CN112484259B - Humidity control method and device, electronic equipment and storage medium - Google Patents
Humidity control method and device, electronic equipment and storage medium Download PDFInfo
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- CN112484259B CN112484259B CN202011415161.9A CN202011415161A CN112484259B CN 112484259 B CN112484259 B CN 112484259B CN 202011415161 A CN202011415161 A CN 202011415161A CN 112484259 B CN112484259 B CN 112484259B
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/64—Electronic processing using pre-stored data
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/88—Electrical aspects, e.g. circuits
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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
- F24F11/89—Arrangement or mounting of control or safety devices
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/10—Temperature
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/20—Humidity
Abstract
The embodiment of the invention relates to a humidity control method, a humidity control device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring temperature and humidity data corresponding to a target air conditioner at present; adding the outdoor temperature data, the humidity data and the set temperature data of the air conditioner into a pre-constructed humidity prediction model to obtain target humidity data; and finishing controlling the target air conditioner according to the target humidity data. In the process, target humidity data are not set according to experience, but are generated by a humidity prediction model obtained after big data training, compared with the prior art, the obtained target humidity data are more scientific, the finally obtained target humidity data are used for controlling the indoor humidity, a user feels more comfortable, and the user experience is improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of electronic equipment, in particular to a humidity control method and device, electronic equipment and a storage medium.
Background
With the improvement of daily living standard and the continuous development of science and technology, people have more and more requirements on functions carried by electronic equipment at the side. For example, air conditioning equipment, which now has not only a temperature control function but also functions such as time monitoring and temperature monitoring. With the continuous improvement of technology, more and more air conditioners are gradually starting to have a humidity control function.
The implementation of the humidity control technique in the prior art may, for example, include an empirical setting. For example, a relation table between the indoor temperature and the target temperature and humidity range is formed, and when a certain temperature is set, the target temperature range where the current temperature is located is inquired, and then the target humidity range corresponding to the target temperature range is matched, so that the indoor humidity is controlled to reach the corresponding target humidity range. Alternatively, the relative humidity may be determined based on a temperature set as needed. And obtaining the current indoor humidity, and calculating the difference value between the current indoor humidity and the relative humidity to adjust the air humidity control.
In the above solutions, it can be found that the optimal humidity setting value is determined according to the indoor temperature, and then the internal operation of the air conditioner is controlled to realize the humidification operation. However, in consideration of the actual complex working conditions, the obtained optimal humidity setting value is often inaccurate and cannot provide a comfortable indoor environment experience for users.
Disclosure of Invention
In view of this, in order to solve the technical problem in the prior art that the indoor humidity data cannot be accurately determined, so that a user cannot obtain more comfortable indoor environment experience, embodiments of the present invention provide a humidity control method and apparatus, an electronic device, and a storage medium.
In a first aspect, an embodiment of the present invention provides a humidity control method, including:
acquiring temperature and humidity data corresponding to a current target air conditioner, wherein the temperature and humidity data corresponding to the target air conditioner respectively comprise: outdoor temperature data and humidity data corresponding to the target air conditioner, and temperature data set by the target air conditioner;
adding the outdoor temperature data, the humidity data and the set temperature data of the air conditioner into a pre-constructed humidity prediction model to obtain target humidity data;
and controlling the target air conditioner according to the target humidity data.
In one possible embodiment, the outdoor temperature data includes first temperature data and second temperature data, and the outdoor humidity data includes first humidity data and second humidity data;
the first temperature data comprises outdoor temperature data obtained from meteorological data of a preset area to which the target air conditioner belongs; the second temperature data includes outdoor temperature data detected by the target air conditioner;
the first humidity data comprises outdoor humidity data obtained from meteorological data of a preset area to which the target air conditioner belongs; the second humidity data includes outdoor humidity data detected by the target air conditioner.
In one possible embodiment, the pre-constructed humidity prediction model is: and the humidity prediction model is obtained after training is carried out jointly according to the temperature and humidity data of a preset area where the target air conditioner is located in a preset historical time period, outdoor temperature and humidity data corresponding to different air conditioners in the preset area, temperature data set by each air conditioner and humidity data output by the air conditioners in the preset area.
In one possible embodiment, the humidity data output from the air conditioners in the preset area includes:
humidity data output from each air conditioner in the preset area,
or an average of humidity data output from all air conditioners within a preset area.
