CN113326619A - Sleep pillow design method and system based on ergonomic shape - Google Patents
Sleep pillow design method and system based on ergonomic shape Download PDFInfo
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- 230000007958 sleep Effects 0.000 title claims abstract description 80
- 238000013461 design Methods 0.000 title claims abstract description 28
- 238000000034 method Methods 0.000 title claims abstract description 20
- 230000000704 physical effect Effects 0.000 claims abstract description 24
- 210000003128 head Anatomy 0.000 claims description 38
- 239000000463 material Substances 0.000 claims description 26
- 239000011148 porous material Substances 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 7
- 210000004709 eyebrow Anatomy 0.000 claims description 7
- 238000005259 measurement Methods 0.000 claims description 7
- 239000011159 matrix material Substances 0.000 claims description 5
- 210000003582 temporal bone Anatomy 0.000 claims description 5
- 210000000103 occipital bone Anatomy 0.000 claims description 4
- 239000006261 foam material Substances 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims description 2
- 230000036314 physical performance Effects 0.000 claims description 2
- 230000002123 temporal effect Effects 0.000 claims description 2
- 238000005516 engineering process Methods 0.000 abstract description 2
- 238000004519 manufacturing process Methods 0.000 description 8
- 238000005452 bending Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 4
- 229920000742 Cotton Polymers 0.000 description 2
- 210000000988 bone and bone Anatomy 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005187 foaming Methods 0.000 description 2
- 208000008035 Back Pain Diseases 0.000 description 1
- 206010019233 Headaches Diseases 0.000 description 1
- 206010028391 Musculoskeletal Pain Diseases 0.000 description 1
- 206010028836 Neck pain Diseases 0.000 description 1
- 208000007613 Shoulder Pain Diseases 0.000 description 1
- 208000013738 Sleep Initiation and Maintenance disease Diseases 0.000 description 1
- 241001122767 Theaceae Species 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 231100000869 headache Toxicity 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 206010022437 insomnia Diseases 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
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- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47G—HOUSEHOLD OR TABLE EQUIPMENT
- A47G9/00—Bed-covers; Counterpanes; Travelling rugs; Sleeping rugs; Sleeping bags; Pillows
- A47G9/10—Pillows
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- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47G—HOUSEHOLD OR TABLE EQUIPMENT
- A47G9/00—Bed-covers; Counterpanes; Travelling rugs; Sleeping rugs; Sleeping bags; Pillows
- A47G9/10—Pillows
- A47G2009/1018—Foam pillows
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/20—Design reuse, reusability analysis or reusability optimisation
Abstract
The invention relates to a design technology of a sleep pillow, and discloses a sleep pillow design method and a sleep pillow design system based on ergonomic morphology, wherein a crowd sleep support database is constructed by measuring a human body; acquiring crowd key node information, and acquiring the crowd key node information according to a crowd sleep support database; classifying the crowd key nodes, namely classifying the acquired crowd key node information by a similarity algorithm; determining a head 11 region type surface data supporting condition by classifying the classified data; constructing a physical property database through a regression equation with physical parameters as variables; establishing a sleeping pillow distribution hole array; determining a distribution hole array according to the data support condition of the region type surface of the head and neck 11 and a physical property database; and determining the engineering file of the sleeping pillow through the sleeping pillow distribution hole array. The sleep pillow designed by the engineering file can be accurately matched with a human body through an ergonomic shape.
Description
Technical Field
The invention relates to a design technology of a sleep pillow, in particular to a sleep pillow design method and a sleep pillow design system based on an ergonomic shape.
Background
According to the investigation of the world health organization, the incidence rate of insomnia of Chinese people is as high as 38%. Sleep problems have become a serious public health problem today. The sleep pillow is used as indispensable bedding for sleep activities of people, helps people keep good neck and chest section curvature in sleeping posture through supporting the head and the neck, can relax neck muscles, and can effectively relieve neck pain, shoulder and back pain, headache and the like. Directly affecting the sleep quality of the user. However, in order to obtain the above good sleep aid, the design and manufacture of the parameters of the pillow should be matched with the specific ergonomic shape of the user.
