Rotatable intelligent electric bed and remote control method thereof

Abstract

The invention discloses a rotatable intelligent electric bed and a remote control method thereof, and the method comprises the steps: obtaining the use data of a user when the intelligent electric bed is in a use state; judging whether a preset condition is met or not according to the use data, and when it is determined that the use data meets the getting-up condition, controlling the intelligent electric bed to enter an auxiliary getting-up mode, including determining a first folding angle of the intelligent electric bed according to user setting or an intelligent recommendation mode; detecting the placement position of the shoe, and determining a first rotation angle of the intelligent electric bed according to the placement position of the shoe; according to the first folding angle and the first rotating angle, the intelligent electric bed is remotely controlled to move from the current state to the first target state so as to assist a user in getting up in the first target state of the intelligent electric bed. According to user setting or an intelligent recommendation mode, the folding angle is accurately calculated by considering user preferences, and the rotation angle is determined according to the position of the shoe after getting out of the bed, so that accurate control of the electric bed is realized, and the user experience is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent bed control, and in particular to a rotatable intelligent electric bed and a remote control method thereof. Background Art

[0002] Smart electric beds have received widespread attention in the field of smart devices. They are not only used in homes, but can also be used in the field of medical care. Among them, electric beds play a key role in the medical care process of the elderly or patients, enabling the elderly or patients to get in and out of bed by themselves without supervision, bringing convenience to family and medical work. When controlling current smart electric beds, they usually only select control parameters based on the user himself, or use relatively unified parameter configuration standards to assist use. However, this method lacks pertinence and lacks scientific guidance for different users, resulting in unsatisfactory control effects and poor user experience. Summary of the invention

[0003] In order to solve at least one of the technical problems mentioned above, the present invention provides a rotatable intelligent electric bed and a remote control method thereof.

[0004] In a first aspect, the present invention provides a remote control method for a rotatable intelligent electric bed, the method comprising:

[0005] When the smart electric bed is in use, obtaining the user's usage data, the usage data including the current time, the user's sleeping position and the frequency of turning over;

[0006] Determining whether a preset condition is met according to the usage data, and when it is determined that the usage data meets the getting up condition, controlling the intelligent electric bed to enter an auxiliary getting up mode, including:

[0007] Determine the first folding angle of the smart electric bed according to user settings or intelligent recommendation methods;

[0008] Detecting the placement position of the shoes, and determining the first rotation angle of the intelligent electric bed according to the placement position of the shoes;

[0009] According to the first folding angle and the first rotation angle, the smart electric bed is remotely controlled to move from a current state to a first target state, so as to assist the user in getting up from the bed in the first target state of the smart electric bed.

[0010] When the smart electric bed is in use, that is, when someone is on the bed, the present invention will determine whether the getting up condition needs to be met based on the user's usage data to control whether the smart electric bed enters the assisted getting up mode. During control, the first folding angle of the smart electric bed is determined according to the user's settings or intelligent recommendation method, and the placement of the shoes is taken into consideration, so that the rotation can be accurately rotated to the angle position where the shoes are located, which can greatly improve the convenience of assisting users to use the electric bed, thereby improving the user experience.

[0011] Preferably, judging whether a preset condition is met according to the usage data includes:

[0012] Determine whether the current time has arrived at the wake-up time, where the wake-up time is obtained according to the user setting or the user's preferred wake-up time according to machine learning.

[0013] The wake-up time can be set by the user, or it can be automatically woken up based on machine learning of the user's preferred wake-up time. This ensures the diversity of wake-up scenarios and is suitable for a variety of states.

[0014] Preferably, judging whether a preset condition is met according to the usage data further includes:

[0015] Predicting whether the probability that the user intends to get up is greater than a preset threshold, wherein predicting the probability that the user intends to get up includes:

[0016] Create a logistic regression function:

[0017]

[0018] Where p(X) is the probability that the user intends to get up. These are all model parameters, X1 represents the user's sleeping position, and X2 represents the frequency of turning over;

[0019] Obtain the historical usage data of the user during the historical use of the smart electric bed, including the user's sleeping position, turning frequency, and known intention to get up during the historical use Perform parameter fitting and convert the fitted Substitute into the logistic regression function to generate the target prediction function;

[0020] Among them, the fitting satisfies:

[0021] X1=1 means the user sleeps on his side; X1=0 means the user sleeps on his back;

[0022] X2=1, indicating that the turning frequency is greater than or equal to the preset frequency; X2=0, indicating that the turning frequency is less than the preset frequency;

[0023] The user's sleeping posture and turning frequency in the current usage state are input into the target prediction function to obtain the probability that the user has the intention to get up.

