Control method and control device of home appliance, electronic device, and storage medium

Abstract

The application discloses a kind of control method of household appliance, control device, electronic equipment and storage medium.The control method of household appliance includes: monitoring the life trajectory information of current user;Current user's life trajectory information is compared with the preset life trajectory information of target user;According to the comparison result, it is judged whether the current user is the target user;When the current user is the target user, the control habit parameter of the target user is obtained according to the self-learning model established by the setting parameter of the target user for household appliance, and the control habit parameter of the target user is used to control household appliance.The control method of the above-mentioned household appliance, control device, electronic equipment and storage medium can distinguish different users based on life trajectory information, and then establish a self-learning model for different users, to improve user experience.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of household appliances, and in particular to a control method and control device of a household appliance, an electronic device and a storage medium. BACKGROUND

[0002] With the rapid development of science and technology, more and more intelligent household appliances such as air conditioners enter thousands of households, providing great convenience for people's life. In the related art, the air conditioner has a self-learning function. In the self-learning mode, the air conditioner can record the air conditioner operating parameters set by the user, and determine the setting habits of the user according to the data recorded for multiple times. After that, the user does not need to set the air conditioner operating parameters again when using the air conditioner, and the air conditioner can execute the corresponding air conditioner operating parameters according to the setting habits of the user.

[0003] However, in the case that multiple family members use the same air conditioner, since the setting habits of each family member are quite different, the air conditioner cannot distinguish different family members, so the air conditioner cannot establish a self-learning mode according to each family member, and the air conditioner cannot automatically execute the corresponding air conditioner operating parameters according to the setting habits of each family member. SUMMARY

[0004] The present application aims to at least solve one of the technical problems in the related art. To this end, one object of the present application is to provide a control method of a household appliance, which can distinguish different users based on life trajectory information, and then establish a self-learning model for different users to improve user experience.

[0005] A second object of the present application is to provide a control device of a household appliance.

[0006] A third object of the present application is to provide an electronic device.

[0007] A fourth object of the present application is to provide a computer-readable storage medium.

[0008] In order to achieve the above-mentioned objects, the control method of a household appliance according to the first aspect of the present application comprises: monitoring life trajectory information of a current user; comparing the life trajectory information of the current user with preset life trajectory information of a target user; determining whether the current user is the target user according to the comparison result; when the current user is the target user, obtaining control habit parameters of the target user according to a self-learning model established by the target user for setting parameters of the household appliance, and controlling the household appliance according to the control habit parameters of the target user.

[0009] The control method of the household appliance according to the embodiment of the present application can determine whether the current user is the target user by comparing the life track information of the current user with the preset life track information of the target user, and can self-learn the setting parameters of the household appliance for the target user when the current user is the target user, thereby establishing self-learning models for different users and improving user experience.

[0010] In an embodiment of the present application, before comparing the life track information of the current user with the preset life track information of the target user, the control method further comprises: obtaining an identification condition of the target user, the identification condition comprising an identification time period and an identification position; determining an activity time when an activity object is monitored; inquiring whether the activity object is the target user when the activity time is in the identification time period; establishing a spatial correspondence between an activity track of the activity object and the identification position when the activity object is the target user; and generating the preset life track information of the target user according to the spatial correspondence and the identification time period.

[0011] In an embodiment of the present application, the preset life track information of the target user comprises a preset time period and a preset track, and the life track information of the current user comprises a current time and a current track. Comparing the life track information of the current user with the preset life track information of the target user comprises: judging whether the current time of the current user matches the preset time period of the target user, and judging whether the current track of the current user matches the preset track of the target user.

[0012] In an embodiment of the present application, judging whether the current user is the target user according to the comparison result comprises: determining that the current user is the target user when the current time of the current user is in the preset time period of the target user, and the coincidence degree between the current track of the current user and the preset track of the target user is greater than or equal to a preset threshold.

[0013] In an embodiment of the present application, after judging whether the current user is the target user according to the comparison result, the control method further comprises: when the current user is not the target user, prompting the current user to be filed.

[0014] In an embodiment of the present application, the target user comprises a plurality of target users, and the preset life track information of the plurality of target users is different.

[0015] In order to achieve the above object, the control device of the household appliance according to the second aspect of the present application comprises a monitoring module, a comparison module, a judging module and a learning module. The monitoring module is configured to monitor the life track information of a current user. The comparison module is configured to compare the life track information of the current user with preset life track information of a target user. The judging module is configured to judge whether the current user is the target user according to the comparison result. The learning module is configured to obtain the control habit parameters of the target user according to a self-learning model established by the target user for the setting parameters of the household appliance when the current user is the target user, and control the household appliance according to the control habit parameters of the target user.

[0016] According to the control device of the household appliance, the life track information of the current user is compared with the preset life track information of the target user, so that it can be determined whether the current user is the target user, and the setting parameters of the household appliance for the target user can be self-learned when the current user is the target user, thereby establishing the self-learning model for different users and improving the user experience.

[0017] In order to achieve the above object, the electronic device according to the third aspect of the present application comprises one or more processors and a memory, and the memory stores a computer program. When the computer program is executed by the processor, the steps of the control method of the household appliance according to any one of the above embodiments are implemented.

[0018] According to the electronic device, the life track information of the current user is compared with the preset life track information of the target user, so that it can be determined whether the current user is the target user, and the setting parameters of the household appliance for the target user can be self-learned when the current user is the target user, thereby establishing the self-learning model for different users and improving the user experience.

[0019] In one embodiment of the present application, the electronic device is a household appliance or a server.

[0020] In order to achieve the above object, the computer readable storage medium according to the fourth aspect of the present application stores a computer program, and when the program is executed by a processor, the steps of the control method of the household appliance according to any one of the above embodiments are implemented.

[0021] According to the computer readable storage medium of the embodiment of the present application, by comparing the life track information of the current user with the preset life track information of the target user stored in advance, it can be determined whether the current user is the target user, and the setting parameters of the home appliance device for the target user can be self-learned when the current user is the target user, so as to establish a self-learning model for different users and improve user experience.

