Control method and device of electric bed, electric bed and storage medium

Abstract

The embodiment of the invention discloses an electric bed control method and device, an electric bed and a storage medium, and the method comprises the steps: obtaining historical habit information of a target user, and predicting a first activity corresponding to the target user according to the historical habit information and time information; determining a first zero-gravity sub-mode corresponding to the first activity; when it is detected that the target user is located on the electric bed, target activity information of the target user is obtained, and a second activity corresponding to the target user is predicted according to the target activity information; and if the first activity is different from the second activity, determining a second zero-gravity sub-mode according to the first activity and the second activity. By implementing the embodiment, the diversity of the zero-gravity mode can be improved, and the use convenience of the zero-gravity mode is improved.

Description

Technical Field

[0001] The present application relates to the field of smart home technology, and in particular to a control method and device for an electric bed, an electric bed and a storage medium. Background Art

[0002] The zero-gravity mode of an electric bed refers to adjusting different parts of the electric bed to adjust the body posture of the user lying on the electric bed in a weightless environment, thereby reducing the pressure on various parts of the body. Currently, the zero-gravity mode of an electric bed is usually a fixed posture, which limits the diversity of the zero-gravity mode. Users also need to think about the timing of turning on the zero-gravity mode, which reduces the frequency and convenience of using the zero-gravity mode. Summary of the invention

[0003] The embodiments of the present application disclose a control method and device for an electric bed, an electric bed, and a storage medium, which can improve the diversity of zero-gravity modes and improve the ease of use of the zero-gravity mode.

[0004] The present application embodiment discloses a control method for an electric bed, which is applied to an electric bed, and the electric bed includes multiple zero-gravity sub-modes; the method includes:

[0005] Acquire historical habit information of a target user, and predict a first activity corresponding to the target user according to the historical habit information and time information;

[0006] Determining a first zero-gravity sub-mode corresponding to the first activity, and adjusting the posture of the electric bed according to the first zero-gravity sub-mode;

[0007] When it is detected that the target user is on the electric bed, obtaining target activity information of the target user, and predicting a second activity corresponding to the target user according to the target activity information;

[0008] If the first activity and the second activity are different, a second zero-gravity sub-mode is determined according to the first activity and the second activity, and the posture of the electric bed is adjusted according to the second zero-gravity sub-mode.

[0009] In one embodiment, the electric bed includes one or more sensors, and the target activity information includes user sensor data collected by the sensors; the step of obtaining the target activity information of the target user and predicting the second activity corresponding to the target user according to the target activity information includes:

[0010] Acquire user sensor data collected by the one or more sensors within a first preset time period;

[0011] Determining the activity level of the target user according to the user sensing data;

[0012] If the activity level is not higher than the activity level threshold, predicting a second activity corresponding to the target user according to the user sensing data.

[0013] In one embodiment, determining the second zero-gravity sub-mode according to the first activity and the second activity includes:

[0014] Determining a first prediction confidence corresponding to the first activity and a second prediction confidence corresponding to the second activity;

[0015] A second zero-gravity submode is determined according to the first prediction confidence and the second prediction confidence.

[0016] In one embodiment, determining the second zero-gravity sub-mode according to the first prediction confidence, the second prediction confidence and the confidence difference includes:

[0017] In the case where both the first prediction confidence and the second prediction confidence are not less than the confidence threshold, if the confidence difference between the first prediction confidence and the second prediction confidence is greater than the difference threshold, it is determined that the zero gravity submode corresponding to the target activity is the second zero gravity submode, and the target activity is the activity corresponding to the larger prediction confidence of the first prediction confidence and the second prediction confidence;

[0018] When both the first prediction confidence and the second prediction confidence are less than a confidence threshold, or when the confidence difference is less than the difference threshold, outputting inquiry information, wherein the inquiry information is used to inquire about the real activity of the target user;

[0019] receiving feedback information regarding the inquiry information, and determining the actual activity of the target user according to the feedback information;

[0020] The zero gravity sub-mode corresponding to the real activity is determined to be the second zero gravity sub-mode.

[0021] In one embodiment, predicting the first activity corresponding to the target user according to the historical habit information and the time information includes:

[0022] Predicting a first activity corresponding to the target user by using a first prediction model according to the historical habit information and the time information;

[0023] The predicting, according to the target activity information, a second activity corresponding to the target user includes:

[0024] Predicting a second activity corresponding to the target user by using a second prediction model according to the target activity information;

[0025] After determining that the zero gravity sub-mode corresponding to the real activity is the second zero gravity sub-mode, the method further includes:

[0026] When the real activity is different from the first activity, adjusting the first prediction parameter of the first prediction model according to the real activity and the first activity;

[0027] When the real activity is different from the second activity, the second prediction parameter of the second prediction model is adjusted according to the real activity and the second activity.

[0028] In one embodiment, the method further comprises:

[0029] When the target user is located on the electric bed, a mode parameter modification instruction of the target user is detected;

[0030] According to the mode parameter modification instruction of the target user, the mode parameters corresponding to the current zero gravity sub-mode are adjusted within the preset ranges corresponding to the multiple zero gravity sub-modes.

[0031] In one embodiment, adjusting the posture of the electric bed according to the second zero-gravity sub-mode includes:

[0032] When the second zero-gravity sub-mode is different from the first zero-gravity sub-mode, acquiring state information of the target user, the state information including one or more of position information, sleep information, and posture information;

[0033] If it is determined that the state information meets the mode switching requirement, the posture of the electric bed is adjusted according to the second zero-gravity sub-mode.

[0034] The present application embodiment discloses a control device for an electric bed, which is applied to an electric bed. The electric bed includes multiple zero-gravity sub-modes. The device includes:

[0035] A first prediction module, used to obtain historical habit information of a target user, and predict a first activity corresponding to the target user according to the historical habit information and time information;

[0036] a first adjustment module, configured to determine a first zero-gravity sub-mode corresponding to the first activity, and adjust the posture of the electric bed according to the first zero-gravity sub-mode;

[0037] A second prediction module is used to obtain target activity information of the target user when it is detected that the target user is located on the electric bed, and predict a second activity corresponding to the target user according to the target activity information;

[0038] The second adjustment module is used to determine a second zero-gravity sub-mode according to the first activity and the second activity if the first activity is different from the second activity, and adjust the posture of the electric bed according to the second zero-gravity sub-mode.

