Control Method for Home Appliance, 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 the priority of a Chinese patent application with the application number 202111313832.5 and the invention title "Control Method, Control Device, Electronic Device and Storage Medium of Home Appliance", which was filed on November 8, 2021, and all of its content is incorporated into this application by reference.

[0002] This application relates to the technical field of home appliances, and particularly to a control method, a control device, an electronic device and a storage medium of home appliances.

Background Art

[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 related technologies, an air conditioner has a self-learning function. In the self-learning mode, the air conditioner operation parameters set by the user are recorded, and the user's setting habits are determined based on multiple recorded data. Thereafter, when the user uses the air conditioner, there is no need to reset the air conditioner operation parameters, and the air conditioner can automatically execute the corresponding operation parameters based on the user's setting habits.

[0004] However, when multiple family members share the same air conditioner, since the setting habits of each family member are very different, the air conditioner cannot distinguish different family members and cannot establish a self-learning mode for each family member. Also, the air conditioner cannot automatically execute the corresponding air conditioner operation parameters according to the setting habits of each family member.

Summary of the Invention

Problems to be Solved by the Invention

[0005] This application aims to solve at least one of the technical problems in the related art to a certain extent. For this reason, the first object of this application is to provide a control method for household electrical appliances that can distinguish different users based on life trajectory information, establish a self-learning model for each different user, and improve the user experience.

[0006] The second object of this application is to provide a control device for household electrical appliances.

[0007] The third object of this application is to provide an electronic device.

[0008] The fourth object of this application is to provide a computer-readable storage medium.

Means for Solving the Problems

[0009] To achieve the above object, the control method for household electrical appliances according to an embodiment of the first aspect of this application includes monitoring the life trajectory information of the current user, 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 the target user based on the comparison result, and when the current user is the target user, obtaining the control habit parameters of the target user based on the self-learning model established with the set parameters for the household electrical appliances of the target user, and controlling the household electrical appliances based on the control habit parameters of the target user.

[0010] According to the control method for household electrical appliances according to an embodiment of this application, by comparing the life trajectory information of the current user with the predetermined life trajectory information of the target user saved in advance, it is possible to determine whether the current user is the target user, and when the current user is the target user, by performing self-learning on the set parameters for the household electrical appliances of the target user, it is possible to realize the establishment of a self-learning model for each different user and improve 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, which includes an identification time period and an identification location; determining an activity time when an activity target is detected; when the activity time is within the identification time period, querying whether the activity target is the target user; when the activity target is the target user, establishing a spatial correspondence relationship between the activity trajectory of the activity target and the identification location; and generating predetermined life trajectory information of the target user based on the spatial correspondence relationship and the identification time period.

[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, the life trajectory information of the current user includes the current time and the 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 matches the predetermined time period of the target user and whether the current trajectory of the current user matches the predetermined trajectory of the target user.

[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 when the current time of the current user is within the predetermined time period of the target user and the degree of coincidence 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.

[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 giving an instruction for profiling to the current user when the current user is not the target user.

[0015] 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 from each other.

[0016] To achieve the above object, a control device for a household electrical appliance according to an embodiment of the second aspect of the present application includes a monitoring module used to monitor the life trajectory information of the current user, the life trajectory information of the current user, and a comparison module used to compare the life trajectory information of the current user with the predetermined life trajectory information of the target user, and a determination module used to determine whether the current user is the target user based on the comparison result, and when the current user is the target user, based on the self-learning model established with the setting parameters for the household electrical appliance of the target user, a learning module used to obtain the control habit parameters of the target user and control the household electrical appliance based on the control habit parameters of the target user.

[0017] According to the control device for a household electrical appliance according to an embodiment of the present application, by comparing the life trajectory information of the current user with the predetermined life trajectory information of the target user saved in advance, it is possible to determine whether the current user is the target user, and when the current user is the target user, by performing self-learning on the setting parameters for the household electrical appliance of the target user, it is possible to realize the establishment of a self-learning model for different users and improve the user experience.

[0018] To achieve the above object, an electronic device according to an embodiment of the third aspect of the present application includes one or more processors and a memory, and a computer program is stored in the memory. When the computer program is executed by the processor, the steps of the control method for a household electrical appliance according to any one of the above embodiments are realized.

[0019] According to the electronic device according to an embodiment of the present application, by comparing the current user's life trajectory information with the predetermined life trajectory information of a target user stored in advance, it is possible to determine whether the current user is the target user. When the current user is the target user, self-learning is performed on the setting parameters for the home appliances of the target user, thereby realizing the establishment of a self-learning model for different users 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 object, a computer-readable storage medium according to an embodiment of the fourth aspect of the present application stores a computer program. When the computer program is executed by a processor, it realizes the steps of the control method of the home appliance according to any one of the above embodiments.

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

[0023] Some of the additional aspects and advantages of the present application are shown in the following description, some of which will become apparent from the following description, or will be understood from the practice of the present application.

[0024] The above and / or additional aspects and advantages of the present application will become apparent and be easily understood from the description of the embodiments based on the following drawings.

Brief Description of the Drawings

[0025]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Embodiments for Carrying Out the Invention

[0026] Hereinafter, 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 from the beginning to the end indicate the same or similar elements or elements having the same or similar functions. Hereinafter, the embodiments described with reference to the drawings are exemplary and are used only for interpreting the present application and should not be understood as limiting the present application.

[0027] Also, in the description of the embodiments of the present application, the terms "first" and "second" are used only for the purpose of explanation and should not be understood as indicating or suggesting relative importance or implying the number of technical features. Therefore, the features limited as "first" and "second" may or may not be explicitly or implicitly included in one or more of the above features. In the description of the embodiments of the present application, "a plurality" means two or more unless specifically limited otherwise.

