Bed screen package and health test management method and device, equipment and storage medium

Abstract

The invention discloses a bed screen package and health test management method, device and equipment and a storage medium, and the method comprises the steps: collecting physical test data and subject physiological parameters of a bed screen, and constructing a test data set; performing index quantification on the test data set by using the test index to obtain an index quantification result; based on the test data set, performing multi-dimensional evaluation by using a multi-dimensional health evaluation model to obtain a multi-dimensional evaluation result; scoring and grading the bed screen according to an index quantification result and a multi-dimensional evaluation result; and generating an optimization strategy based on the scoring and grading results. According to the invention, by collecting the physical test data of the bed screen and the physiological parameters of the subject, various performance indexes of the bed screen can be accurately quantified, multi-dimensional health evaluation is carried out, and quantitative evaluation of the wrapping feeling and health of the bed screen is realized. The limitation of subjective experience and conventional mechanical performance testing is avoided, and a scientific and objective basis is provided for design and optimization of the bed screen.

Description

Technical Field

[0001] This invention relates to the field of furniture design and manufacturing technology, and in particular to methods, devices, equipment and storage media for the wrapping and health testing management of headboards. Background Technology

[0002] As people's living standards continue to improve, healthy home furnishing products are receiving increasing attention from society. As an important component of bedroom furniture, upholstered beds play a significant role in enhancing user comfort and promoting physical and mental health. The headboard, as a key part of the upholstered bed structure, not only affects the overall support performance and aesthetics but also directly relates to the user's safety and comfort.

[0003] Most existing upholstered headboard designs focus on basic structural strength, aesthetics, or simple ergonomics, lacking systematic research and evaluation of user comfort and health needs. In practical use, traditional upholstered headboards often neglect the comfortable feeling of being enveloped and supported, and fail to fully integrate theories from multiple disciplines such as ergonomics, materials science, and mechanics. This makes it difficult for upholstered headboards to achieve ideal results in terms of support, pressure distribution, elasticity transmission, and health protection, failing to meet the increasingly personalized, diverse, and scientifically-oriented demands for healthy home furnishings.

[0004] Furthermore, the current market lacks quantitative evaluation methods for the ergonomic feel and health performance of upholstered headboards. Most evaluations remain at the level of subjective experience or routine mechanical performance testing, failing to provide a scientific basis for the innovative design and precise optimization of upholstered headboards. Therefore, there is an urgent need for a new method that can systematically integrate ergonomics, materials science, and mechanical principles to quantitatively assess the ergonomic feel and health benefits of upholstered headboards and provide a reference for their scientific design, thereby promoting the upgrading and development of healthy home upholstered bed products. Summary of the Invention

[0005] This invention provides a method, apparatus, computer equipment, and storage medium for managing the cradle and health testing of headboards, aiming to achieve quantitative evaluation and testing of the cradle and health benefits of headboards.

[0006] In a first aspect, embodiments of the present invention provide a method for managing the wrapping and health testing of a bed headboard, including: Physical test data and physiological parameters of the subjects were collected from the bed screen to be tested, thereby constructing a test dataset; The test dataset is quantified using preset test metrics to obtain the corresponding quantification results. Based on the test dataset, a multi-dimensional health assessment model is used to perform a multi-dimensional assessment, and the corresponding multi-dimensional assessment results are obtained. The bed headboard is scored and graded based on the quantitative results of the aforementioned indicators and the multi-dimensional evaluation results. An optimization strategy for the bed screen is generated based on the scoring and grading results.

[0007] Secondly, embodiments of the present invention provide a bed headboard wrapping and health testing management device, comprising: The data acquisition unit is used to collect physical test data and physiological parameters of the subjects from the bed screen to be tested, thereby constructing a test dataset. The indicator quantization unit is used to quantify the test dataset using preset test indicators to obtain the corresponding indicator quantization results. The data evaluation unit is used to perform multi-dimensional evaluation based on the test dataset using a multi-dimensional health evaluation model, and obtain the corresponding multi-dimensional evaluation results. The scoring and grading unit is used to score and grade the headboard based on the quantitative results of the indicators and the multi-dimensional evaluation results. An optimization management unit is used to generate optimization strategies for the bed screen based on the results of scoring and grading.

[0008] Thirdly, embodiments of the present invention provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the bed headboard wrapping and health test management method as described in the first aspect.

[0009] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the bed headboard wrapping and health testing management method as described in the first aspect.

[0010] This invention provides a method, apparatus, computer device, and storage medium for managing the cradle comfort and health testing of headboards. The method includes: collecting physical test data and physiological parameters of subjects from the headboard to be tested to construct a test dataset; quantifying the test dataset using preset test indicators to obtain corresponding quantification results; performing multi-dimensional evaluation using a multi-dimensional health assessment model based on the test dataset to obtain corresponding multi-dimensional evaluation results; scoring and grading the headboard according to the quantification results and multi-dimensional evaluation results; and generating optimization strategies for the headboard based on the scoring and grading results. This invention, by collecting physical test data and physiological parameters of subjects from the headboard, can accurately quantify various performance indicators of the headboard and perform multi-dimensional health evaluations, achieving a quantitative assessment of the headboard's cradle comfort and health benefits. This not only avoids the limitations of subjective experience and conventional mechanical performance testing but also provides a scientific and objective basis for the design and optimization of headboards. Attached Figure Description

[0011] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A flowchart illustrating a method for managing the wrapping and health testing of a headboard, as provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the appearance of a headboard in a method for managing the wrapping and health testing of headboards according to an embodiment of the present invention; Figure 3 This is a schematic cross-sectional view of the headboard in a method for managing the wrapping and health testing of headboards provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the bed headboard usage status in a bed headboard wrapping and health testing management method provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the test point distribution in a bed headboard wrapping and health testing management method provided in an embodiment of the present invention; Figure 6 This is a schematic block diagram of a bed headboard wrapping and health testing management device provided in an embodiment of the present invention. Detailed Implementation

[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0015] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0016] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0017] Please see below. Figure 1 This invention provides a method for managing the wrapping and health testing of a bed headboard, specifically including steps S101 to S105.

[0018] Step S101: Collect physical test data and physiological parameters of the subject from the bed screen to be tested, thereby constructing a test dataset; Step S102: Quantify the test dataset using preset test metrics to obtain the corresponding metric quantification results; Step S103: Based on the test dataset, perform a multi-dimensional evaluation using a multi-dimensional health evaluation model to obtain the corresponding multi-dimensional evaluation results; Step S104: Score and grade the bed headboard based on the quantitative results of the indicators and the multi-dimensional evaluation results; Step S105: Generate an optimization strategy for the bed screen based on the scoring and grading results.