[0039] In one embodiment, the electric bed includes one or more sensors, and the target activity information includes user sensor data collected by the sensors; the second prediction module is also used to obtain the user sensor data collected by the one or more sensors within a first preset time period; determine the activity level of the target user based on the user sensor data; if the activity level is not higher than the activity level threshold, predict the second activity corresponding to the target user based on the user sensor data.

[0040] In one embodiment, the second adjustment module is further used to determine a first prediction confidence corresponding to the first activity and a second prediction confidence corresponding to the second activity; and determine a second zero-gravity sub-mode according to the first prediction confidence and the second prediction confidence.

[0041] In one embodiment, the second adjustment module is also used to, when both the first prediction confidence and the second prediction confidence are not less than the confidence threshold, if the confidence difference between the first prediction confidence and the second prediction confidence is greater than the difference threshold, determine that the zero gravity sub-mode corresponding to the target activity is the second zero gravity sub-mode, and the target activity is the activity corresponding to the larger prediction confidence of the first prediction confidence and the second prediction confidence; when both the first prediction confidence and the second prediction confidence are less than the confidence threshold, or the confidence difference is less than the difference threshold, output inquiry information, the inquiry information is used to inquire about the real activity of the target user; receive feedback information for the inquiry information, and determine the real activity of the target user based on the feedback information; determine that the zero gravity sub-mode corresponding to the real activity is the second zero gravity sub-mode.

[0042] In one embodiment, the first prediction module is further used to predict the first activity corresponding to the target user through the first prediction model based on the historical habit information and the time information; the second prediction module is further used to predict the second activity corresponding to the target user through the second prediction model based on the target activity information; the control device of the electric bed may also include a prediction parameter adjustment module for adjusting the first prediction parameter of the first prediction model according to the real activity and the first activity when the real activity is different from the first activity; and adjusting the second prediction parameter of the second prediction model according to the real activity and the second activity when the real activity is different from the second activity.

[0043] In one embodiment, the control device of the electric bed may further include a mode parameter adjustment module, which is used to detect the mode parameter modification instruction of the target user when the target user is located on the electric bed; and adjust the mode parameters corresponding to the current zero gravity sub-mode within the preset range corresponding to the multiple zero gravity sub-modes according to the mode parameter modification instruction of the target user.

[0044] In one embodiment, the second adjustment module is also used to obtain status information of the target user when the second zero-gravity sub-mode is different from the first zero-gravity sub-mode, the status information including one or more of position information, sleep information and posture information; if it is determined that the status information meets the mode switching requirements, the posture of the electric bed is adjusted according to the second zero-gravity sub-mode.

[0045] The present application embodiment discloses an electric bed, comprising:

[0046] A memory storing executable program code;

[0047] a processor coupled to the memory;

[0048] The processor calls the executable program code stored in the memory to execute the method described in any one of the above embodiments.

[0049] An embodiment of the present application discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the processor executes the method described in any one of the above embodiments.

[0050] Through the control method, device, electric bed and storage medium of the electric bed disclosed in the embodiments of the present application, the electric bed can obtain the historical habit information of the target user, predict the first activity corresponding to the target user based on the historical habit information and time information, determine the first zero gravity sub-mode corresponding to the first activity, and adjust the posture of the electric bed according to the first zero gravity sub-mode; when it is detected that the target user is located on the electric bed, the target activity information of the target user is obtained, and based on the target activity information, the second activity corresponding to the target user is predicted; if the first activity and the second activity are different, the second zero gravity sub-mode is determined based on the first activity and the second activity, and the posture of the electric bed is adjusted according to the second zero gravity sub-mode. By implementing this embodiment, the electric bed can correspond to various activities of the user by setting multiple zero-gravity sub-modes, thereby improving the diversity of the zero-gravity mode. In addition, the electric bed determines the user's first activity through historical habit information and time information, and can automatically adjust to the first zero-gravity sub-mode corresponding to the first activity at every moment. The user does not need to consider the timing of turning on the zero-gravity mode, thereby improving the convenience of using the zero-gravity mode. In addition, when the user goes to bed, the user's second activity can also be determined according to the user's target activity information. When the first activity and the second activity are different, in order to ensure that the zero-gravity sub-mode of the electric bed matches the user activity, the second zero-gravity sub-mode can also be determined by the first activity and the second activity, thereby improving the accuracy of the zero-gravity mode. The automatic secondary adjustment of the electric bed also further improves the convenience of using the zero-gravity mode. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0052] Figure 1 It is a flow chart of a control method of an electric bed disclosed in an embodiment of the present application;

[0053] Figure 2 It is a flow chart of another control method of an electric bed disclosed in an embodiment of the present application;

[0054] Figure 3 It is a flowchart of a method for determining a second zero-gravity sub-mode according to a first activity and a second activity disclosed in an embodiment of the present application;

[0055] Figure 4 is a flow chart of a method for adjusting the posture of an electric bed according to a second zero-gravity sub-mode disclosed in an embodiment of the present application;

[0056] Figure 5 It is a modular schematic diagram of a control device for an electric bed disclosed in an embodiment of the present application;

[0057] Figure 6 It is a structural block diagram of an electric bed disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0059] It should be noted that the terms "including" and "having" in the embodiments of the present application and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0060] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, a first activity may be referred to as a second activity, and similarly, a second activity may be referred to as a first activity without departing from the scope of this application. Both the first activity and the second activity are activities, but they are not the same activity.

[0061] The embodiments of the present application disclose a control method and device for an electric bed, an electric bed, and a storage medium, which can improve the diversity of zero-gravity modes and improve the ease of use of the zero-gravity mode.

