CROSS REFERENCES TO RELATED APPLICATIONS

The present application claims foreign priority of Chinese Patent Application No. 202010175032.0, filed on Mar. 13, 2020 in the State Intellectual Property Office of China, the disclosures of all of which are hereby incorporated by reference.

TECHNICAL FIELD

The disclosure relates to the field of smart manufacturing, and more particularly, to an inhomogeneous sample equalization method and system for a product assembly process.

BACKGROUND

With the rise of smart manufacturing, more and more manufacturing companies apply a machine learning algorithm to actual production to improve production efficiency and reduce labor cost consumption, such as establishing a model through machine learning in a product assembly process to predict production costs of products, required time, and the like. Initial sample data is an important part of the machine learning. Accuracy and homogeneity of the initial data may affect accuracy of a final result and generalization ability of the machine learning algorithm.

However, samples are inhomogeneous in an assembly process of same products with different styles. Since the same products with different styles may have subtle differences in function, these subtle differences may lead to different product assembly processes. One product has a main function and multiple additional functions, and the additional functions have different improvement effects on a sales volume of the product. For the additional functions capable of improving the sales volume of the product, manufacturers may design more styles for the additional functions with great improvement effects on the sales volume, so that products with more styles have the additional functions. For the additional functions with insignificant improvement effects on the sales volume, products with the additional functions have fewer styles, so that a model tends to be more in line with a characteristic of an assembly process of a product with large output, and a generalization ability of the model is insufficient. Moreover, for the assembly process of the same products with different styles, a strong relevance of internal data of the samples causes difficult homogenization of the samples.

SUMMARY

The disclosure aims to provide an inhomogeneous sample equalization method and system for a product assembly process to solve the above problems.

In order to achieve the objectives, the disclosure employs the following technical solutions.

An inhomogeneous sample equalization method for a product assembly process 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.

An inhomogeneous sample equalization system for a product assembly process 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 system includes:

a similarity generation module configured to calculate a similarity among different samples;

a fuzzy compatibility space construction module configured to construct a fuzzy compatibility matrix S for representing the similarity among all the samples, construct a fuzzy compatibility space X with different granule layers through the fuzzy compatibility matrix S, and cluster 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;

an optimal granule layer generation module configured to, based on a granular calculating mode, screen 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

an equalization module configured to carry out equalization processing on a sample of the optimal granule layer.

According to the inhomogeneous sample equalization method for the product assembly process, equalization of inhomogeneous samples of the product assembly process is solved, and an accuracy of a final prediction result and a generalization ability of a model are improved; the similarity of the assembly process topological structures of the same products with different styles is also considered, and the samples are clustered from this point of view, so that a problem that the samples are not easy to be homogenized due to a strong relevance of internal data of the samples is solved, thus being more in line with a characteristic of such sample of the product assembly process, and making a final equalized result more scientific. By constructing the fuzzy compatibility space X, the samples may be clustered into different granule layers, and the samples may be observed and analyzed from multiple granule layers, so as to obtain the optimal granule layer C(λo), so that more representative and accurate sample granule may be obtained, and the number of the samples in each sample granule is homogenized from the optimal granule layer C(λo), so that an equalized effect is more representative.

