Electric bed lifting control method and device with self-adaptive height adjustment function

Abstract

The embodiment of the invention discloses an electric bed lifting control method and device capable of adjusting the height in a self-adaptive mode. The method comprises the steps of querying a shank height median value corresponding to a target user in a preset mapping table based on user information of the target user; determining a user group of the target user based on the user information, and determining a habitual bed height according to an average value of historical adjustment data in a group database corresponding to the user group; and in response to the bed height adjustment instruction, calculating a shank height median value and a weighted value of a habitual bed height, and adjusting the weighted value according to a correction parameter corresponding to the current scene mode to obtain a target height so as to perform lifting control on the electric bed according to the target height. According to the embodiment, the optimal target height can be dynamically calculated according to the user information, the height of the electric bed is intelligently controlled according to the target height, tedious operation is not needed, the electric bed can adapt to different use scene requirements, and the safety and convenience of the electric bed in various use scenes are improved.

Description

TECHNICAL FIELD

[0001] Embodiments of the present specification belong to the field of smart home, in particular to a lifting control method and device of an electric bed with adaptive height adjustment. BACKGROUND

[0002] In modern life, the application of electric beds is becoming more and more widespread, especially in medical, elderly care and other scenarios, providing convenience for people with limited mobility. Although the traditional electric bed has the function of lifting, its adjustment mainly depends on the subjective judgment of the user, and lacks consideration of ergonomic parameters. This leads to the fact that the bed height setting is often unreasonable, not only affecting the comfort of the user, but also possibly causing safety hazards. For example, when the bed height is 121%~140% or greater than 140% of the length of the user's lower leg, the user's feet cannot fully or completely reach the ground when sitting beside the bed, and the feet are in an unstable state, leading to unsafe bed transfer, significantly increasing the risk of falling. At the same time, frequent manual operation is extremely unfriendly to the elderly or people with limited mobility, and it is difficult to adapt to the different needs of users of different genders and ages in different use scenarios such as getting out of bed and getting into bed. SUMMARY

[0003] Embodiments of the present disclosure provide a lifting control method and device of an electric bed with adaptive height adjustment, aiming to solve one or more of the above problems and other potential problems.

[0004] According to a first aspect of the present disclosure, a lifting control method of an electric bed with adaptive height adjustment is provided, the method comprising: querying a target user's lower leg height median value in a preset mapping table based on user information of the target user, the user information comprising gender information, height information, weight information, region information and age information; determining a user group of the target user based on the user information, and determining a habitual bed height according to an average value of each historical adjustment data in a group database corresponding to the user group; and in response to a bed height adjustment instruction, calculating a weighted value of the lower leg height median value and the habitual bed height, and adjusting the weighted value according to a correction parameter corresponding to a current scene mode to obtain a target height, so as to control the lifting of the electric bed according to the target height.

[0005] According to a second aspect of the present disclosure, an adaptive height adjustment electric bed lifting control device is provided, the device comprising a calf height query module configured to query a calf height median value corresponding to a target user in a preset mapping table based on user information of the target user, the user information comprising gender information, height information, weight information, region information and age information; a habitual bed height determination module configured to determine a user group of the target user based on the user information, and determine a habitual bed height according to an average value of each historical adjustment data in a group database corresponding to the user group; and a height control module configured to calculate a weighted value of the calf height median value and the habitual bed height in response to a bed height adjustment instruction, and obtain a target height by adjusting the weighted value according to a correction parameter corresponding to a current scene mode, so as to perform lifting control on the electric bed according to the target height.

[0006] According to a third aspect of the present disclosure, an electronic device is provided, comprising one or more processors, and a memory associated with the one or more processors, the memory being configured to store program instructions, the program instructions being configured to perform the method provided by the first aspect when read and executed by the one or more processors.

[0007] According to a fourth aspect of the present disclosure, a computer program product is provided, comprising a computer program configured to implement the method provided by the first aspect when executed by a processor.