[0062] The following is a detailed description with reference to the accompanying drawings.

[0063] like Figure 1 As shown, Figure 1 1 is a flow chart of a control method for an electric bed disclosed in an embodiment of the present application. The control method for an electric bed can be applied to an electric bed. The control method for an electric bed may include the following steps:

[0064] Step 110 , obtaining historical habit information of the target user, and predicting a first activity corresponding to the target user based on the historical habit information and time information.

[0065] The historical habit information may refer to the target user's behavior and preference data related to the electric bed in the historical time period. Optionally, the target user's historical habit information may include the activity habits corresponding to each time period. For example, the activity habit corresponding to 10:30 pm to 11 pm may be applying a facial mask, the activity habit corresponding to the time period from 12 pm to 12:30 pm may be playing with a mobile phone to prepare for sleep, the activity habit corresponding to 1 am to 3 am may be deep sleep, and the activity habit corresponding to 5 am to 6 am may be light sleep.

[0066] Optionally, the target user may refer to a user bound to the electric bed, and the electric bed may be bound to one or more target users. When bound to one target user, the electric bed may obtain the historical habit information of the target user. When bound to multiple target users, the electric bed may detect the target user currently on the electric bed and obtain the historical habit information of the target user currently on the electric bed. If no target user exists on the electric bed, the historical habit information of the target user with the highest priority may be obtained according to the priorities corresponding to the multiple target users.

[0067] The time information may include the current time and / or the current time period, and the electric bed may determine the historical activity habit corresponding to the time information based on the historical habit information and time information of the target user. For example, if the current time is 10:30 p.m., the historical activity habit corresponding to the time information is determined to be applying a facial mask. In one embodiment, the electric bed may predict the first activity corresponding to the target user through a first prediction model based on the historical habit information and time information.

[0068] Optionally, the electric bed can calculate the frequency of various activities in each time period based on historical habit information, and determine the current time period based on time information, select the activity with the highest frequency in the current time period as the prediction result, and obtain the first activity corresponding to the target user. For example, if the frequency of applying a facial mask for the target user is 80% in the time period from 10:30 to 11:00 p.m., the electric bed can predict that the target user is applying a facial mask in this time period.

[0069] Optionally, the first prediction model may be a classification model. The electric bed may extract feature information from historical habit information, train a classification model using a machine learning algorithm, and input time information into the classification model, so that the classification model may predict the first activity corresponding to the target user from a variety of activities. The machine learning algorithm and the classification model may correspond.

[0070] Step 120: determine a first zero-gravity submode corresponding to the first activity, and adjust the posture of the electric bed according to the first zero-gravity submode.

[0071] The electric bed may include multiple zero-gravity sub-modes, which may correspond to multiple activities of the user one by one. The posture of the electric bed corresponding to each zero-gravity sub-mode may be different. The posture of the electric bed is adjusted based on the zero-gravity sub-mode to ensure the comfort and support of the user in different activities. Optionally, the multiple zero-gravity sub-modes may include but are not limited to reading mode, leisure mode, sleep mode, massage mode, and work mode, etc. The activities corresponding to each zero-gravity sub-mode are not limited. The activity corresponding to the reading mode may be reading, or an activity similar to reading, such as playing games.

[0072] The electric bed can determine the first zero gravity sub-mode corresponding to the first activity, and obtain the mode parameters corresponding to the first zero gravity sub-mode, so as to adjust the posture of the electric bed according to the mode parameters. The mode parameters corresponding to each zero gravity sub-mode of the electric bed may include the angle, curvature and height of each part of the electric bed. It should be understood that although various zero gravity sub-modes correspond to different activities, each zero gravity sub-mode must make the user's body reach a zero gravity posture. Therefore, the mode parameters corresponding to multiple zero gravity sub-modes are within the preset range, and the difference between the mode parameters corresponding to each zero gravity sub-mode is small. The mode parameters corresponding to each zero gravity sub-mode are obtained after adjustment and determination within the preset range. For example, the mode parameters corresponding to each zero gravity sub-mode include the head tilt angle corresponding to the head area of ​​the electric bed, and the head tilt angle can be limited to the preset head tilt angle range.

[0073] Since steps 110 to 120 do not use the real-time data of the target user, the electric bed can perform steps 110 to 120 when the target user has not gotten into bed, that is, the electric bed can set the zero-gravity mode before the user gets into bed to prompt and attract the user to get into bed to perform the first activity, thereby increasing the frequency of use of the zero-gravity mode of the electric bed. Optionally, the electric bed can also perform the above steps 110 to 120 when the user is on the electric bed, and there is no limitation to this.

[0074] Step 130 , when it is detected that the target user is on the electric bed, the target activity information of the target user is obtained, and a second activity corresponding to the target user is predicted according to the target activity information.

[0075] The electric bed may include one or more sensors, such as a pressure sensor, an infrared sensor, a body temperature sensor, etc. Through the data detected by one or more sensors, the electric bed can detect whether the target user is located on the electric bed. For example, the pressure sensor can detect the pressure on the electric bed, thereby determining whether the target user is located on the electric bed. The electric bed can also use the user sensing data detected by one or more sensors as the target activity information of the target user when it is detected that the target user is located on the electric bed, thereby predicting the second activity corresponding to the target user based on the target activity information.

[0076] Among them, the electric bed can also predict the second activity corresponding to the target user through the second prediction model according to the target activity information. The second prediction model is a different model from the first prediction model to process different input data respectively. Optionally, the second prediction model can be a time series prediction model. The electric bed can arrange the user sensor data detected by each sensor in chronological order to form a time series, and then analyze the activity characteristics of the target user through the time series analysis method, so as to predict the second activity corresponding to the target user according to the activity characteristics of the target user and through the time series prediction model. Among them, the time series analysis method may include but is not limited to analysis methods such as moving average and exponential smoothing, and the time series prediction model may include but is not limited to the ARIMA model.

