WO2023077835A1 - Procédé de commande d'appareil électroménager, appareil de commande, dispositif électronique et support d'enregistrement - Google Patents

Procédé de commande d'appareil électroménager, appareil de commande, dispositif électronique et support d'enregistrement Download PDF

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WO2023077835A1
WO2023077835A1 PCT/CN2022/102310 CN2022102310W WO2023077835A1 WO 2023077835 A1 WO2023077835 A1 WO 2023077835A1 CN 2022102310 W CN2022102310 W CN 2022102310W WO 2023077835 A1 WO2023077835 A1 WO 2023077835A1
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target user
user
current
preset
target
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PCT/CN2022/102310
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English (en)
Chinese (zh)
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陈锦敏
王庆仙
宋分平
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广东美的制冷设备有限公司
美的集团股份有限公司
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Publication of WO2023077835A1 publication Critical patent/WO2023077835A1/fr

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B15/00—Systems controlled by a computer
    • G05B15/02—Systems controlled by a computer electric
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/26—Pc applications
    • G05B2219/2642—Domotique, domestic, home control, automation, smart house
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Definitions

  • the present application relates to the technical field of household appliances, and in particular to a control method, a control device, electronic equipment, and a storage medium for household appliances.
  • the air conditioner has a self-learning function.
  • the air conditioner can record the operating parameters of the air conditioner set by the user, and determine the user's setting habits based on the data recorded multiple times. After that, the user does not need to set the air conditioner again when using the air conditioner.
  • the air conditioner can execute the corresponding air conditioner operating parameters according to the user's setting habits.
  • an object of the present application is to propose a control method for home appliances, which can distinguish different users based on life trajectory information, and then implement self-learning models for different users to improve user experience.
  • the second purpose of the present application is to propose a control device for household appliances.
  • the third object of the present application is to provide an electronic device.
  • the fourth object of the present application is to provide a computer-readable storage medium.
  • the control method of the home appliance in the embodiment of the first aspect of the present application includes: monitoring the life track information of the current user; comparing the life track information of the current user with the preset life track information of the target user; Judging whether the current user is the target user based on the comparison result; when the current user is the target user, obtain the target user's information according to the self-learning model established by the target user for the setting parameters of the home appliance. control habit parameters, and control the household appliances according to the control habit parameters of the target user.
  • the control method of the home appliance in the embodiment of the present application by comparing the life trajectory information of the current user with the preset life trajectory information of the target user stored in advance, it can be determined whether the current user is the target user, and it can be determined whether the current user is the target user.
  • self-learning is performed on the setting parameters of home appliances for the target user, so as to realize the establishment of self-learning models for different users and improve user experience.
  • the control method before comparing the life trajectory information of the current user with the preset life trajectory information of the target user, the control method further includes: acquiring the identification condition of the target user,
  • the recognition conditions include a recognition period and a recognition position; when an active object is monitored, determine the activity moment; when the activity moment is within the recognition period, ask whether the active object is the target user;
  • When the object is the target user establish a spatial correspondence between the activity trajectory of the active object and the identified location; generate preset life trajectory information of the target user according to the spatial correspondence and the identification period.
  • the preset life trajectory information of the target user includes a preset time period and a preset trajectory
  • the current user's life trajectory information includes the current moment and the current trajectory
  • the current user's life trajectory The trajectory information is compared with the preset life trajectory information of the target user, including: judging whether the current moment of the current user matches the preset time period of the target user, and judging whether the current trajectory of the current user is consistent with the target user’s Whether the preset trajectory matches.
  • judging whether the current user is the target user according to the comparison result includes: the current user is in the target user's preset time period at the current moment, the current user's current When the coincidence degree between the trajectory and the preset trajectory of the target user is greater than or equal to a preset threshold, it is determined that the current user is the target user.
  • control method further includes: when the current user is not the target user, Prompt for filing.
  • the target users include multiple target users, and the preset life trajectory information of the multiple target users is different from each other.
  • the control device for household appliances in the embodiment of the second aspect of the present application includes a monitoring module, a comparing module, a judging module and a learning module.
  • the monitoring module is used to monitor the life track information of the current user.
  • the comparison module is used to compare the life track information of the current user with the preset life track information of the target user.
  • the judging module is used to judge whether the current user is the target user according to the comparison result.
  • the learning module is used to obtain the control habit parameters of the target user according to the self-learning model established by the target user for the setting parameters of the home appliance when the current user is the target user, and to obtain the control habit parameters of the target user according to the target user
  • the control habit parameter controls the home appliance.
  • control device for household appliances by comparing the current user's life trajectory information with the pre-stored target user's preset life trajectory information, it can determine whether the current user is a target user, and can determine whether the current user is a target user.
  • self-learning is performed on the setting parameters of home appliances for the target user, so as to realize the establishment of self-learning models for different users and improve user experience.
  • the electronic device in the embodiment of the third aspect of the present application includes one or more processors and memory, the memory stores a computer program, and when the computer program is executed by the processor, any of the above The steps of the control method for household appliances described in an embodiment.