In one possible embodiment, the humidity data output from the air conditioners in the preset area includes:
humidity data outputted from the air conditioner in the preset area, which is the same as the temperature data set by the target air conditioner,
or the like, or, alternatively,
and an average value of humidity data outputted from the air conditioners within the preset region, the average value being the same as the temperature data set by the target air conditioner.
In one possible embodiment, before controlling the target air conditioner based on the target humidity data, the method further comprises:
determining whether humidity data set by a user is acquired;
and when the humidity data set by the user is acquired, controlling the target air conditioner according to the humidity data set by the user.
In one possible embodiment, after acquiring the humidity data set by the user, the method further includes:
and adding humidity data set by a user and temperature and humidity data corresponding to the target air conditioner into a pre-constructed humidity prediction model together, and performing iterative training on the humidity prediction model.
In a second aspect, an embodiment of the present invention provides a humidity control apparatus, including:
an acquisition unit for acquiring temperature and humidity data corresponding to a target air conditioner, wherein the temperature and humidity data corresponding to the target air conditioner respectively include: outdoor temperature data and humidity data corresponding to the target air conditioner, and temperature data set by the target air conditioner;
the processing unit is used for adding the outdoor temperature data, the humidity data and the set temperature data of the air conditioner into a pre-constructed humidity prediction model to obtain target humidity data;
and the control unit is used for controlling the target air conditioner according to the target humidity data.
In a third aspect, an embodiment of the present invention provides an electronic device, including: at least one processor, memory, a communication device, and at least two sensors;
the communication device and the at least one sensor are jointly used for acquiring outdoor temperature data and humidity data corresponding to the target air conditioner;
the other sensors in the at least two sensors are used for acquiring temperature data set by the target air conditioner;
the processor is configured to execute the humidity control program stored in the memory to implement the humidity control method as described in any one of the embodiments of the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer storage medium, where one or more programs are stored, and the one or more programs are executable by the electronic device as described in the third aspect to implement the humidity control method as described in any one of the embodiments of the first aspect.
According to the humidity control method provided by the embodiment of the invention, the temperature and humidity data corresponding to the target air conditioner at present are obtained, wherein the temperature and humidity data comprise the outdoor temperature data and the humidity data corresponding to the target air conditioner and the temperature data set by the target air conditioner, then the temperature and humidity data are added into the pre-constructed humidity prediction model, and the target humidity data are obtained through the output of the humidity prediction model. And controlling the target air conditioner according to the target humidity data. In this way, not only the temperature data set by the target air conditioner is referred to, but also the temperature data and the humidity data outside the room are combined to determine the more optimal target humidity data. In addition, in the process, the target humidity data is not set according to experience, but is generated by a humidity prediction model obtained after big data training, compared with the prior art, the obtained target humidity data is more scientific, the finally obtained target humidity data is utilized to complete indoor humidity control, a user feels more comfortable, and the user experience is improved.
Drawings
FIG. 1 is a schematic flow chart of a humidity control method according to an embodiment of the present invention;
FIG. 2 is a logic diagram illustrating a humidity control principle provided by the present invention;
FIG. 3 is a schematic structural diagram of a humidity control apparatus according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
For the convenience of understanding of the embodiments of the present invention, the following description will be further explained with reference to specific embodiments, which are not to be construed as limiting the embodiments of the present invention.
Fig. 1 is a schematic flow chart of a humidity control method according to an embodiment of the present invention, as shown in fig. 1, the method includes:
and step 110, acquiring temperature and humidity data corresponding to the target air conditioner at present.
Specifically, the temperature and humidity data corresponding to the target air conditioner respectively include: outdoor temperature data and humidity data corresponding to the target air conditioner, and temperature data set by the target air conditioner.
Optionally, in a specific example, the outdoor temperature data may include outdoor temperature data including first temperature data and second temperature data, and the outdoor humidity data includes first humidity data and second humidity data;
the first temperature data comprises outdoor temperature data obtained from meteorological data of a preset area to which the target air conditioner belongs; the second temperature data includes outdoor temperature data detected by the target air conditioner;
the first humidity data comprises outdoor humidity data obtained from meteorological data of a preset area to which the target air conditioner belongs; the second humidity data includes outdoor humidity data detected by the target air conditioner.
