CN107095378A - A kind of intelligent bust assay method - Google Patents
A kind of intelligent bust assay method Download PDFInfo
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- CN107095378A CN107095378A CN201710310905.2A CN201710310905A CN107095378A CN 107095378 A CN107095378 A CN 107095378A CN 201710310905 A CN201710310905 A CN 201710310905A CN 107095378 A CN107095378 A CN 107095378A
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- bust
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- A—HUMAN NECESSITIES
- A41—WEARING APPAREL
- A41H—APPLIANCES OR METHODS FOR MAKING CLOTHES, e.g. FOR DRESS-MAKING OR FOR TAILORING, NOT OTHERWISE PROVIDED FOR
- A41H1/00—Measuring aids or methods
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
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- Life Sciences & Earth Sciences (AREA)
- Textile Engineering (AREA)
- Artificial Intelligence (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
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Abstract
The invention discloses a kind of intelligent bust assay method, comprise the following steps：A, collection mass data sample；B, training bust converter；C, height, body weight, waistline to people are measured；D, the height that measurement is obtained, body weight, waistline data input obtain bust value to bust converter.The present invention is to use machine learning algorithm and big data parser, accurately generate clothing manufacturing data being difficult to measure and obtain in the case of somatic data arrives, calculate accurate somatic data with the height, body weight, waistline data for easily measuring exact value, and and then calculate clothing manufacturing data.The method solves individual and measures the inaccurate problem of somatic data, makes it possible that one measures, remotely measured, and greatly reduce the manufacturing cost of manufacturer and the stand-by period of client simultaneously.A variety of intelligent uses can be integrated into from now on, and economic value is created in fields such as custom made clothings.
Description
Technical field
The present invention relates to, with the automatic generation method of human body relevant portion and realizing field, proposed simultaneously in clothing manufacturing data
Realize the big data generation method of the clothing manufacturing data generation based on mass data.
Background technology
Traditional clothing manufacturing data generation is artificial using tape measure progress direct measurement, and this method is often due to measurement
The reason such as the human factor of surveyor such as position is inaccurate, the elasticity diversity factor of tape measure is larger, artificial reading is inaccurate, causes to include
Measured value including bust is inaccurate, while largely also increasing the cost of a production and operation using manual measurement, results in the need for big
The surveyor of amount intervenes the collection of customer data, regardless of whether be to visit to measure or be the place measurement that client specifies to producer,
The time cost of client can be increased and cause the delay of delivery, therefore, traditional measuring method can cause inaccurate measurement
Value, higher cost of labor and longer stand-by period, these factors are acted on manufactures field to garment production at present, can cause
Products ＆ services can not be combined current Personalized production and consumption demand.
The content of the invention
Prior art can not meet demand, to make up the deficiencies in the prior art, the present invention is intended to provide a kind of new ready-made clothes system
Make data creation method.
To achieve the above object, the present invention provides following technical scheme：Intelligent bust assay method；
A, collection mass data sample；
B, training bust converter；
C, height, body weight, waistline to people are measured；
D, the height that measurement is obtained, body weight, waistline data input obtain bust value to bust converter.
It is preferred that, in the step A, collect mass data sample, it is desirable to which sample covers various types of crowds, including he
Height H, body weight W, waistline L, bust B exact value.
It is preferred that, in the step B, according to human body classification of type standard, human body is divided into five types；It is then based on back
Return analysis method, regression analysis is to determine a kind of statistical analysis of complementary quantitative relationship between two or more variable
Method, with quite varied, regression analysis is divided into simple regression and multiple regression analysis according to the number for the variable being related to,
In linear regression, according to dependent variable number, simple regression analysis and multiple regression analysis can be divided into, according to independent variable and because become
Relationship type between amount, can be divided into linear regression analysis and nonlinear regression analysis, if in regression analysis, only including one
Individual independent variable and a dependent variable, and the relation of the two can use straight line approximate representation, this regression analysis is referred to as unitary line
Property regression analysis, if regression analysis includes the presence of linear correlation between two or more independents variable, and independent variable,
Then it is referred to as multiple linear regression analysis, the data in step A is trained, using H, W, L as input, B builds model for output
M, and input bust converter.
It is preferred that, in the step C, in actual applications, user measures and provides H, W, L exact value.
It is preferred that, in the step D, three point datas of H, W, L in step C are input to bust converter, user is obtained
Bust data B exact value.
Compared with prior art, the present invention is to use machine learning algorithm and big data parser, is being difficult to measure
Accurately generate clothing manufacturing data to obtain in the case of somatic data arrives, with the height, body weight, waistline data meter for easily measuring exact value
Calculate accurate somatic data, and and then calculate clothing manufacturing data.The method solves personal measurement somatic data not
Accurate problem, make it possible single measurement, long-range measurement, and greatly reduces manufacturing cost and the client of manufacturer simultaneously
Stand-by period.A variety of intelligent uses can be integrated into from now on, and economic value is created in fields such as custom made clothings.
