能够获取到与该用户匹配的群体用户。应说明的是，群体用户对应的推荐值可根据实际情况进行标定，也可为符合群体用户的实际用户的推荐值的平均值。例如，若用户相对于湿度为新用户，则可将所处环境的当前室外湿度、室内湿度、当前月份、具体时间段作为第一数据，然后将第一数据输入至群体自学习模型，以获取用户的群体属性，假设用户的群体属性为a，根据群体属性a获取的群体用户为A，可获取该群体用户A对应的湿度的推荐值，作为该用户的湿度的推荐值。
还应当理解的是，在上述实施例中的群体学习模型偏重于基于用户当前所处的环境和时间，其原因在于环境和时间对空气调节设备的使用具有较大的影响，例如，十月份左右，我国北方天气寒冷，北方用户在该时间段使用空气调节设备时通常使用制热功能，而南方沿海地区城市天气依然温和，南方用户在该时间段使用空气调节设备时通常使用制冷功能。但是，即使同样在制热和/或制冷状态下，由于个体差异性，使得不同用户选择的目标温度不同，因此，还可在群体自学习模型中添加用户的喜好特征，以通过用户的喜好特征对用户进行群体分析，例如，喜欢运动的人群无论采用制冷模式还是制热模式通常会选取比不喜欢运动的人群温度低的温度作为目标温度，又如，女性使用空气调节设备的温度普遍高于男性等，由此，群体自学习模型可不仅包含关于用户当前所处环境和时间等的地域性群体学习，还可包括用户喜好、性别等特征信息的群体学习。
其中，可通过用户移动终端等获取用户的喜好、性别等特征信息。
然而，还存部分用户连用户特征信息也无法获取的客户，例如年龄较大的老人、涉密人员或者部队等，这些用户通常不使用具有人工智能的设备，例如部队环境还很难获取到定位信息，因此无法获取到所处环境信息等，本申请还建立了通用自学习模型。
当目标模型为通用自学习模型时，如图4所示，根据目标模型，获取多维监控参数中每维监控参数的推荐值，包括：
S401：获取空气调节设备的使用数据，作为第一数据。
需要说明的是，在本申请实施例中的空气调节设备的使用数据为全网用户对空气调节设备的使用数据。
S402：将第一数据输入值通用自学习模型，获取全用户通用的推荐值，作为用户的推荐值。
也就是说，可对全网用户的空气调节设备的使用数据进行获取，然后不区分用户的特性，对全网用户的使用数据通过通用自学习模型进行分析，得到可适用于全用户的推荐值，并将该推荐值作为用户的推荐值。
S104：根据多维监控参数中的任意一维监控参数的推荐值和监控值，对与任意一维监控参数对应的调节组件进行调节。
需要说明的是，每维监控参数可对应一个或者多个调节组件，调节组件能够独立控制，也可以进行联动控制，以对调节组件对应的监控参数进行调节，而且每维监控参数对应的调节组件的调节是相互独立的。
由此，本申请能够根据用户的特征信息对使用的自学习模型进行筛选，以获取当前最适合用户的自学习模型，使得推荐参数更符合用户的需求，同时对多个监控参数进行调节，而且各个监控参数的调节过程是相互独立的，提高了空气调节设备的灵活性。进一步地，能够根据监控参数的推荐值和监控值，对与监控参数对应的调节组件进行调节，以对监控参数进行调节。
根据本申请一个实施例，如图5所示，根据特征信息对至少一个候选模型进行筛选，得到适用于用户的目标模型，包括：
S501：获取候选模型的优先级。
基于上述对通过自学习模型获取推荐值的过程的描述，可知，含有用户更多的历史使用记录的个体自学习模型的优先级高于仅获取部分用户信息的群体自学习模型，群体自学习模型的优先级高于没有用户信息的通用自学习模型。
S502：按照优先级顺序将特征信息逐次与候选模型进行匹配。
S503：获取与特征信息相匹配的候选模型，并将其作为目标模型。
例如，可在获取到用户的特征信息之后，先将特征信息与优先级最高的个体自学习模型进行匹配，如果特征信息中存在对空气调节设备的历史使用记录，确定用户特征信息与个体自学习模型匹配，则进一步将个体自学习模型作为目标模型以通过历史使用数据获取用户的推荐值，如果特征信息中不存在对空气调节设备的历史使用记录，确定用户特征信息与个体自学习模型不匹配，则进一步将特征信息与优先级其次的群体用户自学习模型进行匹配，如果特征信息中存在用户所处环境和时间信息或用户喜好、性别等特征信息，确定特征信息与群体自学习模型匹配，则进一步将群体自学习模型作为目标模型以通过用户的特征信息获取用户的推荐值，如果特征信息中不存在用户所处环境和时间信息或用户喜好、性别等特征信息，则直接将优先级最低的通用自学习模型作为用户的目标模型，并通过通用自学习模型获取用户的推荐值。
