CN118859791B - User awakening method and system for intelligent electric bed of Internet of things - Google Patents
User awakening method and system for intelligent electric bed of Internet of things Download PDFInfo
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Abstract
The invention discloses a user awakening method and a system for an intelligent electric bed of the Internet of things, wherein the method comprises the steps of determining a target awakening mode for awakening a user, monitoring a sleep state of the user to obtain a sleep period stage of the user, and recommending a first awakening mode; the method comprises the steps of recommending a second awakening mode according to identity information and health state information of a user, generating a third awakening mode according to user-defined awakening setting information of the user, determining a target awakening mode according to the first awakening mode, the second awakening mode and the third awakening mode, executing awakening operation on the user, monitoring the awake state of the user in the awakening process, generating a change curve of the awake state, and adaptively adjusting the target awakening mode according to the change curve of the awake state. According to the invention, multidimensional influence factors are considered to recommend the user awakening mode, and the awakening mode is adaptively adjusted according to the user awakening state so as to match the user situation and the actual requirement, thereby improving the user awakening experience.
Description
Technical Field
The invention relates to the technical field of intelligent home control, in particular to a user awakening method and system for an intelligent electric bed of the Internet of things.
Background
The intelligent electric bed is an important component in the field of intelligent home, combines the adjustable function of the traditional electric bed with the modern intelligent technology, and provides more comfortable and personalized sleep experience for users. For the user wake-up process of the intelligent electric bed, although some intelligent beds have a wake-up function, a targeted wake-up policy cannot be provided because individual differences are not considered. Even if the user can set the wake-up mode by himself in some scenes, the wake-up mode set by the user can only meet the user preference, and the scientific health guidance is lacking, so that the unhealthy wake-up mode is caused, and the physical health of the user is influenced. In addition, the current wake-up mode has single consideration factor, and the preset program is usually only mechanically executed in the wake-up process, and a dynamic adjustment mechanism is lacked, so that the wake-up diversity degree is lower, and the user's requirement on wake-up diversity experience cannot be met.
Disclosure of Invention
In order to solve at least one technical problem, the invention provides a user awakening method and system for an intelligent electric bed of the Internet of things.
In a first aspect, the present invention provides a user wake-up method for an intelligent electric bed of the internet of things, the method comprising:
Determining a target wake-up mode for waking up a user, comprising:
Monitoring the sleep state of a user to obtain sleep cycle stages of the user, and recommending corresponding first awakening modes according to different sleep cycle stages;
Acquiring identity information and health state information of a user, and recommending a second awakening mode according to the identity information and the health state information of the user;
acquiring user-defined wake-up setting information of a user, and generating a third wake-up mode according to the user-defined wake-up setting information;
determining a target wake-up mode according to the first wake-up mode, the second wake-up mode and the third wake-up mode;
And executing wake-up operation on the user according to the target wake-up mode, monitoring the wake-up state of the user in the wake-up process, generating a change curve of the wake-up state, and self-adaptively adjusting the target wake-up mode according to the change curve of the wake-up state.
Preferably, the determining the target wake-up mode according to the first wake-up mode, the second wake-up mode and the third wake-up mode includes:
constructing an evaluation factor set according to the first awakening mode, the second awakening mode and the third awakening mode, and determining a corresponding grade set;
based on each factor in the evaluation factor set, constructing a corresponding fuzzy evaluation matrix, wherein the items in the fuzzy evaluation matrix are used for representing the membership degrees of different evaluation factors on different grades;
Determining the weight of each factor by using an analytic hierarchy process, and determining a fuzzy evaluation vector according to the weight of each factor and a fuzzy evaluation matrix;
And comparing the fuzzy evaluation vector with the class set, screening out a target class corresponding to the maximum membership degree and a target factor corresponding to the target class, and taking a wake-up mode under the target factor as a target wake-up mode.
Preferably, the identity information of the user comprises age and occupation information of the user, and the health state information comprises alcohol influence state, physiological cycle condition and physical disease state of the user.
Preferably, the target wake-up mode comprises wake-up time, wake-up action and wake-up auxiliary environment setting, and the wake-up action comprises any one or more of body position transformation wake-up, biological vibration wake-up and sound control wake-up.
Preferably, the performing a wake-up operation on the user according to the target wake-up mode further includes:
Monitoring body movement data of a user in a wake-up process, and determining the falling risk of the user in a current target wake-up mode according to the body movement data of the user;
when the falling risk is determined to be greater than a preset threshold, generating an alarm prompt, and optimizing a target awakening mode.