[0028] Please refer to FIG. 1. The control method for a household electrical appliance according to the embodiment of the present application includes S11 for monitoring the current user's life trajectory information, and S13 that compares the current user's life trajectory information with the predetermined life trajectory information of the target user, S15 that determines whether the current user is the target user based on the comparison result, When the current user is the target user, based on the self-learning model established with the setting parameters for the target user's household appliances, obtain the control habit parameters of the target user, and control the household appliances based on the control habit parameters of the target user, including S17.

[0029] According to the control method of household appliances according to the embodiments of the present application, by comparing the current user's life trajectory information with the predetermined life trajectory information of the target user saved in advance, it can be determined whether the current user is the target user to be self-learned regarding the current user's operation habits. Moreover, if the current user is the target user to be self-learned regarding the current user's operation habits, by performing self-learning regarding the setting parameters for the target user's household appliances, the establishment of a self-learning model for each different user is realized, and the user experience is improved.

[0030] In related technologies, when a household appliance is in the self-learning mode, no matter which user operates the household appliance, the household appliance regards it as being operated by the same user, and at the same time, it is understood that the household appliance can self-learn the setting parameters for the household appliances of multiple users. That is, the electronic device in related technologies can only establish one self-learning model with itself as the unit. However, usually, a family includes multiple family members, and the control habits of the setting parameters for the household appliances of each family member are different. Therefore, if the household appliance cannot distinguish different users, it is impossible to establish individual self-learning models for each family member. As a result, the household appliance cannot perform automatic operation individually 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 and processed in chronological order to obtain one comprehensive self-learning model, it is impossible to realize control according to individuals and meet the usage needs of different family members.

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

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

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

[0034] The target user can be understood as the user for whom the household appliance should establish an individual self - learning model and determine control habit parameters. The predetermined life trajectory information may be the life trajectory information within the monitoring range of the household appliances of the target user saved in advance. 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 respectively. In this way, different target users can be distinguished based on the predetermined life trajectory information, and the establishment of an individual self - learning model can be realized for each target user.

[0035] In some embodiments, the target user may include housewives, workers within the family, students, etc.

[0036] In one example, the same household appliance can establish five self - learning models. In another example, the same household appliance can perform self - learning regarding the control habits of 15 target users.

[0037] After obtaining the current user's life trajectory information, by comparing the current user's life trajectory information with the predetermined life trajectory information of the target user, it is possible to determine whether the current user is the target user who should self-learn regarding the current user's operation habits, and thereby determine whether to learn regarding the operation habits of the current user's household appliances. If the comparison result indicates that the current user is the target user who should self-learn regarding the current user's operation habits, set the setting parameters for the current user's household appliances as the setting parameters for the target user's household appliances, and establish a self-learning model corresponding to the target user.

[0038] The setting parameters may include at least one of a temperature parameter, an air volume mode parameter, and a wind 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 household appliances of the same target user are obtained a total of 7 times, the self-learning regarding the control habits of the target user will be completed. Perform a weighting process on the 7 sets of obtained setting parameters for the household appliances of the target user to obtain the final self-learning model corresponding to the user, and determine the control habit parameters of the target user. When it is detected that the target user appears within the monitoring range next time, the target user does not need to manually adjust the setting parameters of the household appliances, and the household appliances can directly operate based on the control habit parameters of the target user, thereby simplifying the operation and improving the user experience.

[0040] It should be noted that the control method for household appliances according to the embodiments of the present application can be realized by the household appliances, or by the server, or jointly by both the household appliances and the server, and is not limited herein.

[0041] Please refer to Figure 2. In one embodiment of the present application, before step S13, the control method is as follows Obtaining an identification condition of a target user, the identification condition including an identification time period and an identification position, S21; When an activity target is detected, determining an activity time, S23; When the activity time is within the identification time period, querying whether the activity target is the target user for the activity target, S25; When the activity target is the target user, establishing a spatial correspondence relationship between the activity track of the activity target and the identification position, S27; Further including generating predetermined life track information of the target user based on the spatial correspondence relationship and the identification time period, S29.

[0042] In this way, before formally starting self-learning, the correspondence relationship between the identification position and the actual space can be established, and the predetermined life track information of the target user can be determined, thereby facilitating the comparison between the predetermined life track information of the target user and the life track information of the current user. Since the identification position is the name of a certain place, that is, the identification position itself does not include spatial information such as the coordinates of the position, the range, the distance to the household electrical appliance, and the orientation with respect to the household electrical appliance, the household electrical appliance cannot determine whether the current user is the target user to be self-learned regarding the operation habits of the current user by comparing the identification position of the target user and the movement track of the current user. It is understood that it is necessary to establish in advance the correspondence relationship between the identification position and the actual space, so that other spatial information other than the name of the identification position can be determined.

[0043] Specifically, in step S21, the target user may customize the identification condition of the target user by himself / herself. In an example, the target user or other users may input the identification condition of the target user by means of terminal devices such as mobile phones, tablets, notebook computers, and remote controls.

[0044] The identified time period may include a start time, an end time, and other times between the start time and the end time. The identified location may include at least one of a kitchen, an entrance, a sofa, a dining table, a desk, and a balcony. In the description of the embodiments 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 identified time period in the identification condition is 6:00-7:00, and the identified location in the identification condition is the kitchen. In another example, the target user is a worker within the family, the identified time period in the identification condition is 7:30-8:00, and the identified location in the identification condition is the dining table. In another example, the target user is a student within the family, the identified time period in the identification condition is 18:00-19:00, and the identified location in the identification condition is the desk.

