A method, apparatus, device, and storage medium for determining airbag adjustment information.

Abstract

This invention discloses a method, apparatus, device, and storage medium for determining airbag adjustment information, comprising: acquiring target parameter information of a target object, wherein the target parameter information includes at least sleep parameter information of the target object during sleep; determining target muscle group information related to the sleep quality of the target object based on the sleep parameter information; and determining airbag adjustment information matching the target object based on the target muscle group information. This technical solution solves the problems of limited adjustment methods and lack of flexibility, enabling flexible airbag adjustment based on the user's individual circumstances, thus improving the user experience.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method, apparatus, device, and storage medium for determining airbag adjustment information. Background Technology

[0002] As people's living standards improve, their demands for mattress comfort are also increasing. To improve comfort, existing mattresses often incorporate air cushions to provide users with a more comfortable experience.

[0003] Currently, the firmness of existing air mattresses is adjusted entirely by inflating the air bladders inside the mattress after it is filled with air. This adjustment is fixed and rigid, and cannot be adapted to the user's own situation. The adjustment method is singular and lacks flexibility, which cannot meet the needs of different users, resulting in a poor user experience. Summary of the Invention

[0004] This invention provides a method, apparatus, device, and storage medium for determining airbag adjustment information, which solves the problems of limited adjustment methods and lack of flexibility. It enables flexible airbag adjustment based on the user's own situation, thereby improving the user experience.

[0005] In a first aspect, embodiments of this disclosure provide a method for determining airbag adjustment information, including:

[0006] Obtain target parameter information of the target object, wherein the target parameter information includes at least the sleep parameter information of the target object during sleep;

[0007] Based on the sleep parameter information, determine the target muscle group information related to the sleep quality of the target object;

[0008] Based on the target muscle group information, airbag adjustment information matching the target object is determined.

[0009] Secondly, embodiments of this disclosure provide an airbag adjustment information determining device, comprising:

[0010] The parameter information acquisition module is used to acquire target parameter information of the target object, wherein the target parameter information includes at least the sleep parameter information of the target object during sleep.

[0011] A muscle group information determination module is used to determine target muscle group information related to the sleep quality of the target object based on the sleep parameter information.

[0012] The adjustment information determination module is used to determine the airbag adjustment information matching the target object based on the target muscle group information.

[0013] Thirdly, embodiments of this disclosure provide an electronic device, including:

[0014] At least one processor; and

[0015] A memory that is communicatively connected to at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by at least one processor, such that the at least one processor is able to perform the airbag adjustment information determination method provided in the first aspect embodiment described above.

[0017] Fourthly, embodiments of this disclosure provide a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the airbag adjustment information determination method provided in the first aspect of the embodiments described above.

[0018] This invention discloses a method, apparatus, device, and storage medium for determining airbag adjustment information. The method involves acquiring target parameter information of a target object, including at least sleep parameter information during the target object's sleep period; determining target muscle group information related to the target object's sleep quality based on the sleep parameter information; and determining airbag adjustment information matching the target object based on the target muscle group information. This technical solution solves the problems of limited adjustment methods and lack of flexibility, enabling flexible airbag adjustment based on the user's individual circumstances and improving the user experience.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of a method for determining airbag adjustment information provided in Embodiment 1 of the present invention;

[0022] Figure 2 This is a flowchart of a method for determining airbag adjustment information provided in Embodiment 2 of the present invention;

[0023] Figure 3This is a schematic diagram of the structure of an airbag adjustment information determination device provided in Embodiment 3 of the present invention;

[0024] Figure 4 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first," "second," and "target," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] Example 1

[0028] Figure 1 This is a flowchart of an airbag adjustment information determination method provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation where adaptive adjustment information of airbags in a smart mattress is determined based on user information. The method can be executed by an airbag adjustment information determination device, which can be implemented in hardware and / or software.