[0022] Additional aspects and advantages of the present application will be described in the following description, become apparent from the following description, or be learned by practice of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0023] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood by considering the following detailed description, including the accompanying drawings, in which:

[0024] Figure 1 is a flowchart of a control method of a home appliance device according to an embodiment of the present application;

[0025] Figure 2 is a flowchart of a control method of a home appliance device according to an embodiment of the present application;

[0026] Figure 3 is a flowchart of a control method of a home appliance device according to an embodiment of the present application;

[0027] Figure 4 is a flowchart of a control method of a home appliance device according to an embodiment of the present application;

[0028] Figure 5 is a flowchart of a control method of a home appliance device according to an embodiment of the present application;

[0029] Figure 6 is a structural block diagram of a control device of a home appliance device according to an embodiment of the present application;

[0030] Figure 7 is a structural block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0031] Embodiments of the present application are described in detail below with reference to the accompanying drawings, in which the same or similar components have the same or similar reference numbers throughout. The embodiments described below are exemplary only, and are not intended to be limiting of the present application.

[0032] In the description of the embodiments of the present application, the terms "first", "second" are only used for descriptive purpose, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the embodiments of the present application, the meaning of "multiple" is two or more, unless otherwise explicitly and specifically limited.

[0033] Please refer to Figure 1 The control method of the household appliance in the embodiments of the present application comprises:

[0034] S11: monitoring the life trajectory information of the current user;

[0035] S13: comparing the life trajectory information of the current user with the preset life trajectory information of the target user;

[0036] S15: judging whether the current user is the target user according to the comparison result;

[0037] S17: when the current user is the target user, obtaining the control habit parameters of the target user according to the self-learning model established by the target user for the setting parameters of the household appliance, and controlling the household appliance according to the control habit parameters of the target user.

[0038] According to the control method of the household appliance in the embodiments of the present application, by comparing the life trajectory information of the current user with the preset life trajectory information of the target user, it can be determined whether the current user is the target user who needs to self-learn the operation habit, and when the current user is the target user who needs to self-learn the operation habit, the setting parameters of the household appliance for the target user can be self-learned, so as to establish the self-learning model for different users and improve the user experience.

[0039] It can be understood that in the related art, when the household appliance is in a self-learning mode, no matter how many users operate the household appliance, the household appliance defaults to the operation of the same user and simultaneously self-learns the setting parameters of the household appliance for multiple users, that is, the electronic device in the related art can only establish a self-learning model in units of itself. However, in general, a family includes multiple family members, and the control habits of the setting parameters of the household appliance of each family member are different. In the case that the household appliance cannot distinguish different users, the household appliance cannot establish a self-learning model for each family member, and cannot automatically run the household appliance according to the control habits of each family member. Even if the control habits of each family member on the same household appliance are weighted in the order of time to obtain a comprehensive self-learning model, it is impossible to achieve personalized control and meet the use requirements of different family members.

[0040] Specifically, the household appliance includes but is not limited to an air conditioner, a humidifier, an air purifier, a television, a smart speaker, and the like.

[0041] The current user can be understood as a moving object appearing in the monitoring range of the household appliance.

[0042] In some embodiments, the household appliance can include a monitoring device capable of collecting sound data, image data, radar signal data, or the like in the monitoring range at a preset frequency. According to the data collected by the monitoring device, it can be determined whether there is a moving object in the monitoring range, and when there is a moving object in the monitoring range, the motion trajectory of the moving object can be continuously tracked and life trajectory information can be generated. In one example, the detection device includes a radar.

[0043] The target user can be understood as a user who needs the household appliance to individually establish a self-learning model and determine control habit parameters. The preset life trajectory information can be the life trajectory information of the target user in the monitoring range of the household appliance. In one embodiment of the present application, the target user includes multiple target users, and the preset life trajectory information of the multiple target users is different. In this way, different target users can be distinguished based on the preset life trajectory information, and a self-learning model can be established for each target user.

[0044] In some embodiments, the target user can include a housewife, an office worker at home, a student at home, and the like.

[0045] In one example, the same household appliance can establish five self-learning models. In one example, the same household appliance can self-learn the control habits of 15 target users.

[0046] After obtaining the life track information of the current user, by comparing the life track information of the current user with preset life track information of the target user, it can be determined whether the current user is the target user who needs to self-learn the operation habit, so as to determine whether to learn the control habit of the current user for the household appliance. If the comparison result shows that the current user is the target user who needs to self-learn the operation habit, the setting parameter of the current user for the household appliance is taken as the setting parameter of the target user for the household appliance, and a self-learning model corresponding to the target user is established.

[0047] The setting parameter can include at least one of a temperature parameter, an air outlet mode parameter, and a wind direction parameter. The control habit parameter of the target user determined according to the self-learning model can include at least one of the setting parameters.

[0048] In an embodiment, when the number of times of obtaining the setting parameter of the target user for the household appliance reaches 7 times, the self-learning of the control habit of the target user is completed. The setting parameter of the target user for the household appliance obtained 7 times is weighted to obtain the final self-learning model corresponding to the target user, so as to determine the control habit parameter of the target user. When the target user is monitored to appear in the monitoring range next time, the target user does not need to manually adjust the setting parameter of the household appliance, and the household appliance can directly operate according to the control habit parameter of the target user, so as to simplify the operation and improve the user experience.

[0049] It should be pointed out that the control method of the household appliance of the embodiment of the present application can be realized by the household appliance, or by the server, or by the household appliance and the server together, which is not limited here.

[0050] Please refer to Figure 2 In an embodiment of the present application, before step S13, the control method further comprises:

[0051] S21: Obtain the identification condition of the target user, and the identification condition includes an identification period and an identification position;

[0052] S23: When the moving object is monitored, determine the moving time;

[0053] S25: When the moving time is in the identification period, ask whether the moving object is the target user;

[0054] S27: When the moving object is the target user, establish the space correspondence between the moving track of the moving object and the identification position;

[0055] S29: Generate the preset life track information of the target user according to the space correspondence and the identification period.

[0056] Therefore, before starting the self-learning, the correspondence between the identified position and the actual space can be established, and the preset life track information of the target user can be determined, so that the preset life track information of the target user and the life track information of the current user can be compared. It can be understood that, since the identified position is the name of a position, that is, the identified position itself does not include the spatial information such as the coordinates, range, distance from the home appliance, and orientation relative to the home appliance of the position, the home appliance cannot determine whether the current user is the target user who needs to self-learn the operation habit by comparing the identified position of the target user and the motion track of the current user, and the correspondence between the identified position and the actual space needs to be established in advance to determine the spatial information other than the name of the identified position.