For example, the patent names: a combined pillow, the patent application number is: CN201720190430.3, filed as follows: 2017-02-28, the patent application discloses a combined pillow which comprises a plurality of pillow bags filled in a pillow case to form the combined pillow; the pillow bag comprises a head bearing pillow bag, a neck bearing pillow bag and a side sleeping supporting pillow bag. The three pillow bags are combined according to human engineering, and each pillow bag has multiple pillow height models. Each pillow bag is filled with one of latex, memory cotton, tea, PE hose and down, and each pillow bag has 5 types filled with different materials.
The sleep pillow designed by the prior art is mostly a full code, the specific use object cannot be accurately matched, and the colleagues cannot directly generate the manufacturing file to guide production.
Disclosure of Invention
The invention provides a sleep pillow design method and system based on ergonomic shapes, aiming at the defects that most of sleep pillows designed in the prior art are all-in-one, specific use objects cannot be accurately matched, and colleagues cannot directly generate manufacturing files to guide production.
In order to solve the technical problem, the invention is solved by the following technical scheme:
a sleep pillow design method based on human engineering form comprises,
constructing a crowd sleep support database, and constructing a classified crowd sleep support database by measuring big data of a human body;
acquiring classified crowd key node data, and acquiring standard average data of crowd key nodes according to a crowd sleep support database;
classifying crowd key nodes, and matching the crowd key node information of a design target by a similarity algorithm;
determining neck 11 region type surface data supporting conditions, and determining head 11 region type surface data supporting conditions for the classified data;
determining a pillow material physical property database, and constructing the physical property database through a regression equation with physical parameters as variables;
establishing a sleeping pillow distribution hole array; determining a distribution hole array according to the data support condition of the head and neck 11 region type surface and a pillow material physical property database;
and (4) generating a sleep pillow engineering file, and determining the sleep pillow engineering file through the sleep pillow distribution hole array.
Preferably, a crowd sleep support database is constructed that includes,
constructing a human head and neck database through measurement of human-computer data materials;
classifying the ergonomic shape samples of the head and the neck by a clustering algorithm formula 1 to obtain a classified human head and neck database;
n represents the number of coordinate points; c represents the number of population fractions; v. ofkA cluster center representing a kth class; mu.sikRepresenting the membership degree of the ith sample to the kth class; | xi-vk||2Representing samples xi to the cluster center vkSquared euclidean distance of; m represents a blur index.
Preferably, the crowd key nodes comprise an occipital apex salient point in a lateral curve, a first cervical vertebra node bending point, a second back physiological bending salient point, an eyebrow apex end point, a temporal apex end point, a lower jaw and neck junction point and a peak salient point in the lateral curve.
Preferably, the crowd critical node categories include,
analyzing the key node data of the classified population to obtain standard values including,
calculating the median of each coordinate of the human-computer contour curve to obtain the average contour curve of various crowds; extracting convex points of an occipital bone apex, a first cervical vertebra bend and a second back physiological bend in a rear side curve; and 7 key point data of the eyebrow tip, the temporal bone tip, the junction point of the lower jaw and the neck and the peak salient point in the side curve are measured and recorded.
Preferably, the determining of the head and neck 11 region type surface data support condition includes determining a user population classification through calculation of cosine similarity; and calculating the cosine similarity of the coordinate data of each classified key node according to a formula 2; the crowd with the smallest calculated value is regarded as the conforming crowd; and extracting the left side of the classified key node which accords with the population median curve to obtain the bed-leaving height of each node in the two prone positions, thereby determining the data support condition of the region type surface of the head and neck 11.
In formula 2, Ai、BiRespectively representing the coordinates of the user node and the key node of the classified crowd of the database, and n represents the number of digits of the coordinate point.