由此，本申请能够通过依次匹配的方式减少匹配过程的运算量，并且按照优先级顺序进行匹配能够尽快得到最能够表达用户习惯和需求的目标模型，使得最终采用的目标模型能够尽可能的符合用户历史使用习惯和需求，提高了用户的舒适度，提升用户的体验。
根据本申请另一个实施例，如图6所示，根据特征信息对至少一个候选模型进行筛选，得到适用于用户的目标模型，包括：
S601：将特征信息逐个与候选模型进行匹配。
S602：获取与特征信息相匹配的候选模型的个数。
S603：在个数大于预设阈值时，将匹配候选模型按照优先级进行排序。
也就是说，本申请能够在获取到用户的特征信息之后，分别将特征信息同时与候选模型进行匹配，例如，当特征信息包含有用户对空气调节设备的历史使用数据时，特征信息能够同时匹配到个体自学习模型、群体自学习模型和通用自学习模型，当特征信息仅包含有用户所处环境和时间或者用户的喜好和性别等信息时，特征信息能够同时匹配到群体自学习模型和通用自学习模型，当特征信息不包含上述数据时，则确定特征信息仅能匹配到通用自学习模型。
应当理解的是，预设阈值可为1，即，当特征信息仅能匹配到通用自学习模型时，可直接将通用自学习模型作为用户的目标模型获取推荐值，但是，当根据特征信息匹配到的模型大于1时，例如同时匹配到群体自学习模型和通用自学习模型或者同时匹配到个体自学习模型、群体自学习模型和通用自学习模型时，则需要对多个候选模型进行进一步筛选，以选择出唯一的目标模型使得推荐值具有唯一的确定性。因此，可进一步根 据候选模型的优先级对匹配到的候选模型进行筛选。
例如，在同时匹配到群体自学习模型和通用自学习模型时，由于群体自学习模型的优先级高于通用自学习模型，则确定群体自学习模型为用户的目标模型，在同时匹配到个体自学习模型、群体自学习模型和通用自学习模型时，则按照优先级进行排序得到个体自学习模型＞群体自学习模型＞通用自学习模型，因此，可将个体自学习模型作为用户的目标模型获取推荐值。
由此，本实施例可同时对三个候选模型进行匹配操作，有效节约对候选模型进行匹配的时间，提升筛选目标模型的效率。
根据本申请又一个实施例，如图7所示，根据特征信息对至少一个候选模型进行筛选，得到适用于用户的目标模型，包括：
S701：将特征信息随机与候选模型进行匹配。
S702：识别存在与特征信息相匹配的候选模型。
S703：将特征信息相匹配的候选模型作为目标模型。
也就是说，本申请可将特征信息与候选模型之间进行随机的匹配，例如，可将特征信息先与个体自学习模型进行匹配，也可将特征信息先与群体自学习模型进行匹配，还可将特征信息先与通用自学习模型匹配，若匹配成功，则直接将匹配到的候选模型作为目标模型。
举例来说，当获取到的用户的特征信息仅包括用户所处环境信息和时间，此时，若随机获取到与特征信息匹配的是个体自学习模型，则识别到不存在与特征信息相匹配的候选模型，若随机获取与特征信息匹配的是群体自学习模型或通用自学习模型，则识别到存在与特性信息相匹配的候选模型，并可直接将随机匹配到的候选模型作为目标模型。
可选地，调节组件集成或者独立于空气调节设备，该方法能够提高调节组件的适用性和灵活性，使得本申请可以更广泛地应用于空气调节设备。
举例来说，对于温度、空气质量指数和空气中污染物含量进行调节时，均会涉及到将室内空气进行回收的操作，即，温度调节时的室内回风、空气质量指数和空气中污染物含量的回收过滤等，因此，可对温度调节、空气质量指数调节和空气中污染物调节的回风口进行集成设置，即，只设置一个回风口，或者，可根据实际情况设置多个回风口，例如，由于空气中污染物的质量较大会产生下沉现象，因此，可将控制中污染物调节的回风口设置在空气调节设备的下部，以使回收到的空气含污染物量较高，从而提高空气中污染物含量调节的效率，并将温度调节的回风口设置于空气调节设备的上部，以使回 收到的空气含污染物量较低，降低通过温度调节送风造成空气二次污染。又如，由于湿度调节包括向室内吹送雾化水，为了防止雾化水在空气调节设备内部造成凝露等现象影响其他监控参数的调节，因此，可独立设置湿度调节的送风口。
需要说明的是，空气调节设备进入多维调节模式后，可对每维监控参数进行监控，以获取每维监控参数的监控值。
