Intelligent sleep environment adaptation method and system based on user personal parameters

Abstract

The invention discloses an intelligent sleep environment adaptation method and system based on personal parameters of a user. The method comprises the steps that the personal parameters of the user and real-time monitoring data are acquired; substituting the acquired personal parameters of the user and the real-time monitoring data into a pressure calculation model to calculate an expected pressure value of each sampling contact point; substituting the acquired personal parameters of the user and the real-time monitoring data into a joint angle model to calculate an expected joint angle; substituting the calculated expected pressure value and the expected joint angle into an optimization function for optimization so as to obtain an optimal adaptive parameter; and adjusting the height of the mattress based on the optimal adaptive parameters. The method has the advantages of personalized optimization, customized design, real-time feedback and adjustment, energy consumption reduction, user friendliness and high adaptability.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of computer system engineering, and particularly relates to an intelligent sleep environment adaptation method and system based on user personal parameters. BACKGROUND

[0002] With the improvement of people's living standards and the enhancement of health awareness, sleep quality has gradually become the focus of attention. Studies have shown that good sleep quality has an important influence on physical health, psychological state and daily life efficiency. Therefore, how to improve the sleep environment and increase the comfort of the mattress has become a problem to be solved.

[0003] Traditional mattress design mostly uses fixed materials and structures, which cannot be adjusted according to the individual needs of users. This "one-size-fits-all" approach often leads to user discomfort during use, thereby affecting sleep quality. For example, many mattresses do not take into account the physiological characteristics and sleep habits of users of different age groups, resulting in significant differences in experience among teenagers, adults and the elderly on the mattress. In addition, users have different needs for mattresses in different motion states (such as turning over, adjusting posture, etc.), and traditional mattresses are difficult to adapt to these dynamic changes.

[0004] In recent years, the rapid development of intelligent technology has provided new possibilities for mattress adaptation. Many smart mattresses have begun to introduce sensor technology to monitor the sleep state and motion of users. However, most existing technologies still have limitations. For example, although some products can provide basic sleep data monitoring, they lack a deep understanding of users' individual needs and real-time adaptation capabilities. In addition, these products do not perform well in terms of energy consumption, and intelligent functions often come with higher energy consumption, increasing the user's usage cost. SUMMARY

[0005] In view of the defects in the prior art, the present application provides an intelligent sleep environment adaptation method based on user personal parameters, comprising the following steps: Step S101, acquiring user personal parameters and real-time monitoring data, the user personal parameters including the user's age, height, weight, arm length, leg length, joint angle and group type, and the real-time monitoring data including sleep posture, contact point pressure distribution and motion state; Step S103, substituting the collected user personal parameters and real-time monitoring data into a pressure calculation model to calculate the expected pressure value of each sampling contact point; Step S105, substituting the collected user personal parameters and real-time monitoring data into a joint angle model to calculate the expected joint angle; Step S107, substituting the calculated expected pressure value and expected joint angle into the optimization function for optimization to obtain the best fitting parameter; Step S109, adjusting the height of the mattress based on the best fitting parameter.

[0006] wherein the pressure calculation model in step S103 is calculated by the following formula: wherein represents the pressure value of the th contact point; represents a weight distribution function related to the height, age and group of the user, represents a correction coefficient based on the sleep posture, represents the contact area of the th contact point.

[0007] wherein the weight distribution function is represented as wherein represents a constant related to the type of contact point, represents the average height of the group, the function c(age) represents a correction coefficient related to the age, and the posture correction coefficient .

[0008] wherein the elbow joint angle in the joint angle model in step S105 is calculated by the following formula: ; the knee joint angle is calculated by the following formula: wherein , , age represents the age of the user, and the posture adjustment coefficient .

[0009] wherein the optimization function in step S107 is represented by the following formula: wherein the comfort , the angle , E represents the energy consumed in the adjustment of the mattress settings, the contact point pressure after the motion state correction , the influence function of the motion state is represented as , the comfort weight is represented as , the joint angle weight is represented as , and the energy consumption weight is represented as .

[0010] The group type is divided into teenagers, adults and the elderly, wherein the age less than 20 years is defined as a teenager, the age between 20 years and 60 years is defined as an adult, and the age greater than or equal to 60 years is defined as an elderly.

[0011] The step S107 further comprises selecting a particle swarm optimization operation target function to find the best fitting parameter.