[0045] In step S23, the household appliance may include a radar. It is possible to monitor whether there is an activity target with the radar. The activity target may be a target user to be self-learned regarding the operation habits of the activity target, a non-target user not to be self-learned regarding the operation habits of the activity target, an animal, or any other movable object. The activity time can be understood as the time when the radar confirms the detection of the activity target. That is, the activity time may be the time when the radar first detects the appearance of the activity target within the monitoring range, or the time when the radar redetects that the activity target is within the monitoring range during continuous monitoring of the objects within the monitoring range at a predetermined frequency, but is not limited thereto.

[0046] In step S25, the fact that the activity time is within the identification time zone can be understood as the activity time being equal to the start time of the identification time zone, equal to the end time of the identification time zone, or equal to another time between the start time and the end time. In some embodiments, considering the possibility that the actual appearance time of the target user may deviate slightly from the preset identification time zone, in order to ensure that the target user can be timely discovered even in such a case, a first time deviation may be set. That is, any activity time within the first time deviation before the start time of the identification time zone or within the first time deviation after the end time of the identification time zone is regarded as being within the identification time zone. That is to say, the fact that the activity time is within the identification time zone can 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 zone, or equal to the time of the first time deviation after the end time of the identification time zone, 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 numerical values, but is not limited thereto.

[0047] When it is determined that the activity time is within the identification time zone, in order to verify whether the activity target is a target user to be self-learned regarding the operation habits of the activity target, terminal devices such as mobile phones, tablets, notebook computers, and remote controls may be used to inquire of the activity target whether it is a target user to be self-learned regarding the operation habits. In one example, inquiry information such as "Are you the target user A who entered the identification conditions?" is displayed on the APP of the mobile phone. If a signal indicating "Yes" is received, proceed to step S27. If a signal indicating "No" is received, or if no signal is received at all, proceed again to the step of monitoring the activity target.

[0048] In some embodiments, considering the possibility that the time zones within the first time deviation in the identification time zone of target user A may overlap with the time zones within the first time deviation in the identification time zone of target user B, and as a result, it may be impossible to determine which target user to inquire, the inquiry logic in this case may be predefined. In one example, first inquire the target user with an earlier identification time zone. If it is not that target user, then inquire another target user with a later identification time zone. In one example, first inquire the target user with a later identification time zone. If it is not that target user, then inquire another target user with an earlier identification time zone. In one example, first inquire the target user corresponding to the identification time zone with a smaller time deviation. If it is not that target user, then inquire another target user corresponding to the identification time zone with a larger time deviation. In this way, the inquiry can be carried out normally, avoiding omissions in inquiries to target users and improving the accuracy of inquiries.

[0049] In step S27, the radar of the household electrical appliance can track the activity trajectory of the activity target, and the activity trajectory tracked by the radar may include the activity orientation and distance with respect to the radar of the activity target. When it is determined that the activity target is a target user who should be self-learned regarding the operation habits of the activity target, by establishing the spatial correspondence relationship between the identification position and the activity trajectory of the activity target with the activity trajectory of the activity target as the spatial information of the identification position of the target user, the spatial information of the identification position can be determined based on the spatial correspondence relationship.

[0050] In step S29, based on the spatial correspondence relationship and the identification time zone of the identification conditions of the target user, the predetermined life trajectory information of the target user is generated. That is, the predetermined life trajectory of the target user includes the identification time zone of the target user and the activity trajectory of the target user specified in advance.

[0051] In one example, if the identification time zone of target user A to be self-learned regarding operation habits is from 6:00 am to 7:00 am, the identification position of target user A to be self-learned regarding operation habits is in the kitchen, and the first time deviation is 30 minutes, then if the activity time is at any time between 5:30 am and 7:30 am, it is determined that the activity time is within the identification time zone from 6:00 am to 7:00 am.

[0052] Furthermore, the identification time zone of target user B to be self-learned regarding operation habits is from 7:30 am to 8:00 am. The activity time when the detected activity target appears within the monitoring range is 7:10 am, and the activity trajectory of the detected activity target within the monitoring range is that it appears on the left side of the monitoring range and moves from a position 8.5 meters in front of the left side of the household electrical appliance to a position 11.5 meters in front of the left side, and stops or makes fine adjustments to the operation near the position 11.5 meters in front of the left side of the household electrical appliance.

[0053] Since the time deviation between the activity time and the identification time zone from 6:00 am to 7:00 am is small, while the time deviation between the activity time and the identification time zone from 7:30 am to 8:00 am is large, first, it may be queried whether the activity target is target user A to be self-learned regarding the operation habits of the activity target. If it is determined that it is not target user A to be self-learned regarding the operation habits, then next, it is queried whether the activity target is target user B to be self-learned regarding the operation habits of the activity target. If it is determined that the activity target is target user A to be self-learned regarding the operation habits, the activity trajectory of the activity target is used as the spatial information of the kitchen to establish the spatial correspondence relationship between the activity trajectory of the activity target and the kitchen, and the content of "appearing on the left side of the monitoring range from 6:00 to 7:00, moving to the range from 8.5 meters to 11.5 meters in front of the left side, and stopping or making fine adjustments to the operation" is used as the predetermined life trajectory information of target user A to be self-learned regarding the operation habits.

[0054] Please refer to FIG. 3. In one embodiment of the present application, the predetermined life trajectory information of the target user includes a predetermined time zone and a predetermined trajectory, the life trajectory information of the current user includes the current time and the current trajectory, and step S13 is It includes S131 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.