Preferably, the determining the falling risk of the user in the current target wake-up mode according to the body movement data of the user includes:
detecting weight distribution of a user on the electric bed surface, and acquiring average weight distribution of the user outside the safe range of the bed surface;
detecting heart rate variability and user activity of a user, and calculating an awake state coefficient of the user according to the heart rate variability;
Acquiring a head lifting angle of an electric bed, and calculating a falling risk index according to the head lifting angle, the average weight distribution, the user activity degree and the awake state coefficient:
FRI=w1G×cosθ+w2(1-M)+w3Cs+w4(Ee+Eh);
Wherein FRI represents a drop risk index, G represents an average weight distribution of a user outside a safe range of a bed surface, M represents a user activity level, θ represents a head of a bed lifting angle, C s represents a user's awake state coefficient, E e、Eh represents an environmental compensation factor and an age compensation factor, and w 1、w2、w3、w4 represents weights, respectively.
In a second aspect, the present invention also provides a user wake-up system for an intelligent electric bed of the internet of things, the system comprising:
the wake-up mode determining module is used for determining a target wake-up mode for waking up a user, and comprises the following steps:
the first recommending unit is used for monitoring the sleep state of the user to obtain sleep cycle stages of the user, and recommending corresponding first awakening modes according to different sleep cycle stages;
The second recommending unit is used for acquiring the identity information and the health state information of the user and recommending a second awakening mode according to the identity information and the health state information of the user;
the third recommendation unit is used for acquiring user-defined wake-up setting information of the user and generating a third wake-up mode according to the user-defined wake-up setting information;
The target mode generating unit is used for determining a target awakening mode according to the first awakening mode, the second awakening mode and the third awakening mode;
The self-adaptive adjustment module is used for executing the wake-up operation on the user according to the target wake-up mode, monitoring the wake-up state of the user in the wake-up process, generating a change curve of the wake-up state, and self-adaptively adjusting the target wake-up mode according to the change curve of the wake-up state.
In a third aspect, the invention also provides an electronic device comprising a processor and a memory for storing computer program code comprising computer instructions which, when executed by the processor, cause the electronic device to perform a method as in the first aspect and any one of its possible implementations.
In a fourth aspect, the present invention also provides a computer readable storage medium having stored therein a computer program comprising program instructions which, when executed by a processor of an electronic device, cause the processor to perform a method as in the first aspect and any one of the possible implementations thereof.
Compared with the prior art, the invention has the beneficial effects that:
1) The user awakening method provided by the invention can monitor the sleep state of the user to obtain sleep cycle stages of the user, recommend corresponding first awakening modes according to different sleep cycle stages, acquire identity information and health state information of the user, recommend second awakening modes according to the identity information and the health state information of the user, acquire custom awakening setting information of the user, generate third awakening modes according to the custom awakening setting information, and determine target awakening modes according to the first awakening mode, the second awakening mode and the third awakening mode. The first awakening mode is mainly scientifically recommended according to a scientific sleep cycle, and the second awakening mode mainly considers the identity information and health state information of a user and influence between awakening of the user, wherein the identity information of the user comprises age and occupation information of the user, and the health state information comprises alcohol influence state, physiological cycle condition and physical disease state of the user. The third wake-up mode is mainly based on the preference of the user, and the optimal target wake-up mode under the current wake-up scene of the user is determined by combining the three modes, so that the user preference can be met, a scientific and healthy wake-up mode is provided, and the user health work and rest can be guided more conveniently.
2) When the wake-up operation is executed on the user according to the target wake-up mode, the wake-up mode of the user is monitored in the wake-up process, the change curve of the wake-up state is generated, and the target wake-up mode is adaptively adjusted according to the change curve of the wake-up state. This may provide the user with a buffering process to provide the user with a better wake-up experience. And the dynamic mechanism can reduce the energy consumption of equipment to a certain extent and reduce the resource waste.
3) According to the method, the system and the device, body movement data of the user can be monitored in the awakening process, the falling risk of the user in the current target awakening mode is determined according to the body movement data of the user, when the falling risk is determined to be larger than the preset threshold value, an alarm prompt is generated, and the target awakening mode is optimized, so that the falling risk of the user when the user gets up can be reduced, and the use safety of the intelligent electric bed is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
In order to more clearly describe the embodiments of the present invention or the technical solutions in the background art, the following description will describe the drawings that are required to be used in the embodiments of the present invention or the background art.
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the technical aspects of the disclosure.
