Home appliance control method, control device, electronic device, and storage medium

Abstract

A control method, a control device, an electronic device, and a storage medium for a home appliance are provided. The control method for a home appliance includes: monitoring life trajectory information of a current user; comparing the life trajectory information of the current user with predetermined life trajectory information of a target user; judging whether the current user is the target user based on a comparison result; if the current user is the target user, obtaining control habit parameters of the target user based on a self-learning model established by setting parameters for the home appliance of the target user, and controlling the home appliance based on the control habit parameters of the target user.

Description

[Technical field]

[0001] This application claims priority to a Chinese patent application bearing application number 202111313832.5 filed on November 8, 2021 and titled "Control method, control device, electronic device, and storage medium for household appliances," the entire contents of which are incorporated herein by reference.

[0002] The present application relates to the technical field of home appliances, and in particular to a control method, a control device, an electronic device, and a storage medium for home appliances. [Background technology]

[0003] With the rapid development of science and technology, more and more smart home appliances such as air conditioners are introduced into every household, bringing great convenience to people's lives. In the related technology, air conditioners are equipped with a self-learning function, and in self-learning mode, they record the air conditioner operating parameters set by the user and determine the user's setting habits based on the recorded data multiple times. After that, the user does not need to reset the air conditioner operating parameters when using the air conditioner, and the air conditioner can automatically execute the corresponding operating parameters based on the user's setting habits.

[0004] However, when multiple family members share the same air conditioner, the setting habits of each family member are significantly different, so the air conditioner cannot distinguish between different family members and cannot establish a self-learning mode for 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 of the Invention [Problem to be solved by the invention]

[0005] The present application aims to solve at least one of the technical problems in the related art to a certain extent. Therefore, a first objective of the present application is to provide a control method for a home appliance, which can distinguish different users based on life trajectory information, realize the establishment of a self-learning model for each different user, and improve the user experience.

[0006] A second object of the present application is to provide a control device for a home appliance.

[0007] A third object of the present application is to provide an electronic device.

[0008] A fourth object of the present application is to provide a computer-readable storage medium. [Means for solving the problem]

[0009] In order to achieve the above-mentioned object, a method for controlling a home appliance according to an embodiment of a first aspect of the present application includes: monitoring life trajectory information of a current user; comparing the life trajectory information of the current user with predetermined life trajectory information of a target user; determining whether the current user is the target user based on a comparison result; and if the current user is the target user, obtaining control habit parameters of the target user based on a self-learning model established by setting parameters of the target user for the home appliance, and controlling the home appliance based on the control habit parameters of the target user.

[0010] According to the control method for a home appliance of an embodiment of the present application, by comparing the life trajectory information of a current user with the predetermined life trajectory information of a target user stored in advance, it is possible to determine whether the current user is a target user, and if the current user is the target user, it is possible to perform self-learning regarding the setting parameters of the home appliance of the target user, thereby realizing the establishment of a self-learning model for each different user and improving the user experience.

[0011] In one embodiment of the present application, before comparing the life trajectory information of the current user with the predetermined life trajectory information of the target user, the control method further includes: obtaining an identification condition of the target user, the identification condition including an identification time zone and an identification location; if an activity object is detected, determining an activity time; if the activity time is within the identification time zone, inquiring of the activity object whether the activity object is the target user; if the activity object is the target user, establishing a spatial correspondence between the activity trajectory of the activity object and the identification location; and generating predetermined life trajectory information of the target user based on the spatial correspondence and the identification time zone.

[0012] In one embodiment of the present application, the predetermined life trajectory information of the target user includes a predetermined time period and a predetermined trajectory, and the life trajectory information of the current user includes a current time and a current trajectory, and comparing the life trajectory information of the current user with the predetermined life trajectory information of the target user includes determining whether the current time of the current user and the predetermined time period of the target user match, and whether the current trajectory of the current user and the predetermined trajectory of the target user match.

[0013] In one embodiment of the present application, determining whether the current user is the target user based on the comparison result includes determining that the current user is the target user if the current time of the current user is in a predetermined time zone of the target user and the degree of matching between the current trajectory of the current user and the predetermined trajectory of the target user is greater than or equal to a predetermined threshold.

[0014] In one embodiment of the present application, after determining whether the current user is the target user based on the comparison result, the control method further includes instructing the current user to perform profiling if the current user is not the target user.

[0015] In one embodiment of the present application, the target users include a plurality of target users, and the predetermined life trajectory information of the plurality of target users is different from each other.

[0016] In order to achieve the above-mentioned object, a control device for a home appliance according to an embodiment of the second aspect of the present application includes: a monitoring module used for monitoring life trajectory information of a current user; a comparison module used for comparing the life trajectory information of the current user with predetermined life trajectory information of a target user; a judgment module used for determining whether the current user is the target user based on a comparison result; and a learning module used for obtaining control habit parameters of the target user based on a self-learning model established by setting parameters of the target user for the home appliance, and controlling the home appliance based on the control habit parameters of the target user.

[0017] According to the control device of a home appliance in an embodiment of the present application, by comparing the life trajectory information of a current user with the pre-stored predetermined life trajectory information of a target user, it is possible to determine whether the current user is a target user, and if the current user is the target user, it is possible to perform self-learning regarding the setting parameters of the home appliance of the target user, thereby realizing the establishment of a self-learning model for each different user and improving the user experience.

[0018] In order to achieve the above-mentioned object, an electronic device according to an embodiment of a third aspect of the present application includes one or more processors and a memory, and a computer program is stored in the memory, and when the computer program is executed by the processor, it realizes the steps of a control method for a home appliance according to any one of the above-mentioned embodiments.

[0019] According to the electronic device of the embodiment of the present application, by comparing the life trajectory information of the current user with the pre-stored predetermined life trajectory information of the target user, it is possible to determine whether the current user is the target user, and if the current user is the target user, it is possible to perform self-learning regarding the setting parameters of the home appliances of the target user, thereby realizing the establishment of a self-learning model for each different user and improving the user experience.

[0020] In one embodiment of the present application, the electronic device is a home appliance or a server.

[0021] In order to achieve the above-mentioned object, a computer-readable storage medium according to an embodiment of a fourth aspect of the present application stores a computer program, which, when executed by a processor, realizes the steps of a method for controlling a home appliance according to any one of the above-mentioned embodiments.

[0022] According to a computer-readable storage medium of an embodiment of the present application, by comparing the life trajectory information of a current user with the pre-stored predetermined life trajectory information of a target user, it is possible to determine whether the current user is a target user; and if the current user is the target user, it is possible to perform self-learning on the setting parameters of the target user's home appliances, thereby realizing the establishment of a self-learning model for each different user, and improving the user experience.

[0023] Additional aspects and advantages of the present application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the present application.