The temperature data and the humidity data include temperature and humidity data in the meteorological data, and temperature and humidity data detected by the target air conditioner, because the meteorological data represents regional temperature data. For example, if the air conditioner is located in a house in a cell in the XX district of the XX city. Then, the preset area to which the target air conditioner is located may be set as the XX zone, and the existing weather data is also basically refined to a certain area of a certain city. Therefore, the current weather data cannot directly represent the outdoor temperature and humidity data corresponding to the position of the air conditioner. The air conditioner may be, for example, an air conditioner, or other devices having both temperature control and humidity control, or a combination of a device having temperature control and a device having humidity control. In any case, outdoor temperature data and outdoor humidity data collected by the device are data specifying a certain position, and the data themselves are difficult to have certain representativeness. For example, an air conditioner outdoor unit is generally installed in a complex environment, so that it is difficult to directly represent temperature data and humidity data of an outdoor location where the air conditioner is located by using temperature data and humidity data respectively collected by a temperature sensor and a humidity sensor installed on the outdoor unit of the air conditioner. Therefore, after comprehensive consideration, the temperature and humidity data acquired through the meteorological data and the outdoor temperature and humidity data detected by the target air conditioner can be used as the outdoor temperature data and humidity data corresponding to the target air conditioner. The meteorological data can be acquired through a plurality of paths, for example, the meteorological data can be acquired through a cloud data platform, or can be acquired through other equipment or APP and the like which are in communication connection with the air conditioner. Referring specifically to fig. 2, a logical diagram of the humidity control principle is shown in fig. 2. The acquisition of meteorological data via the cloud is shown in fig. 2. Outdoor temperature and humidity data detected by the target air conditioner can be obtained through the sensor.
In order to determine the final target humidity data, not only the outdoor temperature and humidity data but also the indoor temperature data need to be considered. If the indoor temperature is too high, the humidity in the room is low and the room is too dry. A more comfortable environment cannot be provided for the user. Therefore, the temperature and humidity data corresponding to the target air conditioner includes temperature data set by the target air conditioner.
And step 120, adding the outdoor temperature data, the humidity data and the set temperature data of the air conditioner into a pre-constructed humidity prediction model to obtain target humidity data.
Specifically, the pre-constructed humidity prediction model is a pre-trained humidity prediction model. Specifically, during training, the input parameter data may include: the temperature and humidity data of a preset area where the target air conditioner is located in a preset historical time period, outdoor temperature and humidity data corresponding to different air conditioners in the preset area, temperature data set by each air conditioner and humidity data output by the air conditioners in the preset area.
Namely, the humidity prediction model obtained after training is performed jointly according to the temperature and humidity data of the preset area to which the target air conditioner belongs within the preset historical time period, the outdoor temperature and humidity data respectively corresponding to different air conditioners in the preset area, the temperature data set by each air conditioner, and the humidity data output by the air conditioners in the preset area.
It should be noted that the air conditioner of the preset area referred to herein actually means an air conditioner that has been connected to the network. After the air conditioners are connected to the network, outdoor temperature and humidity data respectively corresponding to different air conditioners, temperature data set by each air conditioner and humidity data output by the air conditioners in a preset area can be obtained.
Optionally, in a specific example, the humidity data output by the air conditioner in the preset area includes:
humidity data output from each air conditioner in the preset area,
or an average of humidity data output from all air conditioners within a preset area.
In another specific example, the humidity data output from the air conditioners in the preset area includes:
humidity data outputted from the air conditioner in the preset area, which is the same as the temperature data set by the target air conditioner,
or the like, or, alternatively,
and an average value of humidity data outputted from the air conditioners within the preset region, the average value being the same as the temperature data set by the target air conditioner.
The above description is merely an example of two specific examples, and how to define the humidity data output from the air conditioner in the preset area may be set according to actual conditions, and will not be described in detail here.
Further optionally, the humidity prediction model may include, but is not limited to, a BP humidity prediction model, or a Radial Basis Function (RBF) neural network.
In a specific example, when the neural network is a BP neural network, and the target humidity data is obtained, see the following:
the number of the neural network layers can be set to be 3-7, the number of the neurons of the input layer is 5, and the neural network layers respectively correspond to the local meteorological temperature and humidity, the outdoor temperature and humidity detected by the air conditioner and the temperature data set by the target air conditioner; the number of neurons in an output layer is 1, and the neurons correspond to target humidity data; the number of the neurons in the middle layer can be 6-10.
