CN112432328A - Control method and device of air conditioner, storage medium and electronic device - Google Patents
Control method and device of air conditioner, storage medium and electronic device Download PDFInfo
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- 238000004378 air conditioning Methods 0.000 abstract description 7
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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
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
- F24F11/72—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure
- F24F11/74—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling air flow rate or air velocity
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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
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
- F24F11/72—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure
- F24F11/79—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling the direction of the supplied air
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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
- F24F2120/00—Control inputs relating to users or occupants
- F24F2120/10—Occupancy
- F24F2120/14—Activity of occupants
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Abstract
The application discloses a control method and device of an air conditioner, a storage medium and an electronic device. Wherein, the method comprises the following steps: predicting the activity track of a target user according to the historical activity area of the target user; and controlling the air conditioner according to the predicted moving track. The application solves the technical problem that energy is wasted in air conditioning in the related technology.
Description
Technical Field
The application relates to the field of smart home, in particular to a control method and device of an air conditioner, a storage medium and an electronic device.
Background
With the development of sensing technology, more and more advanced sensors are applied to the field of household appliances at present, so that diversified detection on people, objects and scenes is realized, and the sensor is applied to the whole household appliance to realize intelligent interaction, energy-saving control and the like.
The existing air conditioner is not intelligent enough, and cannot actively adjust according to the activity state or the activity historical track of a user, so that energy waste and poor experience are caused.
Aiming at the problem of energy waste of the air conditioner, an effective solution is not provided at present.
Disclosure of Invention
The embodiment of the application provides a control method and device of an air conditioner, a storage medium and an electronic device, and aims to at least solve the technical problem that the air conditioner wastes energy in the related technology.
According to an aspect of an embodiment of the present application, there is provided a control method of an air conditioner, including: predicting the activity track of a target user according to the historical activity area of the target user; and controlling the air conditioner according to the predicted moving track.
Optionally, when the air conditioner is controlled according to the predicted moving track, controlling the air supply position of the air conditioner according to the predicted moving track; and/or controlling the air outlet volume of the air conditioner according to the predicted activity track.
Optionally, when the activity track of the target user is predicted according to the historical activity area of the target user, sampling the activity area of the target user according to a preset time interval to obtain sampling data; and generating the activity track of the target user according to the adoption data, wherein the activity track comprises activity time and a corresponding activity position.
Optionally, the activity track includes a plurality of activity times and an activity position at each activity time, wherein generating the activity track of the target user according to the adoption data includes determining the activity position at each activity time as follows: counting activity positions of the target user in the same activity time in a plurality of time periods; and taking the activity position with the highest occurrence frequency as the activity position matched with the activity time in the activity track.
Optionally, in the process of sampling the activity area of the target user at preset time intervals, the target user is identified as follows: acquiring a scene depth map of a scene; carrying out background difference processing, noise elimination processing, outline filling and refining processing and binarization processing on the multiple frames of scene depth images to obtain a binarization image; and identifying the target user from the binary image according to the human body contour features.
Optionally, in the process of sampling the activity area of the target user at preset time intervals, the scene object is identified as follows: acquiring depth information of each point of a scene and generating a depth map; and identifying a scene area from the depth map, and marking the identified scene area.
Optionally, when a scene object is identified from the depth map, a scene region is identified from the depth map by using a deep neural network model, wherein the deep neural network model is a pre-trained model, and the scene region includes a sofa region, a dining region, a learning region, and a motion region.
According to another aspect of the embodiments of the present application, there is also provided a control apparatus of an air conditioner, including: the prediction unit is used for predicting the activity track of the target user according to the historical activity area of the target user; and the control unit is used for controlling the air conditioner according to the predicted movable track.
Optionally, the prediction unit is further configured to, when the air conditioner is controlled according to the predicted movement trajectory, control an air supply position of the air conditioner according to the predicted movement trajectory; and/or controlling the air outlet volume of the air conditioner according to the predicted activity track.
Optionally, the prediction unit is further configured to, when predicting the activity track of the target user according to the historical activity area of the target user, sample the activity area of the target user according to a preset time interval to obtain sample data; and generating the activity track of the target user according to the adoption data, wherein the activity track comprises activity time and a corresponding activity position.
Optionally, the activity track includes a plurality of activity times and an activity position at each activity time, wherein the prediction unit is further configured to determine the activity position at each activity time according to the following manner in the process of generating the activity track of the target user according to the adoption data: counting activity positions of the target user in the same activity time in a plurality of time periods; and taking the activity position with the highest occurrence frequency as the activity position matched with the activity time in the activity track.