例如，监控参数包括温度时，可通过在空气调节设备的室内机上安装温度传感器来获取温度的监控值。监控参数包括风速时，可通过在空气调节设备的室内机的出风口处安装风速传感器来获取风速的监控值。
需要说明的是，监控参数及其对应的调节组件可根据实际情况进行标定，并预先设置在空气调节设备的存储空间中。例如，监控参数为风速时，对应的调节组件可包括风机。监控参数为温度时，对应的调节组件可包括压缩机、风机。
可选地，可预先建立监控参数和调节组件之间的映射关系或者映射表，在获取到监控参数后，查询映射关系或者映射表，能够确定出该监控参数对应的调节组件，然后对调节组件进行调节。
进一步地，根据多维监控参数中任意一维监控参数的推荐值和监控值，对与任意一维监控参数对应的调节组件进行调节，可包括确定与任意一维监控参数对应的至少一个调节组件，然后根据任意一维监控参数的推荐值和监控值，生成针对调节组件的调节指令，并按照调节指令对调节组件进行调节。
例如，监控参数为风速时，对应的调节组件可包括风机，可根据风速的推荐值和监控值，生成针对风机的调节指令，并按照调节指令对风机进行调节。
可以理解的是，风机转速越高，则风速越大。
进一步地，若风速的监控值大于风速的推荐值，说明此时风速过大，需要降低风速，可生成降低风机转速的调节指令，并按照调节指令降低风机的转速，以降低风速。若风速的监控值小于风速的推荐值，说明此时风速过小，需要提高风速，可生成提高风机转速的调节指令，并按照调节指令提高风机的转速，以提高风速。若风速的监控值与风速的推荐值相等，可不生成针对风机的调节指令，使得风机按照当前转速继续运行。
该方法能够通过调节风机的转速来对风速进行调节，能够使风速的监控值趋近于风速的推荐值，提高了用户的舒适度。
或者，监控参数为温度时，对应的调节组件可包括压缩机、风机，可根据温度的推荐值和监控值，分别生成针对压缩机、风机的调节指令，并按照调节指令对压缩机、风机进行调节。
可以理解的是，以空气调节设备运行在制热模式为例，压缩机的运行频率、风机转速越高，则空气调节设备的制热负荷越大，温度越高。
进一步地，以空气调节设备运行在制热模式为例，若温度的监控值大于温度的推荐值，说明此时温度过高，需要降低温度，可分别生成降低压缩机的运行频率、降低风机转速的调节指令，并按照调节指令降低压缩机的运行频率和风机的转速，以降低温度。若温度的监控值小于温度的推荐值，说明此时温度过低，需要提高温度，可分别生成提高压缩机的运行频率、提高风机转速的调节指令，并按照调节指令提高压缩机的运行频率和风机的转速，以提高温度。若温度的监控值与温度的推荐值相等，可不生成针对压缩机、风机的调节指令，使得压缩机按照当前运行频率继续运行，以及风机按照当前转速继续运行。
该方法能够通过调节压缩机的运行频率、风机的转速来对温度进行调节，能够使温度的监控值趋近于温度的推荐值，提高了用户的舒适度。
在本申请的一个实施例中，响应用于开启空气调节设备的多维调节模式的第一指令之前，还包括获取从多维监控参数中选取至少两维监控参数的选取指令，说明用户对至少两维监控参数有调节意愿，需要空气调节设备开启多维调节模式，此时可根据选取指令生成用于开启空气调节设备的多维调节模式的第一指令，使得空气调节设备根据第一指令进入多维调节模式，以对选取的至少两维监控参数进行调节。
该方法使得用户可依据个人意愿从多维监控参数中选取至少两维监控参数，作为空气调节设备需要调节的监控参数，具有较高的灵活性。
例如，多维监控参数包括湿度、温度、风速时，若获取选取湿度、温度的选取指令，说明用户对湿度、温度有调节意愿，需要空气调节设备开启多维调节模式，此时可根据选取指令生成用于开启空气调节设备的多维调节模式的第一指令，使得空气调节设备根据第一指令进入多维调节模式，以对湿度、温度进行调节。
可选地，用户可通过遥控器、移动终端中的空气调节设备APP、空气调节设备的机身上的操控面板，通过语言、手势等非接触类方式从多维监控参数中选取监控参数，并生成选取指令。
以用户通过空气调节设备机身上的操控面板生成选取指令为例，可在操控面板上预先设置可供用户选择监控参数的选择菜单，以及对用户在选择界面的菜单选择操作进行监控，当监控到菜单选择操作后，显示选择菜单，并获取用户在选择菜单上的操作位置，然后根据操作位置识别用户所选定的监控参数，当识别用户所选定的监控参数的数量大于或者等于二时，可生成用于开启空气调节设备的多维调节模式的第一指令，使得空气 调节设备响应第一指令，以进入多维调节模式。