[0024] The above and / or additional aspects and advantages of the present application will become apparent and will be readily understood from the following detailed description of the embodiments taken in conjunction with the accompanying drawings. [Brief description of the drawings]

[0025] [Figure 1] FIG. 2 is a schematic diagram showing a flow of a control method for a home appliance according to an embodiment of the present application. [Diagram 2] FIG. 2 is a schematic diagram showing a flow of a control method for a home appliance according to an embodiment of the present application. [Diagram 3] FIG. 2 is a schematic diagram showing a flow of a control method for a home appliance according to an embodiment of the present application. [Figure 4] FIG. 2 is a schematic diagram showing a flow of a control method for a home appliance according to an embodiment of the present application. [Diagram 5] FIG. 2 is a schematic diagram showing a flow of a control method for a home appliance according to an embodiment of the present application. [Figure 6] 1 is a block diagram showing a structure of a control device for a home appliance according to an embodiment of the present application; [Figure 7] FIG. 1 is a block diagram showing a structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0026] Hereinafter, the embodiments of the present application will be described in detail. Examples of the embodiments to be described are shown in the drawings, and the same or similar notations throughout indicate the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the drawings are illustrative and are used only to interpret the present application, and cannot be understood as limiting the present application.

[0027] In addition, in the description of the embodiments of the present application, the terms "first" and "second" are used for explanatory purposes only and cannot be understood as indicating or suggesting the relative importance or the number of technical features. Thus, a feature defined as "first" or "second" may be expressly or implied to include one or more of said features. In the description of the embodiments of the present application, "plurality" means two or more than two, unless otherwise specifically limited.

[0028] Please refer to FIG. 1. In the method for controlling a home appliance according to the embodiment of the present application, The S11 currently monitors the user's daily life track information, S13 comparing the life trajectory information of the current user with the predetermined life trajectory information of the target user; determining whether the current user is a target user based on the comparison result (S15); If the current user is the target user, the method includes obtaining control habit parameters of the target user based on a self-learning model established with the setting parameters for the target user's home appliance, and controlling the home appliance based on the control habit parameters of the target user (S17).

[0029] According to the control method for a home appliance of an embodiment of the present application, by comparing the life trajectory information of a current user with the pre-stored predetermined life trajectory information of a target user, it is possible to determine whether the current user is a target user who should be self-learned regarding the operation habits of the current user; and if the current user is a target user who should be self-learned regarding the operation habits of the current user, self-learning is performed regarding the setting parameters of the target user's home appliance, thereby realizing the establishment of a self-learning model for different users and improving the user experience.

[0030] It will be understood that in the related art, when a home appliance is in a self-learning mode, no matter how many users operate the home appliance, the home appliance will regard it as an operation by the same user, and will self-learn the setting parameters for the home appliances of multiple users at the same time. That is, the electronic device in the related art can only establish one self-learning model for itself. However, a home usually includes multiple family members, and each family member has different control habits for the setting parameters for the home appliances, so if the home appliance cannot distinguish between different users, it cannot establish an individual self-learning model for each family member. As a result, the home appliance cannot perform automatic operation individually according to the control habits of each family member, and even if the control habits of each family member are weighted and processed according to the time order on the same home appliance to obtain one comprehensive self-learning model, it cannot realize control tailored to each person, and cannot meet the usage needs of different family members.

[0031] Specifically, home appliances include, but are not limited to, air conditioners, humidifiers, air purifiers, televisions, smart speakers, etc.

[0032] Currently, a user can be understood as a moving object that appears within the monitoring range of a home appliance.

[0033] In some embodiments, the home appliance includes a monitoring device, and the monitoring device can collect voice data, image data, or radar signal data, etc., within a monitoring range at a predetermined frequency. Based on the data collected by the monitoring device, it is possible to determine whether a moving object exists within the monitoring range, and if a moving object exists within the monitoring range, to continuously track the moving trajectory of the moving object to generate life trajectory information. In one example, the detection device includes a radar.

[0034] The target user may be understood as a user for whom the home appliance should individually establish a self-learning model and determine control habit parameters. The predetermined life trajectory information may be pre-stored life trajectory information of the target user within the monitoring range of the home appliance. In one embodiment of the present application, there are multiple target users, and the predetermined life trajectory information of the multiple target users is different. In this way, different target users can be distinguished based on the predetermined life trajectory information, and a self-learning model can be established individually for each target user.

[0035] In some embodiments, the target users may include housewives, working family members, students, and so on.

[0036] In one example, the same home appliance can establish five self-learning models, and in another example, the same home appliance can self-learn about the control habits of 15 target users.

[0037] After obtaining the life trajectory information of the current user, the life trajectory information of the current user is compared with the predetermined life trajectory information of the target user to determine whether the current user is a target user who should be self-learned regarding the operation habits of the current user, and thus determine whether to learn regarding the operation habits of the current user for the home appliance. If the comparison result indicates that the current user is a target user who should be self-learned regarding the operation habits of the current user, the setting parameters for the home appliance of the current user are set as the setting parameters for the home appliance of the target user to establish a self-learning model corresponding to the target user.

[0038] The setting parameters may include at least one of a temperature parameter, an airflow mode parameter, and an airflow direction parameter. The control habit parameters of the target user determined based on the self-learning model may include at least one of the setting parameters.

[0039] In one embodiment, when the setting parameters for the home appliance of the same target user are acquired a total of seven times, the self-learning of the control habits of the target user is completed. The seven acquired setting parameters for the home appliance of the target user are weighted to obtain a final self-learning model corresponding to the user, and the control habits parameters of the target user are determined. When the target user is detected to appear in the monitoring range next time, the target user does not need to manually adjust the setting parameters of the home appliance, and the home appliance can directly operate according to the control habits parameters of the target user, which simplifies the operation and improves the user experience.

[0040] It should be noted that the method for controlling a home appliance according to the embodiment of the present application may be realized by the home appliance, by the server, or even by both the home appliance and the server, and is not limited thereto.

[0041] Please refer to FIG. 2. In one embodiment of the present application, before step S13, the control method includes: S21: acquiring a target user's identification condition, the identification condition including an identification time period and an identification location; If an activity object is detected, determining a time of the activity (S23); If the activity time is within the identification time period, inquire of the activity object whether the activity object is a target user (S25); If the activity object is a target user, establish a spatial correspondence between the activity trajectory of the activity object and the identification position (S27); The method further includes generating predetermined life trajectory information of the target user according to the spatial correspondence and the identified time period (S29).