Optionally, the prediction unit is further configured to, during sampling of the activity area of the target user at preset time intervals, identify the target user as follows: acquiring a scene depth map of a scene; carrying out background difference processing, noise elimination processing, outline filling and refining processing and binarization processing on the multiple frames of scene depth images to obtain a binarization image; and identifying the target user from the binary image according to the human body contour features.
Optionally, the prediction unit is further configured to, during sampling of the activity area of the target user at preset time intervals, identify a scene object as follows: acquiring depth information of each point of a scene and generating a depth map; and identifying a scene area from the depth map, and marking the identified scene area.
Optionally, the prediction unit is further configured to, when a scene object is identified from the depth map, identify a scene region from the depth map by using a deep neural network model, where the deep neural network model is a pre-trained model, and the scene region includes a sofa region, a dining region, a learning region, and a motion region.
According to another aspect of the embodiments of the present application, there is also provided a storage medium including a stored program which, when executed, performs the above-described method.
According to another aspect of the embodiments of the present application, there is also provided an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the above method through the computer program.
In the embodiment of the application, the activity track of the target user is predicted according to the historical activity area of the target user, the air conditioner is controlled according to the predicted activity track, product intelligence can be improved, timely and advanced active air supply is achieved, the manual operation of the user is reduced, the air supply area is adjusted, and the comfort experience of the user is better. Meanwhile, the air supply amount is reduced for the area where the user is inactive, more effective energy saving is realized, and the technical problem of energy waste of air conditioning in the related technology can be solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a flowchart of an alternative control method of an air conditioner according to an embodiment of the present application;
FIG. 2 is a schematic diagram of an alternative control scheme for an air conditioner according to an embodiment of the present application;
FIG. 3 is a schematic diagram of an alternative user identification scheme according to an embodiment of the present application;
FIG. 4 is a schematic view of an alternative sofa identification solution according to embodiments of the present application;
fig. 5 is a schematic view of an alternative control apparatus of an air conditioner according to an embodiment of the present application;
and
fig. 6 is a block diagram of a terminal according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. 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 application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
According to an aspect of embodiments of the present application, there is provided an embodiment of a control method of an air conditioner. Fig. 1 is a flowchart of an alternative control method of an air conditioner according to an embodiment of the present application, and as shown in fig. 1, the method may include the following steps:
and step S1, predicting the activity track of the target user according to the historical activity area of the target user.
And step S2, controlling the air conditioner according to the predicted activity track.
Through the steps, the activity track of the target user is predicted according to the historical activity area of the target user, the air conditioner is controlled according to the predicted activity track, product intelligence can be improved, timely and early active air supply is achieved, the manual operation of the user is reduced, the air supply area is adjusted, and the comfortable experience of the user is better. Meanwhile, the air supply amount is reduced for the area where the user is inactive, more effective energy saving is realized, and the technical problem of energy waste of air conditioning in the related technology can be solved.
Optionally, when the air conditioner is controlled according to the predicted moving track, controlling the air supply position of the air conditioner according to the predicted moving track; and/or controlling the air outlet volume of the air conditioner according to the predicted activity track.
Optionally, when the activity track of the target user is predicted according to the historical activity area of the target user, sampling the activity area of the target user according to a preset time interval to obtain sampling data; and generating the activity track of the target user according to the adoption data, wherein the activity track comprises activity time and a corresponding activity position.
Optionally, the activity track includes a plurality of activity times and an activity position at each activity time, wherein generating the activity track of the target user according to the adoption data includes determining the activity position at each activity time as follows: counting activity positions of the target user in the same activity time in a plurality of time periods; and taking the activity position with the highest occurrence frequency as the activity position matched with the activity time in the activity track.
Optionally, in the process of sampling the activity area of the target user at preset time intervals, the target user is identified as follows: acquiring a scene depth map of a scene; carrying out background difference processing, noise elimination processing, outline filling and refining processing and binarization processing on the multiple frames of scene depth images to obtain a binarization image; and identifying the target user from the binary image according to the human body contour features.
Optionally, in the process of sampling the activity area of the target user at preset time intervals, the scene object is identified as follows: acquiring depth information of each point of a scene and generating a depth map; and identifying a scene area from the depth map, and marking the identified scene area.
Optionally, when a scene object is identified from the depth map, a scene region is identified from the depth map by using a deep neural network model, wherein the deep neural network model is a pre-trained model, and the scene region includes a sofa region, a dining region, a learning region, and a motion region.
As an alternative example, the technical solution of the present application is further described below with reference to specific embodiments.
The TOF (Time-of-Flight) imaging principle is to calculate depth information from a detector pixel to a scene according to Time information of propagation of measurement light in space, and further obtain a three-dimensional structure of the scene.