[0019] In this embodiment, a test dataset is first constructed by collecting physical test data of the bed screen to be tested and physiological parameters of the subjects. Then, the dataset is quantified using preset test indicators to obtain the quantification results. Based on the test dataset, a multi-dimensional health evaluation model is used to conduct a multi-dimensional evaluation to obtain the multi-dimensional evaluation results. The bed screen is then scored and graded according to the quantification results and the multi-dimensional evaluation results. Finally, an optimization strategy for the bed screen is generated based on the scoring and grading results.

[0020] This embodiment, by collecting physical test data of the headboard and physiological parameters of the subjects, can accurately quantify various performance indicators of the headboard and conduct multi-dimensional health evaluations, achieving a quantitative assessment of the headboard's enveloping comfort and health benefits. This not only avoids the limitations of subjective experience and conventional mechanical performance testing but also provides a scientific and objective basis for the design and optimization of headboards.

[0021] In one embodiment, step S101 includes: The loading-unloading loop test method is adopted. The loading and unloading loop test is performed on different areas of the bed headboard at a preset rate, and the force-displacement curve of each area is recorded during the test. Then, the force-displacement curve is used as the physical test data. When the subject is in a natural sitting posture with respect to the headboard, pressure sensors are used to collect data on the back pressure distribution of the subject with respect to the headboard. The three-dimensional point cloud data of the subject in a leaning posture was acquired in a non-contact manner, and a digital human musculoskeletal model was constructed for the subject. The iterative nearest point algorithm is used to perform spatial registration and posture fitting on the three-dimensional point cloud data and the digital human musculoskeletal model to obtain the sitting back spinal morphology data. The back pressure distribution data and the sitting back spinal morphology data are then summarized into the physiological parameters of the subject.

[0022] In this embodiment, physical test data is obtained by performing load-unload cyclic tests on different areas of the headboard at a preset rate and recording the force-displacement curves for each area. The acquisition of the subject's physiological parameters includes two aspects: first, when the subject is naturally leaning against the headboard, pressure sensors are used to collect back pressure distribution data; second, three-dimensional point cloud data of the subject in a leaning posture is acquired non-contactly, a digital human musculoskeletal model is constructed, and then the iterative nearest-point algorithm is used to spatially register and posture-fit the three-dimensional point cloud data and the model to obtain the sitting back spinal morphology data. Finally, the back pressure distribution data and the sitting back spinal morphology data are summarized to form the subject's physiological parameters. Specifically: (1) When obtaining physical test data, the environmental conditions should be adjusted first. The soft bed screen sample to be tested should be placed in a standard test environment with the ambient temperature controlled at 23±2℃ and the relative humidity controlled at 55±5%. The sample needs to be left to stand in the above environment for at least 24 hours to allow its moisture content and material properties to reach a balanced state.

[0023] After assembling the soft bed screen, the sample was pre-loaded to eliminate the mechanical hysteresis caused by initial stress and material relaxation during production. The equivalent hardness (SHR) test was performed as follows: a load of 450N was applied to the sample using the testing equipment, with a loading rate set to 10 cycles / min, and the loading cycle was repeated at least 3 times. After pre-loading, the zero points of displacement and force were recorded as the initial values ​​for subsequent tests. The indentation hardness index (IH) test was performed by setting the loading control mode to displacement control. Each test point required 3 pre-loading-unloading cycles of up to 30% of the nominal thickness before formal measurement to eliminate the effects of initial stress and relaxation in the material.

[0024] Then, a load-unload cycle test is performed. The equivalent hardness (SHR) is measured using a rigid cylindrical-ellipsoidal composite indenter with a diameter of 100 mm as the loading tool. Loading and unloading cycles are sequentially applied to the shoulder and neck area, upper back area, and lumbar area of ​​the soft headboard at a predetermined rate (preferably 50 mm / min). During the test, the force-displacement curves of each test area are collected and recorded in real time to analyze its physical support characteristics. The indentation hardness index (IH) is measured using a rigid cylindrical-ellipsoidal composite indenter with a diameter of 100 mm as the loading tool. Before testing, the "nominal thickness" of the sample must be clearly defined, i.e., the free thickness measured after the indenter lightly touches (e.g., with a 5 N micro-load) to the reference plane without preload. For soft headboards with curved or irregular shapes, the local normal thickness at each test point should be taken and indicated in the test report. It is recommended that at least three indentation hardness test points be arranged in each of the shoulder and neck area, upper back area, and lumbar area, with a distance of at least 100 mm between adjacent points, avoiding areas with seams, zippers, and hardware.

[0025] When acquiring physiological parameters of the subjects, for back pressure distribution data, a pressure distribution sensor system is first configured. This sensor system uses a thin film, fabric, or foam medium, with a measurement range ≥0kPa~50kPa, spatial resolution ≤20mm, and sampling frequency ≥30Hz. The positioning distance from the ischial tuberosity to the lumbar protrusion of the soft bed headboard is determined based on the subject's physiological characteristics. Specifically, when the subject is female or has a body mass index (BMI) greater than 23, the positioning distance is 12-13cm; when the subject is male, the positioning distance is 8-11cm; and when the subject's BMI is less than 18, the positioning distance is 8-9cm. Then, the subject is controlled in a natural sitting posture with the upper arms hanging naturally and the forearms resting on the thighs. Back pressure distribution cloud maps are acquired under stable posture for ≥10s. The above acquisition steps are repeated 3 times, and the average value is calculated.

[0026] For seated spinal morphology data, this embodiment abandons traditional contact measurement and adopts a method of "3D scanning in use + digital reconstruction of human seated posture". This method can infer the internal spinal morphology from visible surface features even when the subject's back is obscured by a soft headboard. Specifically, a high-precision handheld 3D scanner or multi-view structured light camera array is first used to perform a 360-degree scan of the subject in a natural leaning position against a soft headboard. The scan range covers the visible areas not obscured by the headboard, including the head, neck, upper shoulder surface, sides of the torso, thighs, and arms. High-density 3D point cloud data or mesh models of the subject in the leaning posture are obtained as boundary constraints for posture reconstruction.

[0027] Then, based on the subject's basic anthropometric data (height, weight, major limb length, etc.), a parameterized digital human musculoskeletal model is created or invoked in a computer. This model possesses a complete spinal skeletal kinematic chain structure and can simulate the natural range of motion of human joints. Then, using the Iterative Closest Point (ICP) algorithm or a non-rigid registration algorithm, the initialized digital human musculoskeletal model is spatially registered and pose-fitted with the acquired scanned point cloud data. Here, the spatial registration and pose fitting include: ① Align key anatomical landmarks in the digital model (such as the auricular point, acromion point, anterior superior iliac spine point, knee joint center, etc.) with their corresponding positions in the scan data.

[0028] ② By using an inverse kinematics solver, the spinal curvature and limb angles of the digital model are automatically adjusted under the premise of satisfying biomechanical constraints, so as to minimize the overlap error between the skin on the body surface and the scanned point cloud data, thereby realizing the digital reconstruction of the occluded back area and the spinal morphology inside the body.