Preferably, the determination of the sleeping pillow distribution hole array comprises the steps of determining a physical property database of the sleeping pillow through a fitting algorithm of a formula 3;
kiα d ' + β l ' + γ c ' + … formula 3
In the formula, kiThe comprehensive stiffness coefficient of the material i under a pore array with the pore diameter d, the pore distance l and the height c is shown, and alpha, beta and gamma are coefficients.
The preferable sleeping pillow is made of foam materials, and comprises a 2-layer structure which is distributed up and down; the comprehensive stiffness coefficient of the upper layer structure is smaller than that of the lower layer structure, and the formula is satisfied
kGeneral assembly＝kOn the upper part+kLower part
kOn the upper part≤kLower part
Wherein k isOn the upper partStiffness coefficient of upper layer structure, kLower partStiffness coefficient of underlying structure, kGeneral assemblyCoefficient of total stiffness。
Preferably, the sum of the thicknesses of the 2-layer structures of the sleep pillow is 80-150 mm.
A system for designing a sleep pillow based on ergonomic configuration comprises
Constructing a crowd sleep support database module, and constructing a classified crowd sleep support database module by measuring big data of a human body;
the system comprises a crowd classification key node data acquisition module, a crowd sleep support database module, a standard average data acquisition module and a crowd classification key node data acquisition module, wherein the crowd classification key node data acquisition module acquires standard average data of crowd key nodes according to the crowd sleep support database module;
the crowd key node classification module is used for matching the crowd key node information of the design target by a similarity algorithm;
a module for determining neck 11 region surface data support conditions, which determines head 11 region surface data support conditions for the classified data;
determining a pillow material physical property database module, and constructing a physical property database by using a regression equation with physical parameters as variables;
a sleeping pillow distribution hole array establishing module; determining a distribution hole array according to the data support condition of the head and neck 11 region type surface and a pillow material physical property database;
and the sleep pillow engineering file generating module is used for determining the engineering file of the sleep pillow through the sleep pillow distribution hole array.
Due to the adoption of the technical scheme, the invention has the remarkable technical effects that:
the method integrates coordinate data information of key nodes related to sleep of user groups, and builds a large group sleep support requirement range database through clustering; determining the head 11 region type surface supporting condition of the secondary individual by inputting relevant individual information and matching with the database; constructing a foaming material physical property database; the pillow-shaped structure design aiming at specific people and specific materials and the generation of production files are completed by using 11-region surface supporting conditions and material physical property data as variables and through a double-layer material supporting and non-equidistant hole array algorithm. The sleep pillow designed by the engineering file can be accurately matched with a human body through an ergonomic shape.
Drawings
FIG. 1 is a flow chart of the present invention.
Fig. 2 is a diagram of a pressure distribution matrix of the present invention.
FIG. 3 is a sample acquisition diagram of the present invention.
Fig. 4 is a compression deformation diagram of the sleep pillow of the present invention.
Fig. 5 is a graph of a fit of the present invention.
FIG. 6 is a regression plot of the present invention.
FIG. 7 is a model generation diagram of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Example 1
A sleep pillow design method based on human engineering form comprises,
constructing a crowd sleep support database, and constructing a classified crowd sleep support database by measuring big data of a human body;
acquiring classified crowd key node data, and acquiring standard average data of crowd key nodes according to a crowd sleep support database;
classifying crowd key nodes, and matching the crowd key node information of a design target by a similarity algorithm;
determining neck 11 region type surface data supporting conditions, and determining head 11 region type surface data supporting conditions for the classified data;
determining a pillow material physical property database, and constructing the physical property database through a regression equation with physical parameters as variables;
establishing a sleeping pillow distribution hole array; determining a distribution hole array according to the data support condition of the head and neck 11 region type surface and a pillow material physical property database;
and (4) generating a sleep pillow engineering file, and determining the sleep pillow engineering file through the sleep pillow distribution hole array.