[0042] In this way, before starting self-learning formally, the correspondence between the identification location and the actual space can be established, and the predetermined life trajectory information of the target user can be determined, so that the predetermined life trajectory information of the target user and the life trajectory information of the current user can be easily compared.It can be understood that the identification location is the name of a place, that is, the identification location itself does not include spatial information such as the coordinates of the location, the range, the distance to the home appliance, and the direction to the home appliance, so that the home appliance cannot determine whether the current user is the target user to be self-learned about the operation habits of the current user by comparing the identification location of the target user with the movement trajectory of the current user, and it is necessary to establish the correspondence between the identification location and the actual space in advance, so that other spatial information than the name of the identification location can be determined.

[0043] Specifically, in step S21, the target user may customize the identification conditions of the target user by himself / herself. In one example, the target user or other users may input the identification conditions of the target user through a terminal device such as a mobile phone, a tablet, a laptop, a remote control, etc.

[0044] The identification time period may include a start time, an end time, and other times between the start time and the end time. The identification location may include at least one of the kitchen, the entrance, the sofa, the dining table, the desk, and the balcony. In the description of the embodiment of the present application, the "time", "start time", "end time", "activity time", and "current time" may include hours, may include hours and minutes, and may also include hours, minutes, and seconds, but are not limited thereto. In one example, when the target user is a housewife, the identification time period in the identification condition is 6:00-7:00, and the identification location in the identification condition is the kitchen. In another example, when the target user is a worker in the family, the identification time period in the identification condition is 7:30-8:00, and the identification location in the identification condition is the dining table. In another example, when the target user is a student in the family, the identification time period in the identification condition is 18:00-19:00, and the identification location in the identification condition is the desk.

[0045] In step S23, the home appliance may include a radar. The radar may monitor whether an activity object exists. The activity object may be a target user who should be self-trained on the operation habits of the activity object, a non-target user who should not be self-trained on the operation habits of the activity object, an animal, or any other movable object. The activity time may be understood as the time when the radar confirms the detection of the activity object. That is, the activity time may be, but is not limited to, the time when the radar first detects the appearance of the activity object in the monitoring range, or the time when the radar detects the activity object again in the monitoring range while continuously monitoring the object in the monitoring range at a predetermined frequency.

[0046] In step S25, the activity time being in the identification time slot may be understood as the activity time being equal to the start time of the identification time slot, equal to the end time of the identification time slot, or equal to another time between the start time and the end time. In some embodiments, a first time deviation may be set in consideration of the possibility that the actual appearance time of the target user may be slightly shifted from the preset identification time slot, and in order to ensure that the target user can be found in a timely manner even in such a case. That is, any activity time within the first time deviation before the start time of the identification time slot or within the first time deviation after the end time of the identification time slot is considered to be in the identification time slot. That is, the activity time being in the identification time slot may be further understood as the activity time being equal to the time of the first time deviation before the start time of the identification time slot, or equal to the time of the first time deviation after the end time of the identification time slot, or equal to another time between the time of the first time deviation before the start time and the time of the first time deviation after the end time. 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 may be set to other values, including but not limited to.

[0047] When it is determined that the activity time is within the identification time period, the activity object may be inquired of whether it is a target user who should be self-trained on the operation habit of the activity object by a terminal device such as a mobile phone, a tablet, a notebook computer, a remote control, etc., in order to verify whether it is a target user who should be self-trained on the operation habit of the activity object. In one example, the mobile phone APP displays inquiry information such as "Are you the target user A who entered the identification condition?", and if a signal indicating "yes" is received, proceed to step S27; if a signal indicating "no" is received or no signal is received, proceed again to the step of monitoring the activity object.

[0048] In some embodiments, the time period within the first time deviation in the identification time zone of the target user A may overlap with the time period within the first time deviation in the identification time zone of the target user B, and as a result, it is not possible to determine which target user should be inquired. In one example, a target user with an earlier identification time zone is inquired first, and if the target user is not the target user, another target user with a later identification time zone is inquired next. In one example, a target user with a later identification time zone is inquired first, and if the target user is not the target user, another target user with an earlier identification time zone is inquired next. In one example, a target user corresponding to an identification time zone with a small time deviation is inquired first, and if the target user is not the target user, another target user corresponding to an identification time zone with a large time deviation is inquired next. In this way, the inquiry can be performed normally, and the target user can be avoided from being overlooked, thereby improving the accuracy of the inquiry.

[0049] In step S27, the radar of the home appliance can track the activity trajectory of the activity object, and the activity trajectory tracked by the radar can include the activity direction and distance of the activity object relative to the radar. If it is determined that the activity object is a target user to be self-learned about the operation habits of the activity object, the activity trajectory of the activity object is taken as the spatial information of the identification position of the target user, and a spatial correspondence between the identification position and the activity trajectory of the activity object can be established, and the spatial information of the identification position can be determined based on the spatial correspondence.

[0050] In step S29, generate predetermined life trajectory information of the target user according to the spatial correspondence and the identification time period of the identification condition of the target user, that is, the predetermined life trajectory of the target user includes the identification time period of the target user and the activity trajectory of the target user specified in advance.

[0051] In one example, when the identification time period of the target user A to be self-trained regarding the operation habits is from 6:00 a.m. to 7:00 a.m., the identification location of the target user A to be self-trained regarding the operation habits is the kitchen, and the first time deviation is 30 minutes, if the activity time is any time between 5:30 a.m. and 7:30 a.m., it is determined that the activity time is in the identification time period from 6:00 a.m. to 7:00 a.m.

[0052] Furthermore, the identification time period of target user B to be self-learned regarding operation habits is from 7:30 a.m. to 8:00 a.m. The activity time when the detected active object appears within the monitoring range is 7:10 a.m., and the activity trajectory of the detected active object within the monitoring range is that it appears on the left side of the monitoring range, moves from a position 8.5 meters in front of the left of the home appliance to a position 11.5 meters in front of the left of the home appliance, and stops or fine-tunes its operation at about 11.5 meters in front of the left of the home appliance.

[0053] Since the time deviation of the activity time from the identification time zone of 6:00 to 7:00 a.m. is small, but the time deviation from the identification time zone of 7:30 to 8:00 a.m. is large, the activity subject may first be inquired as to whether it is a target user A who should be self-learned about the operation habits of the activity subject. If it is determined that the activity subject is not a target user A who should be self-learned about the operation habits of the activity subject, then it is inquired as to whether the activity subject is a target user B who should be self-learned about the operation habits of the activity subject. If it is determined that the activity subject is a target user A who should be self-learned about the operation habits of the activity subject, the activity trajectory of the activity subject is set as the spatial information of the kitchen, and the spatial correspondence between the activity trajectory of the activity subject and the kitchen is established, and the content "6:00-7:00, appear on the left side of the monitoring range, move to the range of 8.5 to 11.5 meters forward to the left, and stop or fine-tune the operation" is set as the predetermined life trajectory information of the target user A who should be self-learned about the operation habits.