  • the electronic device of the embodiment of the present application by comparing the life trajectory information of the current user with the preset life trajectory information of the target user stored in advance, it can be determined whether the current user is the target user, and it can be determined when the current user is the target user According to the target user, self-learning is performed on the setting parameters of home appliances, so as to realize the establishment of self-learning models for different users and improve user experience.
  • the electronic device is a household electrical appliance or a server.
  • the computer-readable storage medium of the embodiment of the fourth aspect of the present application has a computer program stored thereon, and it is characterized in that, when the program is executed by a processor, it can realize any one of the above-mentioned embodiments.
  • the computer-readable storage medium of the embodiment of the present application by comparing the life track information of the current user with the preset life track information of the target user stored in advance, it can be determined whether the current user is the target user, and it can be determined whether the current user is the target user.
  • self-learning is performed on the setting parameters of home appliances for the target user, so as to realize the establishment of self-learning models for different users and improve user experience.
  • FIG. 1 is a schematic flowchart of a control method for a household electrical appliance according to an embodiment of the present application
  • Fig. 2 is a schematic flowchart of a control method for a household electrical appliance according to an embodiment of the present application
  • FIG. 3 is a schematic flowchart of a control method for a household electrical appliance according to an embodiment of the present application
  • Fig. 4 is a schematic flowchart of a control method for a household electrical appliance according to an embodiment of the present application
  • Fig. 5 is a schematic flowchart of a control method for a household electrical appliance according to an embodiment of the present application
  • Fig. 6 is a structural block diagram of a control device for household appliances according to an embodiment of the present application.
  • Fig. 7 is a structural block diagram of an electronic device according to an embodiment of the present application.
  • first and second are used for description purposes only, and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of indicated technical features.
  • a feature defined as “first” or “second” may explicitly or implicitly include one or more of said features.
  • “plurality” means two or more, unless otherwise specifically defined.
  • control method of the household appliances in the embodiment of the present application includes:
  • S17 When the current user is the target user, obtain the target user's control habit parameters according to the self-learning model established by the target user for the setting parameters of the home appliances, and control the home appliances according to the target user's control habit parameters.
  • the control method for household electrical appliances in the embodiment of the present application by comparing the current user's life trajectory information with the pre-stored target user's preset life trajectory information, it can be determined whether the current user needs to self-learn its operating habits Target users, and when the current user is a target user who needs to self-learn their operating habits, self-learn the setting parameters of home appliances for the target user, so as to establish a self-learning model for different users and improve user experience .
  • the home appliance when the home appliance is in the self-learning mode, no matter how many users operate the home appliance, the home appliance defaults to the same user operation, and sets parameters for the home appliance for multiple users at the same time.
  • Carrying out self-learning that is to say, the electronic device in the related art can only establish a self-learning model with itself as a unit.
  • a family includes multiple family members, and each family member has different control habits on the setting parameters of the household appliances. If each family member establishes a self-learning model for each family member, it is impossible to automatically operate the home appliances according to the control habits of each family member. Even if the control habits of each family member on the same household appliance are weighted in chronological order to obtain a comprehensive self-learning model, it is impossible to achieve individual control and meet the needs of different family members.
  • home appliances include but are not limited to air conditioners, humidifiers, air purifiers, TVs, smart speakers, and the like.
  • the current user may be understood as a moving object appearing within the monitoring range of the home appliance.
  • the household appliances may include a monitoring device, which can collect sound data, image data or radar signal data, etc. within the monitoring range according to a preset frequency. There is a moving object, and when there is a moving object within the monitoring range, the trajectory of the moving object can be continuously tracked and life trajectory information can be generated.
  • the detection device includes radar.
  • Target users can be understood as users who need home appliances to independently establish self-learning models and determine control habit parameters.
  • the preset life trajectory information may be pre-stored life trajectory information of the target user within the monitoring range of the home appliance.
  • there are multiple target users and the preset life track information of the multiple target users is different from each other. In this way, different target users can be distinguished based on the preset life trajectory information, and a self-learning model can be independently established for each target user.
  • target users may include housewives, home office workers, home students, and the like.
  • five self-learning models can be built for the same home appliance.
  • the same home appliance can self-learn the control habits of 15 target users.
  • the current user’s life trajectory information After obtaining the current user’s life trajectory information, by comparing the current user’s life trajectory information with the target user’s preset life trajectory information, it can be determined whether the current user is a target user who needs to self-learn its operating habits, so as to determine whether Learn the current user's control habits for home appliances. If the comparison result shows that the current user is a target user who needs to self-learn its operating habits, then use the current user's setting parameters for home appliances as the target user's setting parameters for home appliances, and establish a self-study corresponding to the target user. learning model.
  • the setting parameters may include at least one of a temperature parameter, an air outlet mode parameter, and an air direction parameter.
  • the target user's control habit parameters determined according to the self-learning model may include at least one of setting parameters.
  • the self-learning of the target user's control habits is completed.
  • the setting parameters of the target user for the household appliances obtained seven times are weighted to obtain the final self-learning model corresponding to the user, so as to determine the control habit parameters of the target user.
  • the target user appears within 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 habit parameters of the target user, thereby simplifying operations and improving user experience.
  • control method of the home appliance in the embodiment of the present application may be implemented by the home appliance, may also be implemented by the server, or may be jointly implemented by the home appliance and the server, which is not limited herein.