Fig. 1 is a schematic flow chart of a user wake-up method for an intelligent electric bed of the internet of things according to an embodiment of the present invention;
FIG. 2 is a flow chart illustrating the sub-steps of step S104 in FIG. 1 according to an embodiment of the present invention;
Fig. 3 is a schematic structural diagram of a user wake-up system for an intelligent electric bed of the internet of things according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms first, second and the like in the description and in the claims and in the above-described figures are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
The term "and/or" is merely an association relationship describing the associated object, and means that three relationships may exist, for example, a and/or B may mean that a exists alone, while a and B exist together, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, may mean including any one or more elements selected from the group consisting of A, B and C.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better illustration of the invention. It will be understood by those skilled in the art that the present invention may be practiced without some of these specific details. In some instances, well known methods, procedures, components, and circuits have not been described in detail so as not to obscure the present invention.
The invention provides a user awakening method for an intelligent electric bed of the Internet of things, which considers multidimensional factors and individual differences, considers scientific awakening modes and user preferences and adds a dynamic mechanism, so that the user awakening mode is high in diversity and can meet the requirements of users on awakening diversity experiences.
Referring to fig. 1, fig. 1 is a flow chart of a user wake-up method for an intelligent electric bed of the internet of things according to an embodiment of the present invention. According to the illustration in fig. 1, a user wake-up method for an intelligent electric bed of the internet of things comprises the following steps:
s10, determining a target wake-up mode for waking up a user, comprising:
S101, monitoring a sleep state of a user to obtain sleep cycle stages of the user, and recommending corresponding first awakening modes according to different sleep cycle stages;
S102, acquiring identity information and health state information of a user, and recommending a second awakening mode according to the identity information and the health state information of the user;
S103, acquiring user-defined wake-up setting information of a user, and generating a third wake-up mode according to the user-defined wake-up setting information;
s104, determining a target awakening mode according to the first awakening mode, the second awakening mode and the third awakening mode;
S20, executing wake-up operation on the user according to the target wake-up mode, monitoring the wake-up state of the user in the wake-up process, generating a change curve of the wake-up state, and self-adaptively adjusting the target wake-up mode according to the change curve of the wake-up state.
The target wake mode typically includes wake time, wake action, and wake auxiliary context settings.
Wake-up time refers to the exact point in time when the user wishes to wake up from sleep. This time is usually set by the user himself according to his work, living habit or health needs. The intelligent motorized bed allows the user to set the wake-up time as accurate as minutes, and may also recommend wake-up time based on the user's sleep cycle stages to provide scientific wake-up guidance, such as to ensure wake-up in the most appropriate light sleep stage to reduce tiredness after getting up.
Generally, based on setting a wake-up time preset by a user, the system starts to perform a wake-up action when approaching the time, and further combines with wake-up in a sleep cycle, monitors a sleep stage of the user by using a biometric sensor, and starts a wake-up process when approaching a shallow sleep stage, so as to reduce interference as much as possible and improve wakefulness after wake-up. The optimal wake-up time is determined by fusing the user preference and the scientific wake-up time of the sleep cycle.
The wake-up action refers to the physical or sensory stimulus pattern that the intelligent mattress uses to actually wake up the user. The wake-up action typically includes a body position change wake-up, a biological vibration wake-up, and a voice-activated wake-up. It will be appreciated that only one wake-up action may be employed in waking up, or that multiple wake-up actions may be used in combination.
The body position is changed and awakened, for example, the angle of the bed body is adjusted, and the mattress automatically adjusts the angle of the bed head or the bed body, so that the user wakes up in a state closer to sitting posture, and the feeling of confusion when the user wakes up is reduced.
Biological vibration wake-up, for example, the air bag or mechanical device built in the mattress vibrates lightly, simulating a gentle massage or hug sensation.
Voice-activated wake-up, e.g. playing soft natural sound or user-preferred music, gradually increasing the volume, waking up the hearing in a gentle way.
Wake-assist environment settings refer to adjustments made to the surrounding environment to facilitate a user's more comfortable, natural wake-up, including but not limited to:
light adjustment, namely imitating sunrise effect, gradually increasing the light brightness in the bedroom and helping the user to wake up naturally.
Room temperature adjustment, namely automatically adjusting the room temperature to a temperature more beneficial to waking up through an intelligent thermostat.
The window shade is opened automatically to make natural light enter the room to help the body to regulate the biological clock.
Air freshening and humidity control, which adjusts the indoor air quality and humidity, creating a more comfortable wake-up environment.