[0057] Specifically, in step S21, the identified condition of the target user can be defined by the target user. In an example, the target user or other users can input the identified condition of the target user through a terminal device such as a mobile phone, a tablet computer, a notebook computer, or a remote controller.

[0058] The identified period can include a start time, an end time, and other times between the start time and the end time. The identified position can include at least one of a kitchen, a porch, a sofa, a dining table, a writing desk, and a balcony. It should be noted that, in the description of the embodiments of the present application, the "time", "start time", "end time", "active time", and "current time" can include hours, can include hours and minutes, or can include hours, minutes, and seconds, which are not limited herein. In an example, the target user is a housewife, the identified period of the identified condition is 6:00-7:00, and the identified position of the identified condition is the kitchen. In another example, the target user is an office worker at home, the identified period of the identified condition is 7:30-8:00, and the identified position of the identified condition is the dining table. In another example, the target user is a student at home, the identified period of the identified condition is 18:00-19:00, and the identified position of the identified condition is the writing desk.

[0059] In step S23, the home appliance can include a radar, and whether there is an active object can be monitored by the radar. The active object can be the target user who needs to self-learn the operation habit, the non-target user who does not need to self-learn the operation habit, an animal, or any other movable object. The active time can be understood as the time when the radar confirms that the active object is monitored. That is, the active time can be the time when the radar first monitors that the active object appears in the monitoring range, or can be the time when the radar again monitors that the active object is in the monitoring range when the radar continuously monitors the objects in the monitoring range according to a preset frequency, which is not limited herein.

[0060] In step S25, the activity time is in the identification period, which can be understood as that the activity time is equal to the start time of the identification period, or the activity time is equal to the end time of the identification period, or the activity time is equal to other time between the start time and the end time. It should be noted that in some embodiments, considering that the actual appearance time of the target user can deviate slightly from the pre-set identification period, in order to ensure that the target user can be found in time in this case, a first time deviation can be set, that is, the activity time before the start time of the identification period by no more than the first time deviation or the activity time after the end time of the identification period by no more than the first time deviation is considered to be in the identification period. That is, the activity time is in the identification period, which can also be understood as that the activity time is equal to the time before the start time of the identification period by the first time deviation, or the activity time is equal to the time after the end time of the identification period by the first time deviation, or the activity time is equal to other time between the time before the start time by the first time deviation and the time after the end time by the first time deviation. In some embodiments, the first time deviation is set to 30 minutes; in some embodiments, the first time deviation is set to 20 minutes; in other embodiments, the first time deviation can also be set to other values, which are not limited here.

[0061] In order to ensure that the activity object is verified whether it is the target user whose operation habit needs to be self-learned, when it is determined that the activity time is in the identification period, the terminal device such as a mobile phone, a tablet computer, a notebook computer, or a remote controller can be used to inquire whether the activity object is the target user whose operation habit needs to be self-learned. In an example, the APP on the mobile phone displays inquiry information such as “Are you the target user A who has entered the identification condition?” If a signal representing “yes” is received, step S27 is entered; if a signal representing “no” is received or no signal is received, the step of monitoring the activity object is re-entered.

[0062] In some embodiments, considering that the period of the identification period of the target user A within the first time deviation and the period of the identification period of the target user B within the first time deviation can coincide, thereby causing that it cannot be determined which target user should be inquired, the inquiry logic in this case can be defined in advance. In an example, the target user in front of the identification period is inquired first, if it is not the target user, the other target user behind the identification period is inquired. In an example, the target user behind the identification period is inquired first, if it is not the target user, the other target user in front of the identification period is inquired. In an example, the target user corresponding to the identification period with smaller time deviation is inquired first, if it is not the target user, the other target user corresponding to the identification period with larger time deviation is inquired. In this way, it can be ensured that the inquiry is carried out normally, the target user is not missed, and the accuracy of the inquiry is improved.

[0063] In step S27, the radar of the home appliance device can track the activity trajectory of the active object, and the activity trajectory tracked by the radar can include the activity orientation of the active object relative to the radar and the distance of the active object relative to the radar. When the active object is determined to be the target user who needs to self-learn the operation habit, the activity trajectory of the active object is taken as the spatial information of the identified position of the target user, and the spatial correspondence between the identified position and the activity trajectory of the active object is established, so that the spatial information of the identified position can be determined according to the spatial correspondence.

[0064] In step S29, the preset life trajectory information of the target user is generated according to the spatial correspondence and the identification period of the identification condition of the target user. That is, the preset life trajectory of the target user includes the identification period of the target user and the activity trajectory of the target user calibrated in advance.

[0065] In one example, the identification period of the target user A who needs to self-learn the operation habit is from 6:00 to 7:00 in the morning, the identified position of the target user A who needs to self-learn the operation habit is the kitchen, the first time deviation is 30 minutes, and if the activity time is any time between 5:30 and 7:30 in the morning, it is determined that the activity time is in the identification period from 6:00 to 7:00 in the morning.

[0066] Further, the identification period of the target user B who needs to self-learn the operation habit is from 7:30 to 8:00 in the morning. The activity time when the active object appears in the monitoring range is 7:10, and the activity trajectory of the active object in the monitoring range is to appear on the left side of the monitoring range, to move from a position 8.5 meters in front of the left side of the home appliance device to a position 11.5 meters in front of the left side of the home appliance device, and to stay or fine-tune the action near the position 11.5 meters in front of the left side of the home appliance device.

[0067] Since the time deviation between the activity time and the identification period from 6:00 to 7:00 in the morning is small, and the time deviation between the activity time and the identification period from 7:30 to 8:00 in the morning is large, the active object can be first asked whether it is the target user A who needs to self-learn the operation habit, and if it is determined that it is not the target user A who needs to self-learn the operation habit, the active object can be asked whether it is the target user B who needs to self-learn the operation habit; if it is determined that it is the target user A who needs to self-learn the operation habit, the activity trajectory of the active object is taken as the spatial information of the kitchen, the spatial correspondence between the activity trajectory of the active object and the kitchen is established, and "6:00-7:00, appearing on the left side of the monitoring range, moving to the range of 8.5-11.5 meters in front of the left side, staying or fine-tuning the action" is taken as the preset life trajectory information of the target user A who needs to self-learn the operation habit.