[0029] like Figure 1 As shown, the method includes:

[0030] S101. Obtain the target parameter information of the target object. The target parameter information shall include at least the sleep parameter information of the target object during sleep.

[0031] In this embodiment, the target object can be understood as the user on the mattress. Target parameter information can be understood as relevant parameter information specific to the target object, such as the target object's height, weight, and body mass index (BMI), as well as sleep-related parameter information during sleep. Sleep parameter information can be understood as relevant information detected in the target object during sleep, including at least electromyography (EMG) information and sleep quality information.

[0032] In this embodiment, electromyography (EMG) signals reflect the degree of muscle relaxation, and a comfortable mattress should promote muscle relaxation. Electrode pads are placed on the target subject, and EMG signals are obtained using an EMG analyzer. The electrode pads can be placed on the left and right deltoid muscles, left and right trapezius muscles, left and right erector spinae and longissimus muscles, left and right multifidus muscles, left and right external oblique muscles, left and right gluteus medius muscles, left and right tensor fasciae latae muscles, and left and right biceps femoris muscles. By monitoring EMG signals, the degree of muscle relaxation in different postures can be assessed. Lower EMG activity levels may indicate that the mattress provides good support and relaxation for the muscles, while higher EMG activity levels may indicate that the mattress provides poor support and relaxation for the muscles.

[0033] In this embodiment, sleep quality information comprehensively reflects the sleep quality of the target subject throughout the entire night, including the total sleep duration and the various sleep stages. The sleep stages are divided into five phases: wakefulness, dreaming, light sleep stage I, light sleep stage II, and deep sleep. Sleep quality information is obtained using polysomnography (PSG).

[0034] Specifically, it obtains electromyography (EMG) information collected by an EMG analyzer and sleep quality information detected by a polysomnography (PSG) monitor during the target subject's sleep.

[0035] S102. Determine the target muscle group information related to the sleep quality of the target subject based on sleep parameter information.

[0036] In this embodiment, the target muscle group information can be understood as the impact of the target subject's muscle relaxation level on sleep quality.

[0037] Specifically, based on the electromyography (EMG) information and sleep quality information in the sleep parameter information, a correlation analysis is performed to determine the correlation between the EMG information of the muscle group at each electrode and the sleep quality information. Furthermore, the degree of influence of the muscle group corresponding to each EMG information on sleep quality is determined, and the name of each muscle group and the degree of influence on sleep quality are recorded as the target muscle group information.

[0038] S103. Determine the airbag adjustment information that matches the target object based on the target muscle group information.

[0039] In this embodiment, the airbag adjustment information can be understood as the adjustment information of each airbag in the target mattress, including the adjustment information of the airbag position and the adjustment information of the airbag pressure.

[0040] Specifically, muscle groups positively correlated with sleep quality in the target muscle group information have a beneficial impact on sleep quality. The displacement required to move the airbag to this muscle group, and the increase or decrease in the airbag pressure value corresponding to this muscle group, are determined to make this muscle group more relaxed during sleep. Conversely, muscle groups negatively correlated with sleep quality in the target muscle group information have a detrimental impact on sleep quality. The displacement required to move the airbag away from this muscle group, and the increase or decrease in the airbag pressure value corresponding to this muscle group, are determined to reduce the detrimental impact of this muscle group on sleep quality during sleep.

[0041] This invention provides a method, apparatus, device, and storage medium for determining airbag adjustment information. The method involves acquiring target parameter information of a target object, including at least sleep parameter information during the target object's sleep period; determining target muscle group information related to the target object's sleep quality based on the sleep parameter information; and determining airbag adjustment information matching the target object based on the target muscle group information. This technical solution solves the problems of limited adjustment methods and lack of flexibility, enabling flexible airbag adjustment based on the user's individual circumstances and improving the user experience.

[0042] As a first optional embodiment of the embodiments, based on the above embodiments, this first optional embodiment further optimizes and adds: adjusting the mattress according to the airbag adjustment information.