[0008] The method provided by the embodiments of the present disclosure can automatically estimate the calf height of a target user according to user information of the target user, and estimate the habitual bed height of the user according to historical adjustment data of a corresponding user group, so as to dynamically calculate an optimal target height according to the weighted values of the calf height and the habitual bed height in combination with intelligent correction of a current scene mode, and intelligently control the height of the electric bed according to the target height, without complicated operations, and adapt to different use scene requirements, thereby improving the safety and convenience of the electric bed in various use scenes. BRIEF DESCRIPTION OF DRAWINGS

[0009] The above and other features, advantages, and aspects of the embodiments of the present disclosure will become more apparent by describing in detail the embodiments thereof with reference to the attached drawings. In the drawings:

[0010] Figure 1 FIG. 1 shows a flowchart of an adaptive height adjustment electric bed lifting control method according to some embodiments of the present disclosure;

[0011] Figure 2 FIG. 2 shows a structural diagram of an adaptive height adjustment electric bed lifting control device according to some embodiments of the present disclosure;

[0012] Figure 3A schematic block diagram of an electronic device showing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0013] For the purposes of the present application, the term "comprising" means including, but not limited to, whatever follows the word "comprising". Generally "comprising" will be understood to encompass the terms "consisting of" and "consisting essentially of". The term "consisting of" means including, and limited to, whatever follows the term "consisting of". The term "consisting essentially of" means including, and limited to, whatever follows the term "consisting essentially of", with the proviso that the method, composition, or article of manufacture excludes any additional steps, compositions, or ingredients that are not recited in the claim. The term "consisting essentially of" is used herein to define a method, composition, or article of manufacture that includes the recited steps, compositions, or ingredients, and further includes other steps, compositions, or ingredients that do not materially affect the basic and novel characteristics of the method, composition, or article of manufacture. The use of the terms "comprising", "comprises", "comprised of", "comprise", "containing", "contains", "contained" or "contain" or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of steps or elements does not include only those steps or elements but can include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. The term "if can be construed to mean "when" or "upon" or "in response to determining" or "in response to detecting", depending on the context within which the term is used.

[0014] The terms "including", "comprising" and "having" and any variations thereof in the specification and claims shall be understood to encompass the terms "consisting of" and "consisting essentially of". The terms "if can be construed to mean "when" or "upon" or "in response to determining" or "in response to detecting", depending on the context within which the term is used.

[0015] Figure 1 A flowchart of an adaptive height adjustment method 100 of an electric bed is shown, which shows some embodiments of the present disclosure. The method 100 can be executed by a terminal, which can include but is not limited to a mobile phone, a tablet computer, a desktop computer, a server, etc. The terminal can be communicatively connected to the electric bed to realize data interaction and control of the electric bed by the terminal. The method 100 can also be executed by a controller of the electric bed, etc. As shown in FIG. 1, at block 102, the method 100 can query a target user's corresponding calf height median value in a preset mapping table based on user information of the target user, the user information including gender information, height information, weight information, region information, and age information. Figure 1

[0016] ​In the embodiment, the terminal first acquires user information of a target user currently using the electric bed to determine gender information, height information, weight information, region information (for example, northeast region), age information, and the like of the target user. The user information can be acquired in a manner such as the target user manually inputting height, weight, age, and the like through a bed body control APP of the electric bed and transmitting the data to the terminal, or acquiring the information through an authorized smart device associated with the target user (for example, the weight data of the user can be acquired through a smart body scale, and the gender, height, and age information can be acquired through historical health data of the user in a motion health application of a smart phone or a smart bracelet). When acquiring the user information, data verification is performed to prompt and require re-input of abnormal values (for example, height less than or equal to 50 cm or greater than or equal to 300 cm). Then, in a preset mapping table, a mapping relationship between different user information and calf height intervals can be set. By querying the user information of the target user in the mapping table, a calf height interval matched with the user information can be determined, and a median value of the interval is taken as a calf height median value corresponding to the target user. In this way, the calf height median value can be taken as a predicted theoretical calf height of the target user. The mapping table can use national human body size survey data (for example, GB / T10000-2023 “Chinese adult human body size”) to acquire human body size data covering different age groups (for example, 18-25 years old, 26-35 years old, 36-60 years old, 61-70 years old, and the like), determine special data of “calf height” from the human body size data, and construct a mapping table by combining gender and region.

[0017] At block 104, the method 100 can determine a user group of the target user based on the user information, and determine a habitual bed height according to an average value of each historical adjustment data in a group database corresponding to the user group.