According to the scheme, the depth information of an indoor scene is detected based on the TOF flight time detection technology, a dining area, a sofa area and the like are distinguished through a neural network algorithm according to a depth map, the activity track is counted and predicted according to the condition of the historical activity area of a user every day, and the air conditioner actively changes the air supply position and the air outlet air volume according to the prediction result so as to achieve the purposes of comfortable and energy-saving air supply.
The hardware scheme is shown in fig. 2: the TOF (time of flight) detection module comprises a TOF identification module, a driving unit, an infrared emitter, a TOF lens, a depth detection unit and a scene identification unit.
The driving unit is used for modulating a pulse type or sine wave type infrared emitter; the infrared emitter is used for lighting according to the driving signal modulated by the driving unit; the wavelength of the infrared light is generally within the range of 850nm +/-20 nm; the TOF lens is an array type imaging sensor, and the resolution is at least 100 x 100 in consideration of cost and detection precision; the horizontal angle of a lens is 80-100 degrees when the device is applied to an air-conditioning scene, the optimal angle is 90 degrees, the device mainly considers that a conventional circular air conditioner is installed at a corner of a living room at 45 degrees, and a detection area can cover all areas under the condition of the horizontal angle of 90 degrees; the depth detection unit calculates distance depth information between the detection unit and a detected scene (room) through the time difference of the pulse light signal emission and the light signal received by each pixel point of the array sensor; the scene information identification unit further identifies the position (angle, distance), the sofa area, the dining area, the high-frequency activity area and the like of the human body according to the depth map detected by the multi-frame detection.
An air conditioning part: the air conditioner statistical unit receives the recognition result of the scene information recognition unit recognized by the TOF recognition module, takes each day as a unit, performs statistics on the time of each area activity of the user every day, and generates a user expected activity track in a self-learning mode, wherein the user expected activity track comprises time information, user information and activity area information; the main control unit receives the expected activity track information and regulates and controls the air outlet angle, the air outlet quantity and the like of the air conditioner according to the expected activity track; and the execution unit comprises an air supply motor, a refrigerating system, a wind sweeping device and the like so as to realize the adjustment of different air blowing directions, cold quantities and the like.
Air conditioner carries on structural scheme: the TOF identification module is assembled at the height of 1.5-1.8 m of the cabinet air conditioner, is horizontal or slightly declined, and has a vertical angle larger than 70 degrees so as to cover the whole living room area with a visual field as far as possible.
The scene detection method comprises the following steps:
(1) human body detection, as shown in fig. 3:
step 1, obtaining depth information and generating a depth map.
The TOF identification module acquires depth information of the array type infrared imaging sensor and generates a depth map. Detecting 2-5 frames every 1s, and carrying out background difference processing through the change of the depth distance information difference value of each frame.
And 2, carrying out multi-frame depth distance change background difference processing.
And 3, denoising, and contour filling and refining.
And 4, processing the human-shaped contour binary image. And performing binary processing on the difference image to generate a binary image.
And 5, judging whether the human body is the human body according to the information such as the contour feature, the pixel quantity, the depth information and the like.
And 6, acquiring the human figure position when the human body is detected.
And 7, outputting the relative position of the human body.
(2) Sofa area and dining area detection
The detection method of the dining area and the sofa area is the same, and only the specific characteristic parameter values are different, so the detection method is described by the sofa area. As shown in fig. 4:
step 1, obtaining depth distance information, obtaining depth information of each point of a scene and generating a depth map.
And 2, carrying out 3D modeling depth map on the depth distance information.
And 3, judging whether an area meeting the sofa characteristics exists according to the depth map.
Judging whether a sofa area exists in a scene or not by the depth map according to a depth neural network algorithm; the neural network algorithm is realized according to preset sofa area characteristics, wherein the sofa area characteristics comprise: the distance from the back wall, the position of the tea table, the proportion of the cushion back to the cushion, the proportion of the whole length and width and the like.
And 4, marking the position information of the sofa area if the sofa area meets the requirement. And if the region blocks meeting the sofa region, marking the corresponding region to be defined as the sofa region.
The detection area is not limited to a sofa area and a dining area, and can be expanded into a learning area, a sports area and the like.
The activity scene statistics is to collect the positions of human bodies every day in a certain period, and specifically for example, the statistics is carried out according to the following rules: according to the time axis progress, identifying the human body and the position every 10min to determine the human body activity condition of each region of the scene; collecting the time of the personnel in each activity area in a scene of a certain number of days (more than 10 days) in the last time; generating a predicted activity trace map, namely an activity area where the time is predicted, according to the sampling data; the activity area of each time point is a prediction area which is the area with the highest frequency activity according to the statistics of historical time points. If 12:00 is detected in the dining area for 20 consecutive days, the activity area of 12:00 today is predicted to be the dining area.