[0043] In this embodiment, based on the changes in airbag position and pressure in the airbag adjustment information matched with the target object, the position of the airbags in the smart mattress is adjusted and the pressure is increased or decreased to ensure that the airbags in the smart mattress are in a state that has the greatest positive correlation with the sleep quality of the target object. The specific direction and amount of airbag movement, as well as the amount of pressure change, are determined based on the relationship between airbag pressure and muscle relaxation level under actual conditions; this embodiment does not impose any limitations on these aspects.

[0044] Example 2

[0045] Figure 2 This is a flowchart of an airbag adjustment information determination method provided in Embodiment 2 of the present invention. This embodiment is a further optimization of any of the above embodiments and can be applied to the situation where adaptive adjustment information of airbags in a smart mattress is determined based on user information. The method can be executed by an airbag adjustment information determination device, which can be implemented in hardware and / or software.

[0046] like Figure 2 As shown, the method includes:

[0047] S201. Obtain the target parameter information of the target object.

[0048] In this embodiment, the target parameter information includes sleep parameter information during the target object's sleep period, and also includes the target object's body mass index (BMI).

[0049] Among them, the Body Mass Index (BMI) is pre-defined into five levels: less than 18, 18-24, 24-28, 28-32, and above 32.

[0050] S202. Based on the BMI level of the target object, input the sleep quality information and at least one electromyographic information from the sleep parameter information into the corresponding level of the correlation analysis model to obtain the correlation results output by the model.

[0051] In this embodiment, sleep quality information can be understood as information comprehensively reflecting the sleep quality of the target subject throughout the entire night's sleep, including the total sleep duration and sleep stages. Electromyography (EMG) information can be understood as the monitored EMG signals reflecting the degree of muscle relaxation; each muscle group corresponds to one EMG signal, and a lower EMG activity level may indicate that the mattress provides better support and relaxation for the muscles. The correlation analysis model can be understood as a model used to analyze the correlation between EMG information and sleep quality information; it is a multiple linear regression model.

[0052] The correlation results can be understood as the correlation between electromyography (EMG) information and sleep quality information, including whether EMG information is positively correlated with sleep quality and whether EMG information is negatively correlated with sleep quality. When EMG information is positively correlated with sleep quality, the correlation results also include a positive correlation value. The higher the positive correlation value, the better the impact of EMG information on sleep quality. When EMG information is negatively correlated with sleep quality, the correlation results also include a negative correlation value. The higher the negative correlation value, the worse the impact of EMG information on sleep quality.

[0053] Specifically, each BMI level has its corresponding correlation sub-model. Based on the BMI level of the target object, the sleep quality information and at least one electromyography (EMG) information from the sleep parameter information are input into the corresponding level's correlation analysis model to obtain the correlation between each EMG information and sleep quality and its correlation value after the model calculation.

[0054] S203. Based on the correlation results, determine the target muscle group information related to the sleep quality of the target subject.

[0055] In this embodiment, based on the correlation results and correlation values ​​between electromyography information and sleep quality, the correlation values ​​are sorted under positive and negative correlation dimensions to obtain sorting information. The sorting information under positive correlation and the sorting information under negative correlation are determined as target muscle group information.

[0056] Optionally, target muscle group information related to the sleep quality of the target subject can be determined based on the correlation results, including:

[0057] S2031. Based on the positive correlation value under positive correlation in the correlation results, determine the positive ranking information of the muscle groups corresponding to the electromyographic information. The positive ranking information includes the name, location and ranking value of the positively correlated muscle groups.

[0058] In this embodiment, the positive correlation value can be understood as the positive correlation influence value. The higher the positive correlation value, the stronger the beneficial influence of the muscle group corresponding to the electromyographic signal on sleep quality. The positive ranking information can be understood as the information determined according to the ranking of positive correlation values, including the name of the positively correlated muscle group, its location in the body, and the ranking of the positive correlation values.