[0012] The step S109 adjusts the height of the mattress by using the following formula: the adjusted height of the mattress , , is the basic mattress height, is the target knee joint angle, which is the ideal angle set by the user; is the target elbow joint angle, which is the ideal angle set by the user; k(age, group) is a coefficient dynamically adjusted according to the age and the group.

[0013] The acceleration is used to judge the motion state of the user.

[0014] The application further provides an intelligent sleep environment fitting device based on user personal parameters, comprising An information acquisition module is configured to acquire user personal parameters and real-time monitoring data, wherein the user personal parameters comprise the age, height, weight, arm length, leg length, joint angle and group type of the user, and the real-time monitoring data comprises the sleep posture, contact point pressure distribution and motion state; A pressure calculation module is configured to substitute the acquired user personal parameters and real-time monitoring data into a pressure calculation model to calculate the expected pressure value of each sampling contact point; A joint angle calculation module is configured to substitute the acquired user personal parameters and real-time monitoring data into a joint angle model to calculate the expected joint angle; A fitting module is configured to substitute the calculated expected pressure value and expected joint angle into an optimization function for optimization to obtain the best fitting parameter; An adjustment module is configured to adjust the height of the mattress based on the best fitting parameter.

[0015] Compared with the prior art, the application has the following advantages: According to the age, group type and motion state of the user, the system can adjust the parameters in real time to provide personalized comfortable experience.

[0016] By using advanced algorithms such as particle swarm optimization, the optimal mattress settings can be found, significantly improving the user's sleep quality and comfort. Users can choose different adaptation parameters according to their own needs, thus realizing customized sleep environment.

[0017] Real-time monitoring of user status using acceleration, pressure, etc. sensors, timely adjustment, enhanced user experience. The system can continuously learn and optimize based on user feedback, improving the accuracy of adaptation. Through intelligent algorithm optimization of mattress energy consumption settings, energy saving effect is achieved, unnecessary energy consumption is reduced. Combined with material properties and user needs, more efficient materials are selected to further reduce energy consumption.

[0018] Intuitive system interface and operation, users can easily set and adjust parameters. Based on user historical data and preferences, intelligent recommendations are provided to simplify the decision-making process.

[0019] Suitable for different types of users (such as teenagers, adults, the elderly), widely applicable to homes, hospitals, nursing homes, etc. The system can be extended according to technological progress and user needs, adding new functions or supporting new devices. BRIEF DESCRIPTION OF DRAWINGS

[0020] The above and other objects, features and advantages of the exemplary embodiments of the present disclosure will be more apparent from the following detailed description taken in conjunction with the accompanying drawings, in which several embodiments of the present disclosure are shown by way of example, and wherein like or corresponding elements show like or corresponding parts, in which: Figure 1 is a flow chart showing an intelligent sleep environment adaptation method based on user personal parameters according to an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be described in further detail below with reference to the drawings. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0022] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "an" and "the" used in the embodiments of the present application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. "Multiple" generally includes at least two.

[0023] It should be understood that, although the terms first, second, third, etc. can be employed in describing the … in the embodiments of the present application, these … should not be limited to these terms. These terms are only used to distinguish one … from another. For example, without departing from the scope of the embodiments of the present application, the first … can also be referred to as the second …, and similarly, the second … can also be referred to as the first ….

[0024] It should be understood that the term "and / or" used herein is only to describe the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can mean that A exists alone, A and B exist together, and B exists alone. In addition, the character " / " herein generally represents that the associated objects before and after are in an "or" relationship.

[0025] Depending on the context, the word "if" as used herein can be interpreted as meaning "when" or "upon" or "in response to a determination" or "in response to a detection". Similarly, depending on the context, the phrase "if determined" or "if detecting (a stated condition or event)" can be interpreted as meaning "when determined" or "in response to a determination" or "when detecting (a stated condition or event)" or "in response to a detection (of a stated condition or event)".

[0026] It should also be noted that the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusion, so that a product or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such product or device. Without more limitations, the element defined by the sentence "including a …" does not exclude the presence of another identical element in the product or device including the element.