[0077] Optionally, the electric bed can also communicate with the target user's wearable device and obtain the target activity information of the target user detected by the wearable device when it is detected that the target user is located on the electric bed. The target activity information may include the target user's pulse, blood pressure, etc. Compared with the data detected by the sensor of the electric bed, the wearable device can be close to the user's body, and the detected data is more accurate, thereby improving the accuracy of the target activity information. Only when it is detected that the target user is located on the electric bed will the wearable device communicate with the target user, which can also improve data security and privacy.

[0078] It is understandable that the electric bed can perform steps 110 to 120 before the user gets on the bed, that is, when the target user is detected on the electric bed, the electric bed has been adjusted to the posture corresponding to the first zero-gravity sub-mode. In the case of postures corresponding to different zero-gravity sub-modes, the scheme for the electric bed to detect the target activity information of the target user may be different. When the mode parameters corresponding to the zero-gravity sub-mode are limited to a preset range, different activities require different detection accuracy and sensitivity, thereby providing more accurate and suitable adjustments.

[0079] As an optional implementation, the electric bed can determine the key area on the electric bed corresponding to the first activity, and determine the sensor set in the key area among the one or more sensors included in the electric bed as the first sensor, and determine the sensors set in other areas as the second sensor, so as to determine the first user sensor data and the second user sensor data detected by the first sensor as the target activity information of the target user, and predict the second activity corresponding to the target user through the second prediction model. In the prediction process of the second prediction model, the weight of the first user sensor data can be higher than the weight of the second user sensor data.

[0080] Step 140: If the first activity and the second activity are different, determine a second zero-gravity submode according to the first activity and the second activity, and adjust the posture of the electric bed according to the second zero-gravity submode.

[0081] If the first activity and the second activity are different, it means that the determined first activity may be incorrect, and the first zero-gravity submode corresponding to the first activity may also be incorrect. Since the body postures corresponding to the various zero-gravity submodes are not very different, the target user cannot determine whether the current zero-gravity submode is correct. Therefore, the electric bed can obtain the first activity and the second activity through two predictions, and when the first activity and the second activity are different, the electric bed can determine the second zero-gravity submode based on the first activity and the second activity, thereby improving the accuracy of the second zero-gravity submode and adjusting the posture of the electric bed according to the second zero-gravity submode.

[0082] Optionally, the second zero gravity sub-mode may be the zero gravity sub-mode corresponding to the second activity, or the zero gravity sub-mode corresponding to the first activity, or a new zero gravity sub-mode generated based on the zero gravity sub-mode corresponding to the second activity and the zero gravity sub-mode corresponding to the first activity. The embodiment of the present application does not limit the method for determining the second zero gravity sub-mode based on the first activity and the second activity.

[0083] In one embodiment, since the mode parameters corresponding to multiple zero-gravity sub-modes are within a preset range, and the difference between the mode parameters corresponding to each zero-gravity sub-mode is small, even if the mode parameters corresponding to the zero-gravity sub-mode are adjusted, the target user cannot accurately adjust the mode parameters corresponding to the current zero-gravity sub-mode. Therefore, in the case where it is difficult to adjust the mode parameters corresponding to the zero-gravity sub-mode, in order to provide human-computer interactivity between the target user and the electric bed, when the target user is located on the electric bed, the target user can issue a mode parameter modification instruction, and the electric bed can detect the target user's mode parameter modification instruction, and according to the target user's mode parameter modification instruction, adjust the mode parameters corresponding to the current zero-gravity sub-mode within the angle range corresponding to the multiple zero-gravity sub-modes.

[0084] Among them, the current zero gravity sub-mode may include the first zero gravity sub-mode or the second zero gravity sub-mode, the mode parameter modification instruction does not include the numerical modification content for a certain parameter, and the mode parameter modification instruction is used to characterize the experience of a certain body part of the target user. The electric bed can determine the target part according to the mode parameter corresponding to the current zero gravity sub-mode and the mode parameter modification instruction, and adjust the mode parameter of the target part, wherein the target part is a part determined according to the body part of the target user. It should be understood that the body part represented by the mode parameter modification instruction may not match the target part. For example, the model parameter modification instruction may be an instruction of "I feel uncomfortable in my buttocks", but the target part may be the head determined according to the buttocks of the target user. If the target user adjusts it manually, he may be obsessed with adjusting the parameters corresponding to the buttocks of the electric bed, but he cannot solve the problem of the target user. The electric bed can adjust the mode parameters of the target part through comprehensive analysis to affect the experience of other body parts of the target user. Implementing this embodiment can improve the human-computer interaction between the target user and the electric bed, and improve the convenience of mode parameter adjustment.

[0085] In an embodiment of the present application, the electric bed can obtain historical habit information of the target user, predict a first activity corresponding to the target user based on the historical habit information and time information, determine a first zero-gravity sub-mode corresponding to the first activity, and adjust the posture of the electric bed according to the first zero-gravity sub-mode. When it is detected that the target user is located on the electric bed, the target activity information of the target user is obtained, and based on the target activity information, the second activity corresponding to the target user is predicted. If the first activity and the second activity are different, the second zero-gravity sub-mode is determined based on the first activity and the second activity, and the posture of the electric bed is adjusted according to the second zero-gravity sub-mode. By implementing this embodiment, the electric bed can correspond to various activities of the user by setting multiple zero-gravity sub-modes, thereby improving the diversity of the zero-gravity mode. In addition, the electric bed determines the user's first activity through historical habit information and time information, and can automatically adjust to the first zero-gravity sub-mode corresponding to the first activity at every moment. The user does not need to consider the timing of turning on the zero-gravity mode, thereby improving the convenience of using the zero-gravity mode. In addition, when the user goes to bed, the user's second activity can also be determined according to the user's target activity information. When the first activity and the second activity are different, in order to ensure that the zero-gravity sub-mode of the electric bed matches the user activity, the second zero-gravity sub-mode can also be determined by the first activity and the second activity, thereby improving the accuracy of the zero-gravity mode. The automatic secondary adjustment of the electric bed also further improves the convenience of using the zero-gravity mode.