Of course, the above is only a specific example, and actually, the structure (number of layers and number of neurons) of the BP network can be adjusted according to actual situations, and can be determined according to the chip computing capability of the air conditioner. If the calculation capability is limited, the number of nerve layers and the number of nerve cells can be reduced, and the accuracy is sacrificed; if the computing power is stronger, the number of the neurons or the number of the layers can be increased. Moreover, the activation function of the BP network has a plurality of modes, and a plurality of methods for optimizing the learning speed can be selected according to the needs.
And step 130, controlling the target air conditioner according to the target humidity data.
Finally, the target air conditioner only needs to adjust the operation of the internal components based on the target humidity data to complete the indoor humidity control.
Optionally, if the user sets the humidity control parameter by himself before executing step 130, that is, obtains the humidity data set by the user. Then, the target air conditioner may be controlled according to the humidity data set by the user.
Further, in order to optimize the humidity prediction model more closely to the user's selection so that the final humidity control conforms to the user's personalized design, the method may further include:
and adding humidity data set by a user and temperature and humidity data corresponding to the target air conditioner into a pre-constructed humidity prediction model together, and performing iterative training on the humidity prediction model. The above process can be seen in the method flowchart of fig. 1, and also in the logic control diagram of fig. 2. The description in fig. 1 to 2 is referred to above in particular and will not be explained more than here.
According to the humidity control method provided by the embodiment of the invention, the temperature and humidity data corresponding to the target air conditioner at present are obtained, wherein the temperature and humidity data comprise the outdoor temperature data and the humidity data corresponding to the target air conditioner and the temperature data set by the target air conditioner, then the temperature and humidity data are added into the pre-constructed humidity prediction model, and the target humidity data are obtained through the output of the humidity prediction model. And controlling the target air conditioner according to the target humidity data. In this way, not only the temperature data set by the target air conditioner is referred to, but also the temperature data and the humidity data outside the room are combined to determine the more optimal target humidity data. In addition, in the process, the target humidity data is not set according to experience, but is generated by a humidity prediction model obtained after big data training, compared with the prior art, the obtained target humidity data is more scientific, the finally obtained target humidity data is utilized to complete indoor humidity control, a user feels more comfortable, and the user experience is improved.
Fig. 3 is a humidity control apparatus according to an embodiment of the present invention, which includes: an acquisition unit 301, a processing unit 302 and a control unit 303.
An obtaining unit 301, configured to obtain temperature and humidity data corresponding to a current target air conditioner, where the temperature and humidity data corresponding to the target air conditioner respectively include: outdoor temperature data and humidity data corresponding to the target air conditioner, and temperature data set by the target air conditioner;
the processing unit 302 is configured to add the outdoor temperature data, the humidity data, and the air conditioner set temperature data to a pre-constructed humidity prediction model to obtain target humidity data;
a control unit 303 for controlling the target air conditioner according to the target humidity data.
Optionally, the outdoor temperature data includes first temperature data and second temperature data, and the outdoor humidity data includes first humidity data and second humidity data;
the first temperature data comprises outdoor temperature data obtained from meteorological data of a preset area to which the target air conditioner belongs; the second temperature data includes outdoor temperature data detected by the target air conditioner;
the first humidity data comprises outdoor humidity data obtained from meteorological data of a preset area to which the target air conditioner belongs; the second humidity data includes outdoor humidity data detected by the target air conditioner.
Optionally, the pre-constructed humidity prediction model is as follows: and the humidity prediction model is obtained after training is carried out jointly according to the temperature and humidity data of a preset area where the target air conditioner is located in a preset historical time period, outdoor temperature and humidity data corresponding to different air conditioners in the preset area, temperature data set by each air conditioner and humidity data output by the air conditioners in the preset area.
Optionally, the humidity data output by the air conditioner in the preset area includes:
humidity data output from each air conditioner in the preset area,
or an average of humidity data output from all air conditioners within a preset area.
Optionally, the humidity data output by the air conditioner in the preset area includes:
humidity data outputted from the air conditioner in the preset area, which is the same as the temperature data set by the target air conditioner,
or the like, or, alternatively,
and an average value of humidity data outputted from the air conditioners within the preset region, the average value being the same as the temperature data set by the target air conditioner.
Optionally, the processing unit 302 is further configured to determine whether the obtaining unit 301 obtains humidity data set by a user;
when the acquiring unit 301 acquires the humidity data set by the user, the processing unit 302 is further configured to control the target air conditioner according to the humidity data set by the user.