[0024] The above scheme takes into account the factor of wake-up time, and here the user's intention to get up is judged. In some scenarios, even if the user is very sleepy, the wake-up operation must be performed considering the time factor. In other scenarios, even if the time does not meet the requirements, if the probability of the user's intention to get up is greater than the preset threshold, it is considered that the preset conditions are met and the electric bed posture is adjusted to assist in waking up the user. When predicting the intention to get up, this scheme considers constructing a logistic regression function, using the user's sleeping posture and turning frequency as variables, and fitting the unknown parameters in the function with historical data to determine the final expression of the function. When predicting the intention to get up, you only need to input the user's sleeping posture and turning frequency in the current state into the function model, and you can quickly predict the user's intention to get up, thereby accurately controlling the electric bed.

[0025] Preferably, the step of determining the first folding angle of the intelligent electric bed comprises:

[0026] When the first folding angle is determined according to the intelligent recommendation method, the seat folding angle and the leg folding angle are determined respectively according to the user characteristic data:

[0027]

[0028] Where θ1 and θ2 are the seat folding angle and leg folding angle respectively; H0 is the height of the bed, L g is the user's leg length; α is the ideal angle between the user's torso and thigh, and β is the ideal angle between the user's thigh and calf.

[0029] At present, the folding angle is mostly based on the user's own settings, but this method ignores scientific guidance. For example, the folding angle should be scientifically adjusted according to the user's own physical characteristics, so that the user's comfort is optimal, and it will not harm the user's health. It is also safer when assisting the use of smart electric beds. Therefore, in addition to providing a user setting method, this solution also generates the folding angle through intelligent recommendation, taking into account the physical data of different people, which can improve the safety and comfort of electric beds, better meet the needs of different users, and improve user experience.

[0030] Preferably, when the first folding angle is determined according to the intelligent recommendation method, the method further includes:

[0031] Reading the user's identity information, inputting the identity information into a preference analysis model, and predicting the folding angle of the user's preference; the preference analysis model is trained based on a random forest algorithm;

[0032] The seat folding angle and the leg folding angle are compensated according to the folding angle preferred by the user to determine a final first folding angle.

[0033] The scientifically recommended assisted sitting posture for electric beds usually appears without the user's own settings, for the user's reference. However, the scientifically recommended method cannot meet the needs of individual users in some cases. Therefore, this solution also takes user preferences into consideration, and compensates the scientifically recommended folding angle through user preferences, so as to further make the folding posture of the electric bed more in line with user habits, thereby improving the user experience.

[0034] Preferably, the method further comprises:

[0035] When the smart electric bed is not in use, the user's activity is identified and the arrival probability of the user reaching the location of the smart electric bed within a preset time period is calculated;

[0036] When the arrival probability is greater than a preset probability, calculating an angle formed by a line connecting the user's location and the edge of the intelligent electric bed and the edge of the intelligent electric bed, and determining a second rotation angle according to the angle;

[0037] Determine the second folding angle of the smart electric bed according to user settings or intelligent recommendation methods;

[0038] According to the second folding angle and the second rotation angle, the intelligent electric bed is remotely controlled to move from a current state to a second target state, so as to assist the user to perform the operation of getting into bed in the second target state of the intelligent electric bed.

[0039] The above scheme takes into account the assisted getting-up scenario when the user is using an electric bed. In other scenarios, the user may be waiting to use the electric bed. At this time, this scheme will consider the arrival probability of the user reaching the location of the smart electric bed within a preset time period. If it is greater than the preset probability, it is considered that the user will reach the smart bed and start using it within the preset time period. At this time, during the time when the user arrives at the electric bed, the electric bed can be adjusted in advance, including calculating the angle formed by the line connecting the user's location and the edge of the smart electric bed and the edge of the smart electric bed, and determining the second rotation angle based on the angle. The folding angle is also generated according to the user setting or intelligent recommendation method. In this way, the electric bed can be accurately controlled in advance, so that the user can directly enter the assisted getting-in-bed operation when he arrives at the electric bed without waiting for on-site adjustment, thereby further increasing the convenience and intelligence of use.

[0040] Preferably, the method further comprises:

[0041] When the user performs a getting-up operation in the first target state of the smart electric bed, or the user performs a getting-in-bed operation in the second target state of the smart electric bed, determining whether it is necessary to enter the linkage control mode;

[0042] When it is determined to enter the linkage control mode, the linkage control scheme is matched for the waking up scene or the going to bed scene according to the user identity, and other smart devices are linked and controlled according to the linkage control.