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

[0056] Specifically, the predetermined time zone may be the above-mentioned identification time zone. The predetermined time zone 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 activity trajectory of the activity target of the above-mentioned pre-specified target user.

[0057] The current time can be understood as the time when the current user is detected. Specifically, it may be the time when the current user is first detected, or the time when the current user is detected again at a predetermined frequency, but it is not limited to this.

[0058] It will be understood that step S131 includes a time zone matching step and a trajectory matching step. Here, first, the time zone matching step may be executed, and then the trajectory matching step may be executed. Or first, the trajectory matching step may be executed, and then the time zone matching step may be executed, but it is not limited to this.

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

[0060] Please refer to FIG. 4. In one embodiment of the present application, step S15 includes: S151 of determining that the current user is the target user when the current time of the current user is within the predetermined time period of the target user and the degree of coincidence 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 comparison results of the time period and the comparison results of the trajectories, it is possible to more accurately determine whether the current user is the target user who should self-learn regarding the operation habits of the current user.

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

[0063] In some embodiments, the predetermined threshold is 85%. That is, when the degree of coincidence between the current trajectory of the current user and the predetermined trajectory of the target user reaches or exceeds 85% (for example, in the cases of 90%, 95%, 100%), the matching between the current trajectory and the predetermined trajectory can be regarded as successful. When the degree of coincidence between the current trajectory of the current user and the predetermined trajectory of the target user is less than 85%, the current trajectory can be regarded as unable to match the predetermined trajectory.

[0064] In one example, the predetermined time period of the target user to be self-learned regarding operation habits is from 6:00 a.m. to 7:00 a.m. Also, the predetermined trajectory of the target user to be self-learned regarding operation habits is to appear on the left side of the monitoring range, move to the range from 8.5 meters to 11.5 meters in the front left, and stop or perform fine adjustment of 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 range from 8.4 meters to 11.5 meters in the front left, and stop. Since the degree of coincidence between the current trajectory and the predetermined trajectory exceeds 85% and the current time corresponds to the predetermined time period, it can be determined that the current user is the target user to be self-learned regarding the operation habits of the current user.

[0065] Please refer to FIG. 5. In one embodiment of the present application, after step S15, the control method further includes S19 of giving an instruction for profiling to the current user if the current user is not the target user.

[0066] In this way, when the current user completes profiling according to the profiling instruction, self-learning can be performed regarding the control habits of the current user for the household electrical appliances.

[0067] Specifically, an instruction for profiling may be given by a terminal device such as a mobile phone, a tablet, a notebook computer, or a remote control. The method of the profiling instruction may include a text instruction and / or a voice instruction. The content of the profiling instruction may include "Is self-learning regarding control habits necessary? If so, please input identification conditions according to the instruction."

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

[0069] In one example, a mobile phone is used to instruct the current user to perform profiling. The mobile phone includes a display screen. When it is necessary to instruct the user to perform profiling, the display screen of the mobile phone displays an instruction text "Do you need self-learning regarding control habits? If so, please enter the identification conditions according to the instructions". At the same time, the display screen of the mobile phone provides a selection button indicating "Yes" and a cancel button indicating "No". If it is detected that the cancel button is triggered, it is determined that profiling of the current user is not necessary, and the current interface is exited. If it is detected that the selection button is triggered, after determining that profiling of the current user is necessary, an interactive interface for inputting identification conditions is provided.

[0070] In another example, the user instructs the current user to perform profiling. The mobile phone is equipped with a voice recognition function and includes a speaker and a display screen. When the user needs to give an instruction for profiling, the instruction voice "Do you need self-learning regarding control habits? If so, please enter the identification conditions according to the instructions" is transmitted from the speaker of the mobile phone. According to the voice uttered by the current user, if the current user receives an instruction that profiling is not necessary, the currently detected current user is ignored. According to the voice uttered by the current user, if the current user receives an instruction that profiling is necessary, an interactive interface for entering identification conditions is provided by the display screen.

[0071] Please refer to FIG. 6. In the control device 100 of the home appliance according to the embodiment of the present application, a monitoring module 12, a comparison module 14, a determination module 16, and a learning module 18 are included. The monitoring module 12 is used to monitor the life trajectory information of the current user. The comparison module 14 is used to compare the life trajectory information of the current user with the predetermined life trajectory information of the target user. The determination module 16 is used to determine whether the current user is the target user based on the comparison result. The learning module 18, when the current user is the target user, is used to obtain the control habit parameters of the target user based on the self-learning model established with the setting parameters for the home appliances of the target user, and to control the home appliances based on the control habit parameters of the target user.

[0072] According to the control device 100 of the household electrical appliance according to the embodiment of the present application, by comparing the current user's life trajectory information with the predetermined life trajectory information of the target user stored in advance, it can be determined whether the current user is the target user who should self-learn regarding the operation habits of the current user. Moreover, when the current user is the target user who should self-learn regarding the operation habits of the current user, by performing self-learning on the setting parameters for the household electrical appliances of the target user, the establishment of a self-learning model for each different user can be realized, and the user experience can be improved.

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

[0074] The current user can be understood as a moving object that appears within the monitoring range of the household electrical appliance. In some embodiments, the household electrical appliance includes a monitoring device, and the monitoring device can collect audio data, image data, radar signal data, etc. within the monitoring range at a predetermined frequency. Based on the data collected by the monitoring device, it is determined whether there is a moving object within the monitoring range. When there is a moving object within the monitoring range, it is possible to continuously track the movement trajectory of the moving object and generate life trajectory information. In one example, the detection device includes a radar.

