Inhomogeneous sample equalization method and system for product assembly process

Patent metadata

Field	Value
Publication number	US-11429070-B2
Application number	US-202017134369-A
Country	US
Kind code	B2
Filing date	Dec 26, 2020
Priority date	Mar 13, 2020
Publication date	Aug 30, 2022
Grant date	Aug 30, 2022

Abstract

The disclosure discloses an inhomogeneous sample equalization method and system for a product assembly process. The method includes the following steps of: A: calculating a similarity among different samples; B: constructing a fuzzy compatibility matrix S for representing the similarity among all the samples, and constructing a fuzzy compatibility space X with different granule layers through the fuzzy compatibility matrix S; C: based on a granular calculating mode, screening out a granule layer with a maximum comprehensive value of an information increment and the similarity among the samples from the fuzzy compatible space X to serve as an optimal granule layer; and D: carrying out equalization processing on the sample of the optimal granule layer.

First claim

What is claimed is:

1. An inhomogeneous sample equalization method for a product assembly process which takes an assembly process topological structure of a product as a sample, and takes assembly process topological structures of same products with different styles as different samples, wherein the method comprises the following steps of:
step A: calculating a similarity among different samples;
step B: constructing a fuzzy compatibility matrix S for representing the similarity among all the samples, constructing a fuzzy compatibility space X with different granule layers through the fuzzy compatibility matrix S, and clustering all samples through the fuzzy compatibility space X, wherein the fuzzy compatibility space X is divided into a plurality of different granule layers according to the similarity among the samples;
step C: based on a granular calculating mode, screening out a granule layer with a maximum comprehensive value of an information increment and the similarity among the samples from the fuzzy compatible space X to serve as an optimal granule layer; and
step D: carrying out equalization processing on a sample of the optimal granule layer;
the step D specifically comprises:
step D1: calculating an average number G i of samples of each sample granule G i in the optimal granule layer C(λ o ):

G
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i

=

1
g

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k
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g

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G

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;

wherein, a threshold is set as λ, and values of the threshold λ are respectively λ 1 , λ 2 , λ 3 , . . . , λ n , and 1=λ 1 >λ 2 >λ 3 > . . . >λ n =0, and wherein G i, k is a kth sample granule in the granule layer C(λ i ); |G i, k | is a number of samples contained in the kth sample granule; g represents a number of sample granules in the granule layer C(λ i );
step D2: increasing and decreasing a number of samples of each sample granule G i in the optimal granule layer C(λ o ) by a random sampling method, so that the number of the samples in each sample granule G i is the same to complete the equalization processing:
if |G i, k |> G i , randomly eliminating extra samples of the sample granule G i, k , so that the number |G i, k | of the samples in the sample granule G i, k is decreased to G i ; and
if |G i, k |< G i , copying original samples in the sample granule G i, k into the sample granule G i, k again, so that the number |G i, k | of the samples in the sample granule G i, k is increased to G i .

2. The inhomogeneous sample equalization method for the product assembly process according to claim 1 , wherein the step A specifically comprises:
step A1: calculating a node similarity among different samples:

S
node

(

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i

,

v
j

)

=

2
×

m

i
,
j

e
i

+

e
j

wherein i represents a same product of an i th type, j represents a same product of a j th type, and S node (v i , v j ) represents a node similarity between an assembly process topological structure v i the same product of the i th type and an assembly process topological structure v j of the same product of the j th type; m i, j represents a number of nodes matched in the assembly process topological structure v i and the assembly process topological structure v j ; e i represents a sum of a number of all nodes in the assembly process topological structure v i ; and e j represents a sum of a number of all nodes in the assembly process topological structure v j ;
step A2: calculating a topological relation similarity among different samples:

S
rel

(

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i

,

v
j

)

=

2
×

M

i
,
j

E
i

+

E
j

wherein S rel (v i , v j ) represents a topological relation similarity between the assembly process topological structure v i the same product of the i th type and the assembly process topological structure v j of the same product of the j th type; M i, j represents a number of relation edges matched in the assembly process topological structure v i and the assembly process topological structure v i ; E i represents a sum of a number of all relation edges in the assembly process topological structure v i ; and E j represents a sum of a number of all relation edges in the assembly process topological structure v j ; and
step A3: calculating a topological structure similarity among different samples:
S ( i,j )= S node ( v i ,v j )× W node +S rel ( v i ,v j )× W rel ,

wherein S(i, j) represents the topological structure similarity between the assembly process topological structure v i of the same product of the i th type and the assembly process topological structure v j of the same product of the j th type, W node is a preset node weight parameter, and W rel is a preset relation edge weight parameter.

3. The inhomogeneous sample equalization method for t

Assignees

• Univ Guangdong Technology

Inventors

• Leng Jiewu
• Zhou Man
• Ruan Guolei
• Liu Qiang
• Zhang Ding
• Yan Duxi

Classifications

• G05B13/0275 Primary

  using fuzzy logic only · CPC title

• G06F18/2321 Primary

  using statistics or function optimisation, e.g. modelling of probability density functions · CPC title

• G06F18/22 Primary

  Matching criteria, e.g. proximity measures · CPC title

• G06F17/16

  Matrix or vector computation {, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization (matrix transposition G06F7/78)} · CPC title

• G06Q50/04

  Manufacturing · CPC title