[0018] In the embodiment, according to the user information, a user group of the target user can also be determined. The user group can be divided by selecting one or more data in the user information according to different application scenarios and requirements. For example, north China region, male, and 170 cm height can be taken as a user group, historical adjustment data generated by users in the user group when actively adjusting the height of the electric bed can be aggregated into a group database, and an average value of all historical adjustment data in the group database can be taken as a habitual bed height of the target user in the user group. In this way, the habitual bed height can be taken as a predicted actual habitual height of the target user.

[0019] At block 106, the method 100 can calculate a weighted value of the calf height median value and the habitual bed height in response to a bed height adjustment instruction, adjust the weighted value according to a correction parameter corresponding to a current scene mode to obtain a target height, and control the electric bed to ascend or descend according to the target height.

[0020] In the embodiment, when the target user wants the electric bed to automatically adjust the height, the bed height adjustment instruction can be generated by, for example, operating the terminal, voice inputting the corresponding scene mode, pre-setting the time of triggering the adjustment control operation, etc. After detecting the bed height adjustment instruction, the weighted value can be obtained according to the weighted calculation of the calf height median value and the habitual bed height. The weights of the calf height median value and the habitual bed height can be set to 0.6 and 0.4 respectively in the default state, and the weights can be adjusted according to the actual needs of the target user. For example, the target user pays more attention to scientific recommendation to increase the height, and the weight of the calf height median value obtained by scientific statistics can be higher, and the target user pays more attention to individualized adjustment according to habits, and the weight of the habitual bed height obtained from the historical adjustment data of the user group can be higher. In addition, different scene modes can set different correction parameters. After adjusting the weighted value according to the correction parameter corresponding to the current scene mode, the target height required by the electric bed can be obtained, and the current height of the electric bed can be controlled to reach the target height. As an example, the scene mode can include a getting-on-bed mode, a getting-off-bed mode, a nursing mode and a lying-in-bed mode. The correction parameter of the getting-off-bed mode can be -3cm to prevent the target user from falling down, the correction parameter of the getting-off-bed mode can be +5cm to facilitate the target user to sit and lie, the correction parameter of the nursing mode can be +3cm to optimize the nursing space, the correction parameter of the lying-in-bed mode can be 0 without correction, etc. In addition, the user information of the target user will be updated at a certain interval (for example, 1 day), and the target height will also change dynamically with the change of the user information.

[0021] In an implementable manner, the weighted value of the calf height median value and the habitual bed height is calculated, comprising:

[0022] determining a gender coefficient and a body state coefficient based on the user information;

[0023] calculating a first height based on the product of the gender coefficient, the calf height median value and the calf-bed height mapping coefficient corresponding to the electric bed;

[0024] calculating a second height based on the product of the body state coefficient and the habitual bed height; and

[0025] calculating the weighted value of the first height and the second height.

[0026] In the embodiment, to make the calculated weighting value more accurate, the gender coefficient and the body shape coefficient can also be determined according to the user information. For example, the gender coefficient of men is 1.05, and the gender coefficient of women is 0.95, to highly compensate the case that the average height of men is higher. The body shape coefficient can be 1 when the BMI is less than or equal to 28, and increase by numerical interval when the BMI is greater than 28, to increase the height of obese users when using, and meet the needs of obese users for higher support. In addition, according to different models of the electric bed, the structure of the electric bed may be different, so that the safe bed height range of different models of the electric bed is different. Therefore, different calf-bed height mapping coefficients can be set in advance according to the model of the electric bed to compensate and adjust the calculation result, for example, the coefficient is 1 by default, and in some models, the coefficient can be adjusted between 0.9 and 1.2. Finally, the calculation formula of the weighting value may be:

[0027]

[0028] wherein, is the gender coefficient, is the body shape coefficient, is the weight of the first height, is the calf-bed height mapping coefficient, is the weight of the second height, is the habitual height, is the correction parameter, is the calf height median value.

