CN106097135A - A kind of key factor extracting method towards spinning quality fluctuation - Google Patents
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Abstract
The invention discloses a kind of key factor extracting method towards spinning quality fluctuation, it is characterized in that, comprise the steps: S1, by multi-Agent technology, constructing production process uncertainty forecast model based on multi-Agent, described uncertain forecast model includes system administration Agent, Object Management group Agent, performs Agent, man machine interface Agent, data-interface Agent, source data Agent, initialization Agent, target data Agent, inquiry Agent, statistics Agent and analyze Agent；S2, complete to accept the design of function and influence function；S3, complete the standardization of data；S4, carry out the calculating of correlation coefficient: S5, carry out relativity evaluation based on DEA；S6, the extraction of key factor.The present invention solves the fluctuation in spinning process and extracts, and improves product efficiency, is fallen below by uncontrollable factor minimum.
Description
Technical field
The present invention relates to spinning quality fluctuation monitoring field, be specifically related to a kind of key factor towards spinning quality fluctuation
Extracting method.
Background technology
Under normal circumstances, the uncertain factor affecting process of textile production has a lot, such as board itself, the most different
Often, production schedule change, hot job arrival, environmental effect and other situations etc., manage according to man-machine-environment system engineering
Opinion, it can be divided into board factor, anthropic factor, system factor, plan target factor, environmental factors and other factors etc., and
And each big class can be divided into again multiple group also dependent on concrete condition.As board factor can be divided into mechanical breakdown, electrically event
Barrier and control system fault etc.；Anthropic factor can be divided into maloperation fault, personnel amendment exception, consummate degree abnormal.So,
In order to make uncertain factor obtain Accurate Prediction in advance, can preferably meet the needs of production management, abnormal conditions are carried out
Feed back timely and process, then needing the prediction to production process uncertain factor typically to refine to each abnormal mishap.
In terms of the production management of Trade of China Textile Enterprises, enterprise pursues the development of " with using IT to propel industrialization " simply
Thinking, it is intended to utilize progressive enterprise, the fast lifting of Workshop Production Management level of promoting of information technology, and by researching and developing energetically
Or the monitoring system that cooperative development is relevant, sharing of the integrated and data of promotion system, it is achieved enterprise's comprehensive IT application, net
Networkization manages.But result is the most unsatisfactory, cause business economic exponential increase point too low.The crux of its problem is that
To weaving process causes the basic reason of yield and quality error in data, scientifically do not analyze from root and judge, only
It is " artificially " to conclude simply, never payes attention to how ensureing the fundamental research of Various types of data correctness in weaving process,
The most not relevant theory is as instructing foundation.
Summary of the invention
For solving the problems referred to above, the invention provides a kind of key factor extracting method towards spinning quality fluctuation.
For achieving the above object, the technical scheme that the present invention takes is:
A kind of key factor extracting method towards spinning quality fluctuation, comprises the steps:
S1, by multi-Agent technology, construct production process uncertainty forecast model based on multi-Agent, described not
Deterministic forecasting model includes that system administration Agent, Object Management group Agent, execution Agent, man machine interface Agent, data connect
Mouth Agent, source data Agent, initialization Agent, target data Agent, inquiry Agent, statistics Agent and analysis Agent；
S2, complete to accept the design of function and influence function；
S3, completed the standardization of data by below equation:
Wherein,
XijFor the initial data that j index is over the years；ZijFor the data after standardization；mijThe average of period is being chosen for j index
Value；sijStandard deviation for index；
S4, carried out the calculating of correlation coefficient by below equation:
Wherein: W (i/j) is i-th factor dependency to jth factor；XiIt it is the actual value of the i-th combined factors level；XiFor