Atmosphere music or white noise, playing the audio conducive to waking up, creating a positive morning atmosphere.
Before determining the target wake-up mode in this embodiment, three wake-up modes are first determined based on three different dimensions, and each wake-up mode generally needs to provide wake-up time, wake-up action, and wake-up auxiliary environment to set up these contents, and finally the final target wake-up mode is determined by integrating the wake-up modes in the three dimensions.
Step S101 aims to recommend a first wake-up pattern according to the sleep cycle. Various sensors are commonly configured in intelligent electric beds, including pressure sensors, heart rate sensors, body temperature sensors, accelerometers, etc., for monitoring physiological indicators and body movements of a user in real time, and in some special scenarios, non-contact radar wave detection and sound monitoring (e.g., snoring) may also be applied in advanced models to more fully capture sleep characteristics. The multi-dimensional data acquisition can be performed firstly based on the sensor data, and the sleep cycle stages of the user can be identified through a sleep cycle identification algorithm, wherein the sleep cycle stages comprise different stages of rapid eye movement sleep (REM), light sleep, deep sleep and the like.
In one embodiment, recommending the corresponding first wake-up mode according to different sleep cycle stages includes:
the user is awakened in the light sleep stage, the user is easy to awaken in the stage, the user feel more fresh after awakening, and the user can select the best opportunity to start the awakening program when entering the light sleep stage.
And the wake-up in the deep sleep stage is avoided, namely the user is prevented from being awakened in the deep sleep stage as much as possible, and the wake-up at the moment can cause the user to feel stunned and uncomfortable, so that the work and learning efficiency of the user after getting up is influenced.
The above manner determines the wake-up time according to the sleep cycle, and for the wake-up action and the wake-up auxiliary environment setting, reference may be made to the content of the above embodiment, so that the wake-up time may be scientifically provided by distinguishing sleep cycle stages, and then the first wake-up manner is recommended in combination with the wake-up action and the wake-up auxiliary environment setting.
In one embodiment, the identity information of the user includes user age and occupation information, and the health status information includes alcohol impact status, physiological cycle status, and physical disease status of the user. Step S102 is to recommend a second wake-up mode according to the identity information and the health status information of the user, specifically:
the user's age may be easier for young people to adapt to earlier or abrupt wake patterns, while elderly people may need a milder, progressive wake process, as their sleep cycle and physical fitness may be different.
Professional information for professions requiring high concentration, such as flyers, doctors, lawyers, etc., it is recommended that the light sleep stage wake up shortly after the end of deep sleep to ensure the optimal cognitive state throughout the day. While creative workers may allow more flexible wake-up times, allowing them more creative thinking time after they wake up naturally.
Health status information:
alcohol effect state if the user drinks the alcohol in the last night, the intelligent system should delay the wake-up time until after alcohol metabolism and take a milder way, such as light gradually lightens and natural sound, to reduce discomfort, considering that alcohol may prolong deep sleep and affect sleep quality.
Physiological cycle conditions-women may have varying sleep quality and need during different phases of the menstrual cycle. For example, longer sleep times and milder wake-up processes may be recommended before and after menstrual periods to alleviate possible fatigue and mood swings.
Physical condition-a user suffering from a particular condition, such as heart disease, sleep apnea, may need to pay special attention to the manner of arousal. For example, for users with heart problems, a sudden, intense shock wake-up is avoided, while a gentle sound or light change is selected. For sleep disordered breathing patients, it is ensured that the posture adjustment at arousal will not exacerbate the symptoms.
Step S103 mainly considers user preferences, and although steps S101 and S102 scientifically recommend wake-up modes based on sleep cycle, user identity and health status, respectively, if individual preferences of the user are ignored, the user' S better getting-up experience cannot be satisfied as well, so step S103 mainly generates a third wake-up mode according to the user-defined wake-up setting information.
After the first wake-up mode, the second wake-up mode and the third wake-up mode are determined, in order to facilitate determination of the target wake-up mode, the wake-up time, the wake-up action and the wake-up auxiliary environment setting in each wake-up mode may be displayed in a list, so that the target wake-up mode is determined rapidly according to the first wake-up mode, the second wake-up mode and the third wake-up mode in S104.