Constructing a crowd sleep support database which includes,
constructing a human head and neck database through measurement of human-computer data materials;
classifying the ergonomic shape samples of the head and the neck by a clustering algorithm formula 1 to obtain a classified human head and neck database;
n represents the number of coordinate points; c represents the number of population fractions; v. ofkA cluster center representing a kth class; mu.sikRepresenting the membership degree of the ith sample to the kth class; | xi-vk||2Representing samples xi to the cluster center vkSquared euclidean distance of; m represents a blur index.
The crowd key nodes comprise an occipital bone tip salient point in a side curve, a first cervical vertebra node bending point, a second back physiological bending salient point, an eyebrow bone tip endpoint, a temporal bone tip endpoint, a lower jaw and neck junction point and a peak salient point in a side curve.
Analyzing the key node data of the classified population to obtain standard values including,
calculating the median of each coordinate of the human-computer contour curve to obtain the average contour curve of various crowds; extracting convex points of an occipital bone apex, a first cervical vertebra bend and a second back physiological bend in a rear side curve; and 7 key point data of the eyebrow tip, the temporal bone tip, the junction point of the lower jaw and the neck and the peak salient point in the side curve are measured and recorded.
Determining head and neck 11 region type surface data support conditions, wherein the conditions comprise that user crowd classification is determined through cosine similarity calculation; and calculating the cosine similarity of the coordinate data of each classified key node according to a formula 2; the crowd with the smallest calculated value is regarded as the conforming crowd; and extracting the left side of the classified key node which accords with the population median curve to obtain the bed-leaving height of each node in the two prone positions, thereby determining the data support condition of the region type surface of the head and neck 11.
In formula 2, Ai、BiRespectively representing the coordinates of the user node and the key node of the classified crowd of the database, and n represents the number of digits of the coordinate point.
The method comprises the following steps of determining a physical performance database of the sleeping pillow through a fitting algorithm of a formula 3;
kiα d ' + β l ' + γ c ' + … formula 3
In the formula, kiThe comprehensive stiffness coefficient of the material i under a pore array with the pore diameter d, the pore distance l and the height c is shown, and alpha, beta and gamma are coefficients.
The sleeping pillow is made of foam material, and comprises a 2-layer structure which is distributed up and down; the comprehensive stiffness coefficient of the upper layer structure is smaller than that of the lower layer structure, and the formula is satisfied
kGeneral assembly＝kOn the upper part+kLower part
kOn the upper part≤kLower part
Wherein k isOn the upper partStiffness coefficient of upper layer structure, kLower partStiffness coefficient of underlying structure, kGeneral assemblyThe overall stiffness coefficient.
The sum of the thicknesses of the 2-layer structures of the sleep pillow is 80-150 mm.
Example 2
Based on the method of embodiment 1, the sleep pillow design system based on ergonomic morphology is realized, which comprises a crowd sleep support database module, a classified crowd sleep support database module is constructed by measuring big data of human body;
the system comprises a crowd classification key node data acquisition module, a crowd sleep support database module, a standard average data acquisition module and a crowd classification key node data acquisition module, wherein the crowd classification key node data acquisition module acquires standard average data of crowd key nodes according to the crowd sleep support database module;
the crowd key node classification module is used for matching the crowd key node information of the design target by a similarity algorithm;
a module for determining neck 11 region surface data support conditions, which determines head 11 region surface data support conditions for the classified data;
determining a pillow material physical property database module, and constructing a physical property database by using a regression equation with physical parameters as variables;
a sleeping pillow distribution hole array establishing module; determining a distribution hole array according to the data support condition of the head and neck 11 region type surface and a pillow material physical property database;
and the sleep pillow engineering file generating module is used for determining the engineering file of the sleep pillow through the sleep pillow distribution hole array.