Optionally, the processing unit 302 is further configured to add humidity data set by a user and temperature and humidity data corresponding to the target air conditioner to a pre-constructed humidity prediction model together, and perform iterative training on the humidity prediction model.
The functions performed by the functional components of the humidity control apparatus provided in this embodiment have been described in detail in the embodiment corresponding to fig. 1, and therefore are not described herein again.
The humidity control device provided by the embodiment of the invention obtains the temperature and humidity data corresponding to the target air conditioner at present, wherein the temperature and humidity data comprise the outdoor temperature data and the humidity data corresponding to the target air conditioner and the temperature data set by the target air conditioner, then the temperature and humidity data are added into the pre-constructed humidity prediction model, and the target humidity data are obtained through the output of the humidity prediction model. And controlling the target air conditioner according to the target humidity data. In this way, not only the temperature data set by the target air conditioner is referred to, but also the temperature data and the humidity data outside the room are combined to determine the more optimal target humidity data. In addition, in the process, the target humidity data is not set according to experience, but is generated by a humidity prediction model obtained after big data training, compared with the prior art, the obtained target humidity data is more scientific, the finally obtained target humidity data is utilized to complete indoor humidity control, a user feels more comfortable, and the user experience is improved.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, where the electronic device may be an air conditioner itself or other electronic devices with functions described below. The electronic device 400 shown in fig. 4 includes: at least one processor 401, memory 402, at least one network interface 403, other user interfaces 404, communication devices 405, and sensors 406. The various components in the electronic device 400 are coupled together by a bus system 407. It will be appreciated that the bus system 407 is used to enable communications among the components connected. The bus system 407 includes a power bus, a control bus, and a status signal bus in addition to a data bus. But for the sake of clarity the various buses are labeled as bus system 407 in figure 4.
The user interface 404 may include, among other things, a display, a keyboard, or a pointing device (e.g., a mouse, trackball, touch pad, or touch screen, among others.
It will be appreciated that memory 402 in embodiments of the invention may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of illustration and not limitation, many forms of RAM are available, such as Static random access memory (Static RAM, SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic random access memory (Synchronous DRAM, SDRAM), Double Data Rate Synchronous Dynamic random access memory (ddr Data Rate SDRAM, ddr SDRAM), Enhanced Synchronous SDRAM (ESDRAM), synchlronous SDRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memory 402 described herein is intended to comprise, without being limited to, these and any other suitable types of memory.
In some embodiments, memory 402 stores the following elements, executable units or data structures, or a subset thereof, or an expanded set thereof: an operating system 4021 and application programs 4022.
The operating system 4021 includes various system programs, such as a framework layer, a core library layer, a driver layer, and the like, and is configured to implement various basic services and process hardware-based tasks. The application programs 4022 include various application programs, such as a Media Player (Media Player), a Browser (Browser), and the like, for implementing various application services. A program for implementing the method according to the embodiment of the present invention may be included in the application 4022.
In the embodiment of the present invention, by calling a program or an instruction stored in the memory 402, specifically, a program or an instruction stored in the application 4022, the processor 401, the communication device 405, the sensor 406, and the like cooperate to perform the method steps provided by each method embodiment, for example, the method steps include:
the communication device and the at least one sensor are jointly used for acquiring outdoor temperature data and humidity data corresponding to the target air conditioner;
the other sensors in the at least two sensors are used for acquiring temperature data set by the target air conditioner;
the processor is used for executing the following steps:
adding the outdoor temperature data, the humidity data and the set temperature data of the air conditioner into a pre-constructed humidity prediction model to obtain target humidity data;
and controlling the target air conditioner according to the target humidity data.
Optionally, the outdoor temperature data includes first temperature data and second temperature data, and the outdoor humidity data includes first humidity data and second humidity data;
the first temperature data comprises outdoor temperature data obtained from meteorological data of a preset area to which the target air conditioner belongs; the second temperature data includes outdoor temperature data detected by the target air conditioner;
the first humidity data comprises outdoor humidity data obtained from meteorological data of a preset area to which the target air conditioner belongs; the second humidity data includes outdoor humidity data detected by the target air conditioner.