  • control method before step S13, the control method also includes:
  • S21 Obtain identification conditions of the target user, the identification conditions include identification time period and identification location;
  • S29 Generate preset life track information of the target user according to the spatial correspondence and the identification period.
  • the identified position is the name of a certain position, that is to say, the identified position itself does not include spatial information such as the position's coordinates, range, distance from the home appliance, and orientation relative to the home appliance. Therefore, Home appliances cannot determine whether the current user needs to self-learn their operating habits by comparing the target user's recognition position with the current user's movement trajectory. The target user needs to establish the correspondence between the recognition position and the actual space in advance to determine the recognition Location Spatial information other than name.
  • the identification condition of the target user can be customized by the target user.
  • the target user or other users can enter the identification conditions of the target user through terminal devices such as mobile phones, tablet computers, notebook computers, and remote controls.
  • the identification 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 writing desk, and a balcony.
  • "time”, “starting time”, “ending time”, “activity time”, and “current time” may include hours, hours and minutes, Hours, minutes and seconds may also be included, but are not limited here.
  • the target user is a housewife
  • the recognition period of the recognition condition is 6:00-7:00
  • the recognition location of the recognition condition is the kitchen.
  • the target user is an office worker at home, the recognition period of the recognition condition is 7:30-8:00, and the recognition position of the recognition condition is a dining table.
  • the target user is a student at home, the recognition period of the recognition condition is 18:00-19:00, and the recognition position of the recognition condition is a writing desk.
  • the household appliance may include a radar, and the radar may be used to monitor whether there is a moving object.
  • the active object may be a target user who needs to self-learn its operating habits, a non-target user who does not need to self-learn its operating habits, an animal, or any other movable object.
  • the active moment can be understood as the moment when the radar confirms that the active object is detected. That is to say, the active moment can be the moment when the radar detects that the active object appears within the monitoring range for the first time, or it can be when the radar continues to monitor the objects within the monitoring range according to the preset frequency and detects that the active object is within the monitoring range again
  • the time of internal time is not limited here.
  • the active moment is in the identification period, which can be understood as the active moment is equal to the start moment of the identification period, or the active moment is equal to the end moment of the identification period, or is equal to other moments between the start moment and the end moment.
  • the first duration deviation can be set, That is, an activity moment that is earlier than the start moment of the identification period and does not exceed the first duration deviation or an activity moment that is later than the end moment of the identification period and does not exceed the first duration deviation is considered to be in the identification period.
  • the active moment is in the identification period, which can also be understood as the moment when the active moment is equal to the first duration deviation before the start moment of the identification period, or the moment when the active moment is equal to the first duration deviation after the end moment of the identification period, or The activity time is equal to other times between the time of the first duration deviation before the start time and the time of the first duration deviation after the end time.
  • the first duration deviation is set to 30 minutes; in some embodiments, the first duration deviation is set to 20 minutes; in other embodiments, the first duration deviation can also be set to other values, in This is not limited.
  • the APP on the mobile phone displays inquiry information such as "Are you the target user A whose identification conditions have been entered?", and if a signal representing "Yes" is received, enter step S27; If the signal is "No” or the signal is not received, then re-enter the step of monitoring the active object.
  • the target user whose identification period is earlier is firstly asked, and if it is not the target user, another target user whose identification period is later is asked. In one example, the target user whose identification period is later is firstly asked, and if it is not the target user, another target user whose identification period is earlier is asked.
  • the target user corresponding to the recognition period with a smaller duration deviation is firstly inquired, and if it is not the target user, another target user corresponding to the recognition period with a larger duration deviation is inquired. In this way, it is possible to ensure that the inquiry is carried out normally, to avoid missing the target user of the inquiry, and to improve the accuracy of the inquiry.
  • the radar of the household electrical appliance may track the activity trajectory of the moving object, and the activity trajectory tracked by the radar may include the activity orientation of the moving object relative to the radar and the distance of the moving object relative to the radar.
  • the active object is determined to be a target user who needs to self-learn its operating habits, the active object's activity trajectory is used as the spatial information of the target user's recognition position, and the spatial correspondence between the recognition position and the active object's activity trajectory is established, Therefore, the spatial information of the identified position can be determined according to the spatial correspondence.
  • step S29 according to the spatial correspondence and the identification period of the target user's identification condition, the preset life track information of the target user is generated. That is, the preset life trajectory of the target user includes the identification period of the target user and the pre-marked activity trajectory of the target user.
  • the recognition period for target user A who needs to self-learn his operating habits is from 6 am to 7 am
  • the recognition location for target user A who needs self-learning about his operating habits is the kitchen
  • the first duration The deviation is 30 minutes. If the activity time is any time between 5:30 am and 7:30 am, it is determined that the activity time is in the identification period of 6 am-7 am.
  • the identification period for the target user B who needs to self-learn his operating habits is from 7:30 am to 8 am.
  • the activity time when the detected moving object appears in the monitoring range is 7:10, and the moving track of the monitored moving object within the monitoring range is to appear on the left side of the monitoring range, moving from the position 8.5 meters in front of the left front of the home appliance to the home appliance 11.5 meters in front of the left side of the device, and stop or fine-tune actions near the position 11.5 meters in front of the left side of the home appliance.