Brief description of the drawings
Fig. 1 is data product process figure of the invention.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than whole embodiments.It is based on
Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under the premise of creative work is not made
Embodiment, belongs to the scope of protection of the invention.
Referring to Fig. 1, the present invention provides a kind of technical scheme：A kind of intelligent bust assay method, comprises the following steps；
A, collection mass data sample；
B, training bust converter；
C, height, body weight, waistline to people are measured；
D, the height that measurement is obtained, body weight, waistline data input obtain bust value to bust converter.
In step A, collect mass data sample, it is desirable to which sample covers various types of crowds, including they height H,
Body weight W, waistline L, bust B exact value.
In step B, according to human body classification of type standard, human body is divided into five types；It is then based on regression analysis,
Data in step A are trained, using H, W, L as input, B builds model M for output, and inputs bust converter.
In step C, in actual applications, user measures and provides H, W, L exact value.
In step D, three point datas of H, W, L in step C are input to bust converter, obtain user's bust data B's
Exact value.
Although an embodiment of the present invention has been shown and described, for the ordinary skill in the art, can be with
A variety of changes, modification can be carried out to these embodiments, replace without departing from the principles and spirit of the present invention by understanding
And modification, the scope of the present invention is defined by the appended.
Claims (5)
1. a kind of intelligent bust assay method, comprises the following steps：
A, collection mass data sample；
B, training bust converter；
C, height, body weight, waistline to people are measured；
D, the height that measurement is obtained, body weight, waistline data input obtain bust value to bust converter.
2. a kind of intelligent bust assay method according to claim 1, it is characterised in that：In the step A, collect a large amount of
Data sample, it is desirable to which sample covers various types of crowds, includes their height H, body weight W, waistline L, bust B accurate number
Value.
3. a kind of intelligent bust assay method according to claim 1, it is characterised in that：In the step B, according to human body
Human body, is divided into five types by classification of type standard；Regression analysis is then based on, the data in step A are trained,
Using H, W, L as input, B builds model M for output, and inputs bust converter.
4. a kind of intelligent bust assay method according to claim 1, it is characterised in that：In the step C, actually should
In, user measures and provides H, W, L exact value.
5. a kind of intelligent bust assay method according to claim 1, it is characterised in that：In the step D, step C
Middle H, W, L three point datas are input to bust converter, obtain user's bust data B exact value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710310905.2A CN107095378A (en) | 2017-05-05 | 2017-05-05 | A kind of intelligent bust assay method |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710310905.2A CN107095378A (en) | 2017-05-05 | 2017-05-05 | A kind of intelligent bust assay method |
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| Publication Number | Publication Date |
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| CN107095378A true CN107095378A (en) | 2017-08-29 |
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| Application Number | Title | Priority Date | Filing Date |
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| CN201710310905.2A Pending CN107095378A (en) | 2017-05-05 | 2017-05-05 | A kind of intelligent bust assay method |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110135078A (en) * | 2019-05-17 | 2019-08-16 | 上海凌笛数码科技有限公司 | A kind of human parameters automatic generation method based on machine learning |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1843245A (en) * | 2005-04-07 | 2006-10-11 | 郑州轻工业学院 | Garment somatic data intelligentized design method |
| CN102125322A (en) * | 2011-03-30 | 2011-07-20 | 德州学院 | Optimized manual measurement method based on regression equation |
| CN105069239A (en) * | 2015-08-17 | 2015-11-18 | 常州纺织服装职业技术学院 | Individualized clothing template implementation method and system |
| CN106202724A (en) * | 2016-07-08 | 2016-12-07 | 温州法派服饰有限公司 | A kind of clothing manufacturing data creation method |
-
2017
- 2017-05-05 CN CN201710310905.2A patent/CN107095378A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1843245A (en) * | 2005-04-07 | 2006-10-11 | 郑州轻工业学院 | Garment somatic data intelligentized design method |
| CN102125322A (en) * | 2011-03-30 | 2011-07-20 | 德州学院 | Optimized manual measurement method based on regression equation |
| CN105069239A (en) * | 2015-08-17 | 2015-11-18 | 常州纺织服装职业技术学院 | Individualized clothing template implementation method and system |
| CN106202724A (en) * | 2016-07-08 | 2016-12-07 | 温州法派服饰有限公司 | A kind of clothing manufacturing data creation method |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110135078A (en) * | 2019-05-17 | 2019-08-16 | 上海凌笛数码科技有限公司 | A kind of human parameters automatic generation method based on machine learning |
| CN110135078B (en) * | 2019-05-17 | 2023-03-14 | 浙江凌迪数字科技有限公司 | Human body parameter automatic generation method based on machine learning |
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| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170829 |
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| RJ01 | Rejection of invention patent application after publication |