[0075] The target user can be understood as the user for whom the household electrical appliance should establish an individual self-learning model and determine the control habit parameters. The predetermined life trajectory information may be the life trajectory information of the target user within the monitoring range of the household electrical appliance stored in advance. 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 respectively. In this way, different target users can be distinguished based on the predetermined life trajectory information, and the establishment of an individual self-learning model for each target user can be realized. In some embodiments, the target users may include housewives, workers within the family, students, etc. In one example, the same household electrical appliance can perform self-learning regarding the control habits of 15 target users.

[0076] After obtaining the current user's life trajectory information, by comparing the current user's life trajectory information with the predetermined life trajectory information of the target user, it is possible to determine whether the current user is the target user who should self-learn regarding the current user's operation habits, and thereby determine whether to learn regarding the operation habits of the current user's household appliances. If the comparison result indicates that the current user is the target user who should self-learn regarding the current user's operation habits, set the setting parameters for the current user's household appliances as the setting parameters for the target user's household appliances, and establish a self-learning model corresponding to the target user.

[0077] The setting parameters may include at least one of a temperature parameter, an air volume mode parameter, and a wind 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 household appliances of the same target user are obtained a total of 7 times, the self-learning regarding the control habits of the target user will be completed. Perform a weighting process on the 7 sets of setting parameters for the household appliances of the target user that have been obtained to obtain the final self-learning model corresponding to the user, and determine the control habit parameters of the target user. When it is detected that the target user appears within the monitoring range next time, the target user does not need to manually adjust the setting parameters of the household appliances, and the household appliances can directly operate based on the control habit parameters of the target user, thereby simplifying the operation and improving the user experience.

[0079] In one 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 to acquire identification conditions of a target user, including an identification time period and an identification location. The determination module is used to determine an activity time when an activity target is detected. The inquiry module is used to inquire of the activity target whether the activity target is the target user when the activity time is within the identification time period. The establishment module is used to establish a spatial correspondence relationship between the activity track of the activity target and the identification location when the activity target is the target user. The generation module is used to generate predetermined life track information of the target user based on the spatial correspondence relationship and the identification time period.

[0080] In this way, before formally starting self-learning, the correspondence relationship between the identification location and the actual space can be established, and the predetermined life track information of the target user can be determined, thereby facilitating the comparison between the predetermined life track information of the target user and the life track information of the current user. Since the identification location is the name of a certain 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 household appliance, and the orientation with respect to the household appliance, the household appliance cannot determine whether the current user is the target user who should be self-learned regarding the operation habits of the current user by comparing the identification location of the target user with the movement track of the current user. It is understood that it is necessary to establish in advance the correspondence relationship between the identification location and the actual space, so that other spatial information other 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. In one example, the target user or other users may input the identification conditions of the target user by means of terminal devices such as mobile phones, tablets, notebook computers, and remote controls.

[0082] The identified time period may include a start time, an end time, and other times between the start time and the end time. The identified location may include at least one of a kitchen, an entrance, a sofa, a dining table, a desk, and a balcony. In the description of the embodiments 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 identified time period in the identification condition is 6:00-7:00, and the identified location in the identification condition is the kitchen. In another example, the target user is a worker within the family, the identified time period in the identification condition is 7:30-8:00, and the identified location in the identification condition is the dining table. In another example, the target user is a student within the family, the identified time period in the identification condition is 18:00-19:00, and the identified location in the identification condition is the desk.

[0083] The household appliance may include a radar. It is possible to monitor whether there is an activity target with the radar. The activity target may be a target user to be self-learned regarding the operation habits of the activity target, a non-target user not to be self-learned regarding the operation habits of the activity target, an animal, or any other movable object. The activity time can be understood as the time when the radar confirms the detection of the activity target. That is, the activity time may be the time when the radar first detects the appearance of the activity target within the monitoring range, or the time when the radar redetects the presence of the activity target within the monitoring range during continuous monitoring of the objects within the monitoring range at a predetermined frequency, but is not limited thereto.

[0084] When it is said that the activity time is within the identification time zone, it can be understood that the activity time is equal to the start time of the identification time zone, equal to the end time of the identification time zone, or equal to another time between the start time and the end time. Note that in some embodiments, considering the possibility that the actual appearance time of the target user may deviate slightly from the preset identification time zone, in order to ensure that the target user can be timely discovered even in such cases, a first time deviation may be set. That is, any activity time that is within the first time deviation before the start time of the identification time zone or within the first time deviation after the end time of the identification time zone is regarded as being within the identification time zone. That is, when it is said that the activity time is within the identification time zone, it can be further understood that the activity time is equal to the time of the first time deviation before the start time of the identification time zone, or equal to the time of the first time deviation after the end time of the identification time zone, 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 numerical values, but is not limited thereto.

[0085] When it is determined that the activity time is within the identification time zone, in order to verify whether the activity target is a target user to be self-learned regarding the operation habits of the activity target, terminal devices such as mobile phones, tablets, notebook computers, and remote controls may be used to inquire of the activity target whether it is a target user to be self-learned regarding the operation habits. In one example, the inquiry information such as "Are you the target user A who entered the identification conditions?" is displayed on the APP of the mobile phone. If a signal indicating "Yes" is received, proceed to the step of establishing the spatial correspondence relationship. If a signal indicating "No" is received, or if no signal is received at all, proceed again to the step of monitoring the activity target.