在本申请的一个实施例中，空气调节设备进入多维调节模式后，还可根据从多维监控参数中选取至少两维监控参数的选取指令，识别选取的监控参数，然后获取选取的多维监控参数中每维监控参数的推荐值。
也就是说，对于前述的监控参数的选取指令既可发生在响应用于开启空气调节设备的多维调节模式的第一指令之前，也可发生在响应用于开启空气调节设备的多维调节模式的第一指令之后，即，用户可先对监控参数进行选取，然后再根据选取的监控参数生成第一指令以根据第一指令开启多维调节模式对用户选取的监控参数进行调节，也可先根据第一指令开启多维调节模式，然后等待用户选取需要调节的监控参数，在用户确定了需要调节的监控参数之后再根据推荐值对监控参数进行调节。
该方法只需对与选取的监控参数对应的调节组件进行调节，从而只需对选取的监控参数进行调节，能够更好地响应用户的实际需求，不用对每维监控参数进行调节，可节省能耗。
在本申请的一些实施例中，对调节组件进行调节的过程中，还包括检测用户针对多维监控参数中其中一维监控参数的主动调节指令，说明用户有主动调节该监控参数的意愿，此时不需要空气调节设备对该监控参数进行调节，可根据主动调节指令，控制与该监控参数对应的调节组件的调节功能处于锁定状态。
也就是说，用户对监控参数的选取可通过选择保留该监控参数的方式进行选取，也可通过将部分监控参数进行锁定的方式进行选取。
举例来说，多维监控参数包括湿度、温度、风速时，若获取选取湿度、温度的选取指令，用户可以直接选择对湿度、温度作为需要调节的监控参数，也可选择锁定风速监控参数，即，不对风速监控参数进行调节。
该方法可根据用户针对该监控参数的主动调节指令，控制与该监控参数对应的调节组件的调节功能处于锁定状态，能够更好地响应用户的实际需求，灵活性高。
可选地，用户可通过遥控器、移动终端中的空气调节设备APP、空气调节设备的机身上的操控面板，通过语言、手势等非接触类方式对监控参数进行主动调节，并发出主动调节指令。
在本申请的一些实施例中，对调节组件进行调节的过程中，还包括检测用户针对多维监控参数中其中一维监控参数的关闭指令，说明用户有关闭该监控参数的调节功能的意愿，即此时不需要空气调节设备对该监控参数进行调节，可根据关闭指令，控制与该监控参数对应的调节组件处于关闭状态。可选地，可根据关闭指令，控制关闭该监控参 数的监控功能，即不获取该监控参数的监控值，以节约能耗。
该方法可根据用户针对该监控参数的关闭指令，控制与该监控参数对应的调节组件处于关闭状态，能够更好地响应用户的实际需求，灵活性高，也有利于节约能耗。
可选地，用户可通过遥控器、移动终端中的空气调节设备APP、空气调节设备的机身上的操控面板，通过语言、手势等非接触类方式关闭监控参数，并发出关闭指令。
需要说明的是，本申请实施例的空气调节设备的控制方法中未披露的细节，请参照本申请上述实施例中所披露的细节，这里不再赘述。
由此，本申请能够根据选取的多维监控参数中任意一维监控参数的推荐值和监控值，对与任意一维监控参数对应的调节组件进行调节，从而只需对选取的监控参数进行调节，能够更好地响应用户的实际需求，不用对每维监控参数进行调节，可节省能耗。
在本申请的一个实施例中，若监控参数的监控值和推荐值的差值处于预设的允许范围内，说明监控参数的监控值与推荐值之间的差值较小，不需要对该监控参数进行调节。若监控参数的监控值和推荐值的差值未处于预设的允许范围内，说明监控参数的监控值与推荐值之间的差值较大，需要对该监控参数进行调节。
该方法可根据多维监控参数中任意一维监控参数的监控值和推荐值的差值和预设的允许范围，识别差值未处于预设的允许范围后，再对与监控参数对应的至少一个调节组件进行调节，减少了空气调节设备对监控参数进行调节的次数，有利于提高调节效率。
需要说明的是，预设的允许范围可根据实际情况进行标定，不同的监控参数可对应不同的允许范围，并预先设置在空气调节设备的存储空间中。
举例而言，监控参数为温度时，对应的允许范围可为2℃。监控参数为湿度时，对应的允许范围可为5％。监控参数为风速时，对应的允许范围可为0.5m/s。监控参数为PM2.5浓度时，对应的允许范围可为10μg/m 3。监控参数为空气质量指数时，对应的允许范围可为15μg/m 3。监控参数为二氧化碳浓度时，对应的允许范围可为50PPM。