[0068] Please refer to Figure 3In an embodiment of the present application, the preset life track information of the target user comprises a preset time period and a preset track, the life track information of the current user comprises a current time and a current track, and step S13 comprises:

[0069] S131: determining whether the current time of the current user matches the preset time period of the target user, and determining whether the current track of the current user matches the preset track of the target user.

[0070] In this way, by comparing the life track information and the preset life track information, it can be determined more accurately whether the current user is the target user who needs to self-learn the operation habit.

[0071] Specifically, the preset time period can be the above-mentioned identification time period. The preset time period can comprise a start time, an end time, and other times between the start time and the end time. The preset track can be the activity track of the activity object of the target user pre-labeled above.

[0072] The current time can be understood as the time when the current user is monitored. Specifically, it can be the time when the current user is first monitored, or the time when the current user is monitored again according to a preset frequency, which is not limited here.

[0073] It can be understood that step S131 comprises a time period matching step and a track matching step, wherein the time period matching step can be performed first, and then the track matching step can be performed; or the track matching step can be performed first, and then the time period matching step can be performed, which is not limited here.

[0074] Further, since the monitoring of the current user in the monitoring range is a continuous process, that is, the current time obtained is continuously updated, the matching result obtained by matching the current time and the preset time period for the first time does not affect the step of matching the current time and the preset time period for the second time. That is, when the current track of the current user matches the preset track, if it is determined in the first time period matching step that the current time of the current user does not match the preset time period of the target user, it is determined that the current user is not the target user who needs to self-learn the operation habit. However, if it is determined in the second time period matching step that the current time of the current user matches the preset time period of the target user, it is determined that the current user is the target user who needs to self-learn the operation habit.

[0075] Please refer to Figure 4 In an embodiment of the present application, step S15 comprises:

[0076] S151: determining that the current user is the target user when the current time of the current user is in the preset time period of the target user, and the coincidence degree between the current track of the current user and the preset track of the target user is greater than or equal to a preset threshold.

[0077] In this way, in combination with the comparison result of the time period and the comparison result of the trajectory, it can be determined more accurately whether the current user is the target user whose operation habit needs to be self-learned.

[0078] Specifically, the current time is in the preset time period, which can be understood as that the current time is equal to the start time of the preset time period, or the current time is equal to the end time of the preset time period, or the current time is equal to other time between the start time and the end time. It should be pointed out that in some embodiments, considering that the actual appearance time of the target user may deviate slightly from the preset preset time period, in order to ensure that the target user can be found in time in this case, a second time deviation can be set, that is, the current time that is not more than the second time deviation before the start time of the preset time period or the current time that is not more than the second time deviation after the end time of the preset time period is considered to be in the preset time period. That is, the current time is in the preset time period, which can also be understood as that the current time is equal to the time before the start time of the preset time period by the second time deviation, or the current time is equal to the time after the end time of the preset time period by the second time deviation, or the current time is equal to other time between the time before the start time by the second time deviation and the time after the end time by the second time deviation. In some embodiments, the second time deviation is set to 30 minutes; in some embodiments, the second time deviation is set to 20 minutes; in other embodiments, the second time deviation can also be set to other numerical values, which are not limited here.

[0079] In some embodiments, the preset threshold is 85%, that is, when the coincidence degree between the current trajectory of the current user and the preset trajectory of the target user reaches or exceeds 85% (such as 90%, 95%, 100%), it can be considered that the current trajectory is successfully matched with the preset trajectory; when the coincidence degree between the current trajectory of the current user and the preset trajectory of the target user is less than 85%, it can be considered that the current trajectory cannot be matched with the preset trajectory.

[0080] In one example, the preset time period of the target user whose operation habit needs to be self-learned is from 6 am to 7 am. The preset trajectory of the target user whose operation habit needs to be self-learned is to appear on the left side of the monitoring range, to the left front 8.5-11.5 meters, and to stay or fine-tune the action. It is monitored that the current time of the current user is 6:20 am, and the current trajectory of the current user is to appear on the left side of the monitoring range, to the left front 8.4-11.5 meters, and to stay. Since the coincidence degree between the current trajectory and the preset trajectory exceeds 85%, and the current time is compared with the preset time period, it can be determined that the current user is the target user whose operation habit needs to be self-learned.

[0081] Please refer to Figure 5In an embodiment of the present application, after step S15, the control method further comprises:

[0082] S19: When the current user is not the target user, prompting the current user to profile.

[0083] In this way, the control habit of the current user for the home appliance can be self-learned when the current user completes the profiling according to the profiling prompt.

[0084] Specifically, the profiling prompt can be performed by a terminal device such as a mobile phone, a tablet computer, a notebook computer, or a remote controller. The profiling prompt can include a text prompt and / or a voice prompt. The content of the profiling prompt can include "Do you need to self-learn your control habit? If yes, please enter your identification condition according to the prompt".

[0085] In some embodiments, after step S19, the control method further comprises: providing an interactive interface for entering the identification condition when it is determined that the current user needs to profile; determining the identification condition of the current user according to the input information of the interactive interface, and self-learning the control habit of the current user for the home appliance as one of the target users.

[0086] In one example, the current user is prompted to profile by a mobile phone. The mobile phone includes a display screen, and when it is necessary to prompt the user to profile, the display screen of the mobile phone displays prompt words "Do you need to self-learn your control habit? If yes, please enter your identification condition according to the prompt". At the same time, the display screen of the mobile phone provides a selection button representing "yes" and a cancel button representing "no". When it is detected that the cancel button is triggered, it is determined that the current user does not need to profile, and the current interface is exited. When it is detected that the selection button is triggered, it is determined that the current user needs to profile, and then an interactive interface for entering the identification condition is provided.

[0087] In another example, the current user is prompted to profile by a mobile phone, and the mobile phone has a voice recognition function. The mobile phone includes a speaker and a display screen, and when it is necessary to prompt the user to profile, the speaker of the mobile phone plays a prompt voice "Do you need to self-learn your control habit? If yes, please enter your identification condition according to the prompt". When it is determined according to the voice received from the current user that an instruction that the current user does not need to profile is received, the current user monitored this time is ignored. When it is determined according to the voice received from the current user that an instruction that the current user needs to profile is received, an interactive interface for entering the identification condition is provided through the display screen.