[0027] The optional embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0028] Embodiment one, As Figure 1 shown, the present application discloses a kind of intelligent sleep environment adaptation evaluation method based on user personal parameter, comprising the following steps: Step S101, obtain user personal parameter and real-time monitoring data, the user personal parameter includes the age, height, weight, arm length, leg length, joint angle and group type of the user, and the real-time monitoring data includes sleep posture, contact point pressure distribution and motion state; Step S103, the user personal parameter and real-time monitoring data collected are substituted into pressure calculation model, to calculate the expected pressure value of each sampling contact point; Step S105, the user personal parameters and real-time monitoring data collected are substituted into a joint angle model to calculate an expected joint angle; Step S107, the expected pressure value and expected joint angle calculated are substituted into an optimization function for optimization to obtain optimal fitting parameters; Step S109, the height of the mattress is adjusted based on the optimal fitting parameters.

[0029] Embodiment two, The present application proposes an intelligent sleep environment fitting method based on user personal parameters, comprising the following steps: Step S101, user personal parameters and real-time monitoring data are obtained, the user personal parameters including the user's age, height, weight, arm length, leg length, joint angle and group type, and the real-time monitoring data including sleep posture, contact point pressure distribution and motion state; Step S103, the user personal parameters and real-time monitoring data collected are substituted into a pressure calculation model to calculate an expected pressure value of each sampling contact point; Step S105, the user personal parameters and real-time monitoring data collected are substituted into a joint angle model to calculate an expected joint angle; Step S107, the expected pressure value and expected joint angle calculated are substituted into an optimization function for optimization to obtain optimal fitting parameters; Step S109, the height of the mattress is adjusted based on the optimal fitting parameters.

[0030] In the step S103, the pressure calculation model is calculated by the following formula: , wherein represents the pressure value of the i th contact point; represents a weight distribution function related to the user's height, age and group, represents a correction coefficient based on the sleep posture, represents the contact area of the i th contact point. In the weight distribution function represents

[0031] , wherein represents a constant related to the contact point type, represents the average height of the group, the function c(age) represents a correction coefficient related to the age, and the posture correction coefficient . , wherein

[0032] , wherein ​When c(age) = 0.1, for teenagers, the body is more flexible and adaptive, so a higher correction coefficient is given in the pressure calculation; when c(age) = 0.05, for adults, the body state is relatively stable, and the adaptability decreases slightly, so a medium correction coefficient is given; when c(age) = 0, for the elderly, due to the decline of body function, the correction coefficient is set to 0 to reflect that they have higher requirements for the comfort of the mattress and are more susceptible to pressure.

[0033] The function c(age) is an age-related pressure distribution correction coefficient, which is used to reflect the difference in soft tissue elasticity and pressure tolerance of different ages. Its specific definition adopts the following piecewise linear function: | 0.10 (when age < 20, i.e. teenagers) c(age) = | 0.05 (when 20 ≤ age < 60, i.e. adults) | 0.00 (when age ≥ 60, i.e. the elderly) In the actual system, a continuous transition function can also be used, for example, for the adult population, defined as c(age) = 0.05 - 0.001 × (age - 20).

[0034] In the step S105, the elbow joint angle in the joint angle model is calculated using the following formula: ; The knee joint angle is calculated using the following formula: wherein , , age represents the age of the user, and the posture adjustment coefficient .

[0035] The age-related parameters k1(age), b1(age), k2(age), and b2(age) in the joint angle model are obtained by fitting ergonomics data. In one specific implementation, the following empirical formula can be used: Elbow joint angle parameters: k1(age) = 0.8 + 0.002 × age; b1(age) = 15 - 0.1 × age.

[0036] Knee joint angle parameters: k2(age) = 1.2 - 0.001 × age; b2(age) = 10 - 0.05 × age.

[0037] The posture adjustment coefficient k posture is determined according to the sleep posture, for example: when supine, k posture= 1.0, k posture = 1.1, k posture = 0.9.

[0038] wherein the optimization function in step S107 adopts the formula as follows: wherein the comfort degree , the angle , E represents the energy consumed in the process of adjusting the mattress setting, the contact point pressure after the motion state correction , the influence function of the motion state is represented as , the comfort degree weight is represented as , the joint angle weight is represented as , and the energy consumption weight is represented as .

[0039] The comfort degree weight ω c , the joint angle weight ω θ , and the energy consumption weight ω E are user-adjustable normalized parameters, satisfying ω c + ω θ + ω E = 1. The system default setting is ω c = 0.5, ω θ = 0.3, and ω E = 0.2. The user can adjust them according to personal preferences through an interactive interface, or the system can automatically optimize them according to historical sleep quality feedback.