[0086] In some embodiments, considering the possibility that the time zones within the first time deviation in the identification time zone of target user A may overlap with the time zones within the first time deviation in the identification time zone of target user B, and as a result, it may not be possible to determine which target user to inquire, the inquiry logic in this case may be defined in advance. For example, first inquire of the target user with an earlier identification time zone, and if it is not that target user, then inquire of another target user with a later identification time zone. Or, first inquire of the target user with a later identification time zone, and if it is not that target user, then inquire of another target user with an earlier identification time zone. Or, first inquire of the target user corresponding to the identification time zone with a smaller time deviation, and if it is not that target user, then inquire of another target user corresponding to the identification time zone with a larger time deviation. In this way, inquiries can be made normally, avoiding omission of inquiries to target users and improving the accuracy of inquiries.

[0087] The radar of the household electrical appliance can track the activity trajectory of the activity target, and the activity trajectory tracked by the radar may include the activity azimuth and distance with respect to the radar of the activity target. The radar can identify the indoor layout based on the tracked activity trajectory. For example, the kitchen is 10 meters in the front left, the dining room is 5 meters in the front, and the study is 3 meters in the front right.

[0088] When it is determined that the activity target is a target user to be self-learned regarding the operation habits of the activity target, by establishing the spatial correspondence relationship between the activity trajectory of the activity target and the identification position as the spatial information of the identification position of the target user, the spatial information of the identification position can be determined based on the spatial correspondence relationship.

[0089] Generate the predetermined life trajectory information of the target user based on the spatial correspondence relationship and the identification time zone of the identification conditions of the target user. That is, the predetermined life trajectory of the target user includes the identification time zone of the target user and the activity trajectory of the target user specified in advance.

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

[0091] Furthermore, the identification time zone of target user B to be self-learned regarding operation habits is from 7:30 am to 8:00 am. The activity time when the detected activity target appears within the monitoring range is 7:10 am, and the activity track of the detected activity target within the monitoring range is that it appears on the left side of the monitoring range and moves from a position 8.5 meters in front of the left of the household electrical appliance to a position 11.5 meters in front of the left, and stops or makes fine adjustments to the operation near the position 11.5 meters in front of the left of the household electrical appliance.

[0092] While the time deviation between the activity time and the identification time zone from 6:00 am to 7:00 am is small, and the time deviation between the activity time and the identification time zone from 7:30 am to 8:00 am is large. First, it may be queried whether the activity target is target user A to be self-learned regarding the operation habits of the activity target. If it is determined that it is not target user A to be self-learned regarding the operation habits, then next, it is queried whether the activity target is target user B to be self-learned regarding the operation habits of the activity target. If it is determined that the activity target is target user A to be self-learned regarding the operation habits, the activity track of the activity target is used as the spatial information of the kitchen, and the corresponding relationship between the activity track of the activity target and the space of the kitchen is established, and the content of "appearing on the left side of the monitoring range between 6:00 - 7:00, moving from 8.5 meters to 11.5 meters in front of the left, and stopping or making fine adjustments to the operation" is used as the predetermined life track information of target user A to be self-learned regarding the operation habits.

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

[0094] In this way, by comparing the life trajectory information with the predetermined life trajectory information, it is possible to more accurately determine whether the current user is the target user who should self-learn 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 activity trajectory of the activity target of the above-mentioned pre-specified target user.

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

[0097] It will be understood that the comparison module 14 can execute the time period matching step and the trajectory matching step. Here, first, the time period matching step may be executed, and then the trajectory matching step may be executed. Alternatively, first, the trajectory matching step may be executed, and then the time period matching step may be executed, but it is not limited thereto.

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

[0099] In one embodiment of the present application, the learning module 16 is further used to determine that the current user is the target user when the current time of the current user is within the predetermined time period of the target user and the degree of coincidence 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 comparison results of the time period and the comparison results of the trajectory, it is possible to more accurately determine whether the current user is the target user who should self-learn regarding the operation habits of the current user.

[0101] Specifically, that the current time is within a predetermined time period means that the current time can be understood to be equal to the start time of the predetermined time period, equal to the end time of the predetermined time period, or equal to another time between the start time and the end time. In some embodiments, considering the possibility that the actual appearance time of the target user may deviate slightly from the preset identification time period, in order to ensure that the target user can be timely discovered even in such a case, a second time deviation may be set. That is, a current time that is within the second time deviation before the start time of the predetermined time period or within the second time deviation after the end time of the predetermined time period is regarded as being within the predetermined time period. That is, that the current time is within the predetermined time period can be further understood to mean that the current time is equal to the time of the second time deviation before the start time of the predetermined time period, or equal to the time of the second time deviation after the end time of the predetermined time period, or equal to another time between the time of the second time deviation before the start time and the time of 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 be set to other numerical values, but is not limited thereto.

[0102] In some embodiments, the predetermined threshold is 85%. That is, when the degree of coincidence between the current trajectory of the current user and the predetermined trajectory of the target user reaches or exceeds 85% (for example, in the cases of 90%, 95%, 100%), the matching between the current trajectory and the predetermined trajectory can be regarded as successful. When the degree of coincidence between the current trajectory of the current user and the predetermined trajectory of the target user is less than 85%, the current trajectory can be regarded as not being able to match the predetermined trajectory.