[0086] like Figure 2 As shown, Figure 21 is a flow chart of another control method of an electric bed disclosed in an embodiment of the present application. The control method of the electric bed can be applied to an electric bed. The control method of the electric bed may include the following steps:

[0087] Step 210, obtaining historical habit information of the target user, and predicting a first activity corresponding to the target user based on the historical habit information and time information.

[0088] Step 220: determine a first zero-gravity sub-mode corresponding to the first activity, and adjust the posture of the electric bed according to the first zero-gravity sub-mode.

[0089] The methods of steps 210 to 220 may refer to the methods of steps 110 to 120 in the above embodiment, and will not be described in detail here.

[0090] Step 230, when it is detected that the target user is on the electric bed, obtain user sensor data collected by one or more sensors within a first preset time period.

[0091] Among them, the first preset time length can be pre-set. Optionally, the electric bed can obtain user sensor data collected by one or more sensors within the most recent first preset time length, for example, user sensor data collected by one or more sensors within the most recent 5 minutes. Optionally, the electric bed can also obtain user sensor data collected by one or more sensors within the first preset time length after detecting that the target user is located on the electric bed, for example, user sensor data collected by one or more sensors within 5 minutes after detecting that the target user is located on the electric bed.

[0092] Optionally, the electric bed can also adjust the first preset duration according to the first activity. The electric bed can determine the first prediction confidence corresponding to the first activity, and increase or decrease the first preset duration according to the first prediction confidence. The first prediction confidence can be used to characterize the correct probability of the first activity, wherein the higher the first prediction confidence, the smaller the first preset duration, and the lower the first prediction confidence, the larger the first prediction duration. By implementing this embodiment, the first preset duration can be flexibly adjusted to improve the efficiency of determining the target activity information.

[0093] Step 240: Determine the activity level of the target user based on the user sensing data.

[0094] The electric bed can first pre-process the user's sensor data, including data cleaning and smoothing, and then perform feature extraction to obtain multiple target features, which may include but are not limited to gravity distribution, posture changes, body temperature changes, etc., and then determine multiple activity values ​​based on the multiple target features. The multiple activity values ​​correspond to the multiple target features one by one, and the target activity score of the target user is obtained by weighted calculation based on the weights and activity values ​​corresponding to each target feature, so as to compare the target activity score with each score threshold to determine the activity level of the target user. Optionally, the electric bed can also use the target activity score as the activity level of the target user.

[0095] For example, the weight corresponding to the gravity distribution can be 0.6, the weight corresponding to the posture change can be 0.4, and the weight corresponding to the body temperature change can be 0.1. The activity level can include three categories: high, medium, and low. The three activity levels can correspond to two thresholds, which can be 50 points and 80 points respectively. If the target activity score is greater than 80 points, the target user's activity level is determined to be high. If the target activity score is between 50 and 80 points, the target user's activity level is determined to be medium. If the target activity score is less than 50 points, the target user's activity level is determined to be low.

[0096] Step 250: If the activity level is not higher than the activity level threshold, predict a second activity corresponding to the target user based on the user sensing data.

[0097] It is understandable that the activity level threshold can be preset. If the activity level is a score, the activity level threshold is also a score. If the activity level is a category, the activity level is also a category. When the activity level is higher than the activity level threshold, it means that the target user is not in a stable state. When the activity level is not higher than the activity level threshold, it means that the target user is in a stable state. At this time, the user sensor data collected is more suitable for predicting the second activity corresponding to the target user.

[0098] The electric bed can determine whether the activity level is higher than the activity level threshold. If the activity level is higher than the activity level threshold, the electric bed can wait for a second preset time period and then re-execute the step of obtaining user sensor data collected by one or more sensors within the most recent first preset time period until the determined activity level is no higher than the activity level threshold. The electric bed can predict the second activity corresponding to the target user based on the most recently obtained user sensor data.

[0099] Step 260: If the first activity and the second activity are different, determine a second zero-gravity submode according to the first activity and the second activity, and adjust the posture of the electric bed according to the second zero-gravity submode.

[0100] In an embodiment of the present application, the electric bed can also obtain user sensor data collected by one or more sensors within a first preset time period when it is detected that the target user is on the electric bed, and determine the activity level of the target user based on the user sensor data. If the activity level is not higher than the activity level threshold, the second activity corresponding to the target user is predicted based on the user sensor data, thereby improving the prediction accuracy of the user sensor data and improving the accuracy of the second activity.

[0101] like Figure 3 As shown, Figure 3 : is a flowchart of a method for determining a second zero-gravity sub-mode according to a first activity and a second activity disclosed in an embodiment of the present application, which may include the following steps:

[0102] Step 310: Determine a first prediction confidence corresponding to the first activity and a second prediction confidence corresponding to the second activity.

[0103] When the first prediction model predicts the first activity corresponding to the target user, it can also determine the first prediction confidence corresponding to the first activity, and when the second prediction model predicts the second activity corresponding to the target user, it can also determine the second prediction confidence corresponding to the second activity. The first prediction model includes a first fully connected layer, and the second prediction model includes a second fully connected layer. The first fully connected layer is used to output the first activity and the first prediction confidence corresponding to the first activity, and the second fully connected layer is used to output the second activity and the second prediction confidence corresponding to the second activity.

[0104] Step 320: Determine a second zero-gravity sub-mode according to the first prediction confidence and the second prediction confidence.

[0105] The electric bed can process the zero gravity submode corresponding to the first activity and the zero gravity submode corresponding to the second activity according to the first prediction confidence and the second prediction confidence to determine the second zero gravity submode.

[0106] In an embodiment of the present application, the electric bed can also determine a first prediction confidence corresponding to a first activity and a second prediction confidence corresponding to a second activity, and can determine a second zero gravity submode based on the first prediction confidence and the second prediction confidence, thereby improving the accuracy of the second zero gravity submode.