[0088] Please refer to Figure 6The control device 100 of the household appliance in the embodiment of the present application comprises a monitoring module 12, a comparison module 14, a judgment module 16 and a learning module 18. The monitoring module 12 is configured to monitor the life trajectory information of the current user. The comparison module 14 is configured to compare the life trajectory information of the current user with the preset life trajectory information of the target user. The judgment module 16 is configured to determine whether the current user is the target user according to the comparison result. The learning module 18 is configured to obtain the control habit parameters of the target user according to the self-learning model of the target user established for the setting parameters of the household appliance when the current user is the target user, and control the household appliance according to the control habit parameters of the target user.

[0089] The control device 100 of the household appliance in the embodiment of the present application can determine whether the current user is the target user who needs to self-learn the operation habit by comparing the life trajectory information of the current user with the preset life trajectory information of the target user, and can self-learn the setting parameters of the household appliance for the target user when the current user is the target user who needs to self-learn the operation habit, thereby establishing a self-learning model for different users and improving user experience.

[0090] Specifically, the household appliance includes but is not limited to an air conditioner, a humidifier, an air purifier, a television, a smart speaker and the like.

[0091] The current user can be understood as a moving object appearing in the monitoring range of the household appliance. In some embodiments, the household appliance can comprise a monitoring device capable of collecting sound data, image data or radar signal data and the like in the monitoring range at a preset frequency, and determining whether there is a moving object in the monitoring range according to the data collected by the monitoring device, and continuously tracking the motion trajectory of the moving object and generating life trajectory information when there is a moving object in the monitoring range. In one example, the detection device comprises a radar.

[0092] The target user can be understood as a user who needs the household appliance to establish a self-learning model and determine control habit parameters individually. The preset life trajectory information can be the life trajectory information of the target user in the monitoring range of the household appliance. In one embodiment of the present application, the target user includes multiple target users, and the preset life trajectory information of the multiple target users is different. In this way, different target users can be distinguished based on the preset life trajectory information, and a self-learning model can be established for each target user. In some embodiments, the target user can include a housewife, an office worker at home, a student at home and the like. In one example, the same household appliance can self-learn the control habits of 15 target users.

[0093] After obtaining the life trajectory information of the current user, by comparing the life trajectory information of the current user with the preset life trajectory information of the target user, it can be determined whether the current user is the target user who needs to self-learn the operation habit, so as to determine whether to learn the control habit of the current user for the household appliance. If the comparison result shows that the current user is the target user who needs to self-learn the operation habit, the setting parameter of the current user for the household appliance is taken as the setting parameter of the target user for the household appliance, and a self-learning model corresponding to the target user is established.

[0094] The setting parameter can include at least one of a temperature parameter, an air outlet mode parameter, and a wind direction parameter. The control habit parameter of the target user determined according to the self-learning model can include at least one of the setting parameters.

[0095] In an embodiment, when the number of times of obtaining the setting parameter of the target user for the household appliance reaches 7 times, the self-learning of the control habit of the target user is completed. The setting parameter of the target user for the household appliance obtained for 7 times is weighted to obtain the final self-learning model corresponding to the target user, so as to determine the control habit parameter of the target user. When the target user is monitored to appear in the monitoring range next time, the target user does not need to manually adjust the setting parameter of the household appliance, and the household appliance can directly operate according to the control habit parameter of the target user, so as to simplify the operation and improve the user experience.

[0096] In an embodiment of the present application, the control device 100 further includes an obtaining module, a determining module, an inquiring module, an establishing module and a generating module. The obtaining module is used to obtain the identification condition of the target user, and the identification condition includes an identification period and an identification position. The determining module is used to determine an activity time when an activity object is monitored. The inquiring module is used to inquire whether the activity object is the target user when the activity time is in the identification period. The establishing module is used to establish a space correspondence between the activity trajectory of the activity object and the identification position when the activity object is the target user. The generating module is used to generate the preset life trajectory information of the target user according to the space correspondence and the identification period.

[0097] Therefore, before starting the self-learning, the correspondence between the identified position and the actual space can be established, and the preset life track information of the target user can be determined, so that the preset life track information of the target user and the life track information of the current user can be compared. It can be understood that, since the identified position is the name of a position, that is, the identified position itself does not include the spatial information such as the coordinates, range, distance from the home appliance, and orientation relative to the home appliance of the position, the home appliance cannot determine whether the current user is the target user who needs to have the operation habit self-learned by comparing the identified position of the target user and the motion track of the current user, and the correspondence between the identified position and the actual space needs to be established in advance to determine the spatial information other than the name of the identified position.

[0098] Specifically, the identified condition of the target user can be defined by the target user. In an example, the target user or other users can input the identified condition of the target user through a terminal device such as a mobile phone, a tablet computer, a notebook computer, or a remote controller.

[0099] The identified period can include a start time, an end time, and other times between the start time and the end time. The identified position can include at least one of a kitchen, a porch, a sofa, a dining table, a writing desk, and a balcony. It should be noted that, in the description of the embodiments of the present application, the "time", "start time", "end time", "active time", and "current time" can include hours, can include hours and minutes, or can include hours, minutes, and seconds, which are not limited herein. In an example, the target user is a housewife, the identified period of the identified condition is 6:00-7:00, and the identified position of the identified condition is the kitchen. In another example, the target user is an office worker at home, the identified period of the identified condition is 7:30-8:00, and the identified position of the identified condition is the dining table. In another example, the target user is a student at home, the identified period of the identified condition is 18:00-19:00, and the identified position of the identified condition is the writing desk.

[0100] The home appliance can include a radar, and whether there is an active object can be monitored by the radar. The active object can be a target user who needs to have the operation habit self-learned, a non-target user who does not need to have the operation habit self-learned, an animal, or any other movable object. The active time can be understood as the time when the radar confirms that the active object is monitored. That is, the active time can be the time when the radar first monitors that the active object appears in the monitoring range, or can be the time when the radar again monitors that the active object is in the monitoring range when the radar continuously monitors the objects in the monitoring range according to a preset frequency, which is not limited herein.