[0029] Finally, the geometric coordinates of the spine's centerline are directly extracted from the reconstructed digital human body model to calculate key morphological parameters: ① Thoracic kyphosis angle: Calculate the curvature, tangent angle, or chord distance of the fitted curves at the geometric centers of the T1 to T12 vertebrae in the reconstructed model. ② Lumbar lordosis angle: Calculate the curvature, tangent angle, or arc chord distance of the fitted curve of the geometric center of the T12 to L5 vertebrae in the reconstructed model.

[0030] Combination Figures 2 to 4 The headboard consists of the following layers from the outside to the inside: 1. Leather fabric layer; 2. Glue-free cotton; 3. High-density slow-rebound memory foam; 4. Irregularly shaped cut memory foam; 5. Down and velvet blend; 6. High-elasticity wide webbing; and 7. Wooden frame. When the subject is in a natural sitting position, the headboard can provide the subject with a comfortable feeling of being enveloped and supported.

[0031] In one embodiment, step S102 includes: The equivalent surface hardness index is calculated based on the physical test data. Equivalent surface hardness (SHR) is used to characterize the contact stiffness of a soft mattress headboard within the main pressure range supported by the human body. In the calculation, a linear working range of 50N to 450N load can be selected from the force-displacement curve, and the slope within this range can be calculated. The calculation formula is as follows: ; In the formula, This indicates the equivalent surface hardness of the soft bed, expressed in N / mm. The y-coordinate value (i.e., load value) of the corresponding coordinate point when the load reaches 450N, in N; The y-coordinate value (i.e., load value) of the corresponding coordinate point when the load reaches 50N, in N; The x-coordinate value (i.e., displacement value) of the corresponding coordinate point when the load reaches 450N, in mm; The x-coordinate value (i.e., displacement value) of the corresponding coordinate point when the load reaches 50N is given, in mm. Of course, in other embodiments, a linear working interval within the force-displacement curve with a load of other ranges can be selected, and the corresponding slope can be calculated.

[0032] The waist reinforcement ratio is calculated based on the arithmetic mean of the equivalent surface hardness index. The lumbar support ratio (LBR) characterizes the gradient characteristics of the stiffness distribution in the vertical direction of a headboard, specifically measuring the difference in stiffness between the lumbar support and the back support. The calculation formula is as follows: ; In the formula, The SHR (Self-Rating Length) refers to the arithmetic mean of multiple measurements or multi-point measurements taken within the lumbar region of the soft bed headboard test area. The SHR refers to the arithmetic mean of multiple measurements or multi-point measurements taken within the test area on the back of the soft bed screen.

[0033] The hardness gradient index of the bed screen is calculated based on preset test points and equivalent surface hardness index. The method for calculating the hardness gradient HG can be as follows: Select all adjacent test point pairs along the vertical direction (shoulder and neck area - upper back area, upper back area - waist area), calculate |ΔSHR| and divide it by the corresponding test point spacing d to obtain the hardness gradient HG (shoulder and neck area - upper back area, upper back area - waist area). Then calculate the HG value of all adjacent point pairs, where the maximum HG value can be used for judgment.

[0034] Based on the physical test data, the indentation hardness index IH of each preset test point is obtained.

[0035] This embodiment employs a standardized indentation hardness test method to quantitatively characterize the compressive strength and enveloping properties of different zones of a soft headboard, supplementing the evaluation of the "first touch" and local support. During testing, the indenter is vertically pressed into the sample at a loading rate of 50 mm / min to a position of 0.30 × nominal thickness H. The force required to reach this indentation depth is measured and recorded in real time, denoted as IH (30%), in Newtons (N). This value is the indentation hardness index at the measured point, reflecting the overall "softness" and "hardness" feel when leaning against it.

[0036] Furthermore, step S102 also includes: The bed headboard's wrapping properties are determined by combining the equivalent surface hardness index and the waist reinforcement ratio index. The arithmetic average of the compressive hardness indices corresponding to the test points on different sections of the headboard is calculated to obtain the section indentation hardness index, and the support of different sections of the headboard is judged by the section indentation hardness index.

[0037] Based on ergonomic comfort thresholds, this embodiment sets the following dual judgment criteria. When the soft headboard under test simultaneously meets both of the following conditions, it is determined to conform to the "wrap-around" design characteristics: Condition A (Low Hardness Touch): The equivalent surface hardness of the waist area meets SHR < 4.0 N / mm. This indicator shows that the material surface has sufficient deformation capacity to conform to the curve of the human back and create a wrapping feel; Condition B (Flexible Transition Distribution): The lumbar reinforcement ratio (LBR) meets the requirement of ≤1.25. This indicator shows that the support stiffness transitions smoothly from the back to the waist, without any abrupt, rigid pressure on the waist, ensuring the even distribution of overall support pressure.

[0038] In addition, the indentation hardness index of each zone is obtained by taking the arithmetic mean of the IH values ​​at each test point in the headboard's shoulder and neck area, upper back area, and lumbar area. If the IH value exceeds 300N, it indicates that the support in that area is too stiff. In this case, the porosity, density, or flexible layering of the surface material should be optimized first, and the hardness of the underlying skeleton should be avoided to prevent a decrease in overall support performance. If the IH value is below 30N, it indicates that the area is too soft and sagging. In this case, the density and thickness of the upper foam layer should be increased, or a high-modulus material should be used. Furthermore, the IH parameter should be cross-checked with key indicators such as pressure distribution and lumbar lordosis retention rate in the same area to achieve comprehensive structural optimization that achieves "appropriate softness and hardness, and a snug fit."

[0039] Based on the collected force-displacement curve data, this embodiment constructs a quantitative evaluation model and defines the "wrap-around" design features of the soft bed screen by calculating two key characteristic parameters: equivalent surface hardness (SHR) and waist reinforcement ratio (LBR).

[0040] In one embodiment, step S103 includes: Based on the physiological parameters of the subjects, the theoretical surface area and effective contact area of ​​the human waist and back were calculated respectively, and the fit was evaluated by combining the theoretical surface area and effective contact area of ​​the human waist and back. This step combines the subject's physiological parameters with the acquired body pressure distribution data to establish a fit evaluation model, thereby quantifying the effect of the soft headboard on the human back in terms of coverage and support. Specifically, it includes: ① Theoretical surface area of ​​the human waist and back Calculation Based on the subjects' anthropometric data, a modified regression formula was used to calculate the theoretical body surface area of ​​a specific region (lower back) of the subjects. The calculation formula is as follows: ; In the formula, S 理论 This represents the theoretical body surface area of ​​the subject's lower back, expressed in square meters (m²). 2 H represents the subject's height in centimeters (cm); W represents the subject's weight in kilograms (kg). Coefficient The indices 0.725 and 0.425 are empirical constants for fitting based on an adult body size database.