可选地，可预先建立监控参数与预设的允许范围之间的映射关系或者映射表，在获取到监控参数后，查询映射关系或者映射表，能够确定出该监控参数对应的预设的允许范围，然后用于与监控参数的监控值和推荐值的差值进行比较，识别监控参数的监控值和推荐值的差值是否处于预设的允许范围内。
在本申请的一个实施例中，若识别有两维或者两维以上的监控参数对应的调节组件包括同一调节组件，且两维或者两维以上的监控参数均需要调节，即两维或者两维以上的监控参数的监控值与推荐值的差值未处于预设的允许范围内，说明需要对两维或者两维以上的监控参数进行调节，此时可根据该两维或者两维以上的监控参数中每维监控参 数的优先级，确定其中优先级最高的监控参数，然后根据优先级最高的监控参数的推荐值和监控值，对同一调节组件进行调节。
该方法可在需要调节的监控参数的数量为两个或者两个以上，且两个或者两个以上的需要调节的监控参数对应的调节组件包括同一调节组件时，根据两个或者两个以上的需要调节的监控参数中优先级最高的监控参数的推荐值和监控值，对同一调节组件进行调节。
其中，每维监控参数的优先级可根据实际情况进行标定，并预先设置在空气调节设备的存储空间中。可选地，每维监控参数的优先级可在空气调节设备出厂时预先设置，也可由用户自己定义，具有较高的灵活性。
举例而言，若识别温度、风速对应的调节组件都包括风机，且温度、风速的监控值与推荐值的差值均未处于预设的允许范围内，说明需要对温度、风速进行调节，此时可从自身的存储空间中调出温度、风速对应的优先级，以温度的优先级高于风速的优先级为例，则可根据温度的推荐值和监控值，对风机进行调节。
综上所述，本申请能够根据用户的特征信息对使用的自学习模型进行筛选，以获取当前最适合用户的自学习模型，使得推荐参数更符合用户的需求，同时对多个监控参数进行调节，而且各个监控参数的调节过程是相互独立的，提高了空气调节设备的灵活性。进一步地，能够根据监控参数的推荐值和监控值，对与监控参数对应的调节组件进行调节，以对监控参数进行调节。
为了实现上述实施例，本申请还提出一种空气调节设备的控制装置。
图8为根据本申请一个实施例的空气调节设备的控制方法的方框示意图。如图8所示，该空气调节设备的控制装置100包括：模式启动模块10、第一获取模块20、第二获取模块30和调节模块40。
其中，模式启动模块10用于响应用于开启空气调节设备的多维调节模式的第一指令，以进入多维调节模式；第一获取模块20用于获取用户的特征信息，根据所述特征信息对至少一个候选模型进行筛选，得到适用于所述用户的目标模型；第二获取模块30用于根据所述目标模型，获取所述多维监控参数中每维监控参数的推荐值；调节模块40用于根据所述多维监控参数中的任意一维监控参数的推荐值和监控值，对与所述任意一维监控参数对应的调节组件进行调节。
进一步地，第一获取模块20，还用于：获取所述候选模型的优先级；按照所述优先级顺序将所述特征信息逐次与所述候选模型进行匹配；获取与所述特征信息相匹配的所述候选模型，并将其作为所述目标模型。
进一步地，第一获取模块20，还用于：将所述特征信息逐个与所述候选模型进行匹配；获取与所述特征信息相匹配的所述候选模型的个数；在所述个数大于预设阈值时，将匹配的所述候选模型按照优先级进行排序；将所述优先级最高的所述候选模型作为所述目标模型。
进一步地，第一获取模块20，还用于：将所述特征信息随机与所述候选模型进行匹配；识别存在与所述特征信息相匹配的所述候选模型；将与所述特征信息相匹配的所述候选模型作为所述目标模型。
进一步地，所述候选模型包括个体自学习模型、群体自学习模型和通用自学习模型中的至少一个。
进一步地，所述目标模型为所述个体自学习模型时，第二获取模块30，还用于：获取所述用户使用所述空气调节设备的历史使用数据、所处环境的当前环境数据和当前时间信息作为所述第一数据；将第一数据输入至所述个体自学习模型，得到所述推荐值。
进一步地，目标模型为所述群体自学习模型时，第二获取模块30，还用于：获取所述用户所处环境的当前环境数据和/或当前时间信息作为所述第一数据；将所述第一数据输入至所述群体自学习模型，获取所述用户的群体属性；根据所述群体属性，获取群体用户，并获取所述群体用户对应的所述推荐值，作为所述用户的所述推荐值。
进一步地，目标模型为所述通用自学习模型时，第二获取模块30，还用于：获取空气调节设备的使用数据，作为第一数据；将所述第一数据输入至所述通用自学习模型，获取全用户通用的所述推荐值，作为所述用户的所述推荐值。