[0043] In the scene of getting up or going to bed, you can associate smart devices with it. For example, you can open the curtains when getting up and close them when going to bed; or adjust the air conditioning temperature and light brightness to suit the scene. This linkage control method of smart devices can further improve the convenience of using smart electric beds and enhance the user experience.

[0044] Preferably, the remote control method includes one or more of APP remote control, remote controller control and remote voice control.

[0045] By providing a variety of remote control methods to adapt to different scenarios, the bed can be controlled by the user himself or by others, such as the user's family or medical staff, making the control process more convenient and improving the safety of users when using electric beds.

[0046] In a second aspect, the present invention further provides a rotatable intelligent electric bed, which adopts the remote control method of the rotatable intelligent electric bed as described in any one of the above items, and comprises a body of the intelligent electric bed and a control system;

[0047] The control system comprises:

[0048] The sensor module includes a pressure sensor and an infrared sensor; the pressure sensor is used to collect the user's usage data; the infrared sensor detects the placement of the shoes and the user's location;

[0049] A main control module, used to determine a first folding angle or a second folding angle of the smart electric bed according to a user setting or an intelligent recommendation method, and also used to determine a first rotation angle of the smart electric bed according to a placement position of shoes, and also used to determine a second rotation angle of the smart electric bed according to a user location;

[0050] The driving module is used to generate a first driving signal according to a first rotation angle and a first folding angle, and drive the motor to operate according to the first driving signal to control the intelligent electric bed to move to a first target state; and is also used to generate a second driving signal according to a second rotation angle and a second folding angle, and drive the motor to operate according to the second driving signal to control the intelligent electric bed to move to a second target state.

[0051] The intelligent electric bed provided by the present invention can accurately calculate the folding angle according to user settings or intelligent recommendations and consider user preferences, and determine the rotation angle according to the position of shoes after getting out of bed, thereby realizing the precise control process of the electric bed to meet user needs, thereby greatly improving the user experience and providing convenience for intelligent care or home use.

[0052] 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the background technology, the drawings required for use in the embodiments of the present invention or the background technology will be described below.

[0054] The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used to illustrate the technical solutions of the present disclosure together with the specification.

[0055] Figure 1 A schematic flow chart of a remote control method for a rotatable intelligent electric bed provided in an embodiment of the present invention;

[0056] Figure 2 A schematic diagram showing the principle of the rotation angle of a rotatable intelligent electric bed provided in an embodiment of the present invention;

[0057] Figure 3 A schematic structural diagram of a rotatable intelligent electric bed provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

[0060] The term "and / or" herein is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C can represent including any one or more elements selected from the set consisting of A, B, and C.

[0061] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0062] In addition, in order to better illustrate the present invention, numerous specific details are provided in the following specific embodiments. It should be understood by those skilled in the art that the present invention can be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present invention.

[0063] The current smart electric beds are still lacking in intelligence when in use, and cannot provide targeted recommendations for different users. For example, in medical care scenarios, when the elderly use electric beds, they usually only use a unified standard or set the bed posture according to the elderly’s own preferences or the advice of medical staff, so that the user comfort needs to be improved. In addition, when controlling the rotation angle, the prior art ignores the optimal rotation angle. When the shoes are placed on the left side of the bed, they may rotate to the right when rotating. After getting up, the user still has to find the position of the shoes, which brings inconvenience to the operation of getting up. For this reason, the present invention aims to provide a rotatable smart electric bed and its remote control method, which can accurately calculate the folding angle according to user settings or intelligent recommendations, considering user preferences, and determine the rotation angle according to the position of the shoes after getting out of bed, thereby achieving precise control of the electric bed and improving the user experience.

[0064] See also Figure 1 , Figure 1 The flowchart of a remote control method of a rotatable intelligent electric bed provided by an embodiment of the present invention is as follows. Figure 1 As shown, the method includes:

[0065] S10, when the smart electric bed is in use, obtaining the user's usage data, wherein the usage data includes the current time, the user's sleeping position, and the frequency of turning over;

[0066] S20, judging whether a preset condition is met according to the usage data, and when it is determined that the usage data meets the getting up condition, controlling the intelligent electric bed to enter an auxiliary getting up mode, including:

[0067] S201, determining a first folding angle of the smart electric bed according to a user setting or an intelligent recommendation method;

[0068] S202, detecting the placement position of the shoes, and determining a first rotation angle of the intelligent electric bed according to the placement position of the shoes;

[0069] S203. Remotely control the intelligent electric bed to move from a current state to a first target state according to the first folding angle and the first rotation angle, so as to assist the user to get up from the bed in the first target state of the intelligent electric bed.