[0029] As an example, assuming that the user information of the target user is height (175 cm), age (62), weight (60 kg), gender (male), and region (North China), the calf height median value L=42 cm is obtained by querying, and the target height is calculated in combination with the habitual height (the average historical record of this group of people is 45 cm):

[0030]

[0031] In an implementable manner, the electric bed is controlled to rise and fall according to the target height, including:

[0032] obtaining height data of the electric bed, the height data including a bed frame base height, a mattress thickness, and a current height of a motor; and

[0033] determining a motor operation time based on a first ratio of a difference between the target height and the height data and a motor operation speed, to control the motor to work for the motor operation time, so that the height of the electric bed after adjustment is the target height.

[0034] In the embodiment, when the target height of the electric bed is controlled, the motor of the electric bed is actually driven to run, and in order to ensure that the bed body is more stable, it is generally desired that the electric bed can rise / descend at a constant speed under the driving of the motor, that is, the rotating speed of the motor is constant. In addition, in order to make the final target height meet the requirements, the influence of the bed frame base height, the mattress thickness and the current motor height (the motor height changes synchronously with the bed height, and the motor height is 0 in the initial state of the electric bed without height adjustment) needs to be considered during adjustment, therefore, the calculation formula of the motor running time is:

[0035]

[0036] wherein, is the bed frame base height (which can be obtained through the factory parameters of the electric bed), is the mattress thickness (which can be determined according to the mattress model), is the current motor height (which can be determined according to the motor running record), is the motor running speed (which is 2 cm / s by default, and the adjustable range is 1-3 cm / s).

[0037] In other implementable manners, if the initial height of the motor cannot be 0, the current motor height can also be replaced by the relative height change value of the motor relative to the initial height for calculation and the like when calculating the motor running time.

[0038] In an implementable manner, the method further comprises:

[0039] in response to the active control instruction of the target user to the electric bed, obtaining the operation duration of the target user to the electric bed, calculating a second ratio of the operation duration to the maximum adjustment duration of the electric bed, and determining the time parameter based on the difference between 1 and the second ratio;

[0040] determining the scene similarity value based on the scene proportion of the current scene mode in the historical adjustment data;

[0041] weighting the scene similarity value and the time parameter to obtain the user preference confidence; and

[0042] in response to the user preference confidence being greater than the confidence threshold, adding the control height corresponding to the active control instruction as new historical adjustment data to the group database.

[0043] In this embodiment, in addition to the intelligent adjustment of the height, the target user can also actively adjust the height of the electric bed according to his actual habits and needs. When the target user actively adjusts the electric bed, an active control instruction is generated. When the terminal detects the active control instruction, it can record the control height finally adjusted by the target user and add it to the group database as new historical adjustment data, so as to enrich the data amount of the group database and improve the accuracy of the data in the group database. However, in order to avoid the misadjustment operation of the target user being recorded to the group database and causing the data interference problem of the group database, the user preference confidence will be calculated. The calculation formula of the user preference confidence P can be:

[0044]

[0045] wherein, is the maximum adjustment duration, t is the operation duration, is the scene label of the current scene mode, is the scene label of the historical scene mode, is the scene similarity value, 0.7 and 0.3 are weights, which can be adjusted according to needs. The is taken as a time parameter, because the shorter the user operation time is, the more likely it is that the user has a higher certainty for the currently selected scene mode, so the time factor needs to be inversely proportional to the confidence.

[0046] As an example, it is assumed that there are 30 times of scene selection in the group database, including 25 times of getting-into-bed mode, 4 times of getting-out-of-bed mode, and 1 time of lying-in-bed mode. The selected mode this time is the lying-in-bed mode, the operation duration t is 10s, and it is assumed that is 15s, then .

[0047] A confidence threshold (for example, 0.8) is set in advance. If the user preference confidence is greater than the confidence threshold, it is considered that the adjustment this time is effective, and the control height of this adjustment is included in the group database. Otherwise, it is considered to be a misoperation and is not included in the group database.

[0048] In an implementable manner, in response to the user preference confidence being greater than the confidence threshold, the control height corresponding to the control instruction is added to the group database as new historical adjustment data, including:

[0049] obtaining the historical control times of the target user for the electric bed; and

[0050] In response to the user preference confidence being greater than the confidence threshold and the historical control times being greater than the preset times, the control height corresponding to the control instruction is added to the group database as new historical adjustment data.