The i-th combined factors level relevant to jth factor actual value X；K be 1/S, S be the variance of i factor；
S5, carry out relativity evaluation based on DEA；
The fractional programming form of DEA model is:
J=1,2 ..., n, U >=0, V >=0；
This planning carries out the form after Charnes-Cooper linear transformation and dualistic transformation is:
Min h
λj>=0, S->=0, S+>=0, δ=0 or 1
When δ=0, planning type is C2R model, when δ=1, planning type is C2GS2Model；
S6, the extraction of key factor:
S61, now to define all of uncertain factor be x1, x2... xn, and by two pairwise correlations between each factor
Sexual relationship is defined as xixj, and according to the calculating of dependency each other and evaluation procedure, it is divided into the most relevant, strong correlation, relatively phase
Close, be correlated with and unrelated；
S62, according to above-mentioned xixjBetween structural relation, obtaining fuzzy relation matrix corresponding to uncertain factor is:
With uncertain factor as row in matrix M, with abnormal phenomena for row；Wherein mij(i-1,2 ..., m, j-1,2 ...,
N) it is that expert is rule of thumb given, represents the weight shared by jth kind factor in the uncertain factor of i-th kind of abnormal phenomena,
Therefore have:J=1,2 ..., m；
As such, it is possible to acquisition transfer matrix:
S63, rightIt is added by row, obtains sum Corresponding set of factors is obtained further after standardization
Weight coefficient
In formula, n is factor element number；Existing k position expert, according to weight wi(i=1,2 ..., k), and w1+w2+……+
wk=1, utilize average weighted method, process of textile production is passed judgment on, and sets forth respective fuzzy relation square
Battle array Mi(i=1,2 ..., k), and M=w1M1+w2M2+……+wkMkS44；
S64, now setting and have m kind abnormal phenomena to occur in production process, corresponding uncertain factor may have a n kind, so,
Abnormal phenomena y and uncertain factor x can be expressed as:
Y=(y1, y2, y3..., ym), X=(x1, x2, x3..., xn)；
The vector making uncertain factor membership function is U (X), and U (X)=(u1(x1), u2(x2) ..., un(xn)), with
Time, according to the classification of uncertain factor, factor is divided into artificially, humiture, machine, forceful electric power, and other several big classes, note
For U1, U2, U3, U4, U5；Then, compositing factor collection U={U1, U2, U3, U4, U5}, and it is carried out Further Division, formed
Second level；
S65, according to system core topology degree, be defined as a uncertain system core, detailed process is as follows:
If G is a connected graph, it is assumed that G has a m summit, K (G) represents all cut set set of G, then | V (G) |=m >=
4, its core degree h (G) is: h (G)=MAX | ω (G-J*)-|J*|, J ∈ K (G)；
If J*Meet: h (G)=ω { (G-J*)-|J*|, then in system T, G is its Connected undigraph, and ω (G) is G's
Connection branch amount, then J*Core for G；Again because T is a G connected system, then the core of G is exactly the core of T, simultaneously the core degree h of G
(G) being exactly core degree h (x) of system T, and the core degree of system is the biggest, Appraisal process is the most accurate.
Wherein, described production process uncertainty forecast model based on multi-Agent is built by following steps:
S11, an Agent structure are made up of multiple Agent objects, form set N={A1, A2..., An, need
Task be T, system resource is R, and ability is X；Wherein, each AgentAiAbility X according to selfiWith system resource RiGo
The task of performing to have needed is Ti, as single AgentAiWhen cannot complete task, by multiple Agent by mutual cooperation
Complete；Make Q=(A, O, R), wherein: A represents a series of Agent；O represents operational set；R is set of relationship R=A ∪ (A ∪
O), Q will constitute a non-directed graph, and its point set is made up of A ∪ O, and limit collection is made up of relation R；
If S12 has n (n ＞ 1, but n is unsuitable excessive) private mortgage loan, its own task is expressed as T1, T2,
T3..., Tn, after carrying out the refinement of system task, performed subtask is expressed as T11, T12, T13..., T1n；T21, T22,
T23..., T2n；T31, T32, T33..., T3n；..., Tn1, Tn2, Tn3..., Tnn.So, the cooperating process of whole system can describe
For: R (((T11→T12→T13)→T1)∪((T21∪T22∪T23)→T2)∪((T31→T32→T33)→T3) ..., → Tn)。
Wherein, the described function that accepts uses and fuzzy accept function or dynamically accept function.
Wherein, described influence function is built by following steps: be mapped as affecting artificial ant by all optimization constraintss
The inducer of ant search behavior, is modified to rule of conduct individual in group space by influence function, makes group space
Obtain higher efficiency of evolution.