[0041] ② Effective contact area S 接触 Extraction Based on the collected back pressure distribution cloud map (i.e., the back pressure distribution data), the effective contact area is extracted using image processing algorithms. An effective pressure threshold is set ( The actual effective contact area S between the soft bed screen and the subject's back is obtained by integrating or summing the areas of pixels in the pressure cloud map that are greater than the threshold. 接触 (Units converted to m)2 ).

[0042] ③ Calculation and evaluation of back fit (T) A back-fit index T is constructed to characterize the degree of conformity between the headboard and the curve of the human back. The calculation formula is as follows: ; ④ Judgment Criteria Based on ergonomic comfort standards, a fit evaluation grade is established. When the calculated fit degree T > 80%, the headboard is judged to have "preferred fit performance". At this time, the headboard can effectively fill the physiological gaps created by the lumbar lordosis and thoracic kyphosis, significantly increasing the support area, thereby reducing local peak pressure and achieving "wrap-like" health support.

[0043] Pressure features are extracted based on the back pressure distribution data, and body pressure distribution features are evaluated based on the pressure features. Based on pressure distribution cloud map data, the distribution of interfacial pressure is analyzed to assess the risk of localized pressure buildup. Specifically, features are first extracted, including peak pressure. - Maximum pressure value and average pressure in the pressure cloud map - Arithmetic mean of pressure within the effective contact area, pressure gradient - Calculate the ratio of the rate of pressure change per unit distance between all adjacent transverse sensing units in the entire back contact area, percentage of medium-high pressure zone - Percentage of contact area within the ≥4.27 kPa pressure threshold range in the entire back contact area. Then, based on the extracted features, a health threshold is set according to the judgment criteria. This threshold is set with reference to arteriolar capillary pressure and skin capillary closure pressure. Keeping the pressure below this value can effectively prevent the risk of local soft tissue ischemia and pressure sores caused by prolonged leaning, ensuring smooth blood circulation.

[0044] The lumbar lordosis retention rate is calculated based on the back spinal morphology data of the seated posture, and a spinal biomechanical evaluation is performed based on the lumbar lordosis retention rate.

[0045] Using data obtained from spinal morphology testing, the ability of a soft headboard to maintain the physiological curvature of the spine was assessed by calculating the lumbar lordosis retention rate (LLR). The LLR calculation model selected the chordal distance of the lumbar curve (i.e., the vertical distance from the apex of lumbar extension to the line connecting T12 and S1) as the morphological characteristic parameter, as shown in the following formula: ; In the formula, E represents the lumbar chord distance of the subject in a seated posture (using a soft headboard); E0 represents the lumbar chord distance of the subject in a neutral standing posture (standing naturally).

[0046] The effective scope of health support is defined as follows The judgment criteria are as follows: when When this occurs, it indicates that the lumbar lordosis is too small (straightened), which can easily lead to increased pressure on the posterior side of the intervertebral disc; when When this occurs, it indicates that the lumbar lordosis is too large (excessive lumbar support), which can easily lead to compression of the facet joints; When the LLR is within this range, it is determined that the soft headboard can keep the spine in a natural physiological state with low load.

[0047] This embodiment combines the acquired physical test data with the physiological parameters of the subjects to establish a multi-dimensional health evaluation model. This model includes three core dimensions: fit evaluation, body pressure distribution characteristic evaluation, and spinal biomechanics evaluation. By comprehensively considering these three dimensions, the impact of soft headboards on human health can be fully and accurately assessed.

[0048] In one embodiment, step S104 includes: A linear weighting method was used to calculate a comprehensive score based on the quantitative results of the indicators and the multi-dimensional evaluation results. The headboard is graded based on the comprehensive score.

[0049] The overall score of the product is calculated using a linear weighted method, and the calculation formula is as follows: ; In the formula, Q represents the overall performance score of the soft bed screen; P n This represents the objective physical performance score after normalization (derived from indicators such as SHR and LBR in step two); H n The score represents the normalized physiological health dimension score (derived from indicators such as fit T and spinal morphology LLR); the α and β distributions represent the weighting coefficients. For example, setting α=0.5 and β=0.5 means that physical support and human health are equally important.

[0050] Specifically, the step of classifying the headboard into grades based on the comprehensive score includes: Based on preset threshold constraints, it is determined whether the quantitative results of the indicators and the multi-dimensional evaluation results trigger the level restriction logic; wherein, the threshold constraints are set based on the physiological health dimension. When the level restriction logic is triggered, the bed headboard is classified according to the preset level classification strategy. If it is determined that the level restriction logic has not been triggered, the bed headboard is classified into levels based on the comprehensive score.

[0051] Physiological health dimensions are set as threshold constraints. Key health indicators (including lumbar lordosis retention rate (LLR), mean pressure P) are considered.mean If any item in the above criteria scores zero (i.e., does not meet the aforementioned health threshold conditions), the rating restriction logic is triggered. At this time, regardless of the overall score Q, the overall rating of the product is forcibly restricted to below "qualified". This mechanism ensures that "healthy soft bed screens" must prioritize meeting the basic medical needs of low spinal load and smooth blood circulation, avoiding pseudo-healthy designs that are "physically excellent but damage the human body".

[0052] In order to achieve a comprehensive quantitative evaluation of the performance of the soft headboard, this embodiment establishes a comprehensive evaluation model based on the weighted summation method and introduces a "health indicator veto" mechanism. Through this model and mechanism, the importance of health factors can be highlighted while taking into account both the physical support performance of the headboard and its impact on human health.

[0053] In one embodiment, step S105 includes: Determine whether the scoring and grading results have reached the preset result threshold; If the scoring and grading results do not reach the preset result threshold, the scoring and grading results are optimized and analyzed to obtain the corresponding optimization direction, and an optimization strategy is generated based on the optimization direction; wherein, the optimization direction includes optimizing the quantitative results of the indicators and optimizing the multi-dimensional evaluation results.

[0054] Specifically, the determination of whether the quantitative results and / or multi-dimensional evaluation results corresponding to the scoring and grading results need optimization, thereby generating an optimization strategy, includes: When the optimization direction is to optimize the quantitative results of the indicators, a first optimization strategy for regulating the material of the bed screen is generated. When the optimization direction is to optimize the multi-dimensional evaluation results, a second optimization strategy is generated to correct the functional dimensions and geometric shape of the headboard.

[0055] In this embodiment, when generating optimization strategies based on the scoring and grading results, it first determines whether the scoring and grading results have reached a preset threshold. If not, it needs to first determine whether the optimization direction is aimed at the quantitative results of the indicators or the multi-dimensional evaluation results. If the optimization direction is the former, a first optimization strategy for regulating the headboard material is generated; if it is the latter, a second optimization strategy for correcting the functional dimensions and geometric shape of the headboard is generated.