[0101] The activity time being in the identification period can be understood as the activity time being equal to the start time of the identification period, or the activity time being equal to the end time of the identification period, or the activity time being equal to other time between the start time and the end time. It should be noted that in some embodiments, considering that the actual appearance time of the target user can deviate slightly from the pre-set identification period, in order to ensure that the target user can be found in time in this case, a first time deviation can be set, that is, the activity time preceding the start time of the identification period by no more than the first time deviation or the activity time later than the end time of the identification period by no more than the first time deviation is considered to be in the identification period. That is, the activity time being in the identification period can also be understood as the activity time being equal to the time before the start time of the identification period by the first time deviation, or the activity time being equal to the time after the end time of the identification period by the first time deviation, or the activity time being equal to other time between the time before the start time by the first time deviation and the time after the end time by the first time deviation. In some embodiments, the first time deviation is set to 30 minutes; in some embodiments, the first time deviation is set to 20 minutes; in other embodiments, the first time deviation can also be set to other numerical values, which are not limited here.

[0102] In determining that the activity time is in the identification period, in order to ensure that it is verified whether the activity object is the target user whose operation habit needs to be self-learned, the terminal device such as a mobile phone, a tablet computer, a notebook computer, or a remote controller can be used to inquire whether the activity object is the target user whose operation habit needs to be self-learned. In an example, the APP on the mobile phone displays inquiry information such as "Are you the target user A who has entered the identification condition?", if a signal representing "yes" is received, the step of establishing the space corresponding relationship is entered; if a signal representing "no" is received or no signal is received, the step of monitoring the activity object is re-entered.

[0103] In some embodiments, considering that the period of the identification period of the target user A within the first time deviation and the period of the identification period of the target user B within the first time deviation can coincide, and thus it cannot be determined which target user should be inquired, the inquiry logic in this case can be defined in advance, for example, the target user in the earlier identification period is inquired first, if it is not the target user, the other target user in the later identification period is inquired; or, the target user in the later identification period is inquired first, if it is not the target user, the other target user in the earlier identification period is inquired; or, the target user corresponding to the identification period with smaller time deviation is inquired first, if it is not the target user, the other target user corresponding to the identification period with larger time deviation is inquired. In this way, it can be ensured that the inquiry is carried out normally, the target user is avoided to be missed, and the accuracy of the inquiry is improved.

[0104] The radar of the home appliance device can track the activity trajectory of the active object. The activity trajectory tracked by the radar can include an activity direction of the active object relative to the radar and a distance of the active object relative to the radar. The radar can identify the layout in the house through the tracked activity trajectory, for example, 10 meters in front of the left is a kitchen, 5 meters in front is a dining room, and 3 meters in front of the right is a study.

[0105] When the active object is determined to be a target user who needs to self-learn the operation habit, the activity trajectory of the active object is taken as the spatial information of the identified position of the target user, and a spatial correspondence between the activity trajectory of the active object and the identified position is established, so that the spatial information of the identified position can be determined according to the spatial correspondence.

[0106] According to the spatial correspondence and the identification time period of the identification condition of the target user, preset life trajectory information of the target user is generated. That is, the preset life trajectory of the target user includes the identification time period of the target user and the activity trajectory of the target user pre-calibrated.

[0107] In one example, the identification time period of the target user A who needs to self-learn the operation habit is from 6 am to 7 am, the identified position of the target user A who needs to self-learn the operation habit is the kitchen, the first time deviation is 30 minutes, and if the activity time is any time between 5:30 am and 7:30 am, it is determined that the activity time is in the identification time period from 6 am to 7 am.

[0108] Further, the identification time period of the target user B who needs to self-learn the operation habit is from 7:30 am to 8 am. The activity time when the monitored active object appears in the monitoring range is 7:10, and the activity trajectory of the monitored active object in the monitoring range is to appear on the left side of the monitoring range, move from a position 8.5 meters in front of the left of the home appliance device to a position 11.5 meters in front of the left of the home appliance device, and stay or fine-tune the action near the position 11.5 meters in front of the left of the home appliance device.

[0109] Since the time length deviation between the activity time and the identification time period of 6:00-7:00 in the morning is smaller, and the time length deviation between the activity time and the identification time period of 7:30-8:00 in the morning is larger, the activity subject can be first inquired whether to be the target user A requiring self-learning of the operation habit, if it is determined that the activity subject is not the target user A requiring self-learning of the operation habit, the activity subject can be inquired whether to be the target user B requiring self-learning of the operation habit, if it is determined that the activity subject is the target user A requiring self-learning of the operation habit, the activity track of the activity subject is taken as the space information of the kitchen, the activity track of the activity subject and the space of the kitchen are established in correspondence, and "6:00-7:00, appearing on the left side of the monitoring range, moving to the left front 8.5-11.5 meters, staying or fine-tuning action" is taken as the preset life track information of the target user A requiring self-learning of the operation habit.

[0110] In an embodiment of the present application, the comparison module 14 is further used to judge whether the current time of the current user matches the preset time period of the target user, and judge whether the current track of the current user matches the preset track of the target user.

[0111] In this way, by comparing the life track information and the preset life track information, whether the current user is the target user requiring self-learning of the operation habit can be determined more accurately.

[0112] Specifically, the preset time period can be the above-mentioned identification time period. The preset time period can include a starting time, an ending time and other times between the starting time and the ending time. The preset track can be the activity track of the activity subject of the target user pre-labeled.

[0113] The current time can be understood as the time when the current user is monitored, specifically, it can be the time when the current user is first monitored, or the time when the current user is monitored again according to the preset frequency, which is not limited here.

[0114] It can be understood that the comparison module 14 can perform the matching time period step and the matching track step, wherein the matching time period step can be performed first, and then the matching track step is performed; or the matching track step can be performed first, and then the matching time period step is performed, which is not limited here.

[0115] Further, since the monitoring of the current user in the monitoring range is a continuous process, that is, the current time obtained is continuously updated, the matching result obtained by matching the current time and the preset time period for the first time does not affect the step of matching the current time and the preset time period for the second time. That is, when the current trajectory of the current user and the preset trajectory are matched with each other, if it is determined in the first matching time period step that the current time of the current user and the preset time period of the target user do not match, the current user is determined to be not the target user who needs to self-learn the operation habit, but if it is determined in the second matching time period step that the current time of the current user and the preset time period of the target user match with each other, the current user is determined to be the target user who needs to self-learn the operation habit.