[0059] Specifically, the correlation results are divided into positive and negative correlations, and the positive correlation impact value of each electromyographic (EMG) signal on sleep quality under the positive correlation category is recorded. Based on the positive correlation values ​​between each EMG signal and sleep quality under the positive correlation category, the positive correlation values ​​are sorted from largest to smallest. The name and location of the muscle group corresponding to each EMG signal in each positive correlation value ranking are determined, and the muscle group name, location, and ranking of the positive correlation value (ranking value) are determined as positive ranking information.

[0060] S2032. Based on the negative correlation values ​​under negative correlation in the correlation results, determine the negative ranking information of the muscle groups corresponding to the electromyographic information. The negative ranking information includes the name, location, and ranking value of the negatively correlated muscle groups.

[0061] In this embodiment, the negative correlation value can be understood as the negative correlation effect value. The higher the negative correlation value, the weaker the beneficial effect of the muscle group corresponding to the electromyographic signal on sleep quality. The negative ranking information can be understood as information determined according to the ranking of negative correlation values, including the name of the negatively correlated muscle group, its location in the body, and the ranking of the negative correlation values.

[0062] Specifically, the correlation results are divided into positive and negative correlations, and the negative correlation impact value of each electromyographic (EMG) signal on sleep quality under the negative correlation category is recorded. Based on the negative correlation values ​​of each EMG signal and sleep quality under the negative correlation category, the negative correlation values ​​are sorted from largest to smallest. The name and location of the muscle group corresponding to each EMG signal in each negative correlation value ranking are determined, and the muscle group name, location, and ranking of the negative correlation value (ranking value) are determined as negative ranking information.

[0063] S2033. The positive and negative sorting information is determined as target muscle group information related to the sleep quality of the target object.

[0064] In this embodiment, by combining the names, locations, and ranking values ​​of each muscle group in the positive and negative ranking information, comprehensive muscle group information related to the sleep quality of the target object is determined.

[0065] S204. Determine the changes in airbag position and airbag pressure for the positively correlated muscle groups of the target object based on the positive sorting information in the target muscle group information.

[0066] In this embodiment, when the muscle group corresponding to the electromyographic information is positively correlated with sleep quality, it indicates that the muscle group has a beneficial effect on sleep quality. Based on the top-ranked positive correlation values ​​and corresponding muscle groups in the positive ranking information of the target muscle groups, the changes in air sac position and pressure are determined one by one, traversing the ranking from top to bottom. Muscle groups with larger correlation values ​​also have correspondingly larger changes in air sac position and pressure.

[0067] For example, if the presence of an airbag and increased airbag pressure can further relax the muscle group and improve sleep quality, then the displacement by which the airbag is moved to the muscle group in that area, as well as the increase or decrease in the airbag pressure corresponding to that muscle group, are determined to make the muscle group more relaxed during sleep. The specific direction and amount of airbag movement, as well as the pressure change, are determined based on the actual relationship between airbag pressure and muscle relaxation; this embodiment does not impose any limitations on these aspects.

[0068] S205. Determine the changes in airbag position and airbag pressure for the negatively related muscle groups of the target object based on the negative sorting information in the target muscle group information.

[0069] In this embodiment, when the muscle group corresponding to the electromyographic information is negatively correlated with sleep quality, it indicates that the muscle group has no beneficial effect on sleep quality. Based on the top-ranked negative correlation values ​​and corresponding muscle groups in the negative ranking information of the target muscle groups, the changes in air sac position and pressure are determined one by one, traversing the ranking from top to bottom. Muscle groups with larger correlation values ​​also have correspondingly larger changes in air sac position and pressure.