  • the target user A who needs to self-learn his operating habits is determined not to be the target user A who needs to self-learn his operating habits, then ask whether the active object is the target user B who needs to self-learn his operating habits; It is the target user A who needs to self-learn its operating habits, then use the activity trajectory of the activity object as the spatial information of the kitchen, establish the corresponding relationship between the activity trajectory of the activity object and the space of the kitchen, and set the "6:00-7:00 , appearing on the left side of the monitoring range, within 8.5m-11.5m to the left, stay or fine-tune the action" as the preset life track information of the target user A who needs to self-learn his operating habits.
  • the preset life trajectory information of the target user includes a preset time period and a preset trajectory
  • the current user's life trajectory information includes the current moment and the current trajectory.
  • S131 Determine whether the current moment of the current user matches the preset time period of the target user, and determine whether the current track of the current user matches the preset track of the target user.
  • the preset time period may be the above-mentioned identification time period.
  • the preset time period may include a start time, an end time and other times between the start time and the end time.
  • the preset trajectory may be the activity trajectory of the target user's activity object marked in advance.
  • the current moment can be understood as the moment when the current user is monitored, specifically, it can be the moment when the current user is detected for the first time, or the moment when the current user is detected again according to a preset frequency, which is not limited here.
  • step S131 includes a matching period step and a matching trajectory step, wherein the matching period step can be performed first, and then the trajectory matching step; or the trajectory matching step can be performed first, and then the period matching step is performed, which is not limited here.
  • the matching result obtained by matching the current time and the preset time period for the first time will not affect The second step to match the current moment and the preset time period. That is to say, when the current trajectory of the current user matches the preset trajectory, if it is determined in the first matching period step that the current moment of the current user does not match the preset period of the target user, then it is determined that the current user is not Target users who need to self-learn their operating habits. However, if it is determined in the second matching period step that the current moment of the current user matches the preset period of the target user, it can be determined that the current user needs to learn their operating habits. Target users for self-learning.
  • step S15 includes:
  • S151 Determine that the current user is the target user when the current moment of the current user is within the preset period of the target user, and the coincidence degree between the current trajectory of the current user and the preset trajectory of the target user is greater than or equal to a preset threshold.
  • the current moment is in the preset period, which can be understood as the current moment is equal to the start moment of the preset period, or the current moment is equal to the end moment of the preset period, or the current moment is equal to other moments between the start moment and the end moment .
  • a second duration deviation can be set That is, the current moment that is earlier than the start moment of the preset period and does not exceed the second duration deviation or the current moment that is later than the end moment of the preset period and does not exceed the second duration deviation is considered to be in the preset period.
  • the current moment is in the preset time period, which can also be understood as the moment when the current moment is equal to the second time length deviation before the start time of the preset time period, or the current time is equal to the second time length deviation after the end time of the preset time period. time, or other times between the time when the current time is equal to the second time length deviation before the start time and the second time length deviation after the end time.
  • the second duration deviation is set to 30 minutes; in some embodiments, the second duration deviation is set to 20 minutes; in other embodiments, the second duration deviation can also be set to other values, in This is not limited.
  • the preset threshold is 85%, that is, when the overlap between the current trajectory of the current user and the preset trajectory of the target user reaches or exceeds 85% (such as 90%, 95%, 100%) , it can be considered that the current trajectory successfully matches the preset trajectory; when the overlap between the current trajectory of the current user and the preset trajectory of the target user is lower than 85%, it can be considered that the current trajectory cannot match the preset trajectory.
  • the preset time period for target users who need to self-learn their operating habits is 6:00 am to 7:00 am.
  • the preset trajectory of the target user who needs to self-learn their operating habits is to appear on the left side of the monitoring range, 8.5 meters to 11.5 meters in front of the left, stay or fine-tune the action.
  • the current time when the current user is monitored is 6:20 in the morning, and the current trajectory of the current user appears on the left side of the monitoring range, and stays in the range of 8.4-11.5 meters in front of the left. Since the coincidence degree of the current track and the preset track exceeds 85%, and the current moment is compared with the preset time period, it can be determined that the current user is a target user whose operating habits need to be self-learned.
  • control method further includes:
  • the current user's control habits for the household appliances can be self-learned.
  • the filing reminder can be provided through terminal devices such as mobile phones, tablet computers, notebook computers, and remote controls.
  • the way of prompting for filing may include text prompt and/or voice prompt.
  • the content of the filing prompt may include "Do you need to conduct self-study on your control habits? If necessary, please enter your identification conditions according to the prompt".
  • control method further includes: when it is determined that the current user needs to file, providing an interactive interface for inputting identification conditions; determining the identification conditions of the current user according to the input information of the interactive interface , and take the current user as one of the target users, and perform self-learning on the control habit of the current user for the home appliance.
  • the current user is prompted to create a file through the mobile phone.
  • the mobile phone includes a display screen.
  • the display screen of the mobile phone will display the prompt words "Do you need to self-study your control habits? If necessary, please enter your identification conditions according to the prompts".
  • the display screen of the mobile phone provides a selection button representing "Yes” and a cancel button representing "No".