[0054] Refer to FIG. 3. In one embodiment of the present application, the predetermined life trajectory information of the target user includes a predetermined time period and a predetermined trajectory, and the life trajectory information of the current user includes a current time and a current trajectory, and step S13 includes: The method includes determining whether the current time of the current user matches the predetermined time zone of the target user, and whether the current trajectory of the current user matches the predetermined trajectory of the target user in step S131.

[0055] In this manner, by comparing the life trajectory information with the predetermined life trajectory information, it is possible to more accurately determine whether the current user is a target user who should be self-trained regarding the operation habits of the current user.

[0056] Specifically, the predetermined time period may be the above-mentioned identification time period. The predetermined time period may include a start time, an end time, and other times between the start time and the end time. The predetermined trajectory may be the above-mentioned pre-specified activity trajectory of the activity object of the target user.

[0057] The current time may be understood as the time when the current user is detected. Specifically, the current time may be the time when the current user is detected for the first time or the time when the current user is detected again with a predetermined frequency, but is not limited thereto.

[0058] It will be understood that step S131 includes a time zone matching step and a trajectory matching step, where the time zone matching step may be performed first and then the trajectory matching step, or the trajectory matching step may be performed first and then the time zone matching step, but is not limited thereto.

[0059] Furthermore, since the monitoring of the current user within the monitoring range is a continuous process, that is, the obtained current time is continuously updated, the result of the first matching between the current time and the predetermined time zone does not affect the second matching step between the current time and the predetermined time zone. That is, when the current trajectory of the current user and the predetermined trajectory are matched with each other, if it is determined in the first time zone matching step that the current time of the current user does not match the predetermined time zone of the target user, it is determined this time that the current user is not the target user who should be self-trained with respect to the operation habits of the current user. However, if it is determined in the second time zone matching step that the current time of the current user and the predetermined time zone of the target user are matched with each other, it can be determined that the current user is the target user who should be self-trained with respect to the operation habits of the current user.

[0060] Please refer to FIG. 4. In one embodiment of the present application, step S15 includes: The method includes: determining (S151) that the current user is the target user if the current time of the current user is in the predetermined time zone of the target user and the matching degree between the current trajectory of the current user and the predetermined trajectory of the target user is equal to or greater than a predetermined threshold.

[0061] In this way, by combining the time period comparison result and the trajectory comparison result, it is possible to more accurately determine whether the current user is a target user who should be self-trained regarding the operation habits of the current user.

[0062] Specifically, the current time being in the predetermined time zone may be understood as the current time being equal to the start time of the predetermined time zone, the current time being equal to the end time of the predetermined time zone, or the current time being equal to another time between the start time and the end time. In some embodiments, a second time deviation may be set in consideration of the possibility that the actual appearance time of the target user may be slightly shifted from the preset identification time zone, and in order to ensure that the target user can be found in a timely manner even in such a case. That is, the current time being within the second time deviation before the start time of the predetermined time zone, or within the second time deviation after the end time of the predetermined time zone, is considered to be in the predetermined time zone. That is, the current time being in the predetermined time zone may be further understood as the current time being equal to the second time deviation before the start time of the predetermined time zone, or equal to the second time deviation after the end time of the predetermined time zone, or equal to another time between the second time deviation before the start time and 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 may be set to other values, including but not limited to.

[0063] In some embodiments, the predetermined threshold is 85%. That is, if the degree of match between the current trajectory of the current user and the predetermined trajectory of the target user reaches or exceeds 85% (e.g., 90%, 95%, 100%), the matching between the current trajectory and the predetermined trajectory may be considered successful. If the degree of match between the current trajectory of the current user and the predetermined trajectory of the target user is less than 85%, it may be considered that the current trajectory cannot be matched with the predetermined trajectory.

[0064] In one example, the predetermined time period of the target user to be self-learned regarding the operation habits is 6:00 a.m. to 7:00 a.m. In addition, the predetermined trajectory of the target user to be self-learned regarding the operation habits is to appear on the left side of the monitoring range, move to the left front range from 8.5 meters to 11.5 meters, and stop or fine-tune the operation. The current time when the current user is detected is 6:20 a.m., and the current trajectory of the current user is to appear on the left side of the monitoring range, move to the left front range from 8.4 meters to 11.5 meters, and stop. Since the matching rate between the current trajectory and the predetermined trajectory exceeds 85% and the current time falls within the predetermined time period, it can be determined that the current user is a target user to be self-learned regarding the operation habits of the current user.

[0065] See Figure 5. In one embodiment of the present application, after step S15, the control method further includes: S19, if the current user is not the target user, issuing a profiling instruction to the current user.

[0066] In this manner, if the current user completes the profiling according to the profiling instructions, self-learning can be performed regarding the current user's control habits with respect to the home appliance.

[0067] Specifically, the profiling instruction may be given by a terminal device such as a mobile phone, a tablet, a laptop, or a remote control. The profiling instruction may be given in the form of a text instruction and / or a voice instruction. The profiling instruction may include, "Do you need to self-learn about your control habits? If so, please follow the instructions to enter the identification conditions."

[0068] In some embodiments, after step S19, if it is determined that profiling is required for the current user, the control method further includes: providing an interactive interface for inputting identification conditions; determining identification conditions for the current user based on the input information to the interactive interface; and performing self-learning regarding the control habits of the current user for home appliances, with the current user as one of the target users.

[0069] In one example, the mobile phone prompts the current user for profiling. The mobile phone includes a display screen, and when the user needs to be prompted for profiling, the display screen of the mobile phone displays an instruction message saying, "Do you need self-learning regarding control habits? If so, please follow the instructions to enter identification criteria." At the same time, the display screen of the mobile phone is provided with a selection button indicating "Yes" and a cancel button indicating "No." If it is detected that the cancel button has been triggered, it is determined that the current user does not need profiling and is allowed to exit the current interface. If it is detected that the selection button has been triggered, it is determined that the current user needs profiling and is then provided with an interactive interface for entering identification criteria.

[0070] In another example, a user instructs the current user to perform profiling. The mobile phone has a voice recognition function, and includes a speaker and a display screen. When it is necessary to instruct the user to perform profiling, the mobile phone speaker issues a voice instruction such as "Do you need to self-learn about your control habits? If so, please follow the instructions and enter the identification conditions." If the current user receives an instruction that does not need to be profiled according to the voice uttered by the current user, the current user detected this time is ignored. If the current user receives an instruction that does need to be profiled according to the voice uttered by the current user, the display screen provides an interactive interface for inputting the identification conditions.