进一步地，检测所述用户针对所述多维监控参数中其中一维监控参数的主动调节指令，根据所述主动调节指令，控制与所述其中一维监控参数对应的调节组件的调节功能处于锁定状态。
进一步地，检测用户针对所述多维监控参数中其中一维监控参数的关闭指令，根据所述关闭指令，控制与所述其中一维监控参数对应的调节组件处于关闭状态。
进一步地，在所述响应用于开启空气调节设备的多维调节模式的第一指令之前，获取从所述多维监控参数中选取至少两维监控参数的选取指令，根据所述选取指令生成所述第一指令。
进一步地，确定与所述任意一维监控参数对应的至少一个调节组件；根据所述任意一维监控参数的推荐值和监控值，生成针对所述调节组件的调节指令，并按照所述调节指令对所述调节组件进行调节。
进一步地，在所述根据所述任意一维监控参数的推荐值和监控值，生成针对所述调 节组件的调节指令之前，识别有两维或者两维以上的监控参数对应的调节组件包括同一调节组件；且所述两维或者两维以上的监控参数均需要调节，则确定所述两维或者两维以上的监控参数中每维监控参数的优先级，根据优先级最高的监控参数的推荐值和监控值，对所述同一调节组件进行调节。
进一步地，所述多维监控参数包括：湿度、温度、风速、空气中污染物含量和空气质量指数中的两个及两个以上。
进一步地，所述调节组件集成或者独立于所述空气调节设备。
需要说明的是，前述对空气调节设备的控制方法实施例的解释说明也适用于该实施例的空气调节设备的控制装置，此处不再赘述。
为了实现上述实施例，本申请还提出一种空气调节设备200，如图9所示，其包括上述空气调节设备的控制装置100。
本申请实施例的空气调节设备，能够同时对多个监控参数进行调节，而且各个监控参数的调节过程是相互独立的，提高了空气调节设备的灵活性。进一步地，能够根据监控参数的推荐值和监控值，对与监控参数对应的调节组件进行调节，以对监控参数进行调节。
为了实现上述实施例，本申请还提出一种电子设备300，如图10所示，该电子设备300包括存储器31、处理器32。其中，处理器32通过读取存储器31中存储的可执行程序代码来运行与可执行程序代码对应的程序，以用于实现上述空气调节设备的控制方法。
在本说明书的描述中，参考术语“一个实施例”、“一些实施例”、“示例”、“具体示例”、或“一些示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本申请的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不必须针对的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任一个或多个实施例或示例中以合适的方式结合。此外，在不相互矛盾的情况下，本领域的技术人员可以将本说明书中描述的不同实施例或示例以及不同实施例或示例的特征进行结合和组合。
此外，术语“第一”、“第二”仅用于描述目的，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”的特征可以明示或者隐含地包括至少一个该特征。在本申请的描述中，“多个”的含义是至少两个，例如两个，三个等，除非另有明确具体的限定。
流程图中或在此以其他方式描述的任何过程或方法描述可以被理解为，表示包括一 个或更多个用于实现定制逻辑功能或过程的步骤的可执行指令的代码的模块、片段或部分，并且本申请的优选实施方式的范围包括另外的实现，其中可以不按所示出或讨论的顺序，包括根据所涉及的功能按基本同时的方式或按相反的顺序，来执行功能，这应被本申请的实施例所属技术领域的技术人员所理解。
在流程图中表示或在此以其他方式描述的逻辑和/或步骤，例如，可以被认为是用于实现逻辑功能的可执行指令的定序列表，可以具体实现在任何计算机可读介质中，以供指令执行系统、装置或设备(如基于计算机的系统、包括处理器的系统或其他可以从指令执行系统、装置或设备取指令并执行指令的系统)使用，或结合这些指令执行系统、装置或设备而使用。就本说明书而言，"计算机可读介质"可以是任何可以包含、存储、通信、传播或传输程序以供指令执行系统、装置或设备或结合这些指令执行系统、装置或设备而使用的装置。计算机可读介质的更具体的示例(非穷尽性列表)包括以下：具有一个或多个布线的电连接部(电子装置)，便携式计算机盘盒(磁装置)，随机存取存储器(RAM)，只读存储器(ROM)，可擦除可编辑只读存储器(EPROM或闪速存储器)，光纤装置，以及便携式光盘只读存储器(CDROM)。另外，计算机可读介质甚至可以是可在其上打印所述程序的纸或其他合适的介质，因为可以例如通过对纸或其他介质进行光学扫描，接着进行编辑、解译或必要时以其他合适方式进行处理来以电子方式获得所述程序，然后将其存储在计算机存储器中。