[0070] In this embodiment, the user usually refers to an elderly person or patient who needs care or has difficulty in moving. The bed is usually equipped with a pressure sensor that can detect the position and weight distribution of the user on the bed. By analyzing these data, the system can determine whether the user is in bed, the frequency of turning over, the sleeping posture, etc., and then determine whether the elderly is in the state of getting up or preparing to get up.

[0071] The current moment refers to the real time when the user is using the smart electric bed, which can be a specific time point (such as 23:30) or a time period (such as 10 pm to 6 am).

[0072] User sleeping position refers to the user's body position when sleeping. Common sleeping positions include sleeping on your back and sleeping on your side. In some scenarios, the smart bed can also adjust the inclination angle of the bed according to the sleeping position to provide the best spinal support, reduce pressure points, and improve sleep quality. For users who snore or have sleep apnea, it can automatically adjust to a sleeping position that is conducive to breathing.

[0073] The frequency of turning over refers to the number of times a user turns over in a specific period of time, and is usually used to measure the depth and quality of sleep. When the frequency of turning over is high, it may be considered that the user is about to wake up, and the probability of having the intention to get up will increase.

[0074] Judging whether the preset conditions are met based on the above usage data usually refers to judging whether the current moment has reached the wake-up time, or predicting whether the probability that the user has the intention to get up is greater than the preset threshold. When the wake-up time is reached, or it is determined that the user's intention to get up is greater than the preset threshold, the assisted getting-up mode can be entered.

[0075] When entering the assisted getting-up mode, the first rotation angle and the first folding angle of the bed need to be calculated. The first folding angle of the smart electric bed is determined according to the user setting or intelligent recommendation method; the user setting is mainly set according to the folding angle that the user is accustomed to, and the intelligent recommendation method is mainly based on model analysis, recommending a scientific folding angle of the bed, and can consider user preferences to correct the angle to meet the user's comfort needs. Generally speaking, the priority of user settings is greater than that of intelligent recommendations; that is, when the user sets the most accustomed folding angle by himself, the folding angle of the bed will be adjusted according to the user's decision. If the user has not set it, the appropriate folding angle can be intelligently recommended based on the user's physical data, so that the posture of the bed to assist the user to get up is safer and more comfortable, in line with human health needs.

[0076] Existing electric beds often ignore the control of the rotation angle. Although they can achieve angle rotation within a certain range, they cannot recommend a suitable rotation angle.

[0077] See also Figure 2 , Figure 2 A rotation scenario for getting out of bed based on the shoe placement position is provided. Usually, the electric bed will directly rotate from posture A to posture B, that is, it will rotate 90 degrees uniformly, so that the position for getting up corresponds to the center of the bed. However, in this case, if the shoes are placed at a position 40 degrees between the end of the bed and the side of the bed, then when rotating to posture B, the user needs to move to the shoe placement position after getting up and getting out of bed, which will cause inconvenience to the elderly or patients with limited mobility when getting up, and increase safety hazards. Therefore, this embodiment will detect the placement position of the shoes and determine the first rotation angle of the smart electric bed based on the placement position of the shoes. Among them, it is preferred to use an infrared sensor to detect the position of the shoes, and in other embodiments, image recognition can be used to detect the position of the shoes.

[0078] After the first folding angle and the first rotation angle are calculated, the smart electric bed can be remotely controlled to move from the current state to the first target state to assist the user in getting up from the bed in the first target state of the smart electric bed.

[0079] Therefore, this embodiment will determine whether the getting up condition needs to be met based on the user's usage data to control whether the smart electric bed enters the assisted getting up mode. During the control, the first folding angle of the smart electric bed is determined according to the user's settings or intelligent recommendation method, and the placement of the shoes is considered, so that the rotation can be accurately rotated to the angle position where the shoes are located, which can greatly improve the convenience of assisting users to use the electric bed, thereby improving the user experience.

[0080] In one embodiment, judging whether a preset condition is met according to the usage data includes:

[0081] Determine whether the current time has arrived at the wake-up time, where the wake-up time is obtained according to the user setting or the user's preferred wake-up time according to machine learning.

[0082] User settings: Users can directly enter the time they want to be woken up in the settings interface of the smart device, such as setting a specific wake-up time through the alarm function.

[0083] Machine Learning Predictions:

[0084] Data collection: Smart devices collect the user's wake-up time over the past period of time, and may also include other related data, such as sleep quality, weekdays or rest days, sleep time the night before, etc.