[0116] In an embodiment of the present application, the learning module 18 is further configured to determine that the current user is the target user when the current time of the current user is in the preset time period of the target user, and the coincidence degree between the current trajectory of the current user and the preset trajectory of the target user is greater than or equal to the preset threshold.

[0117] In this way, in combination with the comparison result of the time period and the comparison result of the trajectory, whether the current user is the target user who needs to self-learn the operation habit can be determined more accurately.

[0118] Specifically, the current time being in the preset time period can be understood as the current time being equal to the start time of the preset time period, or the current time being equal to the end time of the preset time period, or the current time being equal to other time between the start time and the end time. It should be noted that in some embodiments, considering that the actual appearance time of the target user may deviate slightly from the preset preset time period, in order to ensure that the target user can be found in time in this case, a second time deviation can be set, that is, the current time that is not more than the second time deviation before the start time of the preset time period or the current time that is not more than the second time deviation after the end time of the preset time period are considered to be in the preset time period. That is, the current time being in the preset time period can also be understood as the current time being equal to the time that is the second time deviation before the start time of the preset time period, or the current time being equal to the time that is the second time deviation after the end time of the preset time period, or the current time being equal to other time between the time that is the second time deviation before the start time and the time that is the second time deviation after the end time. In some embodiments, the second time deviation is set to 30 minutes; in some embodiments, the second time deviation is set to 20 minutes; in other embodiments, the second time deviation can also be set to other numerical values, which are not limited herein.

[0119] In some embodiments, the preset threshold is 85%, i.e., when the coincidence between the current trajectory of the current user and the preset trajectory of the target user reaches or exceeds 85% (e.g., 90%, 95%, 100%), it can be considered that the current trajectory is successfully compared with the preset trajectory; when the coincidence between the current trajectory of the current user and the preset trajectory of the target user is less than 85%, it can be considered that the current trajectory cannot be compared with the preset trajectory.

[0120] In one example, the preset time period of the target user who needs to self-learn the operation habit is from 6 am to 7 am. The preset trajectory of the target user who needs to self-learn the operation habit is to appear on the left side of the monitoring range, to the left front 8.5-11.5 meters, and to stay or fine-tune. The current time of the current user is monitored to be 6:20 am, and the current trajectory of the current user is to appear on the left side of the monitoring range, to the left front 8.4-11.5 meters, and to stay. Since the coincidence between the current trajectory and the preset trajectory exceeds 85%, and the current time is compared with the preset time period, it can be determined that the current user is the target user who needs to self-learn the operation habit.

[0121] In one embodiment of the present application, the control device 100 further comprises a filing module. The filing module is used to prompt the current user to file when the current user is not the target user.

[0122] In this way, the control habit of the current user for the household appliance can be self-learned when the current user completes the filing according to the filing prompt.

[0123] Specifically, the filing prompt can be performed through a terminal device such as a mobile phone, a tablet computer, a notebook computer, or a remote controller. The filing prompt can include a text prompt and / or a voice prompt. The content of the filing prompt can include "Do you need to self-learn your control habit? If yes, please enter your identification condition according to the prompt".

[0124] In some embodiments, the control device 100 further comprises an input module. The input module is used to provide an interactive interface for inputting the identification condition when it is determined that the current user needs to file; determine the identification condition of the current user according to the input information of the interactive interface, and self-learn the control habit of the current user for the household appliance as one of the target users.

[0125] In one example, a mobile phone prompts the current user to create a profile. The phone includes a display screen. When prompting the user to create a profile, the screen displays the message, "Do you need to learn your control habits? If so, please enter your recognition criteria as prompted." Simultaneously, the screen provides a "Yes" selection button and a "No" cancellation button. If the cancellation button is triggered, it's determined that the current user does not need to create a profile, and the current interface is exited. If the selection button is triggered, it's determined that the current user needs to create a profile, and an interactive interface for entering recognition criteria is provided.

[0126] In another example, a user is prompted to create a profile via their mobile phone, which has voice recognition capabilities. The phone includes a speaker and a display screen. When prompting the user to create a profile, the speaker announces, "Do you need to learn your control habits? If so, please enter your recognition criteria as prompted." If the system determines that the user does not need to create a profile based on their voice, the current user is ignored. If the system determines that the user needs to create a profile based on their voice, an interactive interface for entering recognition criteria is provided on the display screen.

[0127] It should be noted that the specific values ​​mentioned above are only for illustrating the implementation of the present invention in detail, and should not be construed as limiting the present invention. In other examples, implementation methods, or embodiments, other values ​​may be selected according to the present invention, and no specific limitations are made here.

[0128] Please see Figure 7 The electronic device 200 of the present invention includes one or more processors 22 and a memory 24. The memory 24 stores a computer program 26. When the computer program 26 is executed by the processor 22, it implements the steps of the control method of the home appliance of any of the above embodiments.

[0129] According to an embodiment of the present invention, an electronic device can determine whether the current user is a target user whose operating habits need to be self-learned by comparing the current user's life trajectory information with the preset life trajectory information of the target user stored in advance. When the current user is a target user whose operating habits need to be self-learned, the device can perform self-learning on the setting parameters of the home appliance for the target user, thereby realizing the establishment of a self-learning model for different users and improving the user experience.

[0130] It should be noted that the above explanation of the embodiments and beneficial effects of the control method also applies to the electronic device 200 of this embodiment, and will not be elaborated in detail here to avoid redundancy.

[0131] In one embodiment of the present invention, the processor 22 is used to implement the above steps S11, S13, S15 and S17.

[0132] In one embodiment of the present invention, the processor 22 is used to implement the above steps S21, S23, S25, S27 and S29.

[0133] In one embodiment of the present invention, the processor 22 is used to implement the above step S131.

[0134] In one embodiment of the present invention, the processor 22 is used to implement the above step S151.

[0135] In one embodiment of the present invention, the processor 22 is used to implement the above step S19.

[0136] In one embodiment of the present invention, the electronic device 200 is a home appliance or a server.