The method have the advantages that
In process of textile production, such as board itself, artificial exception, production schedule change, hot job arrival, environment
Impact and other situations etc., all can cause spinning quality, by a kind of key factor towards spinning quality fluctuation of the present invention
The design of extracting method, solves the fluctuation in spinning process and extracts, improve product efficiency, dropped to by uncontrollable factor
Low.
Accompanying drawing explanation
Fig. 1 is the schematic diagram of production process uncertainty forecast model based on multi-Agent in the embodiment of the present invention.
In figure, (a) is calling between single private mortgage loan；B () is calling between two private mortgage loan；(c) be three each and every one
Calling between body Agent；D () is calling between more than four private mortgage loan.
Fig. 2 is the task scheduling mode schematic diagram in the embodiment of the present invention between multi-Agent.
Fig. 3 is the schematic diagram of the design of embodiment of the present invention chinesization evolution and Evolution of Population interaction mechanism.
Detailed description of the invention
In order to make objects and advantages of the present invention clearer, below in conjunction with embodiment, the present invention is carried out further
Describe in detail.Should be appreciated that specific embodiment described herein, only in order to explain the present invention, is not used to limit this
Bright.
Embodiments provide a kind of key factor extracting method towards spinning quality fluctuation, including walking as follows
Rapid:
S1, by multi-Agent technology, construct production process uncertainty forecast model based on multi-Agent, such as Fig. 1
Shown in, described uncertain forecast model includes system administration Agent, Object Management group Agent, performs Agent, man machine interface
Agent, data-interface Agent, source data Agent, initialization Agent, target data Agent, inquiry Agent, statistics Agent
With analysis Agent；
Described production process uncertainty forecast model based on multi-Agent is built by following steps:
S11, an Agent structure are made up of multiple Agent objects, form set N={A1, A2..., An, need
Task be T, system resource is R, and ability is X；Wherein, each AgentAiAbility X according to selfiWith system resource RiGo to hold
The task that row has needed is Ti, as single AgentAiWhen cannot complete task, complete by mutual cooperation by multiple Agent
Become；Make Q=(A, O, R), wherein: A represents a series of Agent；O represents operational set；R is set of relationship R=A ∪ (A ∪
O), Q will constitute a non-directed graph, and its point set is made up of A ∪ O, and limit collection is made up of relation R；
If S12 has n (n ＞ 1, but n is unsuitable excessive) private mortgage loan, its own task is expressed as T1, T2,
T3..., Tn, after carrying out the refinement of system task, performed subtask is expressed as T11, T12, T13..., T1n；T21, T22,
T23..., T2n；T31, T32, T33..., T3n；..., Tn1, Tn2, Tn3..., Tnn.So, the cooperating process of whole system can describe
For: R (((T11→T12→T13)→T1)∪((T21∪T22∪T23)→T2)∪((T31→T32→T33)→T3) ..., → Tn), many
Task scheduling mode between Agent is as shown in Figure 2.
S2 is as it is shown on figure 3, complete to accept the design of function and influence function；The described function that accepts uses fuzzy letter of acceptance
Count or dynamically accept function；Described influence function is built by following steps: be mapped as affecting people by all optimization constraintss
The inducer of work Ant Search behavior, is modified to rule of conduct individual in group space by influence function, makes colony
Space obtains higher efficiency of evolution.
S3, completed the standardization of data by below equation:
Wherein,
XijFor the initial data that j index is over the years；ZijFor the data after standardization；mijThe average of period is being chosen for j index
Value；sijStandard deviation for index；
S4, carried out the calculating of correlation coefficient by below equation:
Wherein: W (i/j) is i-th factor dependency to jth factor；XiIt it is the actual value of the i-th combined factors level；XiFor
The i-th combined factors level relevant to jth factor actual value X；K be 1/S, S be the variance of i factor；
S5, carry out relativity evaluation based on DEA；
The fractional programming form of DEA model is:
J=1,2 ..., n, U >=0, V >=0；
This planning carries out the form after Charnes-Cooper linear transformation and dualistic transformation is:
Min h
λj>=0, S->=0, S+>=0, δ=0 or 1
When δ=0, planning type is C2R model, when δ=1, planning type is C2GS2Model；
S6, the extraction of key factor:
S61, now to define all of uncertain factor be x1, x2... xn, and by two pairwise correlations between each factor
Sexual relationship is defined as xixj, and according to the calculating of dependency each other and evaluation procedure, it is divided into the most relevant, strong correlation, relatively phase
Close, be correlated with and unrelated；Refer to table 1.