[0085] Pattern recognition: Use machine learning algorithms (such as decision trees, support vector machines, neural networks, etc.) to analyze the collected data and identify users’ waking patterns and preferences.

[0086] Predicted wake-up time: Based on the identified patterns, the algorithm predicts the most likely time for the user to wake up and sets that as the wake-up time.

[0087] Therefore, in this embodiment, the wake-up time can be set according to the user, or it can be automatically woken up according to the user's preferred wake-up time learned by machine learning. This can ensure the diversity of wake-up scenes and is suitable for a variety of states.

[0088] In one embodiment, judging whether a preset condition is met according to the usage data further includes:

[0089] Predicting whether the probability that the user intends to get up is greater than a preset threshold, wherein predicting the probability that the user intends to get up includes:

[0090] Create a logistic regression function:

[0091]

[0092] Where p(X) is the probability that the user intends to get up. These are all model parameters, X1 represents the user's sleeping position, and X2 represents the frequency of turning over;

[0093] Obtain the historical usage data of the user during the historical use of the smart electric bed, including the user's sleeping position, turning frequency, and known intention to get up during the historical use Perform parameter fitting and convert the fitted Substitute into the logistic regression function to generate the target prediction function;

[0094] Among them, the fitting satisfies:

[0095] X1=1 means the user sleeps on his side; X1=0 means the user sleeps on his back;

[0096] X2=1, indicating that the turning frequency is greater than or equal to the preset frequency; X2=0, indicating that the turning frequency is less than the preset frequency;

[0097] The user's sleeping posture and turning frequency in the current usage state are input into the target prediction function to obtain the probability that the user has the intention to get up.

[0098] The above scheme takes into account the factor of wake-up time, and here the user's intention to get up is judged. In some scenarios, even if the user is very sleepy, the wake-up operation must be performed considering the time factor. In other scenarios, even if the time does not meet the requirements, if the probability of the user's intention to get up is greater than the preset threshold, it is considered that the preset conditions are met and the electric bed posture is adjusted to assist in waking up the user.

[0099] Predicting the probability of the user's intention to get up can be achieved by building a model based on the user's behavior pattern. This model can use a machine learning algorithm, especially a classification algorithm, such as logistic regression, support vector machine, decision tree or neural network. Preferably, this embodiment uses logistic regression as the basic model to predict the probability of the intention to get up.

[0100] Assume that there are two main features: sleeping position and turning frequency. Sleeping position can be represented by an angle, while turning frequency can be represented by the number of turnings per unit time. To simplify the explanation, sleeping position is simplified to a binary classification, i.e., side sleeping and back sleeping, and turning frequency is also simplified to a binary classification, i.e., a preset frequency is set, and a frequency greater than or equal to the preset frequency is considered high frequency, and vice versa. In practical applications, these features are usually quantized into continuous values ​​and may be normalized.

[0101] After constructing the above logistic regression function, the model parameters need to be solved by fitting During fitting training. The training process involves using historical data, including the user's sleeping position, turning frequency, and known intention to get up, to fit parameters so that the model can best predict the intention to get up. During fitting, 1 can be defined as the intention to get up, and 0 can be defined as no intention to get up. Once the parameters are determined, the final expression of the function can be determined. When predicting the intention to get up, you only need to input the user's sleeping position and turning frequency in the current state into the function model, and you can quickly predict the user's intention to get up, thereby accurately controlling the electric bed.

[0102] Therefore, in this embodiment, by considering the intention to get up, it can be determined whether the electric bed posture needs to be adjusted, thereby achieving a more accurate control process.

[0103] In one embodiment, determining the first folding angle of the intelligent electric bed includes:

[0104] When the first folding angle is determined according to the intelligent recommendation method, the seat folding angle and the leg folding angle are determined respectively according to the user characteristic data:

[0105]

[0106] Where θ1 and θ2 are the seat folding angle and leg folding angle respectively; H0 is the height of the bed, L g is the user's leg length; α is the ideal angle between the user's torso and thigh, and β is the ideal angle between the user's thigh and calf.

[0107] In this embodiment, H0+L g sinα represents the vertical distance from the buttocks to the edge of the smart electric bed, H0+L g sinβ represents the horizontal distance from the buttocks to the edge of the intelligent electric bed. L g cosα、L g cosβ are the vertical distance and horizontal distance from the knee joint to the edge of the smart electric bed. The above formula can accurately calculate the folding angle of the bed that meets the human comfort.