In summary, the target wake-up mode provided by the invention considers the wake-up mode under the multi-dimensional factor, the first wake-up mode is mainly scientifically recommended according to a scientific sleep period, the second wake-up mode mainly considers the identity information of the user and the influence between health state information and the wake-up of the user, wherein the identity information of the user comprises the age and occupation information of the user, and the health state information comprises the alcohol influence state, the physiological period condition and the physical disease state of the user. The third wake-up mode is mainly based on the preference of the user, and the optimal target wake-up mode under the current wake-up scene of the user is determined by combining the three modes, so that the user preference can be met, a scientific and healthy wake-up mode is provided, and the user health work and rest can be guided more conveniently.
In one embodiment, when the target wake-up mode is obtained, in step S20, a wake-up operation is performed on the user according to the target wake-up mode, and the awake state of the user is monitored and a change curve of the awake state is generated during the wake-up process, and the target wake-up mode is adaptively adjusted according to the change curve of the awake state.
In this embodiment, the validity of the current wake-up mode is dynamically evaluated according to the wake-up state change curve. If the user is found to be awake slower than expected or there is evidence of repeated light sleep, the wake policy is automatically adjusted. For example:
Enhanced stimulation-if the user reacts slowly, the volume of music or the brightness of light can be increased step by step.
Soft wake-up, in which if the pressure response of the user to the current wake-up mode (such as too fast heart rate) is detected, the stimulus intensity is reduced, and the mode is changed into a milder mode, such as using natural sound only.
Therefore, compared with the existing mechanized wake-up procedure, the embodiment can dynamically adjust the wake-up mode, for example, as the user wakes up more and more, the relevant configuration of the bed body, music, light and the like can be adaptively changed, for example, the music is soft to loud, the light is slowly enhanced and the like. This may provide the user with a buffering process to provide the user with a better wake-up experience. And the dynamic mechanism can reduce the energy consumption of equipment to a certain extent and reduce the resource waste.
Referring to fig. 2, in one embodiment, the determining the target wake mode according to the first wake mode, the second wake mode and the third wake mode includes the following sub-steps:
S1041, constructing an evaluation factor set according to the first wake-up mode, the second wake-up mode and the third wake-up mode, and determining a corresponding level set:
evaluation factor set u= { F 1、F2、F3 };
rank set v= { V1, V2, V3, V4, V5};
Wherein, F1, F2 and F3 respectively represent a first awakening mode, a second awakening mode and a third awakening mode;
V1, V2, V3, V4, V5 each represent 5 fitness levels, such as very unsuitable, less suitable, generally suitable, more suitable, and very suitable.
S1042, constructing a corresponding fuzzy evaluation matrix based on each factor in the evaluation factor set, wherein the terms in the fuzzy evaluation matrix are used for representing the membership degree of different evaluation factors on different grades.
For each factor F i, a fuzzy evaluation matrix R i is constructed, where R ij represents the membership of the ith factor on the jth scale. These values can typically be obtained by expert scoring, historical data analysis, or user investigation, and by normalization processing ensure that the sum of membership under each factor is 1.
S1043, determining the weight of each factor by using an analytic hierarchy process, and determining a fuzzy evaluation vector according to the weight of each factor and the fuzzy evaluation matrix.
Determining weights a i of the factors by AHP (analytic hierarchy process) to ensure that the sum of all a i is 1
Determining a fuzzy evaluation vector:
Where n is the number of factors, a i is the weight of the ith factor, and R i is the fuzzy evaluation matrix of the ith factor.
Where m is the number of elements of the ranking set V, r ij represents the membership of the ith factor to the jth ranking, V kj is a vector representation of the ranking V j.
S1044, comparing the fuzzy evaluation vector with the class set, screening out a target class corresponding to the maximum membership degree and a target factor corresponding to the target class, and taking a wake-up mode under the target factor as a target wake-up mode.
And comparing the comprehensive fuzzy evaluation vector B with the class set V to find out the class corresponding to the maximum membership degree, wherein the class is the indication of the suitability degree of the final awakening mode. Specifically, the membership function of each level may be calculated, and the level with the largest membership function is selected as the recommended wake-up mode.
Therefore, by the fuzzy comprehensive evaluation method, all influence factors of the first awakening mode, the second awakening mode and the third awakening mode and relative importance among the influence factors can be comprehensively considered, so that a final awakening strategy can be more accurately determined, uncertainty and subjective judgment can be avoided, and awakening decisions are closer to complex user behavior modes.
With the execution of the wake-up operation, if the lifting angle of the bed head is not suitable or the pose of the user is at risk, the risk of falling under the bed may occur, and potential safety hazard is brought. To this end, in one embodiment, the wake-up method further includes, when performing a wake-up operation on a user according to a target wake-up mode:
Monitoring body movement data of a user in a wake-up process, and determining the falling risk of the user in a current target wake-up mode according to the body movement data of the user;
when the falling risk is determined to be greater than a preset threshold, generating an alarm prompt, and optimizing a target awakening mode.