[0107] In one embodiment, the electric bed can determine that the zero gravity sub-mode corresponding to the target activity is the second zero gravity sub-mode when both the first prediction confidence and the second prediction confidence are not less than the confidence threshold, and if the confidence difference between the first prediction confidence and the second prediction confidence is greater than the difference threshold, and output inquiry information, receive feedback information for the inquiry information, and determine the real activity of the target user based on the feedback information, and determine that the zero gravity sub-mode corresponding to the real activity is the second zero gravity sub-mode. By implementing this embodiment, the accuracy of the determined two zero gravity sub-modes can be improved by comparing the first prediction confidence and the second prediction confidence with the confidence threshold, and comparing the confidence difference with the difference threshold.

[0108] If both the first prediction confidence and the second prediction confidence are less than the confidence threshold, it means that both the first activity and the second activity are untrustworthy. Therefore, it is necessary to inquire the user to obtain the real activity. The electric bed can output the inquiry information through the voice output device to inquire about the real activity of the target user. Among them, the output volume of the voice output device can be determined according to the current time period. When the current time period is in the sleep time period, the electric bed can control the voice output device to output the inquiry information according to the first volume. When the current time period is not in the sleep time period, the electric bed can control the voice output device to output the inquiry information according to the second volume. The second volume can be greater than the first volume. The sleep time period can be determined according to the historical sleep data of the target user or can be preset. There is no restriction on this.

[0109] If both the first prediction confidence and the second prediction confidence are not less than the confidence threshold, it means that the first activity and the second activity are both trustworthy. However, in order to select the correct user activity, when the confidence difference between the first prediction confidence and the second prediction confidence is greater than the difference threshold, the electric bed can determine the activity corresponding to the larger prediction confidence of the first prediction confidence and the second prediction confidence as the target activity. When the confidence difference between the first prediction confidence and the second prediction confidence is not greater than the difference threshold, the electric bed can also output the inquiry information through the voice output device.

[0110] Optionally, when both the first prediction confidence and the second prediction confidence are not less than the confidence threshold, and the confidence difference between the first prediction confidence and the second prediction confidence is not greater than the difference threshold, if the current time period is a sleeping time period, the query information may not be output, but an activity is randomly determined from the first activity and the second activity as the target activity to avoid disturbing the target user during the sleeping time period.

[0111] As an optional implementation, the first prediction model may include a first prediction parameter, and the second prediction model may include a second prediction parameter. The first prediction parameter of the first prediction model may be determined through training, and the second prediction parameter of the second prediction model is determined through training. In order to further improve the accuracy of the first prediction parameter and the second prediction parameter, after the electric bed outputs inquiry information to inquire about the real activity of the target user and determines that the zero gravity sub-mode corresponding to the real activity is the second zero gravity sub-mode, when the real activity is different from the first activity, the first prediction parameter of the first prediction model may be adjusted according to the real activity and the first activity; when the real activity is different from the second activity, the second prediction parameter of the second prediction model may be adjusted according to the real activity and the second activity.

[0112] It can be understood that the first activity is the predicted value of the first prediction model, the second activity is the predicted value of the second prediction model, and the real activity is the real value. The electric bed can determine the first loss through the first loss function according to the first activity and the real activity, and adjust the first prediction parameter of the first prediction model according to the first loss. The electric bed can also determine the second loss through the second loss function according to the second activity and the real activity, and adjust the second prediction parameter of the second prediction model according to the second loss.

[0113] By implementing this embodiment, the first prediction parameter of the first prediction model or the second prediction parameter of the second prediction model can be adjusted by querying the user each time, thereby improving the prediction accuracy of the first prediction model and the second prediction model.

[0114] like Figure 4 As shown, Figure 4 : is a flow chart of a method for adjusting the posture of an electric bed according to the second zero-gravity sub-mode disclosed in an embodiment of the present application, which may include the following steps:

[0115] Step 410: When the second zero-gravity sub-mode is different from the first zero-gravity sub-mode, obtain the state information of the target user.

[0116] The status information includes one or more of position information, sleep information and posture information. The position information can be used to characterize the position of the target user on the electric bed, the sleep information can be used to characterize whether the target user has fallen asleep and / or is about to fall asleep, and the posture information is used to characterize the posture of the target user.

[0117] Step 420: If it is determined that the state information meets the mode switching requirement, the posture of the electric bed is adjusted according to the second zero-gravity sub-mode.

[0118] Optionally, the mode switching requirement may include a first requirement corresponding to the position information, a second requirement corresponding to the sleep information, and a third requirement corresponding to the posture information. The first requirement indicates that the position of the target user on the electric bed is not in the edge area, the second requirement indicates that the target user is not asleep or is not about to fall asleep, and the third requirement indicates that the posture of the target user is not a sleeping posture.

[0119] When the state information includes position information, the electric bed can determine whether the position information meets the first requirement. When the state information includes sleep information, the electric bed can determine whether the position information meets the second requirement. When the state information includes posture information, the electric bed can determine whether the position information meets the third requirement. If all information included in the state information meets the corresponding requirements, it is determined that the state information meets the mode switching requirements, and the electric bed can adjust the posture of the electric bed according to the second zero gravity sub-mode.

[0120] As an optional implementation, if the state information of the target user includes location information, and the location information does not meet the first requirement, a prompt message is output, and after a first preset time, the step of adjusting the posture of the electric bed according to the second zero-gravity sub-mode is performed. The prompt message is used to prompt the target user that the electric bed is about to be adjusted so that the target user moves from the edge area to the non-edge area to avoid the risk of falling, thereby improving the safety of the electric bed.

[0121] As an optional implementation, since the mode parameters corresponding to multiple zero gravity sub-modes are within a preset range, and the difference between the mode parameters corresponding to each zero gravity sub-mode is small, that is, the difference between the mode parameters of the first zero gravity sub-mode and the second zero gravity sub-mode is also small, the electric bed can calculate the mode parameter change value from the first zero gravity sub-mode to the second zero gravity sub-mode, and when the mode parameter change value is lower than the change threshold, the mode parameter change value can be divided into a preset number of sub-change values, and the electric bed can be adjusted a preset number of times according to the preset frequency and the sub-change values, so that the posture of the electric bed corresponds to the mode parameter change value of the second zero gravity sub-mode, thereby reducing the perception of the target user and avoiding excessive interference of the posture change to the target user, thereby improving the experience of the target user.