Optionally, the pre-constructed humidity prediction model is as follows: and the humidity prediction model is obtained after training is carried out jointly according to the temperature and humidity data of a preset area where the target air conditioner is located in a preset historical time period, outdoor temperature and humidity data corresponding to different air conditioners in the preset area, temperature data set by each air conditioner and humidity data output by the air conditioners in the preset area.
Optionally, the humidity data output by the air conditioner in the preset area includes:
humidity data output from each air conditioner in the preset area,
or an average of humidity data output from all air conditioners within a preset area.
Optionally, the humidity data output by the air conditioner in the preset area includes:
humidity data outputted from the air conditioner in the preset area, which is the same as the temperature data set by the target air conditioner,
or the like, or, alternatively,
and an average value of humidity data outputted from the air conditioners within the preset region, the average value being the same as the temperature data set by the target air conditioner.
Optionally, before controlling the target air conditioner according to the target humidity data, the method further includes:
determining whether humidity data set by a user is acquired;
and when the humidity data set by the user is acquired, controlling the target air conditioner according to the humidity data set by the user.
Optionally, after the humidity data set by the user is acquired, the method further includes:
and adding humidity data set by a user and temperature and humidity data corresponding to the target air conditioner into a pre-constructed humidity prediction model together, and performing iterative training on the humidity prediction model.
The method disclosed in the above embodiments of the present invention may be applied to the processor 401, or implemented by the processor 401. The processor 401 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 401. The Processor 401 may be a general-purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, or discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software elements in the decoding processor. The software elements may be located in ram, flash, rom, prom, or eprom, registers, among other storage media that are well known in the art. The storage medium is located in the memory 402, and the processor 401 reads the information in the memory 402 and completes the steps of the method in combination with the hardware.
It is to be understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or any combination thereof. For a hardware implementation, the Processing units may be implemented in one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), general purpose processors, controllers, micro-controllers, microprocessors, other electronic units configured to perform the functions of the present Application, or a combination thereof.
For a software implementation, the techniques herein may be implemented by means of units performing the functions herein. The software codes may be stored in a memory and executed by a processor. The memory may be implemented within the processor or external to the processor.
The electronic device provided in this embodiment may be the electronic device shown in fig. 4, and may perform all the steps of the humidity control method shown in fig. 1, so as to achieve the technical effect of the humidity control method shown in fig. 1.
The embodiment of the invention also provides a storage medium (computer readable storage medium). The storage medium herein stores one or more programs. Among others, the storage medium may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, a hard disk, or a solid state disk; the memory may also comprise a combination of memories of the kind described above.
When one or more programs in the storage medium are executable by one or more processors to implement the humidity control method described above as being performed on the electronic device side.
The processor is used for executing the humidity control program stored in the memory to realize the following steps of the humidity control method executed on the electronic equipment side:
acquiring temperature and humidity data corresponding to a current target air conditioner, wherein the temperature and humidity data corresponding to the target air conditioner respectively comprise: outdoor temperature data and humidity data corresponding to the target air conditioner, and temperature data set by the target air conditioner;
adding the outdoor temperature data, the humidity data and the set temperature data of the air conditioner into a pre-constructed humidity prediction model to obtain target humidity data;
and controlling the target air conditioner according to the target humidity data.
Optionally, the outdoor temperature data includes first temperature data and second temperature data, and the outdoor humidity data includes first humidity data and second humidity data;
the first temperature data comprises outdoor temperature data obtained from meteorological data of a preset area to which the target air conditioner belongs; the second temperature data includes outdoor temperature data detected by the target air conditioner;
the first humidity data comprises outdoor humidity data obtained from meteorological data of a preset area to which the target air conditioner belongs; the second humidity data includes outdoor humidity data detected by the target air conditioner.
Optionally, the pre-constructed humidity prediction model is as follows: and the humidity prediction model is obtained after training is carried out jointly according to the temperature and humidity data of a preset area where the target air conditioner is located in a preset historical time period, outdoor temperature and humidity data corresponding to different air conditioners in the preset area, temperature data set by each air conditioner and humidity data output by the air conditioners in the preset area.
Optionally, the humidity data output by the air conditioner in the preset area includes:
humidity data output from each air conditioner in the preset area,
or an average of humidity data output from all air conditioners within a preset area.
Optionally, the humidity data output by the air conditioner in the preset area includes:
humidity data outputted from the air conditioner in the preset area, which is the same as the temperature data set by the target air conditioner,
or the like, or, alternatively,
and an average value of humidity data outputted from the air conditioners within the preset region, the average value being the same as the temperature data set by the target air conditioner.