[0070] For example, if the absence of airbag support or a reduction in airbag pressure would allow the muscle group to relax more effectively, thus improving sleep quality, then the amount of displacement by which the airbag is moved away from the muscle group in that area, as well as the increase or decrease in the airbag pressure corresponding to that muscle group, is determined to allow the muscle group to relax more during sleep. The specific direction and amount of airbag movement, as well as the pressure change, are determined based on the actual relationship between airbag pressure and the degree of muscle relaxation; this embodiment does not impose any limitations on these aspects.

[0071] This invention provides a method, apparatus, device, and storage medium for determining airbag adjustment information. The method involves acquiring target parameter information of a target object; inputting sleep quality information and at least one electromyographic (EMG) data from the sleep parameter information into a correlation analysis model corresponding to the target object's BMI level, and obtaining the model's output correlation results; determining target muscle group information related to the target object's sleep quality based on the correlation results; determining the airbag position change and airbag pressure change for positively correlated muscle groups based on the positive ranking information in the target muscle group information; and determining the airbag position change and airbag pressure change for negatively correlated muscle groups based on the negative ranking information in the target muscle group information. This technical solution solves the problems of limited adjustment methods and lack of flexibility, enabling flexible airbag adjustment based on the user's individual circumstances and improving the user experience.

[0072] As a first optional embodiment of the embodiments, based on the above embodiments, this first optional embodiment further optimizes and adds the construction step of the correlation analysis model, including:

[0073] a1) Using the preset BMI level as the grouping dimension, obtain the sample parameter information of sample objects under at least two BMI levels.

[0074] In this embodiment, the preset BMI level can be understood as a pre-defined level based on BMI ranges, such as being divided into five ranges: less than 18, 18-24, 24-28, 28-32, and above 32. The sample object can be understood as the user on the mattress. The sample parameter information can be understood as relevant parameter information for the sample object, such as the sample object's height, weight, and body mass index (BMI), as well as sleep-related parameter information during sleep.

[0075] Specifically, a predefined range is used as the BMI cluster dimension, with one BMI range constituting one BMI level. Sample parameter information is obtained for at least two sample objects under each BMI level, and sample parameter information is obtained for at least two BMI levels, meaning sample parameter information for at least four sample objects under at least two BMI levels is obtained.

[0076] b1) Under the same BMI level, construct a correlation analysis model for the current BMI level based on the sleep quality information and electromyography information in the sample sleep information of at least two of the sample objects.

[0077] In this embodiment, sample sleep information can be understood as relevant information detected during the sleep of the sample subject, including at least electromyography (EMG) information and sleep quality information. EMG information is obtained by placing electrode pads on the sample subject's body and using an EMG analyzer to detect the sample subject's EMG activity level. Sleep quality information is obtained by using a polysomnography analyzer to detect the sample subject's total sleep duration and each sleep stage, and by combining the total sleep duration and sleep stages to determine the quality information of the sample subject's sleep during sleep.

[0078] Specifically, when at least two sample subjects have BMIs within the same range (i.e., at the same BMI level), the sleep quality information and electromyography (EMG) information corresponding to each muscle group of the sample subjects at that same BMI level are subjected to multiple regression linear analysis to construct a multiple regression linear model. The constructed multiple regression linear model is then determined as the correlation analysis model corresponding to that BMI level. The construction of correlation analysis models for other BMI levels is similar.

[0079] Example 3

[0080] Figure 3 This is a schematic diagram of an airbag adjustment information determination device provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes:

[0081] The parameter information acquisition module 31 is used to acquire target parameter information of the target object, wherein the target parameter information includes at least the sleep parameter information of the target object during sleep.

[0082] The muscle group information determination module 32 is used to determine target muscle group information related to the sleep quality of the target object based on the sleep parameter information.

[0083] The adjustment information determination module 33 is used to determine the airbag adjustment information matching the target object based on the target muscle group information.

[0084] The airbag adjustment information determination device used in this technical solution solves the problems of limited adjustment methods and lack of flexibility. It can be combined with the user's own situation to achieve flexible airbag adjustment and improve the user experience.