Function mode: and under the starting condition, the air conditioner has an energy-saving or intelligent mode, and a user can start the functional mode through a remote controller, panel keys or an App and the like. When the energy-saving or intelligent mode is started, adjusting the air-conditioning wind sweeping direction and the air outlet quantity according to the predicted activity track diagram; the air sweeping direction is changed to the position corresponding to the activity area, if a person is in the sofa area, the air outlet volume is properly reduced on the basis of the rated air volume, and if the person is in the dining area, the air outlet volume is increased on the basis of the rated air volume.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present application.
According to another aspect of the embodiments of the present application, there is also provided a control apparatus of an air conditioner for implementing the control method of an air conditioner described above. Fig. 5 is a schematic diagram of an alternative control device of an air conditioner according to an embodiment of the present application, and as shown in fig. 5, the device may include:
a prediction unit 51, configured to predict an activity track of a target user according to a historical activity area of the target user; and a control unit 53 for controlling the air conditioner according to the predicted activity trajectory.
It should be noted that the prediction unit 51 in this embodiment may be configured to execute step S1 in this embodiment, and the control unit 53 in this embodiment may be configured to execute step S2 in this embodiment.
Through the module, the activity track of the target user is predicted according to the historical activity area of the target user, the air conditioner is controlled according to the predicted activity track, product intelligence can be improved, timely and active air supply in advance is achieved, the air supply area is adjusted through manual operation of the user, and the comfortable experience of the user is better. Meanwhile, the air supply amount is reduced for the area where the user is inactive, more effective energy saving is realized, and the technical problem of energy waste of air conditioning in the related technology can be solved.
Optionally, the prediction unit is further configured to, when the air conditioner is controlled according to the predicted movement trajectory, control an air supply position of the air conditioner according to the predicted movement trajectory; and/or controlling the air outlet volume of the air conditioner according to the predicted activity track.
Optionally, the prediction unit is further configured to, when predicting the activity track of the target user according to the historical activity area of the target user, sample the activity area of the target user according to a preset time interval to obtain sample data; and generating the activity track of the target user according to the adoption data, wherein the activity track comprises activity time and a corresponding activity position.
Optionally, the activity track includes a plurality of activity times and an activity position at each activity time, wherein the prediction unit is further configured to determine the activity position at each activity time according to the following manner in the process of generating the activity track of the target user according to the adoption data: counting activity positions of the target user in the same activity time in a plurality of time periods; and taking the activity position with the highest occurrence frequency as the activity position matched with the activity time in the activity track.
Optionally, the prediction unit is further configured to, during sampling of the activity area of the target user at preset time intervals, identify the target user as follows: acquiring a scene depth map of a scene; carrying out background difference processing, noise elimination processing, outline filling and refining processing and binarization processing on the multiple frames of scene depth images to obtain a binarization image; and identifying the target user from the binary image according to the human body contour features.
Optionally, the prediction unit is further configured to, during sampling of the activity area of the target user at preset time intervals, identify a scene object as follows: acquiring depth information of each point of a scene and generating a depth map; and identifying a scene area from the depth map, and marking the identified scene area.
Optionally, the prediction unit is further configured to, when a scene object is identified from the depth map, identify a scene region from the depth map by using a deep neural network model, where the deep neural network model is a pre-trained model, and the scene region includes a sofa region, a dining region, a learning region, and a motion region.
It should be noted here that the modules described above are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to the disclosure of the above embodiments. It should be noted that the modules as a part of the apparatus may run in a corresponding hardware environment, and may be implemented by software, or may be implemented by hardware, where the hardware environment includes a network environment.
According to another aspect of the embodiment of the present application, there is also provided a server or a terminal for implementing the control method of the air conditioner.
Fig. 6 is a block diagram of a terminal according to an embodiment of the present application, and as shown in fig. 6, the terminal may include: one or more processors 201 (only one shown), memory 203, and transmission means 205, as shown in fig. 6, the terminal may further comprise an input-output device 207.
The memory 203 may be configured to store software programs and modules, such as program instructions/modules corresponding to the method and apparatus for controlling an air conditioner in the embodiment of the present application, and the processor 201 executes various functional applications and data processing by running the software programs and modules stored in the memory 203, that is, implements the above-described method for controlling an air conditioner. The memory 203 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 203 may further include memory located remotely from the processor 201, which may be connected to the terminal over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 205 is used for receiving or sending data via a network, and can also be used for data transmission between a processor and a memory. Examples of the network may include a wired network and a wireless network. In one example, the transmission device 205 includes a Network adapter (NIC) that can be connected to a router via a Network cable and other Network devices to communicate with the internet or a local area Network. In one example, the transmission device 205 is a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
Wherein the memory 203 is specifically used for storing application programs.