[0108] The current folding angle is usually based on the user's own settings, but this method ignores scientific guidance. For example, the folding angle should be scientifically adjusted according to the user's own physical characteristics, so that the user's comfort is optimal, and it will not harm the user's health. It is also safer when assisting the use of smart electric beds. Therefore, in addition to providing a user setting method, this solution also generates the folding angle through intelligent recommendation, taking into account the physical data of different people, which can improve the safety and comfort of the electric bed, better meet the needs of different users, and improve the user experience.

[0109] In one embodiment, when determining the first folding angle according to the intelligent recommendation method, the method further includes:

[0110] Reading the user's identity information, inputting the identity information into a preference analysis model, and predicting the folding angle of the user's preference; the preference analysis model is trained based on a random forest algorithm;

[0111] The seat folding angle and the leg folding angle are compensated according to the folding angle preferred by the user to determine a final first folding angle.

[0112] The scientifically recommended assisted sitting posture for electric beds usually appears without the user's own settings, for the user's reference. However, the scientifically recommended method cannot meet the needs of individual users in some cases. Therefore, this solution also takes user preferences into consideration, and compensates the scientifically recommended folding angle through user preferences, so as to further make the folding posture of the electric bed more in line with user habits, thereby improving the user experience.

[0113] Specifically, performing folding angle compensation may include the following steps:

[0114] Step 1: Collect user identity information:

[0115] Type of information: This may include the user's age, gender, weight, height, health conditions (such as back pain), sleep preferences (such as preferred sleeping position), etc.

[0116] Data collection: collected through the information filled in by the user during registration, or through long-term monitoring by smart devices (such as smart bracelets and smart mattresses).

[0117] Step 2: Build a preference analysis model:

[0118] Model Selection: The random forest algorithm was chosen as the basis for building the preference analysis model due to its robustness and ability to handle high-dimensional data.

[0119] Feature selection: Select the features most relevant to the folding angle preference from the user's identity information, such as height, weight, sleeping preference, etc.

[0120] Data preprocessing: Clean the data, handle missing values, perform feature encoding (such as converting categorical variables into numerical values), and possibly perform feature scaling.

[0121] Model training: Use the collected user data and their known preference fold angles as the training set to train a random forest model.

[0122] Model evaluation: Evaluate the predictive performance of the model through methods such as cross-validation, and adjust parameters until satisfactory prediction accuracy is achieved.

[0123] Step 3: Predict the user's preferred folding angle:

[0124] Input user information: Input the new user’s identity information into the trained preference analysis model.

[0125] Prediction output: The model outputs the seat and leg fold angles preferred by the user.

[0126] Step 4: Compensation and determination of final folding angle:

[0127] Compensation analysis: Considering that user preferences may change over time, as well as the influence of environmental factors (e.g., temperature, humidity), the system may need to fine-tune or compensate for the predicted folding angle.

[0128] User feedback: By allowing users to give feedback on the automatically adjusted folding angles, the system can learn and adjust the model to more accurately predict user preferences.

[0129] Determine the final folding angle: Comprehensively consider the predicted folding angle and the results of the compensation analysis to determine the final first folding angle, and adjust it through the control system of the intelligent electric bed.

[0130] In summary, this embodiment takes into account the user's preference to compensate for the folding angle, so that the adjusted posture of the electric bed can better meet the user's needs, thereby improving the user experience.

[0131] The above scenarios all describe the scenario where the smart electric bed is in use and the user needs to get up from the bed. In some scenarios, the scenario where the smart electric bed is not in use and the user is about to get on the bed is also included.

[0132] In one embodiment, the method further comprises:

[0133] When the smart electric bed is not in use, the user's activity is identified and the arrival probability of the user reaching the location of the smart electric bed within a preset time period is calculated;

[0134] When the arrival probability is greater than a preset probability, calculating an angle formed by a line connecting the user's location and the edge of the intelligent electric bed and the edge of the intelligent electric bed, and determining a second rotation angle according to the angle;

[0135] Determine the second folding angle of the smart electric bed according to user settings or intelligent recommendation methods;

[0136] According to the second folding angle and the second rotation angle, the intelligent electric bed is remotely controlled to move from a current state to a second target state, so as to assist the user to perform the operation of getting into bed in the second target state of the intelligent electric bed.

[0137] The above embodiments consider the assisted getting-up scenario when the user is using an electric bed. In other scenarios, the user may be waiting to use the electric bed. At this time, the present solution will consider the arrival probability of the user reaching the location of the smart electric bed within a preset time period. If the probability is greater than the preset probability, it is considered that the user will reach the smart bed and start using it within the preset time period. At this time, during the time when the user arrives at the electric bed, the electric bed can be adjusted in advance, including calculating the angle formed by the line connecting the user's location and the edge of the smart electric bed and the edge of the smart electric bed, and determining the second rotation angle based on the angle. The folding angle is also generated based on the user's setting or intelligent recommendation method. In this way, the electric bed can be accurately controlled in advance, so that the user can directly enter the assisted getting-in-bed operation when he arrives at the electric bed without waiting for on-site adjustment, thereby further increasing the convenience and intelligence of use.