[0056] This embodiment constructs a closed-loop system of "evaluation-feedback-correction". Based on the obtained quantitative evaluation results, if the soft bed screen does not meet the preset "wrap-in" or "healthy" standards, the material combination, topology, and geometric dimensions of the product are reverse-corrected according to the following strategies until the preset performance indicators are met. Specifically, this may include the following optimization strategies: (1) Material hierarchy optimization strategy based on equivalent hardness (SHR) To address the tactile feedback of the soft bed screen during the initial contact, a "non-homogeneous gradient composite structure" was constructed.

[0057] ① Layered design principle: The soft bed screen is configured from the outside to the inside as a highly flexible surface layer, a gradient transition core layer, and a highly resilient bottom layer.

[0058] ② Optimization logic If the SHR in the waist area is greater than 4.0 N / mm (too hard to the touch), increase the thickness of the soft foam on the surface layer or increase its porosity, and introduce a viscoelastic damping material as a transition layer. If the waist reinforcement ratio (LBR) is greater than 1.25 (causing a prominent waist), the modulus gradient of the core material needs to be adjusted so that the increase in SHR in the waist area relative to the upper back area is controlled within the range of 0% to 10%, thus avoiding a rigid "waist-lifting" feeling.

[0059] (2) Transition smoothing strategy based on hardness gradient (HG) Eliminate the feeling of a foreign object in the structure by targeting the pressure change points in the back contact area.

[0060] Diagnostic indicators: Calculate the hardness gradient between adjacent test points along the longitudinal direction of the body. .

[0061] Corrective measure: If HG > 1.2 N / mm / dm is detected, it is determined that there is stress concentration in the area.

[0062] Option A (Structural Method): Add a 10mm-20mm thick low-density fiber cotton or memory foam transition layer to the surface of the filling layer in the corresponding area; Option B (Process Method): Implement gradient porosity treatment on the filling material in this area, or use CNC cutting technology to pre-fabricate shrinkage grooves inside the material to smooth stress transmission.

[0063] (3) Load-bearing capacity control strategy based on indentation hardness (IH) A two-way correction is made to the macroscopic support capability of soft bed screens under deep pressure.

[0064] Scenario A: If IH > 300N (judged as too stiff, easily leading to shoulder and back fatigue) Corrective measures: Prioritize reducing the compression modulus of the upper foam layer or thinning the base fabric; strictly prohibit directly reducing the stiffness of the bottom skeleton to avoid compromising overall stability.

[0065] Scenario B: If IH < 30N (indicated as too soft, which can easily lead to spinal instability) Corrective measures: Increase the density and indentation force deflection factor (IFD) of the core or bottom layer foam; or implant high-rigidity support inserts in the core stress area of ​​the waist.

[0066] Cross-validation: All hardness adjustments must simultaneously meet the following requirements. a and The constraints ensure that health performance is not sacrificed while improving the tactile experience.

[0067] (4) Precise correction based on functional dimensions and geometry To address issues such as abnormal body pressure distribution or spinal morphology distortion, adjust the product's geometric parameters.

[0068] ① Lumbar support height (L) h )Adjustment: If the lumbar lordosis retention rate (LLR) is abnormal, first check the position of the lumbar support high point. Adjust it to a distance of 140mm-180mm from the seat surface according to the mattress firmness (mattress sinkage 1cm-6.5cm) to accurately match the height of the L3-L4 vertebrae.

[0069] ② Anterior protrusion (L) prot Adjustment of thickness distribution: If the waist P peak If the deviation exceeds the standard, reduce the physical forward protrusion of the waist filling layer, or correct the curvature radius of the surface and increase the contact radius; If there is a gap in the upper back (fit T < 80%), increase the thickness of the upper back padding and correct the backrest angle.

[0070] In practical applications, this embodiment provides a specific application example of an ergonomically based, wrap-around, health-oriented soft headboard design and evaluation method. This application example uses a commercially available prototype soft headboard product as a sample, testing its physical support and physiological health dimensions, and comprehensively evaluating its "wrap-around" characteristics and health performance based on the aforementioned quantitative indicators.

[0071] I. Sample Structure and Testing Conditions S1. Sample Description This embodiment selects a one-piece soft headboard with left and right partitions and continuous internal filling material as the test sample. The overall dimensions of the soft headboard are: width 1950mm, height 1150mm. The waist protrusion is approximately 180mm high from bottom to top relative to the reference plane, with a protrusion distance of approximately 30mm. The tilt angle of the headboard frame is 99°.

[0072] The entire structure adopts an integrated composite filling structure, including the following parts: Fabric layer: Made of leather, providing a basic tactile experience and aesthetic texture for the soft headboard.

[0073] Composite filling layer surface: Made of non-adhesive cotton material, with indentation hardness characteristics of HB25% being 0.42±0.03N, HB40% being 0.89±0.05N, and HB65% being 2.39±0.03N, compression deformation characteristics being 5.67±0.28, and hysteresis loss rate being 0.35±0.03%. It possesses lightweight, breathable, primary cushioning, and flexible spreadability, effectively improving the smoothness of the initial touch.

[0074] Composite filling core layer: High-density slow rebound memory foam is used. The indentation hardness characteristic value of the material is 2.81±0.22N for HB25%, 3.32±0.25N for HB40%, and 5.09±0.26N for HB65%. The compression deformation characteristic is 1.73±0.05, and the hysteresis loss rate is 0.30±0.01%. The whole exhibits high conformity and pressure uniformity characteristics.

[0075] The bottom layer of the composite filling consists of a composite structure made of irregularly shaped flower-cut memory foam and duck feathers. The indentation hardness characteristic values ​​of the memory foam material are 1.39±0.03N for HB25%, 2.45±0.03N for HB40%, and 6.45±0.03N for HB65%, with a compression deformation characteristic of 4.48±0.32 and a hysteresis loss rate of 0.45±0.04%. It has good support and air circulation, while the duck feather part provides a soft, enveloping feel and body pressure diffusion.

[0076] Bottom support structure: The basic support element is a uniformly distributed high-elasticity wide webbing with a spacing of approximately 68mm. The average breaking strength of the wide webbing is 1711.98N and the average elongation at break is 322.89%. This is used to enhance the overall resilience and durability of the soft bed and avoid the risk of collapse during long-term use.

[0077] S2, Environmental Conditioning and Preloading The ambient temperature is controlled at 23±2℃, and the relative humidity is controlled at 55±5%. The soft bed screen sample was left to stand for 48 hours under the above conditions; Before testing the equivalent surface hardness SHR and indentation hardness index IH in the physical support dimension, preloading treatments are performed according to the corresponding requirements.