  • the cancel button When it is detected that the cancel button is triggered, it is determined that the current user does not need to create a profile, and exits the current interface.
  • the selection button is triggered, it is determined that the current user needs to create a file, and then an interactive interface for inputting identification conditions is provided.
  • a mobile phone is used to prompt the current user to create a file, and the mobile phone has a voice recognition function.
  • the mobile phone includes a speaker and a display screen.
  • the speaker of the mobile phone will broadcast the prompt voice of "Do you need to self-learn your control habits? If necessary, please enter your identification conditions according to the prompts".
  • the current user detected this time is ignored.
  • an interactive interface for entering the recognition condition is provided through the display screen.
  • the control device 100 for household electrical appliances includes a monitoring module 12 , a comparing module 14 , a judging module 16 and a learning module 18 .
  • the monitoring module 12 is used for monitoring the life track information of the current user.
  • the comparison module 14 is used to compare the life trajectory information of the current user with the preset life trajectory information of the target user.
  • the judging module 16 is used to judge whether the current user is the target user according to the comparison result.
  • the learning module 18 is used to obtain the control habit parameters of the target user according to the self-learning model established by the target user for the setting parameters of the home appliances when the current user is the target user, and to control the home appliances according to the control habit parameters of the target users.
  • control device 100 for household electrical appliances by comparing the current user's life trajectory information with the pre-stored target user's preset life trajectory information, it can determine whether the current user needs to self-learn its operating habits target users, and when the current user is a target user who needs to self-learn their operating habits, it can self-learn the setting parameters of home appliances for this target user, so as to realize the establishment of self-learning models for different users and improve user experience. experience.
  • home appliances include but are not limited to air conditioners, humidifiers, air purifiers, TVs, smart speakers, and the like.
  • the current user may be understood as a moving object appearing within the monitoring range of the home appliance.
  • the household appliances may include a monitoring device, which can collect sound data, image data or radar signal data, etc. within the monitoring range according to a preset frequency.
  • a monitoring device which can collect sound data, image data or radar signal data, etc. within the monitoring range according to a preset frequency.
  • the detection device includes radar.
  • Target users can be understood as users who need home appliances to independently establish self-learning models and determine control habit parameters.
  • the preset life trajectory information may be pre-stored life trajectory information of the target user within the monitoring range of the home appliance.
  • there are multiple target users and the preset life track information of the multiple target users is different from each other. In this way, different target users can be distinguished based on the preset life trajectory information, and a self-learning model can be independently established for each target user.
  • target users may include housewives, home office workers, home students, and the like. In one example, the same home appliance can self-learn the control habits of 15 target users.
  • the current user’s life trajectory information After obtaining the current user’s life trajectory information, by comparing the current user’s life trajectory information with the target user’s preset life trajectory information, it can be determined whether the current user is a target user who needs to self-learn its operating habits, so as to determine whether Learn the current user's control habits for home appliances. If the comparison result shows that the current user is a target user who needs to self-learn its operating habits, then use the current user's setting parameters for home appliances as the target user's setting parameters for home appliances, and establish a self-study corresponding to the target user. learning model.
  • the setting parameters may include at least one of a temperature parameter, an air outlet mode parameter, and an air direction parameter.
  • the target user's control habit parameters determined according to the self-learning model may include at least one of setting parameters.
  • the self-learning of the target user's control habits is completed.
  • the setting parameters of the target user for the household appliances obtained seven times are weighted to obtain the final self-learning model corresponding to the user, so as to determine the control habit parameters of the target user.
  • the target user appears within 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 habit parameters of the target user, thereby simplifying operations and improving user experience.
  • the control device 100 further includes an acquisition module, a determination module, an inquiry module, an establishment module and a generation module.
  • the obtaining module is used to obtain the identification conditions of the target user, and the identification conditions include identification period and identification location.
  • the determining module is used for determining the active time when an active object is detected.
  • the inquiry module is used for inquiring whether the activity object is the target user when the activity moment is in the identification period.
  • the establishing module is used to establish the spatial correspondence between the activity track of the active object and the recognized position when the active object is the target user.
  • the generation module is used to generate the preset life trajectory information of the target user according to the spatial correspondence and the identification period.
  • the corresponding relationship between the recognized position and the actual space can be established, and the preset life trajectory information of the target user can be determined, so as to facilitate the comparison between the preset life trajectory information of the target user and the current user's life trajectory information.
  • the identified position is the name of a certain position, that is to say, the identified position itself does not include spatial information such as the position's coordinates, range, distance from the home appliance, and orientation relative to the home appliance. Therefore, Home appliances cannot determine whether the current user is a target user who needs to self-learn their operating habits by comparing the recognition position of the target user with the current user's movement trajectory. It is necessary to establish the correspondence between the recognition position and the actual space in advance to determine the identification Location Spatial information other than name.
  • the identification condition of the target user can be defined by the target user.
  • the target user or other users can enter the identification conditions of the target user through terminal devices such as mobile phones, tablet computers, notebook computers, and remote controls.
  • the identification 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 writing desk, and a balcony.
  • "time”, “starting time”, “ending time”, “activity time”, and “current time” may include hours, hours and minutes, Hours, minutes and seconds may also be included, but are not limited here.