应当理解，本申请的各部分可以用硬件、软件、固件或它们的组合来实现。在上述实施方式中，多个步骤或方法可以用存储在存储器中且由合适的指令执行系统执行的软件或固件来实现。如，如果用硬件来实现和在另一实施方式中一样，可用本领域公知的下列技术中的任一项或他们的组合来实现：具有用于对数据信号实现逻辑功能的逻辑门电路的离散逻辑电路，具有合适的组合逻辑门电路的专用集成电路，可编程门阵列(PGA)，现场可编程门阵列(FPGA)等。
本技术领域的普通技术人员可以理解实现上述实施例方法携带的全部或部分步骤是可以通过程序来指令相关的硬件完成，所述的程序可以存储于一种计算机可读存储介质中，该程序在执行时，包括方法实施例的步骤之一或其组合。
此外，在本申请各个实施例中的各功能单元可以集成在一个处理模块中，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模块中。上述集成的模块既可以采用硬件的形式实现，也可以采用软件功能模块的形式实现。所述集成的模块如果以软件功能模块的形式实现并作为独立的产品销售或使用时，也可以存储在一个计算机可读取存储介质中。
上述提到的存储介质可以是只读存储器，磁盘或光盘等。尽管上面已经示出和描述了本申请的实施例，可以理解的是，上述实施例是示例性的，不能理解为对本申请的限制，本领域的普通技术人员在本申请的范围内可以对上述实施例进行变化、修改、替换和变型。

Claims (19)

  1. 一种空气调节设备的控制方法，其中，包括以下步骤：
    响应用于开启空气调节设备的多维调节模式的第一指令，以进入多维调节模式；
    获取用户的特征信息，根据所述特征信息对至少一个候选模型进行筛选，得到适用于所述用户的目标模型；
    根据所述目标模型，获取所述多维监控参数中每维监控参数的推荐值；
    根据所述多维监控参数中的任意一维监控参数的推荐值和监控值，对与所述任意一维监控参数对应的调节组件进行调节。
  2. 根据权利要求1所述的空气调节设备的控制方法，其中，所述根据所述特征信息对至少一个候选模型进行筛选，得到适用于所述用户的目标模型，包括：
    获取所述候选模型的优先级；
    按照所述优先级顺序将所述特征信息逐次与所述候选模型进行匹配；
    获取与所述特征信息相匹配的所述候选模型，并将其作为所述目标模型。
  3. 根据权利要求1所述的空气调节设备的控制方法，其中，所述根据所述特征信息对至少一个候选模型进行筛选，得到适用于所述用户的目标模型，包括：
    将所述特征信息逐个与所述候选模型进行匹配；
    获取与所述特征信息相匹配的所述候选模型的个数；
    在所述个数大于预设阈值时，将匹配的所述候选模型按照优先级进行排序；
    将所述优先级最高的所述候选模型作为所述目标模型。
  4. 根据权利要求1所述的空气调节设备的控制方法，其中，所述根据所述特征信息对至少一个候选模型进行筛选，得到适用于所述用户的目标模型，包括：
    将所述特征信息随机与所述候选模型进行匹配；
    识别存在与所述特征信息相匹配的所述候选模型；
    将与所述特征信息相匹配的所述候选模型作为所述目标模型。
  5. 根据权利要求1-4中任一所述的空气调节设备的控制方法，其中，所述候选模型包括个体自学习模型、群体自学习模型和通用自学习模型中的至少一个。
  6. 根据权利要求5所述的空气调节设备的控制方法，其中，所述目标模型为所述个体自学习模型时，所述根据所述目标模型，获取所述多维监控参数中每维监控参数的推荐值，包括：
    获取所述用户使用所述空气调节设备的历史使用数据、所处环境的当前环境数据和当前 时间信息作为所述第一数据；
    将所述第一数据输入所述个体自学习模型，得到所述推荐值。
  7. 根据权利要求5所述的空气调节设备的控制方法，其中，所述目标模型为所述群体自学习模型时，所述根据所述目标模型，获取所述多维监控参数中每维监控参数的推荐值，包括：