[0071] Please refer to Fig. 6. The home appliance control device 100 according to the embodiment of the present application includes a monitoring module 12, a comparison module 14, a judgment module 16, and a learning module 18. The monitoring module 12 is used for monitoring the life trajectory information of a current user. The comparison module 14 is used for comparing the life trajectory information of the current user with the predetermined life trajectory information of a target user. The judgment module 16 is used for determining whether the current user is the target user based on the comparison result. If the current user is the target user, the learning module 18 is used for obtaining the control habit parameters of the target user based on the self-learning model established by the setting parameters for the home appliance of the target user, and controlling the home appliance according to the control habit parameters of the target user.

[0072] According to the home appliance control device 100 of the embodiment of the present application, by comparing the life trajectory information of the current user with the pre-stored predetermined life trajectory information of the target user, it is possible to determine whether the current user is a target user who should be self-learned regarding the operation habits of the current user; and if the current user is a target user who should be self-learned regarding the operation habits of the current user, self-learning is performed regarding the setting parameters of the target user's home appliances, thereby realizing the establishment of a self-learning model for different users and improving the user experience.

[0073] Specifically, home appliances include, but are not limited to, air conditioners, humidifiers, air purifiers, televisions, smart speakers, etc.

[0074] Currently, a user may be understood as a moving object that appears within a monitoring range of a home appliance. In some embodiments, the home appliance includes a monitoring device, and the monitoring device may collect voice data, image data, radar signal data, or the like within the monitoring range at a predetermined frequency. Based on the data collected by the monitoring device, it is possible to determine whether a moving object exists within the monitoring range, and if a moving object exists within the monitoring range, to continuously track the moving trajectory of the moving object to generate life trajectory information. In one example, the detection device includes a radar.

[0075] The target user may be understood as a user for which the home appliance should individually establish a self-learning model and determine control habit parameters. The predetermined life trajectory information may be pre-stored life trajectory information of the target user within the monitoring range of the home appliance. In one embodiment of the present application, the target user includes a plurality of target users, and the predetermined life trajectory information of the plurality of target users is different. In this way, different target users can be distinguished based on the predetermined life trajectory information, and the establishment of a self-learning model can be realized for each target user individually. In some embodiments, the target users may include housewives, family workers, students, etc. In one example, the same home appliance can perform self-learning on the control habits of 15 target users.

[0076] After obtaining the life trajectory information of the current user, the life trajectory information of the current user is compared with the predetermined life trajectory information of the target user to determine whether the current user is a target user who should be self-learned regarding the operation habits of the current user, and thus determine whether to learn regarding the operation habits of the current user for the home appliance. If the comparison result indicates that the current user is a target user who should be self-learned regarding the operation habits of the current user, the setting parameters for the home appliance of the current user are set as the setting parameters for the home appliance of the target user to establish a self-learning model corresponding to the target user.

[0077] The setting parameters may include at least one of a temperature parameter, an airflow mode parameter, and an airflow direction parameter. The control habit parameters of the target user determined based on the self-learning model may include at least one of the setting parameters.

[0078] In one embodiment, when the setting parameters for the home appliance of the same target user are acquired a total of seven times, the self-learning of the control habits of the target user is completed. The seven acquired setting parameters for the home appliance of the target user are weighted to obtain a final self-learning model corresponding to the user, and the control habits parameters of the target user are determined. When the target user is detected to appear in the monitoring range next time, the target user does not need to manually adjust the setting parameters of the home appliance, and the home appliance can directly operate according to the control habits parameters of the target user, which simplifies the operation and improves the user experience.

[0079] In an embodiment of the present application, the control device 100 includes an acquisition module, a determination module, an inquiry module, an establishment module, and a generation module. Here, the acquisition module is used for acquiring an identification condition of a target user, the identification condition including an identification time zone and an identification location. The determination module is used for determining an activity time when an activity object is detected. The inquiry module is used for inquiring of the activity object whether the activity object is the target user when the activity time is in the identification time zone. The establishment module is used for establishing a spatial correspondence between the activity trajectory of the activity object and the identification location when the activity object is the target user. The generation module is used for generating predetermined life trajectory information of the target user according to the spatial correspondence and the identification time zone.

[0080] In this way, before starting self-learning formally, the correspondence between the identification location and the actual space can be established, and the predetermined life trajectory information of the target user can be determined, so that the predetermined life trajectory information of the target user and the life trajectory information of the current user can be easily compared.It can be understood that the identification location is the name of a place, that is, the identification location itself does not include spatial information such as the coordinates of the location, the range, the distance to the home appliance, and the direction to the home appliance, so that the home appliance cannot determine whether the current user is the target user to be self-learned about the operation habits of the current user by comparing the identification location of the target user with the movement trajectory of the current user, and it is necessary to establish the correspondence between the identification location and the actual space in advance, so that other spatial information than the name of the identification location can be determined.

[0081] Specifically, the target user may customize the identification conditions of the target user by himself / herself. In one example, the target user or other users may input the identification conditions of the target user through a terminal device such as a mobile phone, a tablet, a laptop, a remote control, etc.

[0082] The identification time period may include a start time, an end time, and other times between the start time and the end time. The identification location may include at least one of the kitchen, the entrance, the sofa, the dining table, the desk, and the balcony. In the description of the embodiment of the present application, the "time", "start time", "end time", "activity time", and "current time" may include hours, may include hours and minutes, and may also include hours, minutes, and seconds, but are not limited thereto. In one example, when the target user is a housewife, the identification time period in the identification condition is 6:00-7:00, and the identification location in the identification condition is the kitchen. In another example, when the target user is a worker in the family, the identification time period in the identification condition is 7:30-8:00, and the identification location in the identification condition is the dining table. In another example, when the target user is a student in the family, the identification time period in the identification condition is 18:00-19:00, and the identification location in the identification condition is the desk.

[0083] The home appliance may include a radar. The radar may monitor whether an activity object exists. The activity object may be a target user to be self-trained on the operation habits of the activity object, a non-target user not to be self-trained on the operation habits of the activity object, an animal, or any other movable object. The activity time may be understood as the time when the radar confirms the detection of the activity object. That is, the activity time may be, but is not limited to, the time when the radar first detects the appearance of the activity object in the monitoring range, or the time when the radar detects the activity object again in the monitoring range while continuously monitoring the object in the monitoring range at a predetermined frequency.

[0084] The activity time being within the identification time slot may be understood to mean that the activity time is equal to the start time of the identification time slot, equal to the end time of the identification time slot, or equal to another time between the start time and the end time. In some embodiments, a first time deviation may be set in consideration of the possibility that the actual appearance time of the target user may be slightly shifted from the preset identification time slot, and in order to ensure that the target user can be found in a timely manner even in such a case. That is, any activity time within the first time deviation before the start time of the identification time slot or within the first time deviation after the end time of the identification time slot is considered to be within the identification time slot. That is, the activity time being within the identification time slot may be further understood to mean that the activity time is equal to the time of the first time deviation before the start time of the identification time slot, or equal to the time of the first time deviation after the end time of the identification time slot, or equal to another time between the time of the first time deviation before the start time and the time of the first time deviation after the end time. 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 may be set to other values, but is not limited thereto.