The processor 201 may call the application stored in the memory 203 via the transmission means 205 to perform the following steps:
predicting the activity track of a target user according to the historical activity area of the target user; and controlling the air conditioner according to the predicted moving track.
Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, and this embodiment is not described herein again.
It can be understood by those skilled in the art that the structure shown in fig. 6 is only an illustration, and the terminal may be a terminal device such as a smart phone (e.g., an Android phone, an iOS phone, etc.), a tablet computer, a palm computer, and a Mobile Internet Device (MID), a PAD, etc. Fig. 6 is a diagram illustrating a structure of the electronic device. For example, the terminal may also include more or fewer components (e.g., network interfaces, display devices, etc.) than shown in FIG. 6, or have a different configuration than shown in FIG. 6.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by a program instructing hardware associated with the terminal device, where the program may be stored in a computer-readable storage medium, and the storage medium may include: flash disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
Embodiments of the present application also provide a storage medium. Alternatively, in the present embodiment, the storage medium may be a program code for executing a control method of an air conditioner.
Optionally, in this embodiment, the storage medium may be located on at least one of a plurality of network devices in a network shown in the above embodiment.
Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps:
predicting the activity track of a target user according to the historical activity area of the target user; and controlling the air conditioner according to the predicted moving track.
Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, and this embodiment is not described herein again.
Optionally, in this embodiment, the storage medium may include, but is not limited to: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
The integrated unit in the above embodiments, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in the above computer-readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a storage medium, and including instructions for causing one or more computer devices (which may be personal computers, servers, network devices, or the like) to execute all or part of the steps of the method described in the embodiments of the present application.
In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed client may be implemented in other manners. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.
Claims (10)
1. A control method of an air conditioner, comprising:
predicting the activity track of a target user according to the historical activity area of the target user;
and controlling the air conditioner according to the predicted moving track.
2. The method of claim 1, wherein controlling the air conditioner according to the predicted activity track comprises:
controlling the air supply position of the air conditioner according to the predicted movement track; and/or the presence of a gas in the gas,
and controlling the air outlet volume of the air conditioner according to the predicted motion track.
3. The method of claim 1, wherein predicting the target user's activity track based on the target user's historical activity area comprises:
sampling the activity area of the target user according to a preset time interval to obtain sampling data;
and generating the activity track of the target user according to the adoption data, wherein the activity track comprises activity time and a corresponding activity position.
4. The method of claim 3, wherein the activity track comprises a plurality of activity times and an activity location for each activity time, wherein generating the activity track for the target user based on the adoption data comprises determining the activity location for each activity time as follows:
counting activity positions of the target user in the same activity time in a plurality of time periods;
and taking the activity position with the highest occurrence frequency as the activity position matched with the activity time in the activity track.
5. The method of claim 3, wherein in sampling the active area of the target user at preset time intervals, the method further comprises identifying the target user as follows:
acquiring a scene depth map of a scene;
carrying out background difference processing, noise elimination processing, outline filling and refining processing and binarization processing on the multiple frames of scene depth images to obtain a binarization image;
and identifying the target user from the binary image according to the human body contour features.
6. The method of claim 3, wherein in sampling the target user's activity area at preset time intervals, the method further comprises identifying scene objects as follows:
acquiring depth information of each point of a scene and generating a depth map;
and identifying a scene area from the depth map, and marking the identified scene area.
7. The method of claim 6, wherein identifying scene objects from the depth map comprises:
and identifying a scene area from the depth map by using a deep neural network model, wherein the deep neural network model is a pre-trained model, and the scene area comprises a sofa area, a dining area, a learning area and a motion area.
8. A control device of an air conditioner, characterized by comprising:
the prediction unit is used for predicting the activity track of the target user according to the historical activity area of the target user;
and the control unit is used for controlling the air conditioner according to the predicted movable track.
9. A storage medium, characterized in that the storage medium comprises a stored program, wherein the program when executed performs the method of any of the preceding claims 1 to 7.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the method of any of the preceding claims 1 to 7 by means of the computer program.
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| CN202011322431.1A CN112432328A (en) | 2020-11-23 | 2020-11-23 | Control method and device of air conditioner, storage medium and electronic device |
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| CN202011322431.1A CN112432328A (en) | 2020-11-23 | 2020-11-23 | Control method and device of air conditioner, storage medium and electronic device |
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