[0051] In the embodiment, the active control operation of the target user is recorded into the group database, so that the data in the group database can represent the height habit of the user group corresponding to the target user when adjusting the bed height. When the target user first uses the electric bed, he or she may not be familiar with each scene mode, and may not know which mode he or she is used to and which height he or she is used to in each mode. There will be a process of frequent adjustment, and the adjustment in this process may also be calculated to obtain a user preference confidence greater than a confidence threshold, thereby affecting the accuracy of the group database. Therefore, the historical control times of the target user for the electric bed are also recorded. Only when the historical control times are greater than a preset number of times, it is considered that the target user has become familiar with the electric bed, and the subsequent operation is an adjustment according to the habit of the target user, and then the control height is added to the group database when the user preference confidence is greater than the confidence threshold.

[0052] In an implementable manner, the method further comprises:

[0053] constructing a user database of the target user, and storing each control height in the user database; and

[0054] in response to the cumulative data amount of the user database being greater than a first preset data amount, taking the average value of each control height in the user database as a new habit bed height, so as to calculate the target height according to the new habit bed height.

[0055] In the embodiment, in addition to the group database, a user database dedicated to the target user can also be constructed, and each control height of the active control of the target user is also stored in the user database. Compared with the group database, the user database can better represent the personalized adjustment habit of the target user, so that the habit bed height determined by the user database will be more accurate than the habit bed height determined by the group database. Therefore, after the cumulative data amount of the user database is greater than the first preset data amount, it is considered that the user database has accumulated sufficient data, and the calculation using the user database is relatively accurate. At this time, the average value of each data in the user database is used to replace the average value of each data in the group database as a new habit bed height, and the target height is calculated according to the new habit bed height in the subsequent intelligent adjustment process.

[0056] In an implementable manner, the method further comprises:

[0057] in response to the cumulative data amount being greater than a second preset data amount, increasing the weight of the habit bed height in the weighted calculation.

[0058] In the embodiment, the calf height median value is a predicted value, although it is more scientific. In order to make the target height more accurate, after the cumulative data amount is greater than the second preset data amount, the weight of the habitual bed height in the weighted calculation process of the target height can be increased, so that the calculated target height can consider the actual habitual height of the user more. The weight increase value can be set by itself, and in other implementable manners, after the cumulative data amount is greater than the second preset data amount, the weight of the habitual bed height can be increased by a certain value (but not more than a preset weight upper limit value) every time the cumulative data amount increases by a certain amount.

[0059] Figure 2 A structural diagram of an adaptive height adjustment electric bed lifting control device 200 of some embodiments of the present disclosure is shown. Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to. Each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiment. As shown in the figure, Figure 2 The device 200 includes a calf height query module 201 configured to query a calf height median value corresponding to a target user in a preset mapping table based on user information of the target user, the user information including gender information, height information, weight information, region information, and age information; a habitual bed height determination module 202 configured to determine a user group of the target user based on the user information, and determine a habitual bed height according to an average value of each historical adjustment data in a group database corresponding to the user group; and a height control module 203 configured to calculate a weighted value of the calf height median value and the habitual bed height in response to a bed height adjustment instruction, and adjust the weighted value according to a correction parameter corresponding to a current scene mode to obtain a target height, so as to control the electric bed to rise and fall according to the target height.

[0060] The height control module 203 is further configured to determine a gender coefficient and a body state coefficient based on the user information; calculate a first height based on a product of the gender coefficient, the calf height median value, and a calf-bed height mapping coefficient corresponding to the electric bed; calculate a second height based on a product of the body state coefficient and the habitual bed height; and calculate a weighted value of the first height and the second height.

[0061] The height control module 203 is further configured to obtain height data of the electric bed, the height data including a bed frame base height, a mattress thickness, and a current height of a motor; and determine a motor operation time based on a first ratio of a difference between the target height and the height data and a motor operation speed, so as to control the motor to work for the motor operation time, so that the height of the electric bed after adjustment is the target height.

[0062] The apparatus further comprises a confidence calculation module configured to, in response to the active control instruction of the target user to the electric bed, acquire an operation duration of the target user to the electric bed, calculate a second ratio of the operation duration to a maximum adjustment duration of the electric bed, and determine a time parameter based on a difference between 1 and the second ratio; determine a scene similarity value based on a scene proportion of the current scene mode in the historical adjustment data; perform weighted calculation on the scene similarity value and the time parameter to obtain a user preference confidence; and in response to the user preference confidence being greater than a confidence threshold, add the control height corresponding to the active control instruction as new historical adjustment data to the group database.