[0040] wherein the group type is divided into teenagers, adults, and the elderly, wherein the age less than 20 years old is defined as a teenager, the age between 20 years old and 60 years old is defined as an adult, and the age greater than or equal to 60 years old is defined as the elderly.

[0041] wherein the step S107 further comprises selecting a particle swarm optimization running target function to find the best fitting parameter.

[0042] In order for those skilled in the art to clearly understand and implement the present application, the key parameters in the model are defined and explained as follows: 1. Parameter W (reference force coefficient): Definition: W is a reference force coefficient with a force dimension (unit: Newton, N). Its physical meaning represents a reference value or normalized coefficient of the total support force applied to the body support system under standard conditions (such as standard weight, standard posture).

[0043] It assigns a dimensionless weight distribution function The product of the above-mentioned force F and the posture correction coefficient g is mapped to a range of force values with actual physical meaning, so as to calculate the expected support force of each contact point .

[0044] W can be determined by any of the following ways: a) Function based on body weight: W can be directly related to the user's body weight m (unit: kg), that is, , where g0 is the acceleration of gravity (about 9.8 m / s²), and λ is an empirical coefficient (usually 0.5-0.8) reflecting the proportion of the actual body weight force borne by the mattress.

[0045] b) Experimentally calibrated constant: Through a large number of comfort experiments on the target user group (such as Asian adult males), an optimal average reference force value is statistically obtained, for example, W = 300N.

[0046] In a preferred embodiment of the present application, method a) is adopted, that is, .

[0047] 2, parameter L arm , L leg , h shoulder : L arm (arm length): refers to the straight-line distance from the user's acromion point to the ulnar styloid point. It can be obtained by direct user input, proportional estimation based on height H (such as ), or by depth camera measurement in the mattress / bedroom.

[0048] L leg (leg length): refers to the straight-line distance from the user's greater trochanter point to the lateral malleolus point. The acquisition method is the same as L arm , and the proportional estimation can be .

[0049] h shoulder (shoulder height): when the user is lying on one side, it refers to the vertical height of the acromion point relative to the reference plane of the mattress. This parameter is mainly used for the calculation of joint angles in the side-lying posture, and can be estimated by pressure distribution map or preset typical value (such as h shoulder = 15cm).

[0050] g: posture correction coefficient. This coefficient is a dimensionless correction value determined based on real-time monitoring of the sleeping posture posture, used to adjust the support force required by each part in different postures.

[0051] Model formula , The calculation result corresponds to the pressure (unit: Pa) in physics. However, in the application context and claims of the present invention, "pressure value" is a general term in the industry for the force per unit area on the surface of the mattress in contact with the human body, and the technical essence is the calculation of pressure. Molecules The calculation result is force , divided by the contact area , which is the desired pressure . Based on the formula and the above parameter definitions, those skilled in the art can unambiguously calculate the desired pressure (pressure) value of each contact point. By the following method: the array composed of MxN pressure sensing units is built in the mattress, each unit has a known effective sensing area A unit At each sampling time, the system determines the units with a pressure value greater than a set threshold (such as 50 Pa) as "effective contact units". That is, the sum of the areas A unit of all units in the connected region corresponding to the ith contact point composed of adjacent "effective contact units".

[0052] The functions k1(age), b1(age), and c(age) can be obtained by linear or piecewise linear fitting of experimental data for different age groups. For example, c(age) can be defined as: if age<20, c=0.1; if 20≤age<60, c=0.05; if age≥60, c=0. The weights ω c , ω θ , and ω E can be set by the user according to their preferences, or adjusted adaptively by the system based on user historical data (such as satisfaction feedback after multiple adjustments) through machine learning.

[0053] In an embodiment, the optimization step is illustrated.

[0054] First, determine the size of the particle swarm, such as selecting 2 particles. Each particle represents a possible combination of fitting parameters, such as mattress height, hardness, etc.

[0055] These parameters will be randomly initialized within a certain range, for example, the height can be between 20 and 30 cm, and the material hardness can be within the range of soft to hard.

[0056] For each particle, calculate its corresponding fitness, i.e., the value of the objective function. This involves: calculating the comfort level corresponding to the particle position, calculating the joint angles (knee and elbow), and calculating the energy consumption. The fitness value of each particle will be used to judge its merits.