Example 3
On the basis of the above embodiment, a standard ideal head and neck pressure distribution matrix, which is a pressure distribution matrix shown in fig. 2 and is a support target to be achieved by the pillow, is set, and the construction method is to perform mean calculation on the pressure distribution matrix 10% of the top of the subjective evaluation ranking.
A human head and neck database is constructed based on a large amount of human-computer data materials, the human head and neck database constructs a database through measurement, and the specific method comprises the following steps: the measured person keeps a normal body state during measurement, and the measurement clothes are based on tight-fitting clothes or a small amount of necessary underwear.
During measurement, the instrument is naturally upright, two arms naturally droop, two eyes horizontally and directly look at the mark with the front parallel to the sight line, and the head, the neck and the shoulders are relaxed;
the measurer measures the weight, the height, the neck circumference, the shoulder width and the contour curve of the head side and back of the measured person in sequence by using the scale, the height measuring scale, the circumference measuring tape, the Martin measuring scale and the contour shape taking device to obtain measuring data required by an experiment, measured index data is recorded into an EXCEL form for sorting, and related images are stored in a JPG format.
And recording the coordinates of each measured point in a computer by using the head and neck front and side profile curves of various crowds through data recording and restoring. The frontal curve is from the zygomatic bone to the shoulder, and the posterior lateral line is convex from the occipital apex to the back. The database contains each sample as a set of coordinate data for each point in the curve, as shown in figure 3.
The database is provided with an algorithm, and the human-computer size samples of the head and the neck can be clustered through different judgment conditions. In the process of specific design and production, data can be classified as necessary according to the complexity, cost input and crowd requirements of products.
Calculating the median of each coordinate of each classified human-computer contour curve to obtain the average contour curve of each group, and extracting the convex points of occipital apex, the first segment of cervical vertebra bending and the second physiological bending convex points of the back in the rear side curve; and 7 key node coordinates of the eyebrow tip, the temporal bone tip, the lower jaw and neck junction point and the peak salient point in the side curve are taken as the basis for designing the occipital surface.
And measuring and inputting the data of the 7 key points of the target user, and calculating the cosine similarity of the coordinate data of the key points of each classification.
The more the calculation result value tends to 1, the more the two are approximated. And recording the minimum value similarityB of the calculation result, and then, having the user A belonging to the group B.
And the median node position of the crowd to which the target user belongs. And determining which type of crowd the user belongs to through cosine similarity calculation. The height of each node from the bed in two prone positions is obtained by extracting the left side of the key node of the median curve of the people group, and the heights can be regarded as supporting conditions and are also the states of the pillow after the human body sleeps on the pillow.
Taking memory cotton with 110mm < 40 >, density of 60D and staggered pitch of 30mm as an example, the fitting result is as follows:
d＝3.613k-0.769(6≤d≤21)
because the pillow needs to provide support and wrapping feeling at the same time, the designed sleeping pillow adopts a double-layer structure, the upper layer and the lower layer adopt foaming materials with different hardness, the two materials need to simultaneously meet the requirement that the comprehensive stiffness coefficient of the upper layer material is smaller than that of the lower layer, and the sum of the thicknesses of the double-layer materials is between 90 and 110 before pressure is not applied. With 11-region surface support conditions and material physical property data as variables, specific parameters and hole array arrangement matrixes of the pillow-shaped upper and lower layer designs for specific people and specific materials are determined through fitting, and engineering files for production are generated, as shown in fig. 7.
Claims (9)
1. A sleep pillow design method based on human engineering form comprises,
constructing a crowd sleep support database, and measuring a human body to construct the crowd sleep support database;
acquiring crowd key node information, and acquiring the crowd key node information according to a crowd sleep support database;
classifying the crowd key nodes, namely classifying the acquired crowd key node information by a similarity algorithm;
determining neck 11 region type surface data supporting conditions, and determining head 11 region type surface data supporting conditions for the classified data;
determining a physical property database, and constructing the physical property database through a regression equation with physical parameters as variables;
establishing a sleeping pillow distribution hole array; determining a distribution hole array according to the data support condition of the region type surface of the head and neck 11 and a physical property database;
and (4) generating a sleep pillow engineering file, and determining the sleep pillow engineering file through the sleep pillow distribution hole array.