[0085] When it is determined that the activity time is within the identification time period, the terminal device such as a mobile phone, a tablet, a notebook computer, a remote control, etc. may inquire the activity object whether it is a target user who should be self-trained on the operation habit of the activity object, in order to verify whether the activity object is a target user who should be self-trained on the operation habit of the activity object. In one example, the mobile phone APP displays inquiry information such as "Are you the target user A who entered the identification condition?", and if a signal indicating "yes" is received, proceed to the step of establishing spatial correspondence, and if a signal indicating "no" is received or no signal is received, proceed again to the step of monitoring the activity object.

[0086] In some embodiments, the time period within the first time deviation in the identification time zone of the target user A may overlap with the time period within the first time deviation in the identification time zone of the target user B, and as a result, it is not possible to determine which target user should be inquired. In consideration of this, the inquiry logic for this case may be predefined. For example, the target user with the earlier identification time zone is inquired first, and if the target user is not the target user, another target user with the later identification time zone is inquired next. Or, the target user with the later identification time zone is inquired first, and if the target user is not the target user, another target user with the earlier identification time zone is inquired next. Or, the target user corresponding to the identification time zone with the smaller time deviation is inquired first, and if the target user is not the target user, another target user corresponding to the identification time zone with the larger time deviation is inquired next. In this way, the inquiry can be performed normally, the oversight of the target user can be avoided, and the accuracy of the inquiry can be improved.

[0087] The radar of the home appliance can track the activity trajectory of the object, and the activity trajectory tracked by the radar can include the activity direction and distance of the object relative to the radar. The radar can identify the indoor layout according to the tracked activity trajectory. For example, the kitchen is 10 meters ahead to the left, the dining room is 5 meters ahead, and the study is 3 meters ahead to the right.

[0088] When it is determined that an activity object is a target user who should be self-trained regarding the operation habits of the activity object, the activity trajectory of the activity object is used as spatial information of the identification position of the target user, and a spatial correspondence relationship between the activity trajectory of the activity object and the identification position is established, and the spatial information of the identification position can be determined based on the spatial correspondence.

[0089] According to the spatial correspondence and the identification time period of the identification condition of the target user, predetermined life trajectory information of the target user is generated. That is, the predetermined life trajectory of the target user includes the identification time period of the target user and the activity trajectory of the target user specified in advance.

[0090] In one example, when the identification time period of the target user A to be self-trained regarding the operation habits is from 6:00 a.m. to 7:00 a.m., the identification location of the target user A to be self-trained regarding the operation habits is the kitchen, and the first time deviation is 30 minutes, if the activity time is any time between 5:30 a.m. and 7:30 a.m., it is determined that the activity time is in the identification time period from 6:00 a.m. to 7:00 a.m.

[0091] Furthermore, the identification time period of target user B to be self-learned regarding operation habits is from 7:30 a.m. to 8:00 a.m. The activity time when the detected active object appears within the monitoring range is 7:10 a.m., and the activity trajectory of the detected active object within the monitoring range is that it appears on the left side of the monitoring range, moves from a position 8.5 meters in front of the left of the home appliance to a position 11.5 meters in front of the left of the home appliance, and stops or fine-tunes its operation at about 11.5 meters in front of the left of the home appliance.

[0092] Since the time deviation of the activity time from the identification time zone of 6:00 to 7:00 a.m. is small, but the time deviation from the identification time zone of 7:30 to 8:00 a.m. is large, the activity subject may first be inquired as to whether it is a target user A who should be self-learned about the operation habits of the activity subject. If it is determined that the activity subject is not a target user A who should be self-learned about the operation habits of the activity subject, then it is inquired as to whether the activity subject is a target user B who should be self-learned about the operation habits of the activity subject. If it is determined that the activity subject is a target user A who should be self-learned about the operation habits of the activity subject, the activity trajectory of the activity subject is set as the spatial information of the kitchen, and the spatial correspondence between the activity trajectory of the activity subject and the kitchen is established, and the content "6:00-7:00, appear on the left side of the monitoring range, move to the range of 8.5 to 11.5 meters forward to the left, and stop or fine-tune the operation" is set as the predetermined life trajectory information of the target user A who should be self-learned about the operation habits.

[0093] In one embodiment of the present application, the comparison module 14 is further used for determining whether the current time of the current user matches the predetermined time zone of the target user, and whether the current trajectory of the current user matches the predetermined trajectory of the target user.

[0094] In this manner, by comparing the life trajectory information with the predetermined life trajectory information, it is possible to more accurately determine whether the current user is a target user who should be self-trained regarding the operation habits of the current user.

[0095] Specifically, the predetermined time period may be the above-mentioned identification time period. The predetermined time period may include a start time, an end time, and other times between the start time and the end time. The predetermined trajectory may be the above-mentioned pre-specified activity trajectory of the activity object of the target user.

[0096] The current time may be understood as the time when the current user is detected. Specifically, the current time may be the time when the current user is detected for the first time or the time when the current user is detected again with a predetermined frequency, but is not limited thereto.

[0097] It should be understood that the comparison module 14 can perform a time zone matching step and a trajectory matching step, where the time zone matching step can be performed first and then the trajectory matching step, or the trajectory matching step can be performed first and then the time zone matching step, but is not limited thereto.

[0098] Furthermore, since the monitoring of the current user within the monitoring range is a continuous process, that is, the obtained current time is continuously updated, the result of the first matching between the current time and the predetermined time zone does not affect the second matching step between the current time and the predetermined time zone. That is, when the current trajectory of the current user and the predetermined trajectory are matched with each other, if it is determined in the first time zone matching step that the current time of the current user does not match the predetermined time zone of the target user, it is determined this time that the current user is not the target user who should be self-trained with respect to the operation habits of the current user. However, if it is determined in the second time zone matching step that the current time of the current user and the predetermined time zone of the target user are matched with each other, it can be determined that the current user is the target user who should be self-trained with respect to the operation habits of the current user.

[0099] In one embodiment of the present application, the learning module 16 is further used for determining that the current user is the target user when the current time of the current user is in the predetermined time zone of the target user, and the matching degree between the current trajectory of the current user and the predetermined trajectory of the target user is equal to or greater than a predetermined threshold.

[0100] In this way, by combining the time period comparison result and the trajectory comparison result, it is possible to more accurately determine whether the current user is a target user who should be self-trained regarding the operation habits of the current user.