[0138] In some embodiments, no matter the scenario in which the user needs to get up or go to bed, a corresponding linkage control method can be provided. Specifically:

[0139] When the user performs a getting-up operation in the first target state of the smart electric bed, or the user performs a getting-in-bed operation in the second target state of the smart electric bed, determining whether it is necessary to enter the linkage control mode;

[0140] When it is determined to enter the linkage control mode, the linkage control scheme is matched for the waking up scene or the going to bed scene according to the user identity, and other smart devices are linked and controlled according to the linkage control.

[0141] Smart devices are mainly devices that can be electrically controlled, such as curtains, air conditioners, lights, etc. In this embodiment, assuming that the user needs to go to bed, the curtains can be closed, the light brightness can be reduced, the color temperature can be adjusted, and the air conditioner temperature can be adjusted. If the user needs to get up, the curtains can be opened, the air conditioner can be turned off, the lights can be brightened, and the appropriate color temperature can be adjusted. Through this linkage control method of smart devices, the convenience of using smart electric beds can be further improved, and the user experience can be improved.

[0142] In some embodiments, whether it is a scenario of getting up assistance or going to bed assistance, the remote control method can include one or more of APP remote control, remote remote control and remote voice control. By providing multiple remote control methods to adapt to different scenarios, users can control it themselves or through other people, such as the user's family or medical staff, making the control process more convenient and improving the safety of users when using electric beds.

[0143] It should be noted that the smart electric bed should have an emergency stop button, which can stop the operation in time when the control link fails to ensure the safety of the user. At the same time, once the electric bed fails, it should also have an automatic detection function to troubleshoot the cause of the failure and improve the level of intelligence.

[0144] See also Figure 3 In one embodiment, the present invention further provides a rotatable intelligent electric bed 1, and refers to the remote control method of the rotatable intelligent electric bed as described in any of the above embodiments. The intelligent electric bed 1 includes an intelligent electric bed body 10 and a control system 20;

[0145] The control system 20 includes:

[0146] The sensor module 201 includes a pressure sensor and an infrared sensor; the pressure sensor is used to collect the user's usage data; the infrared sensor detects the placement of the shoes and the user's location;

[0147] The main control module 202 is used to determine the first folding angle or the second folding angle of the intelligent electric bed according to the user setting or the intelligent recommendation method, and is also used to determine the first rotation angle of the intelligent electric bed according to the placement position of the shoes, and is also used to determine the second rotation angle of the intelligent electric bed according to the user's location;

[0148] The driving module 203 is used to generate a first driving signal according to the first rotation angle and the first folding angle, and drive the motor to operate according to the first driving signal to control the intelligent electric bed to move to the first target state; and is also used to generate a second driving signal according to the second rotation angle and the second folding angle, and drive the motor to operate according to the second driving signal to control the intelligent electric bed to move to the second target state.

[0149] It can be understood that the functions or modules included in the intelligent electric bed provided in this embodiment can be used to execute the method described in the above method embodiment. Its specific implementation can refer to the description of the above method embodiment. For the sake of brevity, it will not be repeated here.

[0150] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0151] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. Those skilled in the art can also clearly understand that the descriptions of the various embodiments of the present invention have different focuses. For the convenience and brevity of description, the same or similar parts may not be repeated in different embodiments. Therefore, for parts not described or not described in detail in a certain embodiment, refer to the records of other embodiments.

Claims

1. A remote control method for a rotatable intelligent electric bed, characterized in that: The method comprises: When the smart electric bed is in use, obtaining the user's usage data, the usage data including the current time, the user's sleeping position and the frequency of turning over; Determining whether a preset condition is met according to the usage data, and when it is determined that the usage data meets the getting up condition, controlling the intelligent electric bed to enter an auxiliary getting up mode, including: Determine the first folding angle of the smart electric bed according to user settings or intelligent recommendation methods; Detecting the placement position of the shoes, and determining the first rotation angle of the intelligent electric bed according to the placement position of the shoes; According to the first folding angle and the first rotation angle, the smart electric bed is remotely controlled to move from a current state to a first target state, so as to assist the user in getting up from the bed in the first target state of the smart electric bed.