  • the target user is a housewife
  • the recognition period of the recognition condition is 6:00-7:00
  • the recognition location of the recognition condition is the kitchen.
  • the target user is an office worker at home, the recognition period of the recognition condition is 7:30-8:00, and the recognition position of the recognition condition is a dining table.
  • the target user is a student at home, the recognition period of the recognition condition is 18:00-19:00, and the recognition position of the recognition condition is a writing desk.
  • the home appliances may include radar, and the radar may be used to monitor whether there is a moving object.
  • the active object may be a target user who needs to self-learn its operating habits, a non-target user who does not need to self-learn its operating habits, an animal, or any other movable object.
  • the active moment can be understood as the moment when the radar confirms that the active object is detected. That is to say, the active moment can be the moment when the radar detects that the active object appears within the monitoring range for the first time, or it can be when the radar continues to monitor the objects within the monitoring range according to the preset frequency and detects that the active object is within the monitoring range again
  • the time of internal time is not limited here.
  • the activity moment is in the recognition period, which can be understood as the activity moment is equal to the start moment of the recognition period, or the activity moment is equal to the end moment of the recognition period, or the activity moment is equal to other moments between the start moment and the end moment.
  • the first duration deviation can be set, That is, an activity moment that is earlier than the start moment of the identification period and does not exceed the first duration deviation or an activity moment that is later than the end moment of the identification period and does not exceed the first duration deviation is considered to be in the identification period.
  • the active moment is in the identification period, which can also be understood as the moment when the active moment is equal to the first duration deviation before the start moment of the identification period, or the moment when the active moment is equal to the first duration deviation after the end moment of the identification period, or The activity time is equal to other times between the time of the first duration deviation before the start time and the time of the first duration deviation after the end time.
  • the first duration deviation is set to 30 minutes; in some embodiments, the first duration deviation is set to 20 minutes; in other embodiments, the first duration deviation can also be set to other values, in This is not limited.
  • the APP on the mobile phone displays inquiry information such as "Are you the target user A whose identification conditions have been entered?", and if a signal representing "Yes" is received, the step of establishing a spatial correspondence is entered; if If the signal characterized as "No" is received or the signal is not received, then re-enter the step of monitoring the active object.
  • the query logic in this case, for example, first ask the target user whose identification period is earlier, if it is not the target user, then ask another target user who is later in the identification period; or first ask the target user who is later in the identification period If the target user is not the target user, then ask another target user who is earlier in the recognition period; or, first ask the target user corresponding to the recognition period with a smaller duration deviation, if not the target user, then ask the target user with a larger duration deviation Identify another target user corresponding to the time period. In this way, it is possible to ensure that the inquiry is carried out normally, to avoid missing the target user of the inquiry, and to improve the accuracy of the inquiry.
  • the radar of the home appliance can track the activity track of the moving object, and the activity track tracked by the radar can include the moving direction of the moving object relative to the radar and the distance of the moving object relative to the radar.
  • the radar can identify the layout of the house through the tracked activity trajectory. For example, the kitchen is 10 meters in front of the left, the dining room is 5 meters in front of it, and the study is 3 meters in front of the right.
  • the active track of the active object is used as the spatial information of the recognition position of the target user, and the spatial correspondence between the active track of the active object and the recognition position is established, Therefore, the spatial information of the identified position can be determined according to the spatial correspondence.
  • preset life trajectory information of the target user is generated. That is, the preset life trajectory of the target user includes the identification period of the target user and the pre-marked activity trajectory of the target user.
  • the recognition period for target user A who needs to self-learn his operating habits is from 6 am to 7 am
  • the recognition location for target user A who needs self-learning about his operating habits is the kitchen
  • the first duration The deviation is 30 minutes. If the activity time is any time between 5:30 am and 7:30 am, it is determined that the activity time is in the identification period of 6 am-7 am.
  • the identification period for the target user B who needs to self-learn his operating habits is from 7:30 am to 8 am.
  • the activity time when the detected moving object appears in the monitoring range is 7:10, and the moving track of the monitored moving object within the monitoring range is to appear on the left side of the monitoring range, moving from the position 8.5 meters in front of the left front of the home appliance to the home appliance 11.5 meters in front of the left side of the device, and stop or fine-tune actions near the position 11.5 meters in front of the left side of the home appliance.
  • the target user A who needs to self-learn his operating habits is determined not to be the target user A who needs to self-learn his operating habits, then ask whether the active object is the target user B who needs to self-learn his operating habits; It is the target user A who needs to self-learn its operating habits, then use the activity trajectory of the activity object as the spatial information of the kitchen, establish the corresponding relationship between the activity trajectory of the activity object and the space of the kitchen, and set the "6:00-7:00 , appearing on the left side of the monitoring range, within 8.5m-11.5m to the left, stay or fine-tune the action" as the preset life track information of the target user A who needs to self-learn his operating habits.
  • the comparison module 14 is also used to judge whether the current moment of the current user matches the preset time period of the target user, and judge whether the current trajectory of the current user matches the preset trajectory of the target user.
  • the preset time period may be the above-mentioned identification time period.