[0057] In each iteration, the velocity and position of a particle are updated based on its fitness and the fitness of the global best particle. Specifically, the update of a particle takes into account its own historical best position and the global best position (i.e., the current best solution). This process moves the particle towards a better direction, constantly exploring the solution space.

[0058] The process of evaluating the fitness of particles and updating their positions is repeated until a certain stopping condition is met, such as reaching a maximum number of iterations or a very small change in fitness.

[0059] After multiple iterations, the particle swarm optimization algorithm converges to one or more optimal combinations of fitting parameters. Ultimately, the best mattress height, material hardness, etc. will be obtained. The corresponding minimum objective function value reflects the best comfort, joint angle, and energy consumption.

[0060] Specifically, the derivation process is as follows. The position of each particle After each iteration, the current velocity and position are updated according to the following formula: where, is the current position of the th particle in the tth iteration, is the velocity of the th particle in the tth iteration.

[0061] The velocity of a particle is updated according to the following formula: where w is the inertia weight, controlling the inertia of the particle's advance. c1 and c2 are individual and social learning factors, usually set to 1.5. r1 and r2 are randomly generated numbers between 0 and 1. is the historical best position of the th particle. g is the global best position.

[0062] At each iteration, the conditions for updating the individual best position and the global best position are as follows: (1) For individual ; (2) For global .

[0063] wherein the step S109 adjusts the mattress height using the following formula: adjusted mattress height where , is the base mattress height, is the target knee joint angle, which is the ideal angle set by the user; is the target elbow joint angle, which is the ideal angle set by the user; k(age, group) is a coefficient dynamically adjusted according to age and group, and the specific value rules are as follows: for the "teenager" group, k = 1.0; for the "adult" group, k = 1.0 + 0.005 × (age - 20); for the "elderly" group, k = 1.2.

[0064] Wherein, the user motion state is judged based on acceleration.

[0065] In an embodiment, the user motion state is judged in the following way: The pressure values of different contact points on the mattress are monitored; the change in the readings of the pressure sensor can be used as a trigger condition for motion state judgment. For example, if the pressure value suddenly decreases, it may indicate that the user is adjusting the posture, prompting the system to check the acceleration data. In the case of pressure change, further confirmation is made through the data of the acceleration sensor to ensure the accuracy of the judgment.

[0066] When the contact point pressure changes, the acceleration change of the user's body is triggered to obtain three-dimensional acceleration data ; The total acceleration is calculated .

[0067] The motion state is judged according to the set threshold value:

[0068] For example, in the state of no motion, teenagers , adults , and the elderly ; in the state of slight motion, teenagers , adults , and the elderly ; in the state of greater motion, refer to the above.

[0069] Embodiment three, The present application provides an intelligent sleep environment adaptation device based on user personal parameters, comprising An information acquisition module is used to acquire user personal parameters and real-time monitoring data, wherein the user personal parameters include the user's age, height, weight, arm length, leg length, joint angle, and group type, and the real-time monitoring data includes the sleep posture, contact point pressure distribution, and motion state. A pressure calculation module is used to substitute the collected user personal parameters and real-time monitoring data into a pressure calculation model to calculate the expected pressure value of each sampling contact point. A joint angle calculation module is used to substitute the collected user personal parameters and real-time monitoring data into a joint angle model to calculate the expected joint angle. an adaptation module for fitting the calculated desired pressure value and the desired joint angle into an optimization function for optimization to obtain optimal adaptation parameters; an adjustment module for adjusting the height of the mattress based on the optimal adaptation parameters.

[0070] Embodiment four, The embodiment of the present disclosure provides a nonvolatile computer storage medium, which stores computer executable instructions, and the computer executable instructions can execute the method steps of the above embodiments.

[0071] It should be noted that the computer readable medium of the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more wires, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or component. In the present disclosure, the computer readable signal medium can include a data signal carried in a baseband or as a part of a carrier wave, which carries computer readable program code. Such a propagated data signal can take various forms, including but not limited to an electromagnetic signal, an optical signal or any suitable combination of the above. The computer readable signal medium can also be any computer readable medium other than the computer readable storage medium, which can send, propagate or transmit a program for use by or in conjunction with an instruction execution system, device or component. The program code contained in the computer readable medium can be transmitted by any suitable medium, including but not limited to a wire, a cable, an RF (radio frequency) or the like, or any suitable combination of the above.