2. The ergonomic morphology based sleep pillow design method of claim 1 wherein constructing a crowd sleep support database comprises,
constructing a human head and neck database through measurement of human-computer data materials;
classifying the ergonomic morphology samples of the head and the neck by a clustering algorithm formula 1 to obtain a classified human head and neck database;
in the formula, n represents the number of digits of a coordinate point; c represents the number of population fractions; v. ofkA cluster center representing a kth class; mu.sikRepresenting the membership degree of the ith sample to the kth class; II xi-vk‖2Representing samples xi to the cluster center vkSquared euclidean distance of; m represents a blur index.
3. The ergonomic morphology based sleep pillow design method of claim 1 wherein the crowd critical nodes comprise an occipital apex convex point in a lateral curve, a first cervical vertebra flexion point, a second back physiological flexion convex point, an eyebrow apex end point, a temporal apex end point, a mandibular-cervical junction, and a peak convex point in a lateral curve.
4. The ergonomic morphology based sleep pillow design method of claim 3 wherein the crowd key node classification comprises,
calculating the median of each coordinate of the human-computer contour curve to obtain the average contour curve of various crowds; extracting convex points of an occipital bone apex, a first cervical vertebra bend and a second back physiological bend in a rear side curve; measuring and recording 7 key point data of the eyebrow tip, the temporal bone tip, the junction point of the lower jaw and the neck and the peak salient point in the side curve, and calculating cosine similarity of the coordinate data of each classified key node according to a formula 2;
in formula 2, Ai、BiRespectively representing the coordinates of the user node and the key node of the classified crowd of the database, and n represents the number of digits of the coordinate point.
5. The ergonomic morphology based sleep pillow design method of claim 1 wherein determining head and neck 11 zone profile data support conditions comprises determining user population classifications by cosine similarity calculations; and extracting the left side of the key node of the median curve of the classified similar population to obtain the bed-leaving height of each node in two prone positions, thereby determining the data support condition of the region type surface of the head and neck 11.
6. The ergonomic morphology based sleep pillow design method of claim 1 wherein the sleep pillow distribution aperture matrix establishment comprises determining a physical property database of the sleep pillow by a formula 3 fitting algorithm;
kiα d ' + β l ' + γ c ' + … formula 3
In formula 3, kiThe comprehensive stiffness coefficient of the material i under a pore array with the pore diameter d, the pore distance l and the height c is shown, and alpha, beta and gamma are coefficients.
7. The ergonomic form-based sleep pillow design method as recited in claim 1, wherein the sleep pillow is made of foam material, and comprises a 2-layer structure distributed up and down; and the integrated stiffness coefficient of the upper layer structure is smaller than that of the lower layer structure.
8. The method as claimed in claim 7, wherein the sum of the thicknesses of the 2-layer structure of the pillow is 80-120 mm.
9. The sleep pillow design system based on the human-machine engineering form is characterized by comprising
The crowd sleep support database construction module is used for constructing a crowd sleep support database by measuring a human body;
the crowd key node information acquisition module is used for acquiring the information of the crowd key nodes according to the crowd sleep support database;
the crowd key node classifying module is used for classifying the acquired crowd key node information by a similarity algorithm;
the head and neck 11 region type surface data support condition determining module determines the head 11 region type surface data support condition through the data classified by the human group key node classifying module;
the physical property database determining module is used for constructing a physical property database through a regression equation with physical parameters as variables;
a sleeping pillow distribution hole array establishing module; the sleeping pillow distribution hole array establishing module determines a distribution hole array through a head and neck 11 region type surface data support condition and a physical performance database;
and the sleep pillow engineering file generating module is used for determining the engineering file of the sleep pillow through the sleep pillow distribution hole array.
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