[0078] S3, Test Point Settings To ensure the representativeness and repeatability of the test results, this embodiment adopts a functional test area division method based on the typical human sitting posture, and arranges multiple test points on the surface of the test soft bed headboard. Using the plane where the mattress is placed on the test soft bed headboard in use as the reference zero point, measurements are taken vertically upwards. The contact surface of the test soft bed headboard is divided into the shoulder and neck area, upper back area, and lumbar area according to their functional positions, and the height of the test points is set as follows: Neck and shoulder area: approximately 610mm in height relative to the reference plane; Upper back area: approximately 390mm in height relative to the reference plane; Waist area: approximately 160mm above the reference plane.

[0079] Within each test area, four test points are evenly distributed from left to right, with the following specific numbering: Neck and shoulder area: A1, A2, A3, A4; Upper back area: B1, B2, B3, B4; Waist area: C1, C2, C3, C4.

[0080] The setup of the above test points is as follows: Figure 5 As shown.

[0081] S4, Nominal Thickness The nominal thickness H is a crucial fundamental parameter for the indentation hardness index (IH) test. It is defined as the free thickness measured after the test indenter lightly touches (e.g., with a 5N micro-load) against the reference plane without preload. For soft bed screens with curved or irregular shapes, the local normal thickness at each test point should be taken. The nominal thicknesses corresponding to each test point of the soft bed screen in this embodiment are shown in Table 1 below. Table 1 II. Test Results and Analysis of the Physical Support Dimension of the Soft Bed Headboard S1, Test Results and Analysis of Equivalent Surface Hardness According to the technical solution, the equivalent surface hardness test was carried out at each test point of the soft bed screen, and the arithmetic mean of the four test points in each functional test area (shoulder and neck area, upper back area, and waist area) was calculated. The results are shown in Table 2 below. Table 2 It is evident that the equivalent surface hardness of the tested soft headboard is low in all areas. This indicates that the tested soft headboard can provide necessary support while exhibiting more significant "buffering-fitting" performance, especially in areas that are more sensitive to contact pressure, such as the scapular region and thoracic spine. This performance not only helps to disperse back pressure and avoid the generation of high-pressure points, but also achieves a more natural support and wrapping effect, enhancing the comfort of use.

[0082] S2, the test results of waist reinforcement ratio and the determination of "wrap-around" characteristics Based on the data in Table 2, the calculated lumbar reinforcement ratio (LBR) of the tested soft bed screen is approximately 1.19. The calculation formula is as follows: ; Based on the criteria for determining a "wrap-around" soft bed in this embodiment: Condition A (Low Hardness Touch): The equivalent surface hardness of the waist area meets SHR < 4.0 N / mm. This indicator shows that the material surface has sufficient deformation capacity to conform to the curve of the human back and create a wrapping feel; Condition B (Flexible Transition Distribution): The lumbar reinforcement ratio (LBR) meets the requirement of ≤1.25. This indicator shows that the support stiffness transitions smoothly from the back to the waist, without any abrupt, rigid pressure on the waist, ensuring the even distribution of overall support pressure.

[0083] In this embodiment, the equivalent surface hardness (SHR) of the waist region of the soft bed headboard is approximately 3.24, and the waist reinforcement ratio (LBR) is approximately 1.19. Therefore, the soft bed sample meets the requirements of this embodiment for the low hardness and flexible transition distribution of the "wrap-around" soft bed.

[0084] Test results and analysis of S3 and hardness gradient Based on the data in Table 2, the hardness gradient of the test soft bed screen was calculated and is shown in Table 3 below. Table 3 As can be seen, the hardness gradient of all test points in the shoulder and neck area-upper back area and the upper back area-lower back area is less than 1.2 N / mm / dm. This indicates that the hardness change between different zones of the soft bed headboard is more natural and continuous, effectively avoiding the pressure or feeling of emptiness caused by sudden increase or decrease in local support, and improving the comfort when sitting and the stability during long-term use.

[0085] S4. Test results and analysis of indentation hardness index According to the technical requirements, the indentation hardness index (IH) test was conducted. The indentation hardness index of each test point and each test area of ​​the test soft bed screen in this embodiment is shown in Table 4 below. Table 4 It can be seen that the indentation hardness of each test point and each test area of ​​the soft bed screen is between 300N and 30N, which is within the range of moderate softness and hardness. The overall support performance is quite suitable, neither too hard nor too soft.

[0086] III. Test Results and Analysis of Physiological Health Dimensions of Soft Bed Headboards S1. Test results and analysis of fit In this embodiment, after calculating the data from 30 subjects, the final fit of the soft bed screen was 91.13±17.21%, which is greater than 80%, thus determining that the soft bed screen possesses "preferred fit performance". High fit means that it forms continuous support and wrapping at the contact interface, effectively filling the physiological gaps created by the lumbar lordosis and thoracic kyphosis of the human body, significantly increasing the support area, thereby reducing local peak pressure and achieving "wrap-type" health support.

[0087] S2, Test Results and Analysis of Body Pressure Distribution The final body pressure distribution test results obtained from the data of 30 subjects in this embodiment are as follows: Peak pressure: 5.50 ± 0.71 kPa; Average pressure: 1.47 ± 0.27 kPa; Pressure gradient: 0.35±0.07 kPa / cm; The proportion of medium-to-high pressure areas: 1.52±1.22%.

[0088] As can be seen, the tested soft bed screen exhibits excellent pressure-relieving characteristics and effects, characterized by "low peak and average pressure, smooth pressure gradient transition, and a small proportion of medium- and high-pressure contact area." This indicates that it is unlikely to create a "step-like" feeling or obvious local high-pressure points during use, thus helping to reduce the risk of soft tissue compression.

[0089] S3. Test Results and Analysis of Spinal Biomechanics A handheld 3D scanner was used to perform a full-body multi-view scan of the subjects while they were naturally leaning against the headboard of the test bed, obtaining high-density point cloud data. Based on the subjects' actual height, weight, and limb length parameters, a digital human musculoskeletal model was invoked, and a sitting digital human model matching the scan data was obtained through non-rigid point cloud registration and inverse kinematics solution. In this embodiment, after processing 30 subjects, the final lumbar lordosis retention ratio was 0.97±0.05%. According to the judgment criteria of the present invention, 0.72≤LLR≤1.39 is the healthy support range. In this embodiment, LLR=0.97∈[0.72,1.39], indicating that the headboard can keep the spine in a low-load natural physiological state during use, without obvious straightening or excessive lumbar support.

[0090] Compared with the prior art, the method provided in this embodiment has the following significant advantages: (1) This embodiment overcomes the technical challenge of quantifying the "wrap-around feeling" and constructs a digital representation system. Existing technologies mostly rely on subjective descriptions (such as "soft," "hard," and "comfortable") after a human trial sitting when evaluating the comfort of soft headboards, lacking objective standards. This embodiment innovatively constructs a dual index system of equivalent surface hardness (SHR) and lumbar reinforcement ratio (LBR). SHR is used to accurately define the initial "softness" of the contact interface; LBR is used to quantify the "stiffness gradient change" from the back to the waist. Thus, the abstract "wrap-around feeling" is transformed into measurable and reproducible physical parameters, providing a unified quantitative standard for the refined design of soft headboards.