[0085] Optionally, the target parameter information also includes the target object's body mass index (BMI), and the muscle group information determination module 32 includes:

[0086] The correlation result determination unit is used to input the sleep quality information and at least one electromyographic information from the sleep parameter information into the corresponding correlation analysis model based on the BMI level of the target object, and obtain the correlation result output by the model.

[0087] A muscle group information determination unit is used to determine target muscle group information related to the sleep quality of the target object based on the correlation results.

[0088] Optionally, the device further includes a related model building module, specifically used for:

[0089] Using the preset BMI level as the clustering dimension, obtain sample parameter information of sample objects under at least two BMI levels;

[0090] Within the same BMI level, a correlation analysis model is constructed based on sleep quality information and electromyography information from the sleep information of at least two of the sample subjects.

[0091] Optionally, the correlation results include positive correlation between electromyography (EMG) information and sleep quality, and negative correlation between EMG information and sleep quality.

[0092] When electromyography (EMG) information is positively correlated with sleep quality, the correlation result also includes the positive correlation value;

[0093] When electromyographic information is negatively correlated with sleep quality, the correlation results also include negative correlation values.

[0094] Optionally, the muscle group information determination unit is specifically used for:

[0095] Based on the positive correlation value under positive correlation in the correlation results, the positive ranking information of the muscle group corresponding to the electromyographic information is determined. The positive ranking information includes the name, location and ranking value of the positively correlated muscle group.

[0096] Based on the negative correlation value under negative correlation in the correlation results, the negative ranking information of the muscle group corresponding to the electromyographic information is determined. The negative ranking information includes the name, location and ranking value of the negatively correlated muscle group.

[0097] The positive sorting information and the negative sorting information are determined as target muscle group information related to the sleep quality of the target object.

[0098] Optionally, the adjustment information determining module 33 is specifically used for:

[0099] Based on the positive sorting information in the target muscle group information, determine the change in airbag position and change in airbag pressure for the positively related muscle groups of the target object;

[0100] Based on the negative sorting information in the target muscle group information, determine the changes in air bladder position and air bladder pressure for the negatively related muscle groups of the target object.

[0101] Optionally, the device may also include:

[0102] The intelligent adjustment module is used to adjust the mattress according to the airbag adjustment information.

[0103] The airbag adjustment information determination device provided in the embodiments of the present invention can execute the airbag adjustment information determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0104] Example 4

[0105] Figure 4 A schematic diagram of an electronic device 40 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0106] like Figure 4 As shown, the electronic device 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42 or a random access memory (RAM) 43, communicatively connected to the at least one processor 41. The memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes based on the computer program stored in the ROM 42 or loaded into the RAM 43 from storage unit 48. The RAM 43 may also store various programs and data required for the operation of the electronic device 40. The processor 41, ROM 42, and RAM 43 are interconnected via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0107] Multiple components in electronic device 40 are connected to I / O interface 45, including: input unit 46, such as keyboard, mouse, etc.; output unit 47, such as various types of monitors, speakers, etc.; storage unit 48, such as disk, optical disk, etc.; and communication unit 49, such as network card, modem, wireless transceiver, etc. Communication unit 49 allows electronic device 40 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0108] Processor 41 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 41 performs the various methods and processes described above, such as an airbag adjustment information determination method.

[0109] In some embodiments, an airbag adjustment information determination method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the airbag adjustment information determination method described above may be performed. Alternatively, in other embodiments, processor 41 may be configured to perform an airbag adjustment information determination method by any other suitable means (e.g., by means of firmware).