[0103] In one example, the predetermined time period of the target user to be self-learned regarding operation habits is from 6:00 am to 7:00 am. Also, the predetermined trajectory of the target user to be self-learned regarding operation habits is to appear on the left side of the monitoring range, move to the range from 8.5 meters to 11.5 meters in the front left, and stop or perform fine adjustment of the operation. The current time when the current user is detected is 6:20 am, and the current trajectory of the current user is to appear on the left side of the monitoring range, move to the range from 8.4 meters to 11.5 meters in the front left, and stop. Since the degree of coincidence between the current trajectory and the predetermined trajectory exceeds 85% and the current time corresponds to the predetermined time period, it can be determined that the current user is the 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. The profiling module is used to give an instruction for profiling to the current user when the current user is not the target user.

[0105] In this way, when the current user completes profiling according to the profiling instruction, self-learning can be performed regarding the control habits of the current user for the household electrical appliances.

[0106] Specifically, an instruction for profiling may be given by a terminal device such as a mobile phone, a tablet, a notebook computer, or a remote control. The method of the profiling instruction may include a text instruction and / or a voice instruction. The content of the profiling instruction may include "Is self-learning regarding control habits necessary? If so, please input the identification conditions according to the instruction".

[0107] In some embodiments, the control device 100 further includes an input module. When it is determined that profiling of the current user is necessary, the input module is used to provide an interactive interface for inputting identification conditions, determine the identification conditions of the current user based on the input information to the interactive interface, and use the current user as one of the target users to perform self-learning regarding the control habits of the current user's household appliances.

[0108] In one example, a mobile phone is used to instruct the current user to perform profiling. The mobile phone includes a display screen. When profiling instructions are required for the user, an instruction text "Do you need self-learning regarding control habits? If so, please enter the identification conditions according to the instructions" is displayed on the display screen of the mobile phone. At the same time, a selection button indicating "Yes" and a cancel button indicating "No" are provided on the display screen of the mobile phone. When it is detected that the cancel button is triggered, it is determined that profiling is not required for the current user, and the current interface is exited. When it is detected that the selection button is triggered, after determining that profiling is necessary for the current user, an interactive interface for inputting identification conditions is provided.

[0109] In another example, the user instructs to profile the current user. The mobile phone is equipped with a voice recognition function, and the mobile phone includes a speaker and a display screen. When the user needs to give an instruction for profiling, the instruction voice "Do you need self-learning regarding control habits? If so, please enter the identification conditions according to the instructions" is transmitted from the speaker of the mobile phone. According to the voice uttered by the current user, if the current user receives an instruction that profiling is not required, the currently detected current user is ignored. According to the voice uttered by the current user, if the current user receives an instruction that profiling is required, an interactive interface for inputting identification conditions is provided by the display screen.

[0110] It should be noted that the specific numerical values mentioned above are only provided as examples for explaining the implementation of this application in detail and should not be understood as limitations to this application. In other examples, embodiments, and examples, other numerical values may be selected based on this application, but there is no specific limitation to this.

[0111] Please refer to FIG. 7. The electronic device 200 according to the embodiment of this application includes one or more processors 22 and a memory 24. A computer program 26 is stored in the memory 24. When the computer program 26 is executed by the processor 22, the steps of the control method of the household electrical appliance according to any one of the above embodiments are realized.

[0112] According to the household electrical appliance according to the embodiment of this application, by comparing the life trajectory information of the current user with the predetermined life trajectory information of the target user saved in advance, it can be determined whether the current user is a target user who should self-learn regarding the operation habits of the current user. Moreover, if the current user is a target user who should self-learn regarding the operation habits of the current user, by self-learning regarding the setting parameters for the household electrical appliance of the target user, the establishment of a self-learning model for different users is realized, and the user experience is improved.

[0113] Note that the descriptions and explanations of the embodiments and beneficial effects of the above control method also apply to the electronic device 200 of this embodiment. To avoid redundancy, detailed descriptions are omitted here.

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

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

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

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

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

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

[0120] In this way, the control method of the household appliance may be realized by the household appliance, or the control method of the household appliance may also be realized by the server.

[0121] Specifically, in some embodiments, the control method of another household appliance can be realized by one household appliance. In one example, the control method of the air conditioner may be realized by the smart refrigerator. In another example, the control method of the air conditioner in the living room may be realized by the air conditioner in the bedroom.

[0122] The computer-readable storage medium according to the embodiment of the present application stores a computer program. When the program is executed by a processor, it realizes the steps of the control method for household electrical appliances according to any one of the above-mentioned 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 can be installed in a server or in a household electrical appliance. The household electrical appliance can communicate with the server to obtain the corresponding program.

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

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

[0127] Expressions such as "one embodiment", "some embodiments", "exemplary embodiments", "examples", "specific examples", or "some examples" in the description of this specification mean that the specific features, structures, materials, or characteristics described in the embodiments or examples are included in one or more embodiments or examples of this application. The schematic expressions of the above terms in this specification do not necessarily refer to the same embodiment or example. Also, the specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0128] Any process or method shown in a flowchart or described herein in other ways can be understood as a module, segment, or part of executable instruction code for realizing the steps of a specific logic function or process. And within the scope of the preferred embodiments of this application, additional realizations that execute functions essentially simultaneously or in reverse order based on related functions are also included, without following the order shown or discussed. This will be understood by those skilled in the technical field of this application.

[0129] Logic and / or steps represented by a flowchart or otherwise described herein may be regarded, for example, as an ordered listing of executable instructions for implementing logical functions. This listing can be used by, or in combination with, a command execution system, apparatus, or device (e.g., a computer-based system, a system including a processing module, or other systems that can receive and execute commands from the command execution system, apparatus, or device), and can be specifically implemented on any computer-readable medium. For the purposes of this specification, a "computer-readable medium" is any device that can store, communicate, propagate, or transmit a program used by, or in combination with, a command execution system, apparatus, or device. More specific examples of "computer-readable media" include electrical connection parts having one or more wirings, portable computer disk cases (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber devices, and portable CD-ROMs (non-exhaustive list). Also, a computer-readable medium can even be paper or other suitable media on which a program can be printed. This is because the paper or other media can be optically scanned, then edited, interpreted, or otherwise processed as appropriate, and the program can be obtained electronically and stored in the computer's memory.