[0137] Thus, the aforementioned control method for home appliances can be implemented through home appliances themselves, or it can be implemented through a server.

[0138] Specifically, in some embodiments, the control method of one home appliance can be implemented through another. In one example, the control method of an air conditioner can be implemented through a smart refrigerator. In another example, the control method of the living room air conditioner can be implemented through the air conditioner in the bedroom.

[0139] The computer-readable storage medium of the present invention stores a computer program thereon, characterized in that, when the program is executed by a processor, it implements the steps of the control method for the home appliance of any of the above embodiments.

[0140] In one example, when the program is executed by the processor, steps S11, S13, S15, and S17 of the control method described above can be implemented. In one example, when the program is executed by the processor, steps S21, S23, S25, S27, and S29 of the control method described above can be implemented. In one example, when the program is executed by the processor, step S131 of the control method described above can be implemented. In one example, when the program is executed by the processor, step S151 of the control method described above can be implemented. In one example, when the program is executed by the processor, step S19 of the control method described above can be implemented.

[0141] Specifically, the computer-readable storage medium can be located on a server or in a home appliance, which can communicate with the server to obtain the corresponding program.

[0142] It can be understood that the computer program includes computer program code. The computer program code can be in the form of source code, object code, executable files, or some intermediate forms. The computer-readable storage medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), and software distribution medium, etc.

[0143] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0144] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "exemplary embodiment", "example", "specific example" or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the exemplary description of the above terms does not necessarily mean the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0145] Any process or method descriptions in flow charts or described elsewhere herein can be understood as representing code modules, segments, or portions of code which include one or more executable instructions for performing specific logic functions or steps in the process. The flow diagrams and / or method descriptions can as well be understood as representing some of the operations of a larger machine or process, with the understanding that not all of these steps are required for the operation of the machine or process. The embodiments of the present application should be understood to include all modifications, permutations, equivalents, and alternatives falling within the scope of the application as defined by the appended claims.

[0146] The logic and / or steps represented in flow diagrams or otherwise described herein, for example, can be considered as a sequence of instructions to implement logic functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. In the context of this specification, a "computer-readable medium" can be any means that can contain, store, communicate, propagate or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can be a machine-readable storage device (e.g., magnetic, optical or other) a machine-readable storage diskette (e.g., floppy disk, optical disk, CD- ROM, etc.), a machine- readable storage card (e.g., PCMCIA card, etc.), a machine-readable storage tape (e.g., magnetic tape, optical tape, etc.), a machine-readable storage medium (e.g., RAM, ROM, etc.), a machine-readable signal (e.g., electrical, optical, etc.), a machine-readable medium (e.g., carrier wave, etc.) or any other suitable medium or means of embodying the program. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection having one or more wires (e.g., a bus that has thin film resistors for

[0147] It should be understood that aspects of the embodiments of the present application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, various steps or methods can be implemented in software or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any of the following technologies, known in the art, or combinations thereof, can be used: a discrete logic circuit having logic gates for implementing logic functions upon data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0148] Those of skill in the art will understand that all or a portion of the steps carried out in the above-described embodiments can be performed by a program in accordance with a computer-readable medium, which can be stored in a computer-readable storage medium, and the program is executed when the program is executed, including one or a combination of steps of the method embodiments.

[0149] In addition, each function unit in each embodiment of the present application can be integrated in one processing module, or each unit can be physically present separately, or two or more units can be integrated in one module. The integrated module can be realized in the form of hardware or in the form of a software function module. When the integrated module is realized in the form of a software function module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0150] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0151] Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A control method of a home appliance, characterized by, The control method comprises: monitoring life track information of a current user; comparing the life track information of the current user with preset life track information of a target user; judging whether the current user is the target user according to a comparison result; when the current user is the target user, obtaining control habit parameters of the target user according to a self-learning model established by the target user for setting parameters of the household appliance, and controlling the household appliance according to the control habit parameters of the target user; before comparing the life track information of the current user with the preset life track information of the target user, the control method further comprises: obtaining an identification condition of the target user, the identification condition comprising an identification time period and an identification position; when an active object is monitored, determining an active time; when the active time is in the identification time period, inquiring whether the active object is the target user; when the active object is the target user, establishing a spatial correspondence between an active track of the active object and the identification position; generating the preset life track information of the target user according to the spatial correspondence and the identification time period.

2. The control method of the home appliance according to claim 1, characterized in that, The preset life track information of the target user comprises a preset time period and a preset track, the life track information of the current user comprises a current time and a current track, and comparing the life track information of the current user with the preset life track information of the target user comprises: judging whether the current time of the current user matches the preset time period of the target user, and judging whether the current track of the current user matches the preset track of the target user.

3. The control method of the home appliance according to claim 2, characterized in that, judging whether the current user is the target user according to a comparison result comprises: when the current time of the current user is in the preset time period of the target user, and the coincidence degree between the current track of the current user and the preset track of the target user is greater than or equal to a preset threshold, determining that the current user is the target user.

4. The control method of the home appliance according to any one of claims 1-3, characterized in that, after judging whether the current user is the target user according to a comparison result, the control method further comprises: when the current user is not the target user, filing the current user.

5. The control method of the home appliance according to claim 1, characterized in that, The target user comprises a plurality of target users, and the preset life track information of the plurality of target users is different from each other.

6. A control apparatus of a home electric appliance adapted to employ the control method of the home electric appliance according to any one of claims 1 to 5, characterized by The control device comprises: a monitoring module for monitoring life track information of a current user; a comparison module for comparing the life track information of the current user with preset life track information of a target user; a judging module for judging whether the current user is the target user according to a comparison result; a learning module for, when the current user is the target user, obtaining control habit parameters of the target user according to a self-learning model established by the target user for setting parameters of the household appliance, and controlling the household appliance according to the control habit parameters of the target user.

7. An electronic device, comprising: The electronic device comprises one or more processors and a memory, the memory storing a computer program, the computer program, when executed by the processor, implementing the steps of the control method of the household appliance according to any one of claims 1-6.

8. The electronic device of claim 7, wherein, The electronic device is a household appliance or a server.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program, when executed by the processor, implements the steps of the control method of the household appliance according to any one of claims 1-5.

Images