[0122] In an embodiment of the present application, the electric bed can also obtain status information of the target user when the second zero-gravity sub-mode is different from the first zero-gravity sub-mode. If it is determined that the status information meets the mode switching requirements, the posture of the electric bed is adjusted according to the second zero-gravity sub-mode, and the posture of the electric bed is adjusted at an appropriate time to improve the intelligence of the electric bed.

[0123] like Figure 5 As shown, Figure 5is a modular schematic diagram of a control device for an electric bed disclosed in an embodiment of the present application. The control device 500 for an electric bed can be applied to the electric bed in the above embodiment. The control device 500 for the electric bed can include a first prediction module 510, a first adjustment module 520, a second prediction module 530 and a second adjustment module 540, wherein:

[0124] A first prediction module 510 is used to obtain historical habit information of a target user, and predict a first activity corresponding to the target user based on the historical habit information and time information;

[0125] A first adjustment module 520, configured to determine a first zero-gravity sub-mode corresponding to the first activity, and adjust the posture of the electric bed according to the first zero-gravity sub-mode;

[0126] The second prediction module 530 is used to obtain target activity information of the target user when it is detected that the target user is on the electric bed, and predict a second activity corresponding to the target user according to the target activity information;

[0127] The second adjustment module 540 is configured to determine a second zero-gravity sub-mode according to the first activity and the second activity if the first activity and the second activity are different, and adjust the posture of the electric bed according to the second zero-gravity sub-mode.

[0128] In one embodiment, the electric bed includes one or more sensors, and the target activity information includes user sensor data collected by the sensors; the second prediction module 530 is also used to obtain the user sensor data collected by one or more sensors within a first preset time period; determine the activity level of the target user based on the user sensor data; if the activity level is not higher than the activity level threshold, predict the second activity corresponding to the target user based on the user sensor data.

[0129] In one embodiment, the second adjustment module 540 is further used to determine a first prediction confidence corresponding to the first activity and a second prediction confidence corresponding to the second activity; and determine a second zero-gravity sub-mode according to the first prediction confidence and the second prediction confidence.

[0130] In one embodiment, the second adjustment module 540 is also used to, when both the first prediction confidence and the second prediction confidence are not less than the confidence threshold, if the confidence difference between the first prediction confidence and the second prediction confidence is greater than the difference threshold, determine that the zero gravity sub-mode corresponding to the target activity is the second zero gravity sub-mode, and the target activity is the activity corresponding to the larger prediction confidence of the first prediction confidence and the second prediction confidence; when both the first prediction confidence and the second prediction confidence are less than the confidence threshold, or the confidence difference is less than the difference threshold, output inquiry information, the inquiry information is used to inquire about the real activity of the target user; receive feedback information for the inquiry information, and determine the real activity of the target user based on the feedback information; determine that the zero gravity sub-mode corresponding to the real activity is the second zero gravity sub-mode.

[0131] In one embodiment, the first prediction module 510 is further used to predict the first activity corresponding to the target user through the first prediction model based on the historical habit information and time information; the second prediction module 530 is further used to predict the second activity corresponding to the target user through the second prediction model based on the target activity information; the electric bed control device 500 may also include a prediction parameter adjustment module for adjusting the first prediction parameter of the first prediction model according to the real activity and the first activity when the real activity is different from the first activity; and adjusting the second prediction parameter of the second prediction model according to the real activity and the second activity when the real activity is different from the second activity.

[0132] In one embodiment, the control device 500 of the electric bed may further include a mode parameter adjustment module, which is used to detect the mode parameter modification instruction of the target user when the target user is located on the electric bed; and adjust the mode parameters corresponding to the current zero gravity sub-mode within a preset range corresponding to multiple zero gravity sub-modes according to the mode parameter modification instruction of the target user.

[0133] In one embodiment, the second adjustment module 540 is also used to obtain status information of the target user when the second zero-gravity sub-mode is different from the first zero-gravity sub-mode, the status information including one or more of position information, sleep information and posture information; if it is determined that the status information meets the mode switching requirements, the posture of the electric bed is adjusted according to the second zero-gravity sub-mode.

[0134] In an embodiment of the present application, the electric bed can obtain historical habit information of the target user, predict a first activity corresponding to the target user based on the historical habit information and time information, determine a first zero-gravity sub-mode corresponding to the first activity, and adjust the posture of the electric bed according to the first zero-gravity sub-mode. When it is detected that the target user is located on the electric bed, the target activity information of the target user is obtained, and based on the target activity information, the second activity corresponding to the target user is predicted. If the first activity and the second activity are different, the second zero-gravity sub-mode is determined based on the first activity and the second activity, and the posture of the electric bed is adjusted according to the second zero-gravity sub-mode. By implementing this embodiment, the electric bed can correspond to various activities of the user by setting multiple zero-gravity sub-modes, thereby improving the diversity of the zero-gravity mode. In addition, the electric bed determines the user's first activity through historical habit information and time information, and can automatically adjust to the first zero-gravity sub-mode corresponding to the first activity at every moment. The user does not need to consider the timing of turning on the zero-gravity mode, thereby improving the convenience of using the zero-gravity mode. In addition, when the user goes to bed, the user's second activity can also be determined according to the user's target activity information. When the first activity and the second activity are different, in order to ensure that the zero-gravity sub-mode of the electric bed matches the user activity, the second zero-gravity sub-mode can also be determined by the first activity and the second activity, thereby improving the accuracy of the zero-gravity mode. The automatic secondary adjustment of the electric bed also further improves the convenience of using the zero-gravity mode.

[0135] like Figure 6 As shown, in one embodiment, an electric bed is provided, which may include:

[0136] A memory 610 storing executable program code;

[0137] a processor 620 coupled to the memory 610;

[0138] The processor 620 calls the executable program code stored in the memory 610 to implement the control method of the electric bed provided in the above embodiments.