In this embodiment, when the target wake-up mode is optimized, the current wake-up mode needs to be analyzed first, which includes determining the wake-up mode (such as sound, light, vibration, etc.) currently used and its effect evaluation index (such as response time and false wake-up rate). Further assessing risk context, the optimization strategy should focus on waking up the target quickly and accurately, taking into account the drop risk. For example, in a high drop risk scenario, a more direct and intense wake-up means, such as an emergency shock + high tone alarm, may be required. Finally, a new wake-up mode can be selected, for example, if the original wake-up mode is voice wake-up mode, high-intensity voice combined with vibration dual wake-up mode can be changed
In one embodiment, the determining the falling risk of the user in the current target wake-up mode according to the body movement data of the user includes:
detecting weight distribution of a user on the electric bed surface, and acquiring average weight distribution of the user outside the safe range of the bed surface;
detecting heart rate variability and user activity of a user, and calculating an awake state coefficient of the user according to the heart rate variability;
Acquiring a head lifting angle of an electric bed, and calculating a falling risk index according to the head lifting angle, the average weight distribution, the user activity degree and the awake state coefficient:
FRI=w1G×cosθ+w2(1-M)+w3Cs+w4(Ee+Eh);
Wherein FRI represents a drop risk index, G represents an average weight distribution of a user outside a safe range of a bed surface, M represents a user activity level, θ represents a head of a bed lifting angle, C s represents a user's awake state coefficient, E e、Eh represents an environmental compensation factor and an age compensation factor, and w 1、w2、w3、w4 represents weights, respectively.
In this embodiment, the safe range of the bed surface may be set according to the distance from the bed edge, for example, the bed surface is a rectangular structure with a distance of 1.5×2 meters, and then the safe range may be set within 0.2m of each of the left and right distance edges, so that it may be determined that the average weight distribution of the user is outside the safe range of the bed surface, and a smaller value indicates that the risk is higher as the user gets closer to the bed edge.
The head of a bed lift angle has a better effect on waking up the user, but as the angle gets larger, the potential risk increases, leading to a risk of dropping, this data being typically collected by an angle sensor. User activity can generally be assessed by motion sensor data, with a larger value indicating a stronger activity and a relatively lower risk. The wakefulness coefficient is mainly calculated according to heart rate variability, a value close to 1 indicates complete wakefulness, and a low value indicates possible drowsiness or sleep with higher risk. After the parameters are determined, the falling risk index FRI can be obtained by substituting the parameters into the formula, if the value is larger than a preset threshold value, an alarm prompt is triggered immediately, and meanwhile, a target awakening mode is optimized, for example, the angle of the bed head is adjusted, and the volume of music is reduced.
In the wake-up process, the body movement data of the user are monitored, the falling risk of the user in the current target wake-up mode is determined according to the body movement data of the user, when the falling risk is determined to be greater than the preset threshold value, an alarm prompt is generated, and the target wake-up mode is optimized, so that the falling risk of the user when the user gets up can be reduced, and the use safety of the intelligent electric bed is improved.
In one embodiment, the intelligent electric bed adopted by the above-mentioned wake-up method generally needs to select a proper driving system, such as a driving device with accurate and quiet electric push rod, pneumatic cylinder or linear motor, so as to realize stable lifting of the bed head, the bed tail or the whole bed. The sensors may typically be integrated sensors, and for automatic wake-up, the bed may need to integrate biometric sensors, such as heart rate sensors, body motion sensors, to determine the sleep state of the user, pressure sensors to determine the weight distribution of the user, angle sensors to collect bed angle signals, etc. Thus, the wake-up process can be started at a proper time, and the safety in the wake-up process can be improved.
Further, to implement the linkage with the smart home system, the electric bed should have a standard network interface, such as Wi-Fi, bluetooth, zigbee, etc., and support related communication protocols (such as MQTT, Z-Wave, etc.), so as to exchange data with devices such as smart lamps, speakers, etc. Aiming at the development of a user interface, an easy-to-use mobile phone application program or a bedside control panel can be designed, so that a user is allowed to conveniently set the awakening time, select the awakening mode, view sleep data and link with other intelligent household equipment.
