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Cloud manufacturing service cooperation similarity calculation method based on Word2Vec

A similarity calculation and cloud manufacturing technology, applied in digital transmission systems, data exchange networks, biological neural network models, etc., can solve the problem of low service collaboration similarity

Active Publication Date: 2020-01-07
QINGDAO UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Different from ordinary cloud services, the replacement of cloud manufacturing services not only needs to be considered from the two dimensions of service function and quality, but also needs to consider the service collaboration relationship involved in the establishment process. At present, there are only calculation methods with similar service functions and quality. There are few calculation methods for service collaboration similarity

Method used

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  • Cloud manufacturing service cooperation similarity calculation method based on Word2Vec
  • Cloud manufacturing service cooperation similarity calculation method based on Word2Vec
  • Cloud manufacturing service cooperation similarity calculation method based on Word2Vec

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0061] Such as figure 1 As shown, a Word2Vec-based cloud manufacturing service collaboration similarity calculation method includes the following steps:

[0062] S1. Modeling the cloud manufacturing service process to obtain the cloud manufacturing service process network model;

[0063] S2. Sampling the cloud manufacturing service process network model and obtaining a character sequence;

[0064] S3. Constructing a corpus with the character sequence;

[0065] S4. Using the corpus to train a vector for each cloud manufacturing service through Word2Vec;

[0066] S5. Determine the corresponding cloud manufacturing service collaboration similarity by judging the similarity of any two vectors.

[0067] Among them, Word2Vec (full name Word to Vector) is a toolkit for obtaining Word Vector launched by Google in 2013. It is simple and efficient; through training, Word2Vec can be used to map each word to a vector, which can be used to represent words. relationship between words. ...

Embodiment 2

[0087] Cloud manufacturing service process network:

[0088] A mechanical processing factory R needs to process a set of mechanical parts M due to business expansion, but the processing factory does not have the ability to produce the mechanical parts, so it needs to use the cloud manufacturing service platform for customization. Part M is processed by 6 sub-parts Composition, respectively M 1 -M 6 , factory R can go to the cloud manufacturing service release platform to find 1 -M 6 The cloud manufacturing service combines rent-seeking to service according to business process logic.

[0089] The assembly of parts M exists in the following order: M 1 After production is complete, M 1 The components are decomposed into four parts, respectively by M 2 and M 5 Simultaneous further processing, M 2 , M 3 , M 4 and M 5 After all processing is completed, the service M 6Carry out assembly and processing, and finally deliver to R factory. According to the definition of the ...

Embodiment 3

[0091] Character sequence sampling method description:

[0092] According to the character sequence sampling method described in this patent, figure 2 The cloud manufacturing service process network in contains sequential structure and concurrent structure, and sequential detection sampling method is adopted for the sequential structure to sample the character sequence, and the character sequence M is obtained 1 R 1 and R 2 m 6 ; Sampling the full sequence of triple combinations for the concurrent structure, and the obtained triple combination sequence is: {M 2 m 3 m 4 , M 2 m 4 m 2 , M 3 m 2 m 4 , M 3 m 4 m 2 , M 4 m 2 m 3 , M 4 m 3 m 2 , M 2 m 3 m 5 , M 2 m 5 m 3 , M 3 m 2 m 5 , M 3 m 5 m 2 , M 5 m 2 m 3 , M 5 m 3 m 2 , M 2 m 4 m 5 , M 2 m 5 m 4 , M 4 m 2 m 5 , M 4 m 5 m 2 , M 5 m 2 m 4 , M 5 m 4 m 2 , M 3 m 4 m 5 , M 3 m 5 m 4 , M 4 m 3 m 5 , M 4 m 5 m 3 , M 5 m 3 m 4 , M 5 m 4 m 3}

[0093] Therefo...

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Abstract

The invention discloses a cloud manufacturing service cooperation similarity calculation method based on Word2Vec which comprises the following steps of S1, modeling a cloud manufacturing service process to obtain a cloud manufacturing service process network model; S2, sampling the cloud manufacturing service process network model and obtaining a character sequence; S3, constructing a corpus by using the character sequence; S4, training a vector for each cloud manufacturing service through Word2Vec by utilizing the corpus; and S5, judging the corresponding cloud manufacturing service cooperation similarity by judging the similarity of any two vectors. The manufacturing process needs a plurality of links. When individual services fail or functions are changed , the service with high cooperation similarity can be quickly matched for replacement, the replaced service takes multiple dimensions such as service functions, service quality and process cooperation into consideration, the timeused by a user for self retrieval can be shortened, the adaptation efficiency and compatibility are improved, the replacement period is shortened, and the rent seeking cost of the replacement serviceis reduced.

Description

technical field [0001] The present invention relates to technical fields such as Word2Vec and cloud manufacturing service management, and in particular to a method for calculating similarity of cloud manufacturing service collaboration based on Word2Vec. Background technique [0002] As a typical realization mode of "Internet + intelligent manufacturing", cloud manufacturing deeply integrates information technology and manufacturing technology, and realizes the encapsulation and service release of manufacturing resources and business functions with the help of the Internet and cloud platform. Users search and rent the required manufacturing services according to their needs, which can effectively save manufacturing costs, shorten manufacturing cycles, improve enterprise production efficiency, and make up for the lack of manufacturing business capabilities. [0003] In order to facilitate the invocation of manufacturing services, the function granularity of the services provi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L29/08H04L12/24G06N3/02
CPCH04L67/10G06N3/02H04L41/145H04L67/51
Inventor 胡强秦雪晴杜军威
Owner QINGDAO UNIV OF SCI & TECH
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