Optionally, before controlling the target air conditioner according to the target humidity data, the method further includes:
determining whether humidity data set by a user is acquired;
and when the humidity data set by the user is acquired, controlling the target air conditioner according to the humidity data set by the user.
Optionally, after the humidity data set by the user is acquired, the method further includes:
and adding humidity data set by a user and temperature and humidity data corresponding to the target air conditioner into a pre-constructed humidity prediction model together, and performing iterative training on the humidity prediction model.
Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, a software module executed by a processor, or a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The above embodiments are provided to further explain the objects, technical solutions and advantages of the present invention in detail, it should be understood that the above embodiments are merely exemplary embodiments of the present invention and are not intended to limit the scope of the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method of humidity control, the method comprising:
acquiring temperature and humidity data corresponding to a target air conditioner at present, wherein the temperature and humidity data corresponding to the target air conditioner respectively comprise: outdoor temperature data and humidity data corresponding to the target air conditioner, and temperature data set by the target air conditioner;
inputting the outdoor temperature data, the humidity data and the temperature data set by the target air conditioner into a pre-constructed humidity prediction model so as to enable the humidity prediction model to output target humidity data;
and controlling the target air conditioner according to the target humidity data.
2. The method of claim 1, wherein the outdoor temperature data comprises first temperature data and second temperature data, and the outdoor humidity data comprises first humidity data and second humidity data;
the first temperature data comprises outdoor temperature data obtained from meteorological data of a preset area to which the target air conditioner belongs; the second temperature data includes outdoor temperature data detected by the target air conditioner;
the first humidity data comprises outdoor humidity data obtained from meteorological data of a preset area to which the target air conditioner belongs; the second humidity data includes outdoor humidity data detected by the target air conditioner.
3. The method of claim 1, wherein the pre-constructed humidity prediction model is: and jointly training the humidity prediction model according to the temperature and humidity data of a preset area to which the target air conditioner belongs within a preset historical time period, outdoor temperature and humidity data respectively corresponding to different air conditioners in the preset area, temperature data set by each air conditioner and humidity data output by the air conditioners in the preset area.
4. The method of claim 3, wherein the humidity data output by the air conditioners in the predetermined area comprises:
humidity data output from each of the air conditioners in the preset area,
or an average value of humidity data output from all air conditioners within the preset area.
5. The method of claim 3, wherein the humidity data output by the air conditioners in the predetermined area comprises:
humidity data outputted from the air conditioner in the preset area, which is the same as the temperature data set by the target air conditioner,
or the like, or, alternatively,
and in the preset area, the average value of humidity data output by the air conditioners is the same as the temperature data set by the target air conditioner.
6. The method of any of claims 1-5, wherein prior to controlling the target air conditioner based on the target humidity data, the method further comprises:
determining whether humidity data set by a user is acquired;
and when the humidity data set by the user is acquired, controlling the target air conditioner according to the humidity data set by the user.
7. The method of claim 6, wherein after acquiring the humidity data set by the user, the method further comprises:
and adding the humidity data set by the user and the temperature and humidity data corresponding to the target air conditioner into the pre-constructed humidity prediction model together, and performing iterative training on the humidity prediction model.
8. A humidity control apparatus, characterized in that the apparatus comprises:
an obtaining unit, configured to obtain temperature and humidity data corresponding to a current target air conditioner, where the temperature and humidity data corresponding to the target air conditioner respectively include: outdoor temperature data and humidity data corresponding to the target air conditioner, and temperature data set by the target air conditioner;
the processing unit is used for adding the outdoor temperature data, the humidity data and the temperature data set by the target air conditioner into a pre-constructed humidity prediction model to obtain target humidity data;
and the control unit is used for controlling the target air conditioner according to the target humidity data.
9. An electronic device, characterized in that the electronic device comprises: at least one processor, memory, a communication device, and at least two sensors;
the communication device and the at least one sensor are jointly used for acquiring outdoor temperature data and humidity data corresponding to the target air conditioner;
the other sensors of the at least two sensors are used for acquiring temperature data set by the target air conditioner;
the processor is used for executing the humidity control program stored in the memory so as to realize the humidity control method of any one of claims 1 to 7.
10. A computer storage medium storing one or more programs executable by the electronic device of claim 9 to implement the humidity control method of any one of claims 1 to 7.
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