  • the preset time period may include a start time, an end time and other times between the start time and the end time.
  • the preset trajectory may be the activity trajectory of the target user's activity object marked in advance.
  • the current moment can be understood as the moment when the current user is monitored, specifically, it can be the moment when the current user is detected for the first time, or the moment when the current user is detected again according to a preset frequency, which is not limited here.
  • comparison module 14 can perform the step of matching period and the step of matching trajectory, wherein, the step of matching period can be performed first, and then the step of matching trajectory can be performed; limited.
  • the matching result obtained by matching the current time and the preset time period for the first time will not affect The second step to match the current moment and the preset time period. That is to say, when the current trajectory of the current user matches the preset trajectory, if it is determined in the first matching period step that the current moment of the current user does not match the preset period of the target user, then it is determined that the current user is not Target users who need to self-learn their operating habits. However, if it is determined in the second matching period step that the current moment of the current user matches the preset period of the target user, it can be determined that the current user needs to learn their operating habits. Target users for self-learning.
  • the learning module 16 is also used for when the current user is in the target user's preset period at the current moment, and the coincidence degree between the current user's current trajectory and the target user's preset trajectory is greater than or equal to the preset When the threshold is reached, the current user is determined to be the target user.
  • the current moment is in the preset period, which can be understood as the current moment is equal to the start moment of the preset period, or the current moment is equal to the end moment of the preset period, or the current moment is equal to other moments between the start moment and the end moment .
  • a second duration deviation can be set That is, the current moment that is earlier than the start moment of the preset period and does not exceed the second duration deviation or the current moment that is later than the end moment of the preset period and does not exceed the second duration deviation is considered to be in the preset period.
  • the current moment is in the preset time period, which can also be understood as the moment when the current moment is equal to the second time length deviation before the start time of the preset time period, or the current time is equal to the second time length deviation after the end time of the preset time period. time, or other times between the time when the current time is equal to the second time length deviation before the start time and the second time length deviation after the end time.
  • the second duration deviation is set to 30 minutes; in some embodiments, the second duration deviation is set to 20 minutes; in other embodiments, the second duration deviation can also be set to other values, in This is not limited.
  • the preset threshold is 85%, that is, when the overlap between the current trajectory of the current user and the preset trajectory of the target user reaches or exceeds 85% (such as 90%, 95%, 100%) , it can be considered that the current trajectory is successfully compared with the preset trajectory; when the coincidence degree between the current trajectory of the current user and the preset trajectory of the target user is lower than 85%, it can be considered that the current trajectory cannot be compared with the preset trajectory.
  • the preset time period for target users who need to self-learn their operating habits is 6:00 am to 7:00 am.
  • the preset trajectory of the target user who needs to self-learn their operating habits is to appear on the left side of the monitoring range, 8.5 meters to 11.5 meters in front of the left, stay or fine-tune the action.
  • the current time when the current user is monitored is 6:20 in the morning, and the current trajectory of the current user appears on the left side of the monitoring range, and stays in the range of 8.4-11.5 meters in front of the left. Since the coincidence degree of the current track and the preset track exceeds 85%, and the current moment is compared with the preset time period, it can be determined that the current user is a target user whose operating habits need to be self-learned.
  • control device 100 further includes a filing module.
  • the file building module is used to prompt the current user to file when the current user is not the target user.
  • the current user's control habits for the household appliances can be self-learned.
  • the filing reminder can be provided through terminal devices such as mobile phones, tablet computers, notebook computers, and remote controls.
  • the way of prompting for filing may include text prompt and/or voice prompt.
  • the content of the filing prompt may include "Do you need to conduct self-study on your control habits? If necessary, please enter your identification conditions according to the prompt".
  • control device 100 further includes a recording module.
  • the input module is used to provide an interactive interface for inputting identification conditions when it is determined that the current user needs to file; determine the identification conditions of the current user according to the input information of the interactive interface, and take the current user as one of the target users.
  • the current user performs self-study on control habits of household electrical appliances.
  • the current user is prompted to create a file through the mobile phone.
  • the mobile phone includes a display screen.
  • the display screen of the mobile phone will display the prompt words "Do you need to self-study your control habits? If necessary, please enter your identification conditions according to the prompts".
  • the display screen of the mobile phone provides a selection button representing "Yes” and a cancel button representing "No".
  • the cancel button When it is detected that the cancel button is triggered, it is determined that the current user does not need to create a profile, and exits the current interface.
  • the selection button is triggered, it is determined that the current user needs to create a file, and then an interactive interface for inputting identification conditions is provided.
  • a mobile phone is used to prompt the current user to create a file, and the mobile phone has a voice recognition function.
  • the mobile phone includes a speaker and a display screen.
  • the speaker of the mobile phone will broadcast the prompt voice of "Do you need to self-learn your control habits? If necessary, please enter your identification conditions according to the prompts".
  • the current user detected this time is ignored.
  • an interactive interface for entering the recognition condition is provided through the display screen.
  • the electronic device 200 of the embodiment of the present application includes one or more processors 22 and memory 24, and the memory 24 stores a computer program 26.
  • the computer program 26 is executed by the processor 22, any of the above-mentioned items can be realized.