[0130] It will be understood that each part of the embodiments of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, a plurality of steps or methods can be implemented by software or firmware executed by an appropriate instruction execution system stored in a memory. For example, when implemented in hardware, similar to another embodiment, a discrete logic circuit having a logic gate circuit that realizes a logic function for a data signal, an application-specific integrated circuit (ASIC) having an appropriate combination of logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc., can be implemented by any one or a combination of technologies known in this field.

[0131] Those skilled in the art can understand that all or some of the steps in the methods in the above-described 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, includes one or a combination of the steps of the method embodiments.

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

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

[0134] Although the embodiments of the present application have been shown and described, those skilled in the art will understand that the above embodiments are exemplary and should not be construed as limitations on the present application. It will be understood that within the scope of the present application, changes, combinations, modifications, substitutions, and variations can be made to the above embodiments.

Claims

1. A method for controlling household appliances executed by a computer, comprising: monitoring the current user's life trajectory information; comparing the current user's life trajectory information with the predetermined life trajectory information of a target user; judging whether the current user is the target user based on the comparison result; when the current user is the target user, obtaining the control habit parameters of the target user based on a self-learning model established with the setting parameters for the household appliances of the target user, and controlling the household appliances based on the control habit parameters of the target user; before comparing the current user's life trajectory information with the predetermined life trajectory information of the target user, the method for controlling household appliances further comprises: obtaining identification conditions of the target user, the identification conditions including an identification time period and an identification location; determining an activity time when an activity target is detected; when the activity time is within the identification time period, querying the activity target as to whether the activity target is the target user; when the activity target is the target user, establishing a spatial correspondence relationship between the activity trajectory of the activity target and the identification location; generating predetermined life trajectory information of the target user based on the spatial correspondence relationship and the identification time period.

2. A method for controlling household appliances executed by a computer, comprising: monitoring the current user's life trajectory information; comparing the current user's life trajectory information with the predetermined life trajectory information of a target user; judging whether the current user is the target user based on the comparison result; when the current user is the target user, obtaining the control habit parameters of the target user based on a self-learning model established with the setting parameters for the household appliances of the target user, and controlling the household appliances based on the control habit parameters of the target user; the predetermined life trajectory information of the target user includes a predetermined time period and a predetermined trajectory, the current user's life trajectory information includes the current time and the current trajectory, and comparing the current user's life trajectory information with the predetermined life trajectory information of the target user is A control method for a household electrical appliance, including 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.

3. Based on the comparison result, determining whether the current user is the target user includes When the current time of the current user is within the predetermined time zone of the target user and the degree of coincidence 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, determining that the current user is the target user, The control method of the household electrical appliance according to claim 2.

4. After determining whether the current user is the target user based on the comparison result, the control method of the household electrical appliance When the current user is not the target user, further includes giving an instruction for profiling to the current user, The control method of the household electrical appliance according to any one of claims 1 to 3.

5. The target user includes a plurality, and the predetermined life trajectory information of the plurality of target users is different from each other, The control method of the household electrical appliance according to claim 1 or 2.

6. A control device for a household electrical appliance, A monitoring module used for monitoring the life trajectory information of the current user, A comparison module used for comparing the life trajectory information of the current user with the predetermined life trajectory information of the target user, A determination module used for determining whether the current user is the target user based on the comparison result, When the current user is the target user, based on the self-learning model established with the set parameters of the household electrical appliance for the target user, obtaining the control habit parameters of the target user, and using the control habit parameters of the target user to control the household electrical appliance, including a learning module, The control device of the household electrical appliance An acquisition module for acquiring the identification condition of the target user, which includes an identification time zone and an identification position, A determination module for determining the activity time when an activity target is detected, When the activity time is within the identification time zone, an inquiry module for inquiring of the activity target whether the activity target is the target user, When the activity target is the target user, an establishment module for establishing a spatial correspondence relationship between the activity track of the activity target and the identification position; A generation module for generating predetermined life track information of the target user based on the spatial correspondence relationship and the identification time zone, further included in a control device for household electrical appliances.

7. A control device for household electrical appliances, A monitoring module used for monitoring the life track information of the current user; A comparison module used for comparing the life track information of the current user with the predetermined life track information of the target user; A judgment module used for judging whether the current user is the target user based on the comparison result; When the current user is the target user, based on a self-learning model established with the set parameters for the household electrical appliances of the target user, obtaining the control habit parameters of the target user, and a learning module used for controlling the household electrical appliances based on the control habit parameters of the target user, included; The predetermined life track information of the target user includes a predetermined time zone and a predetermined track, the life track information of the current user includes the current time and the current track, and comparing the life track information of the current user with the predetermined life track information of the target user is The comparison module further judges whether the current time of the current user matches the predetermined time zone of the target user and whether the current track of the current user matches the predetermined track of the target user, a control device for household electrical appliances.

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 steps of the control method for household electrical appliances according to any one of Claims 1 to 3 are realized, an electronic device.

9. The electronic device according to Claim 8, wherein the electronic device is a household electrical appliance or a server.

10. A computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the control method for household electrical appliances according to any one of Claims 1 to 3 are realized, a computer-readable storage medium.

Images