[0063] The confidence calculation module is further configured to acquire a historical control number of the target user to the electric bed; and in response to the user preference confidence being greater than the confidence threshold and the historical control number being greater than a preset number, add the control height corresponding to the control instruction as new historical adjustment data to the group database.

[0064] The apparatus further comprises a user database construction module configured to construct a user database of the target user, store each control height to the user database; and in response to a cumulative data amount of the user database being greater than a first preset data amount, take an average value of each control height in the user database as a new habitual bed height to calculate the target height according to the new habitual bed height.

[0065] The apparatus further comprises a weight adjustment module configured to, in response to the cumulative data amount being greater than a second preset data amount, increase a weight of the habitual bed height in the weighted calculation.

[0066] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this specification is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., Digital Versatile Discs (DVDs)), or semiconductor media (e.g., Solid State Disks (SSDs)).

[0067] Figure 3 A block diagram of an electronic device 300 that can implement various embodiments of the present disclosure is shown. For example... Figure 3 As shown, the electronic device 300 includes a processor 310, a disk drive 320, an input / output interface 330, a network interface 340, and a memory 350. The processor 310, disk drive 320, input / output interface 330, network interface 340, and memory 350 can communicate with each other via a communication bus 360.

[0068] The processor 310 can be implemented using a general-purpose CPU, microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits to execute relevant programs in order to implement the technical solution provided in this application.

[0069] The memory 350 can be implemented in the form of a ROM (Read Only Memory), a RAM (Read Access Memory), a static memory, a dynamic memory device, etc. The memory 350 can store an operating system 351 for controlling the operation of the electronic device 300, a basic input / output system (BIOS) 352 for controlling the low-level operation of the electronic device 300. In addition, a web browser 353, a data storage management system 354, etc. can also be stored. In summary, when the technical solutions provided in the present application are implemented by software or firmware, the relevant program codes are stored in the memory 350 and executed by the processor 310.

[0070] The input / output interface 330 is configured to connect an input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure) or externally connected to the device to provide corresponding functions. The input device can include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device can include a display, a speaker, a vibrator, a prompt light, etc.

[0071] The network interface 340 is configured to connect a communication module (not shown in the figure) to realize the communication interaction between the device and other devices. The communication module can realize communication through a wired manner (such as USB, network cable, etc.) or through a wireless manner (such as mobile network, WIFI, Bluetooth, etc.).

[0072] The bus 360 includes a channel for transmitting information between various components (such as the processor 310, the disk drive 320, the input / output interface 330, the network interface 340, and the memory 350) of the device.

[0073] It should be noted that although the above device only shows the processor 310, the disk drive 320, the input / output interface 330, the network interface 340, the memory 350, the bus 360, etc., in the specific implementation process, the device can also include other components necessary for normal operation. In addition, those skilled in the art can understand that the above device can only contain the components necessary to implement the method of the present application, and does not necessarily contain all the components shown in the figure.

[0074] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, causes the machine to perform the functions / acts specified in the flowcharts and / or block diagrams. The program code can execute entirely on a machine, partly on a machine, as a stand-alone software package, partly on a machine and partly on a remote machine or entirely on a remote machine or server.

[0075] In the context of the present disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium would include one or more lines of a system, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. Further, while operations are depicted in a particular, sequential order, this should not be understood as requiring or implying that the operations are performed in the order shown or in sequential order, or that all illustrated operations are necessary for realizing the desired results. In certain circumstances, multitasking and parallel processing can be advantageous. Likewise, while specific implementations are discussed herein, the scope of the present disclosure is not limited to the specific details and representations herein. Certain features described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. While the subject matter has been described above in the general context of "computer-executable instructions" that can be executed by a computer, those skilled in the art will recognize that the innovation also can be implemented in combination with other program modules or the like. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks and / or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the subject innovation can be practiced with other computer system configurations, including single-processor or multiprocessor computer systems, minicomputers, mainframe computers, as well as computers, hand-held computing devices (e.g., PDA, phone), microprocessor-based or programmable consumer electronics, and the like, each of which can be operatively coupled to one or more associated devices.