[0101] Specifically, the current time being in the predetermined time zone may be understood as the current time being equal to the start time of the predetermined time zone, the current time being equal to the end time of the predetermined time zone, or the current time being equal to another time between the start time and the end time. In some embodiments, a second time deviation may be set in consideration of the possibility that the actual appearance time of the target user may be slightly shifted from the preset identification time zone, and in order to ensure that the target user can be found in a timely manner even in such a case. That is, the current time being within the second time deviation before the start time of the predetermined time zone, or within the second time deviation after the end time of the predetermined time zone, is considered to be in the predetermined time zone. That is, the current time being in the predetermined time zone may be further understood as the current time being equal to the second time deviation before the start time of the predetermined time zone, or equal to the second time deviation after the end time of the predetermined time zone, or equal to another time between the second time deviation before the start time and 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 may be set to other values, including but not limited to.

[0102] In some embodiments, the predetermined threshold is 85%. That is, if the degree of match between the current trajectory of the current user and the predetermined trajectory of the target user reaches or exceeds 85% (e.g., 90%, 95%, 100%), the matching between the current trajectory and the predetermined trajectory may be considered successful. If the degree of match between the current trajectory of the current user and the predetermined trajectory of the target user is less than 85%, it may be considered that the current trajectory cannot be matched with the predetermined trajectory.

[0103] In one example, the predetermined time period of the target user to be self-learned regarding the operation habits is 6:00 a.m. to 7:00 a.m. In addition, the predetermined trajectory of the target user to be self-learned regarding the operation habits is to appear on the left side of the monitoring range, move to the left front range from 8.5 meters to 11.5 meters, and stop or fine-tune the operation. The current time when the current user is detected is 6:20 a.m., and the current trajectory of the current user is to appear on the left side of the monitoring range, move to the left front range from 8.4 meters to 11.5 meters, and stop. Since the matching rate between the current trajectory and the predetermined trajectory exceeds 85% and the current time falls within the predetermined time period, it can be determined that the current user is a target user to be self-learned regarding the operation habits of the current user.

[0104] In one embodiment of the present application, the control device 100 further includes a profiling module, which is used for issuing profiling instructions to the current user if the current user is not the target user.

[0105] In this manner, if the current user completes the profiling according to the profiling instructions, self-learning can be performed regarding the current user's control habits with respect to the home appliance.

[0106] Specifically, the profiling instruction may be given by a terminal device such as a mobile phone, a tablet, a laptop, or a remote control. The profiling instruction may be given in the form of a text instruction and / or a voice instruction. The profiling instruction may include, "Do you need to self-learn about your control habits? If so, please follow the instructions to enter the identification conditions."

[0107] In some embodiments, the control device 100 further includes an input module, which is used for providing an interactive interface for inputting identification conditions when it is determined that profiling is required for the current user, determining the identification conditions of the current user based on the input information to the interactive interface, and performing self-learning on the control habits of the current user for the home appliances by taking the current user as one of the target users.

[0108] In one example, the mobile phone prompts the current user for profiling. The mobile phone includes a display screen, and when the user needs to be prompted for profiling, the display screen of the mobile phone displays an instruction message saying, "Do you need self-learning regarding control habits? If so, please follow the instructions to enter identification criteria." At the same time, the display screen of the mobile phone is provided with a selection button indicating "Yes" and a cancel button indicating "No." If it is detected that the cancel button has been triggered, it is determined that the current user does not need profiling and is allowed to exit the current interface. If it is detected that the selection button has been triggered, it is determined that the current user needs profiling and is then provided with an interactive interface for entering identification criteria.

[0109] In another example, a user instructs the current user to perform profiling. The mobile phone has a voice recognition function, and includes a speaker and a display screen. When it is necessary to instruct the user to perform profiling, the mobile phone speaker issues a voice instruction such as "Do you need self-learning regarding control habits? If so, please follow the instructions and enter the identification conditions." If a command is received according to the voice emitted by the current user that profiling is not necessary for the current user, the current user detected this time is ignored. If a command is received according to the voice emitted by the current user that profiling is necessary for the current user, the display screen provides an interactive interface for inputting the identification conditions.

[0110] In addition, the specific numerical values ​​mentioned above are provided only as examples for explaining the implementation of the present application in detail, and cannot be understood as limitations on the present application. In other examples, embodiments, and examples, other numerical values ​​may be selected based on the present application, but there is no specific limitation thereto.

[0111] Please refer to Fig. 7. The electronic device 200 according to the embodiment of the present application includes one or more processors 22 and a memory 24, and the memory 24 stores a computer program 26. When the computer program 26 is executed by the processor 22, the electronic device 200 realizes the steps of the control method for a home appliance according to any one of the above-mentioned embodiments.

[0112] According to the home appliance of the embodiment of the present application, by comparing the life trajectory information of the current user with the pre-stored predetermined life trajectory information of the target user, it is possible to determine whether the current user is a target user who should be self-learned regarding the operation habits of the current user; and if the current user is a target user who should be self-learned regarding the operation habits of the current user, self-learning is performed regarding the setting parameters of the target user's home appliance, thereby realizing the establishment of a self-learning model for different users and improving the user experience.

[0113] It should be noted that the above-mentioned control method embodiments and explanations and descriptions of the beneficial effects are also applied to the electronic device 200 of this embodiment. To avoid redundancy, detailed explanations are omitted here.

[0114] In one embodiment of the present application, the processor 22 is used to perform the above steps S11, S13, S15, and S17.

[0115] In one embodiment of the present application, the processor 22 is used to perform the above steps S21, S23, S25, S27, and S29.

[0116] In one embodiment of the present application, the processor 22 is used to perform the above-mentioned step S131.

[0117] In one embodiment of the present application, the processor 22 is used to perform the above-mentioned step S151.

[0118] In one embodiment of the present application, the processor 22 is used to perform the above-mentioned step S19.

[0119] In one embodiment of the present application, the electronic device 200 is a home appliance or a server.

[0120] In this way, the above-mentioned control method for a home appliance may be realized by a home appliance, or the control method for a home appliance may be realized by a server.

[0121] Specifically, in some embodiments, one home appliance can realize a control method for another home appliance. In one example, a smart refrigerator can realize a control method for an air conditioner. In another example, an air conditioner in a bedroom can realize a control method for an air conditioner in a living room.

[0122] A computer-readable storage medium according to an embodiment of the present application stores a computer program, which, when executed by a processor, implements the steps of the method for controlling a home appliance according to any one of the above-described embodiments.

[0123] In one example, when the program is executed by a processor, steps S11, S13, S15, and S17 of the above-mentioned control method can be realized. In one example, when the program is executed by a processor, steps S21, S23, S25, S27, and S29 of the above-mentioned control method can be realized. In one example, when the program is executed by a processor, step S131 of the above-mentioned control method can be realized. In one example, when the program is executed by a processor, step S151 of the above-mentioned control method can be realized. In one example, when the program is executed by a processor, step S19 of the above-mentioned control method can be realized.