In one embodiment, since the sleep data and the wake-up data relate to personal privacy of the user, data encryption is performed on the data collected by the sensor to ensure data transmission and storage safety, for example, a symmetric encryption algorithm AES or the like can be used, and the algorithm can play a good role in encryption protection on a large amount of user data and prevent data leakage. Preferably, the data security can be further improved through user authority management, for example, a reasonable user authority system is set, the access and control rights of the user to the personal data are ensured, and relevant data protection regulations are followed.
Referring to fig. 3, in one embodiment, the present invention further provides a user wake-up system for an intelligent electric bed of the internet of things, the system comprising:
A wake mode determining module 100, configured to determine a target wake mode for waking up a user, includes:
The first recommending unit 101 is configured to monitor a sleep state of a user to obtain sleep cycle stages in which the user is located, and recommend corresponding first wake-up modes according to different sleep cycle stages;
A second recommending unit 102, configured to obtain identity information and health status information of a user, and recommend a second wake-up mode according to the identity information and health status information of the user;
A third recommending unit 103, configured to obtain user-defined wake-up setting information of a user, and generate a third wake-up mode according to the user-defined wake-up setting information;
A target mode generating unit 104, configured to determine a target wake mode according to the first wake mode, the second wake mode, and the third wake mode;
The adaptive adjustment module 20 is configured to perform a wake-up operation on a user according to a target wake-up mode, monitor a wake-up state of the user during a wake-up process, generate a change curve of the wake-up state, and adaptively adjust the target wake-up mode according to the change curve of the wake-up state.
It may be understood that, the functions or modules included in the system provided by the present embodiment may be used to perform the method described in the foregoing method embodiment, and specific implementation thereof may refer to the description of the foregoing method embodiment, which is not repeated herein for brevity.
The invention also provides an electronic device comprising a processor and a memory for storing computer program code comprising computer instructions which, when executed by the processor, perform a method as any one of the possible implementations described above.
The invention also provides a computer readable storage medium having stored therein a computer program comprising program instructions which, when executed by a processor of an electronic device, cause the processor to perform a method as any one of the possible implementations described above.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein. It will be further apparent to those skilled in the art that the descriptions of the various embodiments of the present invention are provided with emphasis, and that the same or similar parts may not be described in detail in different embodiments for convenience and brevity of description, and thus, parts not described in one embodiment or in detail may be referred to in description of other embodiments.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present invention, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted across a computer-readable storage medium. The computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital subscriber line (digital subscriber line, DSL)), or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a digital versatile disk (DIGITAL VERSATILEDISC, DVD)), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.
Those of ordinary skill in the art will appreciate that implementing all or part of the above-described method embodiments may be accomplished by a computer program to instruct related hardware, the program may be stored in a computer readable storage medium, and the program may include the above-described method embodiments when executed. The storage medium includes a read-only memory (ROM) or a random access memory (random access memory, RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Claims (7)
1. A user wake-up method for an intelligent electric bed of the internet of things, the method comprising:
Determining a target wake-up mode for waking up a user, comprising:
Monitoring the sleep state of a user to obtain sleep cycle stages of the user, and recommending corresponding first awakening modes according to different sleep cycle stages;
Acquiring identity information and health state information of a user, and recommending a second awakening mode according to the identity information and the health state information of the user;
acquiring user-defined wake-up setting information of a user, and generating a third wake-up mode according to the user-defined wake-up setting information;
determining a target wake-up mode according to the first wake-up mode, the second wake-up mode and the third wake-up mode;
executing wake-up operation on a user according to a target wake-up mode, monitoring the wake-up state of the user in the wake-up process, generating a change curve of the wake-up state, and adaptively adjusting the target wake-up mode according to the change curve of the wake-up state;
The wake-up operation is executed for the user according to the target wake-up mode, and the method further comprises the following steps:
Monitoring body movement data of a user in a wake-up process, and determining the falling risk of the user in a current target wake-up mode according to the body movement data of the user;
When the falling risk is determined to be greater than a preset threshold value, generating an alarm prompt, and optimizing a target awakening mode;
the determining the falling risk of the user in the current target wake-up mode according to the body movement data of the user comprises the following steps:
detecting weight distribution of a user on the electric bed surface, and acquiring average weight distribution of the user outside the safe range of the bed surface;
detecting heart rate variability and user activity of a user, and calculating an awake state coefficient of the user according to the heart rate variability;
Acquiring a head lifting angle of an electric bed, and calculating a falling risk index according to the head lifting angle, the average weight distribution, the user activity degree and the awake state coefficient:
FRI=w1G×cosθ+w2(1-M)+w3Cs+w4(Ee+Eh);
Wherein FRI represents a drop risk index, G represents an average weight distribution of a user outside a safe range of a bed surface, M represents a user activity level, θ represents a head of a bed lifting angle, C s represents a user's awake state coefficient, E e、Eh represents an environmental compensation factor and an age compensation factor, and w 1、w2、w3、w4 represents weights, respectively.