  • the electronic device of the embodiment of the present application by comparing the current user's life track information with the pre-stored target user's preset life track information, it can be determined whether the current user is a target user who needs to self-learn its operating habits, And when the current user is a target user who needs to self-learn its operating habits, it can self-learn the setting parameters of home appliances for the target user, so as to realize the establishment of self-learning models for different users and improve user experience.
  • the processor 22 is configured to implement the above step S11 , step S13 , step S15 and step S17 .
  • the processor 22 is configured to implement the above step S21, step S23, step S25, step S27 and step S29.
  • the processor 22 is configured to implement the above step S131.
  • the processor 22 is configured to implement the above step S151.
  • the processor 22 is configured to implement the above step S19.
  • the electronic device 200 is a home appliance or a server.
  • the above-mentioned control method for a household appliance may be realized by a household appliance, and the method for controlling the above-mentioned household appliance may also be realized by a server.
  • a control method of another household electrical appliance may be implemented by one household electrical appliance.
  • a control method for an air conditioner may be implemented through a smart refrigerator.
  • the method for controlling the air conditioner in the living room can be realized through the air conditioner in the bedroom.
  • the computer-readable storage medium of the embodiment of the present application has a computer program stored thereon, and is characterized in that, when the program is executed by a processor, the steps of the method for controlling a household appliance in any one of the above-mentioned embodiments are implemented.
  • step S11 , step S13 , step S15 and step S17 of the above control method can be implemented.
  • step S21 , step S23 , step S25 , step S27 and step S29 of the above control method can be implemented.
  • step S131 of the above control method can be realized.
  • step S151 of the above control method can be realized.
  • step S19 of the above control method can be realized.
  • the computer-readable storage medium may be set in a server or in a home appliance, and the home appliance can communicate with the server to obtain a corresponding program.
  • a computer program includes computer program code.
  • the computer program code may be in source code form, object code form, executable file or some intermediate form, etc.
  • the computer-readable storage medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, removable hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random memory Access memory (RAM, Random Access Memory), and software distribution media, etc.
  • the processor can be a central processing unit (Central Processing Unit, CPU), or other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • CPU Central Processing Unit
  • DSP Digital Signal Processor
  • ASIC Application Specific Integrated Circuit
  • FPGA Field-Programmable Gate Array
  • references to the terms “one embodiment,” “some embodiments,” “illustrative embodiments,” “example,” “specific examples,” or “some examples” are intended to mean A specific feature, structure, material, or characteristic described by an embodiment or example is included in at least one embodiment or example of the present application.
  • schematic representations of the above terms do not necessarily refer to the same embodiment or example.
  • the specific features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
  • a "computer-readable medium” may be any device that can contain, store, communicate, propagate, or transmit a program for use in or in conjunction with an instruction execution system, device, or device.
  • computer-readable media include the following: electrical connection with one or more wires (electronic device), portable computer disk case (magnetic device), random access memory (RAM), Read Only Memory (ROM), Erasable and Editable Read Only Memory (EPROM or Flash Memory), Fiber Optic Devices, and Portable Compact Disc Read Only Memory (CDROM).
  • the computer-readable medium may even be paper or other suitable medium on which the program can be printed, since the program can be read, for example, by optically scanning the paper or other medium, followed by editing, interpretation or other suitable processing if necessary.
  • the program is processed electronically and stored in computer memory.
  • each part of the embodiments of the present application may be implemented by hardware, software, firmware or a combination thereof.
  • various steps or methods may be implemented by software or firmware stored in memory and executed by a suitable instruction execution system.
  • a suitable instruction execution system For example, if implemented in hardware, as in another embodiment, it can be implemented by any one or combination of the following techniques known in the art: Discrete logic circuits, ASICs with suitable combinational logic gates, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.
  • each functional unit in each embodiment of the present application may be integrated into one processing module, each unit may exist separately physically, or two or more units may be integrated into one module.
  • the above-mentioned integrated modules can be implemented in the form of hardware or in the form of software function modules. If the integrated modules are realized in the form of software function modules and sold or used as independent products, they can also be stored in a computer-readable storage medium.
  • the storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, and the like.

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Abstract

Procédé de commande d'appareil électroménager, appareil de commande, dispositif électronique et support d'enregistrement. Le procédé de commande d'appareil électroménager comprend les étapes consistant à : surveiller des informations de suivi de vie d'un utilisateur actuel ; comparer les informations de suivi de vie de l'utilisateur actuel à des informations de suivi de vie prédéfinies d'un utilisateur cible ; déterminer si l'utilisateur actuel est l'utilisateur cible en fonction d'un résultat de comparaison ; lorsque l'utilisateur actuel est l'utilisateur cible, obtenir un paramètre d'habitude de commande de l'utilisateur cible selon un modèle d'auto-apprentissage établi sur la base d'un paramètre défini par l'utilisateur cible pour l'appareil électroménager, et commander l'appareil électroménager en fonction du paramètre d'habitude de commande de l'utilisateur cible.
PCT/CN2022/102310 2021-11-08 2022-06-29 Procédé de commande d'appareil électroménager, appareil de commande, dispositif électronique et support d'enregistrement WO2023077835A1 (fr)

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