[0124] Specifically, the computer-readable storage medium may be installed in a server, and may be installed in a home appliance, and the home appliance may communicate with the server to obtain the corresponding program.

[0125] It will be understood that a computer program includes computer program code, which may be in source code form, object code form, an executable file, some intermediate form, etc. A computer-readable storage medium may include any entity or device capable of carrying a computer program code, a recording medium, a USB memory, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), and a software distribution medium, etc.

[0126] The processor may be a Central Processing Unit (CPU) or other general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other programmable logic device, a discrete logic or transistor logic device, a discrete hardware component, etc.

[0127] In the description of this specification, the terms "one embodiment," "some embodiments," "exemplary embodiments," "example," "example," or "some examples" mean that the specific features, structures, materials, or characteristics described in the embodiment or example are included in one or more of the embodiments or examples of this specification. The denotative expressions of the above terms in this specification do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics may be combined in any suitable manner in any one or more of the embodiments or examples.

[0128] In any process or method illustrated in a flowchart or otherwise described herein, the steps may be understood as modules, segments, or portions of executable instruction code for implementing specific logical functions or process steps, and the scope of the preferred embodiments of the present application also includes additional implementations that perform functions out of the order illustrated or discussed, essentially simultaneously, or in reverse order based on the associated functionality, as would be understood by one of ordinary skill in the art of the present application.

[0129] The logic and / or steps illustrated in the flow charts or otherwise described herein may be considered, for example, as a sequential listing of executable instructions to implement a logical function. This listing may be tangibly embodied in any computer-readable medium for use by or in combination with an instruction-executing system, device, or appliance (e.g., a computer-based system, a system including a processing module, or other system capable of receiving and executing instructions from the instruction-executing system, device, or appliance). For purposes of this specification, a "computer-readable medium" is any device that can contain, store, communicate, propagate, or transmit a program for use by or in combination with an instruction-executing system, device, or appliance. More specific examples of "computer-readable medium" include an electrical connection having one or more wires, a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable CD-ROM (a non-exhaustive list). The computer readable medium may even be paper or other suitable medium on which the program can be printed, since the paper or other medium can be optically scanned and then edited, interpreted or processed in any other suitable manner, if necessary, to obtain said program in an electronic manner and store it in the memory of the computer.

[0130] It will be understood that each part of each embodiment of the present application can be realized by hardware, software, firmware, or a combination thereof. In the above-mentioned embodiments, a number of steps and methods can be realized by software or firmware executed by a suitable instruction execution system stored in a memory. For example, when realized by hardware, as in the other embodiments, it can be realized by one or a combination of any technologies known in the art, such as discrete logic circuits having logic gate circuits that realize logic functions on data signals, application specific integrated circuits (ASICs) having appropriate combinations of logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0131] Those skilled in the art can understand that all or part of the steps in the method in the above-mentioned embodiments can be realized through instructions to related hardware by a program. The program can be stored in a computer-readable storage medium, and when the program is executed, it includes one or a combination of the steps in the method embodiments.

[0132] In each embodiment of the present application, each functional unit may be integrated into one processing module, may exist physically individually, or two or more units may be integrated into one module. The above-mentioned integrated module may be realized in the form of hardware or in the form of a software functional module. When the integrated module is realized in the form of a software functional module, it may be stored in a computer-readable medium when sold or used as an independent product.

[0133] The above mentioned storage medium may be a read-only memory, a disk, an optical disk, etc.

[0134] Although embodiments of the present application have been shown and described, those skilled in the art will understand that the above-described embodiments are illustrative and cannot be construed as limitations on the present application, and that changes, combinations, modifications, substitutions, and alterations can be made to the above-described embodiments within the scope of the present application.

Claims

1. A method for controlling a home appliance, comprising: Currently, monitoring the user's life trajectory information; Comparing the life trajectory information of the current user with predetermined life trajectory information of a target user; determining whether the current user is the target user based on a comparison result; If the current user is the target user, acquiring control habit parameters of the target user based on a self-learning model established by a setting parameter of the target user for the home appliance, and controlling the home appliance based on the control habit parameters of the target user. A method for controlling home appliances.

2. Before comparing the life trajectory information of the current user with the predetermined life trajectory information of the target user, The control method includes: Obtaining an identification condition of the target user, the identification condition including an identification time period and an identification location; If an activity object is detected, determining a time of the activity; When the activity time is within the identification time period, inquiring of the activity object whether the activity object is the target user; If the activity object is the target user, establishing a spatial correspondence between the activity trajectory of the activity object and the identified location; The method of claim 1 , further comprising: generating predetermined life trajectory information of the target user based on the spatial correspondence and the identified time period.

3. The predetermined life trajectory information of the target user includes a predetermined time period and a predetermined trajectory, and the life trajectory information of the current user includes a current time and a current trajectory. Comparing the life trajectory information of the current user with the predetermined life trajectory information of the target user is: The method for controlling a home appliance according to claim 1 , further comprising determining whether a current time of the current user matches a predetermined time zone of the target user, and whether a current trajectory of the current user matches a predetermined trajectory of the target user.

4. Determining whether the current user is the target user based on a comparison result includes: The method for controlling a home appliance according to claim 3, further comprising: determining that the current user is the target user if the current time of the current user is in a predetermined time zone of the target user and a degree of match between the current trajectory of the current user and the predetermined trajectory of the target user is equal to or greater than a predetermined threshold.

5. After determining whether the current user is the target user based on the comparison result, the control method includes: The method for controlling a home appliance according to claim 1 , further comprising: if the current user is not the target user, issuing a profiling instruction to the current user.

6. The method of claim 1 , wherein the target users include a plurality of target users, and the predetermined life trajectory information of the plurality of target users is different from each other.

7. A control device for a home appliance, A monitoring module for monitoring the current user's life trajectory information; A comparison module is used for comparing the life trajectory information of the current user with the predetermined life trajectory information of the target user; a judging module, which is used for judging whether the current user is the target user based on a comparison result; A learning module is used to obtain control habit parameters of the target user based on a self-learning model established by setting parameters of the target user for the home appliance when the current user is the target user, and to control the home appliance based on the control habit parameters of the target user.

8. An electronic device including one or more processors and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the electronic device realizes the steps of the control method for a home appliance according to any one of claims 1 to 6.

9. The electronic device according to claim 8 , wherein the electronic device is a home appliance or a server.

10. A computer-readable storage medium having a computer program stored therein, the computer-readable storage medium implementing the steps of the method for controlling a home appliance according to any one of claims 1 to 6 when the computer program is executed by a processor.

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