[0091] (2) To address the common problems of traditional soft headboards, such as "soft and weak" causing spinal curvature or "hard and compressive" causing blood flow obstruction, this embodiment introduces the lumbar lordosis retention rate (LLR) and peak pressure ( As a mandatory threshold indicator, a "physiological health veto" evaluation model has been established. No matter how high the physical comfort score is, if the spinal shape or body pressure distribution does not meet the standard, the product is deemed unqualified. This mechanism effectively avoids the design risk of "pseudo-comfort" and ensures that while providing an ultimate wrapping experience, the product can maintain the spine in a low-load natural physiological curvature, truly achieving "spine-protecting comfort".

[0092] (3) Unlike the traditional experience-based development model of "prototyping-trial and error-prototyping again", this embodiment provides a data-based reverse optimization method. This method can directly locate structural abrupt change points based on hardness gradient (HG) anomalies, guiding the design of transition layers; it can also guide the selection of foam density and modulus based on indentation hardness (IH). This enables designers to carry out "targeted improvement", significantly shortening the R&D cycle of new products and reducing trial production costs.

[0093] (4) To address the challenge that the back is obscured when the human body leans against a soft headboard, making it impossible for traditional optical equipment to directly observe the spinal morphology, this embodiment proposes a non-contact measurement method of "visual surface scanning + digital musculoskeletal reconstruction". Without interfering with the subject's natural leaning posture, it achieves high-precision reverse calculation of the spinal geometry (thoracic kyphosis angle and lumbar lordosis angle), significantly improving the accuracy and authenticity of biomechanical data acquisition and providing solid data support for ergonomic evaluation.

[0094] (5) This embodiment is based on the functional size definition derived from the adult body shape database (e.g., lumbar support height). Set between 140-180mm, forward protrusion measurement. The thickness is set between 20 and 40 mm to ensure that the designed headboard can fit most people with a height range of 155cm to 195cm. This not only improves the product's market versatility but also provides a scientific basis for standardized production and quality control in the upholstered furniture industry.

[0095] Figure 6 This is a schematic block diagram of a bed headboard wrapping and health testing management device 600 provided in an embodiment of the present invention. The device 600 includes: The data acquisition unit 601 is used to collect physical test data and physiological parameters of the subject from the bed screen to be tested, thereby constructing a test dataset. The indicator quantization unit 602 is used to quantify the test dataset using preset test indicators to obtain the corresponding indicator quantization results. The data evaluation unit 603 is used to perform multi-dimensional evaluation based on the test dataset using a multi-dimensional health evaluation model, and obtain the corresponding multi-dimensional evaluation results. The scoring and grading unit 604 is used to score and grade the headboard based on the quantitative results of the indicators and the multi-dimensional evaluation results. The optimization management unit 605 is used to generate an optimization strategy for the bed screen based on the results of the scoring and grading.

[0096] In one embodiment, the data acquisition unit 601 includes: The physical acquisition unit is used to perform loading and unloading cyclic testing on different areas of the bed headboard at a preset rate using a loading-unloading cyclic testing method, and record the force-displacement curve of each area during the test, and then use the force-displacement curve as the physical test data; The pressure acquisition unit is used to collect back pressure distribution data of the subject against the headboard when the subject is in a natural sitting posture against the headboard using pressure sensors. The point cloud acquisition unit is used to acquire three-dimensional point cloud data of the subject in a leaning posture in a non-contact manner, and to construct a digital human musculoskeletal model of the subject. The data aggregation unit is used to perform spatial registration and posture fitting on the three-dimensional point cloud data and the digital human musculoskeletal model using the iterative nearest point algorithm to obtain the sitting back spinal morphology data, and to aggregate the back pressure distribution data and the sitting back spinal morphology data into the subject's physiological parameters.

[0097] In one embodiment, the index quantification unit 602 includes: The first index calculation unit is used to calculate the equivalent surface hardness index based on the physical test data. The second index calculation unit is used to calculate the waist reinforcement ratio index based on the arithmetic mean of the equivalent surface hardness index. The third index calculation unit is used to calculate the hardness gradient index of the bed screen based on preset test points and equivalent surface hardness index. The fourth index calculation unit is used to obtain the indentation hardness index of each preset test point based on the physical test data.

[0098] In one embodiment, the index quantification unit 602 further includes: The envelopment judgment unit is used to make an envelopment judgment on the headboard by combining the equivalent surface hardness index and the waist reinforcement ratio index. The support judgment unit is used to calculate the arithmetic average of the compressive hardness index corresponding to the test points on different partitions of the headboard, obtain the partition indentation hardness index, and use the partition indentation hardness index to make a support judgment on different partitions of the headboard.

[0099] In one embodiment, the data evaluation unit 603 includes: The first evaluation unit is used to calculate the theoretical body surface area and effective contact area of ​​the human waist and back based on the physiological parameters of the subject, and to evaluate the fit by combining the theoretical body surface area and effective contact area of ​​the human waist and back. The second evaluation unit is used to extract pressure features based on the back pressure distribution data and to evaluate the body pressure distribution features based on the pressure features. The third evaluation unit is used to calculate the lumbar lordosis retention rate based on the seated back spinal morphology data, and to perform a spinal biomechanical evaluation based on the lumbar lordosis retention rate.

[0100] In one embodiment, the scoring and grading unit 604 includes: The scoring calculation unit is used to calculate a comprehensive score based on the quantitative results of the indicators and the multi-dimensional evaluation results using a linear weighting method. A grading unit is used to grade the headboard based on the comprehensive score.

[0101] In one embodiment, the level division unit includes: A triggering judgment unit is used to determine whether the quantitative results of the indicators and the multi-dimensional evaluation results trigger the level restriction logic based on preset threshold constraints; wherein, the threshold constraints are set based on the physiological health dimension; The first determination unit is used to classify the bed screen according to a preset level classification strategy when the level restriction logic is triggered. The second determination unit is used to classify the bed screen into different levels based on the comprehensive score when it is determined that the level restriction logic has not been triggered.

[0102] In one embodiment, the optimization management unit 605 includes: The result judgment unit is used to determine whether the scoring and grading results have reached the preset result threshold. The optimization analysis unit is used to perform optimization analysis on the scoring and grading results if the results of the determination of the scoring and grading do not reach the preset result threshold, obtain the corresponding optimization direction, and generate an optimization strategy based on the optimization direction; wherein, the optimization direction includes optimization for the quantitative results of the indicators and optimization for the multi-dimensional evaluation results.