    获取所述用户所处环境的当前环境数据和/或当前时间信息作为所述第一数据；
    将所述第一数据输入所述群体自学习模型，获取所述用户的群体属性；
    根据所述群体属性，获取群体用户，并获取所述群体用户对应的所述推荐值，作为所述用户的所述推荐值。
  8. 根据权利要求5所述的空气调节设备的控制方法，其中，所述目标模型为所述通用自学习模型时，所述根据所述目标模型，获取所述多维监控参数中每维监控参数的推荐值，包括：
    获取空气调节设备的使用数据，作为第一数据；
    将所述第一数据输入所述通用自学习模型，获取全用户通用的所述推荐值，作为所述用户的所述推荐值。
  9. 根据权利要求1-8任一项所述的空气调节设备的控制方法，其中，还包括：
    检测所述用户针对所述多维监控参数中其中一维监控参数的主动调节指令，根据所述主动调节指令，控制与所述其中一维监控参数对应的调节组件的调节功能处于锁定状态。
  10. 根据权利要求1-8任一项所述的空气调节设备的控制方法，其中，还包括：
    检测用户针对所述多维监控参数中其中一维监控参数的关闭指令，根据所述关闭指令，控制与所述其中一维监控参数对应的调节组件处于关闭状态。
  11. 根据权利要求1-10任一项所述的空气调节设备的控制方法，其中，所述响应用于开启空气调节设备的多维调节模式的第一指令之前，还包括：
    获取从所述多维监控参数中选取至少两维监控参数的选取指令，根据所述选取指令生成所述第一指令。
  12. 根据权利要求1-11任一项所述的空气调节设备的控制方法，其中，所述根据所述多维监控参数中任意一维监控参数的推荐值和监控值，对与所述任意一维监控参数对应的调节组件进行调节，包括：
    确定与所述任意一维监控参数对应的至少一个调节组件；
    根据所述任意一维监控参数的推荐值和监控值，生成针对所述调节组件的调节指令，并按照所述调节指令对所述调节组件进行调节。
  13. 根据权利要求12所述的空气调节设备的控制方法，其中，所述根据所述任意一维监控参数的推荐值和监控值，生成针对所述调节组件的调节指令之前，还包括：
    识别有两维或者两维以上的监控参数对应的调节组件包括同一调节组件；且所述两维或者两维以上的监控参数均需要调节，则确定所述两维或者两维以上的监控参数中每维监控参数的优先级，根据优先级最高的监控参数的推荐值和监控值，对所述同一调节组件进行调节。
  14. 根据权利要求1-13任一项所述的空气调节设备的控制方法，其中，所述多维监控参数包括：湿度、温度、风速、空气中污染物含量和空气质量指数中的两个及两个以上。
  15. 根据权利要求1-14任一项所述的空气调节设备的控制方法，其中，所述调节组件集成或者独立于所述空气调节设备。
  16. 一种空气调节设备的控制装置，其中，包括：
    模式启动模块，用于响应用于开启空气调节设备的多维调节模式的第一指令，以进入多维调节模式；
    第一获取模块，用于获取用户的特征信息，根据所述特征信息对至少一个候选模型进行筛选，得到适用于所述用户的目标模型；
    第二获取模块，用于根据所述目标模型，获取所述多维监控参数中每维监控参数的推荐值；
    调节模块，用于根据所述多维监控参数中的任意一维监控参数的推荐值和监控值，对与所述任意一维监控参数对应的调节组件进行调节。
  17. 一种空气调节设备，其中，包括：如权利要求16所述的空气调节设备的控制装置。
  18. 一种电子设备，其中，包括存储器、处理器；
    其中，所述处理器通过读取所述存储器中存储的可执行程序代码来运行与所述可执行程序代码对应的程序，以用于实现如权利要求1-15中任一所述的空气调节设备的控制方法。
  19. 一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，其中，该程序被处理器执行时实现如权利要求1-15中任一所述的空气调节设备的控制方法。
PCT/CN2020/106863 2020-03-30 2020-08-04 空气调节设备及其控制方法、装置、电子设备 WO2021196483A1 (zh)

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