2. The remote control method of a rotatable intelligent electric bed according to claim 1, characterized in that: Determining whether a preset condition is met according to the usage data includes: Determine whether the current time has arrived at the wake-up time, where the wake-up time is obtained according to the user setting or the user's preferred wake-up time according to machine learning.

3. The remote control method of a rotatable intelligent electric bed according to claim 1, characterized in that: Judging whether the preset condition is met according to the usage data also includes: Predicting whether the probability that the user intends to get up is greater than a preset threshold, wherein predicting the probability that the user intends to get up includes: Create a logistic regression function: Where p(X) is the probability that the user intends to get up. These are all model parameters, X1 represents the user's sleeping position, and X2 represents the frequency of turning over; Obtain the historical usage data of the user during the historical use of the smart electric bed, including the user's sleeping position, turning frequency, and known intention to get up during the historical use Perform parameter fitting and convert the fitted Substitute into the logistic regression function to generate the target prediction function; Among them, the fitting satisfies: X1=1 means the user sleeps on his side; X1=0 means the user sleeps on his back; X2=1, indicating that the turning frequency is greater than or equal to the preset frequency; X2=0, indicating that the turning frequency is less than the preset frequency; The user's sleeping posture and turning frequency in the current usage state are input into the target prediction function to obtain the probability that the user has the intention to get up.

4. The remote control method of a rotatable intelligent electric bed according to claim 1, characterized in that: The step of determining a first folding angle of the intelligent electric bed comprises: When the first folding angle is determined according to the intelligent recommendation method, the seat folding angle and the leg folding angle are determined respectively according to the user characteristic data: Where θ1 and θ2 are the seat folding angle and leg folding angle respectively; H0 is the height of the bed, L g is the user's leg length; α is the ideal angle between the user's torso and thigh, and β is the ideal angle between the user's thigh and calf.

5. The remote control method of a rotatable intelligent electric bed according to claim 4, characterized in that: The method of determining the first folding angle according to the intelligent recommendation method further includes: Reading the user's identity information, inputting the identity information into a preference analysis model, and predicting the folding angle of the user's preference; the preference analysis model is trained based on a random forest algorithm; The seat folding angle and the leg folding angle are compensated according to the folding angle preferred by the user to determine a final first folding angle.

6. The remote control method of a rotatable intelligent electric bed according to claim 1, characterized in that: The method further comprises: When the smart electric bed is not in use, the user's activity is identified and the arrival probability of the user reaching the location of the smart electric bed within a preset time period is calculated; When the arrival probability is greater than a preset probability, calculating an angle formed by a line connecting the user's location and the edge of the intelligent electric bed and the edge of the intelligent electric bed, and determining a second rotation angle according to the angle; Determine the second folding angle of the smart electric bed according to user settings or intelligent recommendation methods; According to the second folding angle and the second rotation angle, the intelligent electric bed is remotely controlled to move from a current state to a second target state, so as to assist the user to perform the operation of getting into bed in the second target state of the intelligent electric bed.

7. The remote control method of a rotatable intelligent electric bed according to claim 1 or 6, characterized in that: The method further comprises: When the user performs a getting-up operation in the first target state of the smart electric bed, or the user performs a getting-in-bed operation in the second target state of the smart electric bed, determining whether it is necessary to enter the linkage control mode; When it is determined to enter the linkage control mode, the linkage control scheme is matched for the waking up scene or the going to bed scene according to the user identity, and other smart devices are linked and controlled according to the linkage control.

8. The remote control method of a rotatable intelligent electric bed according to claim 1 or 6, characterized in that: The remote control method includes one or more of APP remote control, remote controller control and remote voice control.

9. A rotatable intelligent electric bed, comprising the remote control method for a rotatable intelligent electric bed as claimed in any one of claims 1 to 8, characterized in that: Including the main body and control system of the intelligent electric bed; The control system comprises: The sensor module includes a pressure sensor and an infrared sensor; the pressure sensor is used to collect the user's usage data; the infrared sensor detects the placement of the shoes and the user's location; A main control module, used to determine a first folding angle or a second folding angle of the smart electric bed according to a user setting or an intelligent recommendation method, and also used to determine a first rotation angle of the smart electric bed according to a placement position of shoes, and also used to determine a second rotation angle of the smart electric bed according to a user location; The driving module is used to generate a first driving signal according to a first rotation angle and a first folding angle, and drive the motor to operate according to the first driving signal to control the intelligent electric bed to move to a first target state; and is also used to generate a second driving signal according to a second rotation angle and a second folding angle, and drive the motor to operate according to the second driving signal to control the intelligent electric bed to move to a second target state.

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