[0103] In one embodiment, the optimization analysis unit includes: The first generation unit is used to generate a first optimization strategy for regulating the material of the bed screen when the optimization direction is to optimize the index quantification result. The second generation unit is used to generate a second optimization strategy to correct the functional dimensions and geometric shape of the headboard when the optimization direction is to optimize the multi-dimensional evaluation results.

[0104] Since the embodiments of the apparatus and the embodiments of the method correspond to each other, please refer to the description of the embodiments of the method for the embodiments of the apparatus, which will not be repeated here.

[0105] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed, can perform the steps provided in the above embodiments. The storage medium may include various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0106] This invention also provides a computer device, which may include a memory and a processor. The memory stores a computer program, and when the processor calls the computer program in the memory, it can implement the steps provided in the above embodiments. Of course, the computer device may also include various network interfaces, power supplies, and other components.

[0107] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to in the method section. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of the claims of this application.

[0108] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method for managing the wrapping and health testing of a bed headboard, characterized in that, include: Physical test data and physiological parameters of the subjects were collected from the bed screen to be tested, thereby constructing a test dataset; The test dataset is quantified using preset test metrics to obtain the corresponding quantification results. Based on the test dataset, a multi-dimensional health assessment model is used to perform a multi-dimensional assessment, and the corresponding multi-dimensional assessment results are obtained. The bed headboard is scored and graded based on the quantitative results of the aforementioned indicators and the multi-dimensional evaluation results. An optimization strategy for the bed screen is generated based on the scoring and grading results.

2. The method for managing the wrapping and health testing of bed headboards according to claim 1, characterized in that, The physical test data and physiological parameters of the subject are collected from the bed screen to be tested, thereby constructing a test dataset, including: The loading-unloading loop test method is adopted. The loading and unloading loop test is performed on different areas of the bed headboard at a preset rate, and the force-displacement curve of each area is recorded during the test. Then, the force-displacement curve is used as the physical test data. When the subject is in a natural sitting posture with respect to the headboard, pressure sensors are used to collect data on the back pressure distribution of the subject with respect to the headboard. The three-dimensional point cloud data of the subject in a leaning posture was acquired in a non-contact manner, and a digital human musculoskeletal model was constructed for the subject. The iterative nearest point algorithm is used to perform spatial registration and posture fitting on the three-dimensional point cloud data and the digital human musculoskeletal model to obtain the sitting back spinal morphology data. The back pressure distribution data and the sitting back spinal morphology data are then summarized into the physiological parameters of the subject.

3. The method for managing the wrapping and health testing of bed headboards according to claim 1, characterized in that, The step of quantifying the test dataset using preset test metrics to obtain the corresponding metric quantification results includes: The equivalent surface hardness index is calculated based on the physical test data. The waist reinforcement ratio is calculated based on the arithmetic mean of the equivalent surface hardness index. The hardness gradient index of the bed screen is calculated based on preset test points and equivalent surface hardness index. Based on the physical test data, the indentation hardness index of each preset test point is obtained.

4. The method for managing the wrapping and health testing of bed headboards according to claim 3, characterized in that, The step of quantifying the test dataset using preset test metrics to obtain the corresponding metric quantification results further includes: The bed headboard's wrapping properties are determined by combining the equivalent surface hardness index and the waist reinforcement ratio index. The arithmetic average of the compressive hardness indices corresponding to the test points on different sections of the headboard is calculated to obtain the section indentation hardness index, and the support of different sections of the headboard is judged by the section indentation hardness index.

5. The method for managing the wrapping and health testing of bed headboards according to claim 2, characterized in that, Based on the test dataset, a multi-dimensional health assessment model is used to perform a multi-dimensional assessment, obtaining the corresponding multi-dimensional assessment results, including: Based on the physiological parameters of the subjects, the theoretical surface area and effective contact area of ​​the human waist and back were calculated respectively, and the fit was evaluated by combining the theoretical surface area and effective contact area of ​​the human waist and back. Pressure features are extracted based on the back pressure distribution data, and body pressure distribution features are evaluated based on the pressure features. The lumbar lordosis retention rate is calculated based on the back spinal morphology data of the seated posture, and a spinal biomechanical evaluation is performed based on the lumbar lordosis retention rate.

6. The method for managing the wrapping and health testing of bed headboards according to claim 1, characterized in that, The step of scoring and grading the headboard based on the quantitative results of the indicators and the multi-dimensional evaluation results includes: A linear weighting method was used to calculate a comprehensive score based on the quantitative results of the indicators and the multi-dimensional evaluation results. The headboard is graded based on the comprehensive score.

7. The method for managing the wrapping and health testing of bed headboards according to claim 6, characterized in that, The step of classifying the headboard into grades based on the comprehensive score includes: Based on preset threshold constraints, it is determined whether the quantitative results of the indicators and the multi-dimensional evaluation results trigger the level restriction logic; wherein, the threshold constraints are set based on the physiological health dimension. When the level restriction logic is triggered, the bed headboard is classified according to the preset level classification strategy. If it is determined that the level restriction logic has not been triggered, the bed headboard is classified into levels based on the comprehensive score.

8. The method for managing the wrapping and health testing of bed headboards according to claim 1, characterized in that, The optimization strategy for the bed headboard generated based on the scoring and grading results includes: Determine whether the scoring and grading results have reached the preset result threshold; If the scoring and grading results do not reach the preset result threshold, the scoring and grading results are optimized and analyzed to obtain the corresponding optimization direction, and an optimization strategy is generated based on the optimization direction; wherein, the optimization direction includes optimizing the quantitative results of the indicators and optimizing the multi-dimensional evaluation results.

9. The method for managing the wrapping and health testing of bed headboards according to claim 8, characterized in that, The determination of whether the quantitative results and / or multi-dimensional evaluation results corresponding to the scoring and grading results need optimization is used to generate an optimization strategy, including: When the optimization direction is to optimize the quantitative results of the indicators, a first optimization strategy for regulating the material of the bed screen is generated. When the optimization direction is to optimize the multi-dimensional evaluation results, a second optimization strategy is generated to correct the functional dimensions and geometric shape of the headboard.

10. A device for managing the wrapping and health testing of a bed headboard, characterized in that, include: The data acquisition unit is used to collect physical test data and physiological parameters of the subjects from the bed screen to be tested, thereby constructing a test dataset. The indicator quantization unit is used to quantify the test dataset using preset test indicators to obtain the corresponding indicator quantization results. The data evaluation unit is used to perform multi-dimensional evaluation based on the test dataset using a multi-dimensional health evaluation model, and obtain the corresponding multi-dimensional evaluation results. The scoring and grading unit is used to score and grade the headboard based on the quantitative results of the indicators and the multi-dimensional evaluation results. An optimization management unit is used to generate optimization strategies for the bed screen based on the results of scoring and grading.

11. A computer device, characterized in that, The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the bed headboard wrapping and health testing management method as described in any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the bed headboard wrapping and health testing management method as described in any one of claims 1 to 9.

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