[0072] The above computer readable medium can be contained in the above electronic device; or can exist separately and not be assembled into the electronic device.

[0073] Computer program code for carrying out operations of the present disclosure can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0074] The computer program instructions can also be loaded onto a computer or other programmable information processing apparatus to cause a series of operations to be performed on the computer or other programmable information processing apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable information processing apparatus implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0075] The units described in the embodiments of the present disclosure can be implemented by software, or by hardware. Among them, the name of the unit does not constitute a limitation to the unit itself in some cases.

[0076] The above introduces the preferred embodiment of the present application, which aims to make the spirit of the present application more clear and convenient to understand, and is not intended to limit the present application. Any modification, replacement, improvement made within the spirit and principle of the present application shall be included in the protection scope of the appended claims of the present application.

Claims

1. A method for intelligent sleep environment adaptation based on user personal parameters, characterized in that, Includes the following steps: Step S101: Obtain user personal parameters and real-time monitoring data. The user personal parameters include the user's age, height, weight, arm length, leg length, joint angle, and group type. The real-time monitoring data includes sleep posture, contact point pressure distribution, and movement status. Step S103: Substitute the collected user personal parameters and real-time monitoring data into the pressure calculation model to calculate the expected pressure value for each sampling contact point; Step S105: Substitute the collected user personal parameters and real-time monitoring data into the joint angle model to calculate the desired joint angle; Step S107: Substitute the calculated expected pressure value and expected joint angle into the optimization function for optimization to obtain the best fitting parameters; Step S109: Adjust the height of the mattress based on the optimal fitting parameters.

2. The method as described in claim 1, characterized in that, The pressure calculation model in step S103 uses the following formula for calculation: ,in Indicates the first Pressure value at each contact point; This represents a weighting function related to user height, age, and group. This represents the correction factor based on sleep posture. Indicates the first The contact area of ​​each contact point.

3. The method as described in claim 2, characterized in that, The weight allocation function Represented as ,in This represents a constant related to the type of contact point. This represents the average height of this group, and the function c(age) represents the age-related correction factor and posture correction factor. .

4. The method as described in claim 1, characterized in that, In step S105, the elbow joint angle in the joint angle model is calculated using the following formula: ； The knee joint angle is calculated using the following formula: ,in, , "age" indicates the user's age, and "posture adjustment factor" is also present. .

5. The method as described in claim 1, characterized in that, The optimization function in step S107 is expressed as follows: Among them, comfort ,angle E represents the energy consumed during mattress adjustment, and the contact point pressure after motion correction. Influence function of motion state Represented as The comfort weight is expressed as The joint angle weights are expressed as Energy consumption weight is expressed as .

6. The method as described in claim 1, characterized in that, The group types are divided into teenagers, adults and the elderly. Teenagers are defined as those under 20 years old, adults are defined as those between 20 and 60 years old, and the elderly are defined as those 60 years old or older.

7. The method as described in claim 1, characterized in that, Step S107 further includes selecting the objective function for particle swarm optimization and finding the optimal fitting parameters.

8. The method as described in claim 1, characterized in that, Step S109 adjusts the mattress height using the following formula: Adjusted mattress height ,in , This is the basic mattress height. It is the target knee joint angle, the ideal angle set for the user; is the target elbow joint angle, the ideal angle set for the user; k(age,group) is a coefficient dynamically adjusted based on age and group.

9. The method as described in claim 1, characterized in that, The user's motion state is determined based on acceleration.

10. A smart sleep environment adaptation device based on user personal parameters, comprising: The information acquisition module is used to acquire user personal parameters and real-time monitoring data. The user personal parameters include the user's age, height, weight, arm length, leg length, joint angle, and group type. The real-time monitoring data includes sleep posture, contact point pressure distribution, and movement status. The pressure calculation module is used to input the collected user personal parameters and real-time monitoring data into the pressure calculation model to calculate the expected pressure value for each sampling contact point. The joint angle calculation module is used to input the collected user personal parameters and real-time monitoring data into the joint angle model to calculate the desired joint angle. The adaptation module is used to substitute the calculated expected pressure value and expected joint angle into the optimization function for optimization in order to obtain the best adaptation parameters; An adjustment module is used to adjust the height of the mattress based on the optimal fit parameters.

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