Table 1xixjBetween structural relation
S62, according to above-mentioned xixjBetween structural relation, obtaining fuzzy relation matrix corresponding to uncertain factor is:
With uncertain factor as row in matrix M, with abnormal phenomena for row；Wherein mij(i-1,2 ..., m, j-1,2 ...,
N) it is that expert is rule of thumb given, represents the weight shared by jth kind factor in the uncertain factor of i-th kind of abnormal phenomena,
Therefore have:J=1,2 ..., m；
As such, it is possible to acquisition transfer matrix:
S63, rightIt is added by row, obtains sum Corresponding set of factors is obtained further after standardization
Weight coefficient
In formula, n is factor element number；Existing k position expert, according to weight wi(i=1,2 ..., k), and w1+w2+……+
wk=1, utilize average weighted method, process of textile production is passed judgment on, and sets forth respective fuzzy relation square
Battle array Mi(i=1,2 ..., k), and M=w1M1+w2M2+……+wkMkS44；
S64, now setting and have m kind abnormal phenomena to occur in production process, corresponding uncertain factor may have a n kind, so,
Abnormal phenomena y and uncertain factor x can be expressed as:
Y=(y1, y2, y3..., yn), X=(x1, x2, x3..., xn)；
The vector making uncertain factor membership function is U (X), and U (X)=(u1(x1), u2(x2) ..., un(xn)), with
Time, according to the classification of uncertain factor, factor is divided into artificially, humiture, machine, forceful electric power, and other several big classes, note
For U1, U2, U3, U4, U5；Then, compositing factor collection U={U1, U2, U3, U4, U5}, and it is carried out Further Division, formed
Second level；
S65, according to system core topology degree, be defined as a uncertain system core, detailed process is as follows:
If G is a connected graph, it is assumed that G has a m summit, K (G) represents all cut set set of G, then | V (G) |=m >=
4, its core degree h (G) is: h (G)=MAX | ω (G-J*)-|J*|, J ∈ K (G)；
If J*Meet: h (G)=ω { (G-J*)-|J*|, then in system T, G is its Connected undigraph, and ω (G) is G's
Connection branch amount, then J*Core for G；Again because T is a G connected system, then the core of G is exactly the core of T, simultaneously the core degree h of G
(G) being exactly core degree h (x) of system T, and the core degree of system is the biggest, Appraisal process is the most accurate.
The above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For Yuan, under the premise without departing from the principles of the invention, it is also possible to make some improvements and modifications, these improvements and modifications also should
It is considered as protection scope of the present invention.
Claims (4)
1. the key factor extracting method towards spinning quality fluctuation, it is characterised in that comprise the steps:
S1, by multi-Agent technology, construct production process uncertainty forecast model based on multi-Agent, described uncertain
Property forecast model include system administration Agent, Object Management group Agent, perform Agent, man machine interface Agent, data-interface
Agent, source data Agent, initialization Agent, target data Agent, inquiry Agent, statistics Agent and analysis Agent；
S2, complete to accept the design of function and influence function；
S3, completed the standardization of data by below equation:
Wherein,
XijFor the initial data that j index is over the years；ZijFor the data after standardization；mijThe meansigma methods of period is being chosen for j index；
sijStandard deviation for index；
S4, carried out the calculating of correlation coefficient by below equation:
Wherein: W (i/j) is i-th factor dependency to jth factor；XiIt it is the actual value of the i-th combined factors level；XiFor with
The i-th combined factors level that j factor actual value X is relevant；K be 1/S, S be the variance of i factor；
S5, carry out relativity evaluation based on DEA；
The fractional programming form of DEA model is:
This planning carries out the form after Charnes-Cooper linear transformation and dualistic transformation is:
Min h
Or 1
When δ=0, planning type is C2R model, when δ=1, planning type is C2GS2Model；
S6, the extraction of key factor:
S61, now to define all of uncertain factor be x1, x2... xn, and the dependency two-by-two between each factor is closed
System is defined as xixj, and according to the calculating of dependency each other and evaluation procedure, it is divided into the most relevant, strong correlation, relatively relevant, phase
Close and unrelated；