2. The method for waking up a user of an intelligent electric bed for internet of things according to claim 1, wherein determining a target wake-up mode according to the first wake-up mode, the second wake-up mode and the third wake-up mode comprises:
constructing an evaluation factor set according to the first awakening mode, the second awakening mode and the third awakening mode, and determining a corresponding grade set;
based on each factor in the evaluation factor set, constructing a corresponding fuzzy evaluation matrix, wherein the items in the fuzzy evaluation matrix are used for representing the membership degrees of different evaluation factors on different grades;
Determining the weight of each factor by using an analytic hierarchy process, and determining a fuzzy evaluation vector according to the weight of each factor and a fuzzy evaluation matrix;
And comparing the fuzzy evaluation vector with the class set, screening out a target class corresponding to the maximum membership degree and a target factor corresponding to the target class, and taking a wake-up mode under the target factor as a target wake-up mode.
3. The method for waking up a user of an intelligent electric bed for the internet of things according to claim 1, wherein the identity information of the user includes user age and occupation information, and the health status information includes alcohol influence status, physiological cycle status and physical disease status of the user.
4. The method for waking up the user of the intelligent electric bed for the Internet of things according to claim 1, wherein the target waking up mode comprises waking up time, waking up action and waking up auxiliary environment setting, and the waking up action comprises any one or more of body position transformation waking up, biological vibration waking up and sound control waking up.
5. A user wake-up system for an intelligent electric bed of the internet of things, the system comprising:
the wake-up mode determining module is used for determining a target wake-up mode for waking up a user, and comprises the following steps:
the first recommending unit is used for monitoring the sleep state of the user to obtain sleep cycle stages of the user, and recommending corresponding first awakening modes according to different sleep cycle stages;
The second recommending unit is used for acquiring the identity information and the health state information of the user and recommending a second awakening mode according to the identity information and the health state information of the user;
the third recommendation unit is used for acquiring user-defined wake-up setting information of the user and generating a third wake-up mode according to the user-defined wake-up setting information;
The target mode generating unit is used for determining a target awakening mode according to the first awakening mode, the second awakening mode and the third awakening mode;
The self-adaptive adjustment module is used for executing wake-up operation on the user according to the target wake-up mode, monitoring the wake-up state of the user in the wake-up process, generating a change curve of the wake-up state, and self-adaptively adjusting the target wake-up mode according to the change curve of the wake-up state;
The wake-up operation is executed for the user according to the target wake-up mode, and the method further comprises the following steps:
Monitoring body movement data of a user in a wake-up process, and determining the falling risk of the user in a current target wake-up mode according to the body movement data of the user;
When the falling risk is determined to be greater than a preset threshold value, generating an alarm prompt, and optimizing a target awakening mode;
the determining the falling risk of the user in the current target wake-up mode according to the body movement data of the user comprises the following steps:
detecting weight distribution of a user on the electric bed surface, and acquiring average weight distribution of the user outside the safe range of the bed surface;
detecting heart rate variability and user activity of a user, and calculating an awake state coefficient of the user according to the heart rate variability;
Acquiring a head lifting angle of an electric bed, and calculating a falling risk index according to the head lifting angle, the average weight distribution, the user activity degree and the awake state coefficient:
FRI=w1G×cosθ+w2(1-M)+w3Cs+w4(Ee+Eh);
Wherein FRI represents a drop risk index, G represents an average weight distribution of a user outside a safe range of a bed surface, M represents a user activity level, θ represents a head of a bed lifting angle, C s represents a user's awake state coefficient, E e、Eh represents an environmental compensation factor and an age compensation factor, and w 1、w2、w3、w4 represents weights, respectively.
6. An electronic device comprising a processor and a memory for storing computer program code, the computer program code comprising computer instructions which, when executed by the processor, perform the user wake-up method for an intelligent electric bed of the internet of things as claimed in any one of claims 1 to 4.
7. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein a computer program comprising program instructions which, when executed by a processor of an electronic device, cause the processor to perform the user wake-up method for an intelligent electric bed of the internet of things of any of claims 1 to 4.
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