[0076] Although the subject matter has been described in language specific to structural features and / or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims

1. A method for adaptive height-adjustable power bed lift control, the method comprising: The method comprises: querying a calf height median value corresponding to a target user in a preset mapping table based on user information of the target user, the user information comprising gender information, height information, weight information, region information and age information; determining a user group of the target user based on the user information, and determining a habitual bed height according to an average value of each historical adjustment data in a group database corresponding to the user group; and in response to a bed height adjustment instruction, calculating a weighted value of the calf height median value and the habitual bed height, and adjusting the weighted value according to a correction parameter corresponding to a current scene mode to obtain a target height, so as to control the electric bed to rise and fall according to the target height.

2. The self-adjusting height of power bed lift control method according to claim 1, wherein, The calculation of the weighted value of the calf height median value and the habitual bed height comprises: determining a gender coefficient and a body state coefficient based on the user information; calculating a first height based on a product of the gender coefficient, the calf height median value and a calf-bed height mapping coefficient corresponding to the electric bed; calculating a second height based on a product of the body state coefficient and the habitual bed height; and calculating a weighted value of the first height and the second height.

3. The self-adjusting height of power bed lift control method according to claim 1, wherein, The control of the electric bed to rise and fall according to the target height comprises: obtaining height data of the electric bed, the height data comprising a bed frame base height, a mattress thickness and a current height of a motor; and determining a motor operation time based on a first ratio of a difference between the target height and the height data to a motor operation speed, so as to control the motor to work for the motor operation time, so that an adjusted height of the electric bed is the target height.

4. The self-adjusting height of power bed lift control method according to claim 1, wherein, The method further comprises: in response to an active control instruction of the target user to the electric bed, obtaining an operation duration of the target user to the electric bed, calculating a second ratio of the operation duration to a maximum adjustment duration of the electric bed, and determining a time parameter based on a difference between 1 and the second ratio; determining a scene similarity value based on a scene proportion of a current scene mode in the historical adjustment data; performing weighted calculation on the scene similarity value and the time parameter to obtain a user preference confidence; and in response to the user preference confidence being greater than a confidence threshold, adding a control height corresponding to the active control instruction as new historical adjustment data to the group database.

5. The method of claim 4, wherein the method further comprises: The adding of the control height corresponding to the control instruction as new historical adjustment data to the group database in response to the user preference confidence being greater than the confidence threshold comprises: obtaining a historical control frequency of the target user to the electric bed; and in response to the user preference confidence being greater than the confidence threshold and the historical control frequency being greater than a preset frequency, adding the control height corresponding to the control instruction as new historical adjustment data to the group database.

6. The self-adjusting height of a power bed lift control method according to claim 4, wherein, The method further comprises: building a user database of the target user, and storing each control height to the user database; and In response to the accumulated data quantity of the user database being greater than a first preset data quantity, an average value of each of the control heights in the user database is taken as a new habitual bed height, and the target height is calculated according to the new habitual bed height.

7. The method of claim 6, wherein the method further comprises: The method further comprises: In response to the accumulated data quantity being greater than a second preset data quantity, a weight of the habitual bed height in the weighted calculation is increased.

8. An electric bed lift control device for self-adjusting height, characterized by, The device comprises: a calf height query module configured to query a calf height median value corresponding to a target user in a preset mapping table based on user information of the target user, the user information comprising gender information, height information, weight information, region information and age information; a habitual bed height determination module configured to determine a user group of the target user based on the user information, and determine a habitual bed height according to an average value of each historical adjustment data in a group database corresponding to the user group; and a height control module configured to calculate a weighted value of the calf height median value and the habitual bed height in response to a bed height adjustment instruction, and adjust the weighted value according to a correction parameter corresponding to a current scene mode to obtain a target height, so as to perform lifting control on the electric bed according to the target height. 9.An electronic device comprising: one or more processors, and a memory associated with the one or more processors, the memory configured to store program instructions that, when executed by the one or more processors, perform the steps of the method for adaptive adjustment of the height of the electric bed according to any one of claims 1-7. 10.A computer program product comprising a computer program that, when executed by a processor, implements the method for adaptive adjustment of the height of the electric bed according to any one of claims 1-7.

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