S62, according to above-mentioned xixjBetween structural relation, obtaining fuzzy relation matrix corresponding to uncertain factor is:
With uncertain factor as row in matrix M, with abnormal phenomena for row；Wherein mij(i-1,2 ..., m, j-1,2 ..., n) it is
Expert is rule of thumb given, and represents the weight shared by jth kind factor in the uncertain factor of i-th kind of abnormal phenomena, therefore has:
As such, it is possible to acquisition transfer matrix:
S63, rightIt is added by row, obtains sum The weight system of corresponding set of factors is obtained further after standardization
Number
In formula, n is factor element number；Existing k position expert, according to weight wi(i=1,2 ..., k), and w1+w2+……+wk=
1, utilize average weighted method, process of textile production is passed judgment on, and sets forth respective fuzzy relation matrix Mi
(i=1,2 ..., k), and M=w1M1+w2M2+……+wkMkS44；
S64, now setting and have m kind abnormal phenomena to occur in production process, corresponding uncertain factor may have n kind, so, abnormal
Phenomenon y and uncertain factor x can be expressed as:
Y=(y1, y2, y3..., ym), X=(x1, x2, x3..., xn)；
The vector making uncertain factor membership function is U (X), and U (X)=(u1(x1), u2(x2) ..., un(xn)), meanwhile,
According to the classification of uncertain factor, factor is divided into artificially, humiture, machine, forceful electric power, and other several big classes, it is designated as
U1、U2、U3、U4、U5；Then, compositing factor collection U={U1, U2, U3, U4, U5}, and it is carried out Further Division, form the
Two levels；
S65, according to system core topology degree, be defined as a uncertain system core, detailed process is as follows:
If G is a connected graph, it is assumed that G has a m summit, K (G) represents all cut set set of G, then | V (G) |=m >=4, its
Core degree h (G) is: h (G)=MAX | ω (G-J*)-|J*|, J ∈ K (G)；
If J*Meet: h (G)=ω { (G-J*)-|J*|, then in system T, G is its Connected undigraph, and ω (G) is the company of G
Logical branch amount, then J*Core for G；Again because T is a G connected system, then the core of G is exactly the core of T, simultaneously core degree h (G) of G
Being exactly core degree h (x) of system T, and the core degree of system is the biggest, Appraisal process is the most accurate.
A kind of key factor extracting method towards spinning quality fluctuation the most according to claim 1, it is characterised in that institute
State production process uncertainty forecast model based on multi-Agent to be built by following steps:
S11, an Agent structure are made up of multiple Agent objects, form set N={A1, A2..., An, needed appoints
Business is T, and system resource is R, and ability is X；Wherein, each AgentAiAbility X according to selfiWith system resource RiGo to perform need
Task to be completed is Ti, as single AgentAiWhen cannot complete task, multiple Agent completed by mutual cooperation；
Make Q=(A, O, R), wherein: A represents a series of Agent；O represents operational set；R is set of relationship R=A ∪ (A ∪ O), Q
To constitute a non-directed graph, its point set is made up of A ∪ O, and limit collection is made up of relation R；
If S12 has n (n > 1, but n is unsuitable excessive) private mortgage loan, its own task is expressed as T1, T2, T3..., Tn,
After carrying out the refinement of system task, performed subtask is expressed as T11, T12, T13..., T1n；T21, T22, T23...,
T2n；T31, T32, T33..., T3n；..., Tn1, Tn2, Tn3..., Tnn.So, the cooperating process of whole system can be described as: R
(((T11→T12→T13)→T1)∪((T21∪T22∪T23)→T2)∪((T31→T32→T33)→T3) ..., → Tn)。
A kind of key factor extracting method towards spinning quality fluctuation the most according to claim 1, it is characterised in that institute
State and accept function and use and fuzzy accept function or dynamically accept function.
A kind of key factor extracting method towards spinning quality fluctuation the most according to claim 1, it is characterised in that institute
State influence function to be built by following steps: all optimization constraintss are mapped as affecting the induction of human oasis exploited search behavior
Element, is modified to rule of conduct individual in group space by influence function, makes group space obtain higher effect of evolving
Rate.
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| CN109636014A (en) * | 2018-11-28 | 2019-04-16 | 郑州轻工业学院 | A kind of method of distributing cotton based on yarn quality prediction |
Citations (1)
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