[0139] The memory 610 may include a random access memory (RAM) or a read-only memory (ROM). The memory 610 may be used to store instructions, programs, codes, code sets or instruction sets. The memory 610 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc. The data storage area may also store data created by the electric bed during use, etc.

[0140] The processor 620 may include one or more processing cores. The processor 620 uses various interfaces and lines to connect the various parts of the entire electric bed, and executes various functions of the electric bed and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 610, and calling data stored in the memory 610. Optionally, the processor 620 can be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), and programmable logic array (PLA). The processor 620 can integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing display content; and the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 620, but may be implemented separately through a communication chip.

[0141] It can be understood that the electric bed may include more or fewer structural elements than those in the above structural block diagram, for example, including a power module, physical buttons, WiFi (Wireless Fidelity) module, speakers, Bluetooth module, sensors, etc., and no limitation is made here.

[0142] An embodiment of the present application discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the methods described in the above embodiments.

[0143] In addition, an embodiment of the present application further discloses a computer program product. When the computer program product is run on a computer, the computer can execute all or part of the steps in any one of the control methods for an electric bed described in the above embodiments.

[0144] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0145] The above is a detailed introduction to a control method, device, electric bed and storage medium for an electric bed disclosed in an embodiment of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A control method for an electric bed, characterized in that: Applied to an electric bed, the electric bed includes a plurality of zero-gravity sub-modes; the method includes: Acquire historical habit information of a target user, and predict a first activity corresponding to the target user according to the historical habit information and time information; Determining a first zero-gravity sub-mode corresponding to the first activity, and adjusting the posture of the electric bed according to the first zero-gravity sub-mode; When it is detected that the target user is on the electric bed, obtaining target activity information of the target user, and predicting a second activity corresponding to the target user according to the target activity information; If the first activity and the second activity are different, a second zero-gravity sub-mode is determined according to the first activity and the second activity, and the posture of the electric bed is adjusted according to the second zero-gravity sub-mode.

2. The method according to claim 1, characterized in that The electric bed includes one or more sensors, and the target activity information includes user sensor data collected by the sensors; the step of obtaining the target activity information of the target user and predicting the second activity corresponding to the target user according to the target activity information includes: Acquire user sensor data collected by the one or more sensors within a first preset time period; Determining the activity level of the target user according to the user sensing data; If the activity level is not higher than the activity level threshold, predicting a second activity corresponding to the target user according to the user sensing data.

3. The method according to claim 1, characterized in that The determining of the second zero-gravity sub-mode according to the first activity and the second activity includes: Determining a first prediction confidence corresponding to the first activity and a second prediction confidence corresponding to the second activity; A second zero-gravity submode is determined according to the first prediction confidence and the second prediction confidence.

4. The method according to claim 3, characterized in that Determining a second zero-gravity submode according to the first prediction confidence and the second prediction confidence includes: In the case where both the first prediction confidence and the second prediction confidence are not less than the confidence threshold, if the confidence difference between the first prediction confidence and the second prediction confidence is greater than the difference threshold, it is determined that the zero gravity submode corresponding to the target activity is the second zero gravity submode, and the target activity is the activity corresponding to the larger prediction confidence of the first prediction confidence and the second prediction confidence; When both the first prediction confidence and the second prediction confidence are less than a confidence threshold, or when the confidence difference is less than the difference threshold, outputting inquiry information, wherein the inquiry information is used to inquire about the real activity of the target user; receiving feedback information regarding the inquiry information, and determining the actual activity of the target user according to the feedback information; The zero gravity sub-mode corresponding to the real activity is determined to be the second zero gravity sub-mode.

5. The method according to claim 4, characterized in that The predicting, based on the historical habit information and the time information, the first activity corresponding to the target user includes: Predicting a first activity corresponding to the target user by using a first prediction model according to the historical habit information and the time information; The predicting, according to the target activity information, a second activity corresponding to the target user includes: Predicting a second activity corresponding to the target user by using a second prediction model according to the target activity information; After determining that the zero gravity sub-mode corresponding to the real activity is the second zero gravity sub-mode, the method further includes: When the real activity is different from the first activity, adjusting the first prediction parameter of the first prediction model according to the real activity and the first activity; When the real activity is different from the second activity, the second prediction parameter of the second prediction model is adjusted according to the real activity and the second activity.

6. The method according to claim 1, characterized in that The method further comprises: When the target user is located on the electric bed, a mode parameter modification instruction of the target user is detected; According to the mode parameter modification instruction of the target user, the mode parameters corresponding to the current zero gravity sub-mode are adjusted within the preset ranges corresponding to the multiple zero gravity sub-modes.

7. The method according to claim 1, characterized in that The step of adjusting the posture of the electric bed according to the second zero-gravity sub-mode includes: When the second zero-gravity sub-mode is different from the first zero-gravity sub-mode, acquiring state information of the target user, the state information including one or more of position information, sleep information, and posture information; If it is determined that the state information meets the mode switching requirement, the posture of the electric bed is adjusted according to the second zero-gravity sub-mode.

8. A control device for an electric bed, characterized in that: Applied to an electric bed, the electric bed includes multiple zero-gravity sub-modes; the device includes: A first prediction module, used to obtain historical habit information of a target user, and predict a first activity corresponding to the target user according to the historical habit information and time information; a first adjustment module, configured to determine a first zero-gravity sub-mode corresponding to the first activity, and adjust the posture of the electric bed according to the first zero-gravity sub-mode; A second prediction module is used to obtain target activity information of the target user when it is detected that the target user is located on the electric bed, and predict a second activity corresponding to the target user according to the target activity information; The second adjustment module is used to determine a second zero-gravity sub-mode according to the first activity and the second activity if the first activity is different from the second activity, and adjust the posture of the electric bed according to the second zero-gravity sub-mode.

9. An electric bed, characterized in that: include: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 7.

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