[0110] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0111] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0112] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0113] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0114] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0115] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0116] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0117] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for determining airbag adjustment information, characterized in that, include: Obtain target parameter information of the target object, the target parameter information including at least the sleep parameter information of the target object during sleep and the body mass index (BMI) of the target object, wherein the BMI is divided into five levels: less than 18, 18-24, 24-28, 28-32, and above 32; Based on the sleep parameter information, determine the target muscle group information related to the sleep quality of the target object; Based on the target muscle group information, determine the airbag adjustment information that matches the target object; The step of determining the target muscle group information related to the sleep quality of the target subject based on the sleep parameter information includes: Based on the target object's BMI level, the sleep quality information and at least one electromyographic (EMG) information from the sleep parameter information are input into the corresponding level's correlation analysis model to obtain the model's output correlation results. The correlation results include positive correlation between EMG information and sleep quality and negative correlation between EMG information and sleep quality. When EMG information is positively correlated with sleep quality, the correlation results also include a positive correlation value. When EMG information is negatively correlated with sleep quality, the correlation results also include a negative correlation value. Based on the positive correlation value under positive correlation in the correlation results, the positive ranking information of the muscle group corresponding to the electromyographic information is determined. The positive ranking information includes the name, location and ranking value of the positively correlated muscle group. Based on the negative correlation value under negative correlation in the correlation results, the negative ranking information of the muscle group corresponding to the electromyographic information is determined. The negative ranking information includes the name, location and ranking value of the negatively correlated muscle group. The positive sorting information and the negative sorting information are determined as target muscle group information related to the sleep quality of the target object.

2. The method according to claim 1, characterized in that, The construction of the correlation analysis model includes: Using the preset BMI level as the clustering dimension, obtain sample parameter information of sample objects under at least two BMI levels; Within the same BMI level, a correlation analysis model is constructed based on sleep quality information and electromyography information from the sleep information of at least two of the sample subjects.

3. The method according to claim 1, characterized in that, The step of determining the airbag adjustment information matching the target object based on the target muscle group information includes: Based on the positive sorting information in the target muscle group information, determine the change in airbag position and change in airbag pressure for the positively related muscle groups of the target object; Based on the negative sorting information in the target muscle group information, determine the changes in air bladder position and air bladder pressure for the negatively related muscle groups of the target object.

4. The method according to claim 1, characterized in that, Also includes: The mattress is adjusted based on the airbag adjustment information.

5. A device for determining airbag adjustment information, characterized in that, include: The parameter information acquisition module is used to acquire target parameter information of the target object. The target parameter information includes at least the sleep parameter information of the target object during sleep and the body mass index (BMI) of the target object. The BMI is divided into five levels: less than 18, 18-24, 24-28, 28-32, and above 32. A muscle group information determination module is used to determine target muscle group information related to the sleep quality of the target object based on the sleep parameter information. The adjustment information determination module is used to determine the airbag adjustment information matching the target object based on the target muscle group information; The muscle group information determination module includes: The correlation result determination unit is used to input sleep quality information and at least one electromyographic (EMG) information from the sleep parameter information into a correlation analysis model at the corresponding level, based on the BMI level of the target object, to obtain the correlation result output by the model. The correlation result includes a positive correlation between EMG information and sleep quality and a negative correlation between EMG information and sleep quality. When EMG information is positively correlated with sleep quality, the correlation result also includes a positive correlation value; when EMG information is negatively correlated with sleep quality, the correlation result also includes a negative correlation value. A muscle group information determination unit is used to determine target muscle group information related to the sleep quality of the target object based on the correlation results. The muscle group information determination unit is specifically used for: Based on the positive correlation value under positive correlation in the correlation results, the positive ranking information of the muscle group corresponding to the electromyographic information is determined. The positive ranking information includes the name, location and ranking value of the positively correlated muscle group. Based on the negative correlation value under negative correlation in the correlation results, the negative ranking information of the muscle group corresponding to the electromyographic information is determined. The negative ranking information includes the name, location and ranking value of the negatively correlated muscle group. The positive sorting information and the negative sorting information are determined as target muscle group information related to the sleep quality of the target object.

6. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform an airbag adjustment information determination method according to any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute and implement the airbag adjustment information determination method according to any one of claims 1-4.

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