Design decision-making guidance method based on product feature association degree

A feature association and design decision-making technology, applied in computing, manufacturing computing systems, special data processing applications, etc., can solve problems such as product specification dependency acquisition impact, and achieve the effect of overcoming limitations

Pending Publication Date: 2019-11-08
SHANTOU UNIV
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  • Abstract
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  • Claims
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Problems solved by technology

Although existing methods have been widely used in recent years due to their strong applicability and good interpretability, these methods rely on expert experience and the acquisition of dependencies between product specifications is affected by subjective factors.

Method used

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  • Design decision-making guidance method based on product feature association degree
  • Design decision-making guidance method based on product feature association degree
  • Design decision-making guidance method based on product feature association degree

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Embodiment Construction

[0057] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0058] Such as figure 2 Shown is a schematic structural diagram of the electric vehicle battery pack according to the present invention, wherein there are 25 representative parts of the battery pack, and the list of battery pack parts is shown in Table 1. The method described in the present invention will be further described below by taking an electric vehicle battery pack as an example.

[0059] The first step is to perform market data preprocessing: process the data collected from the market for electric vehicle battery packs, the data includes: samples, sample sales, product features, and feature values ​​included in each feature; in this example, 13 The product features of various electric vehicles, the list is shown in Table 2. Considering that some produc...

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Abstract

The invention relates to a design decision guidance method based on product feature correlation. The method comprises the steps of collecting and processing market data; establishing a product featureassociation degree matrix; establishing a product feature and part relationship model; establishing a product part correlation degree matrix; performing clustering analysis according to customer requirements or designer requirements; and providing guidance for product design decisions according to clustering results. According to the method, the correlation degree of the product characteristics and the parts is calculated according to the market data by applying a mathematical statistics method, different combinations of the product parts are obtained under the condition of avoiding the influence of subjective factors of designers, and guidance is provided for product design decisions.

Description

technical field [0001] The invention relates to a product design decision-making guidance method based on product big data, in particular to a design decision-making guidance method based on product feature correlation. Background technique [0002] The engineering practice of the past few decades has proved that the global competitiveness of manufacturers is closely related to the rapid response of customers to product specification requirements. Changes to product specifications and their combinations are made through adaptations of physical components. Various types of products, including customized products, reconfigurable products, scalable products, adaptable products and open architecture products, are adaptable to meet changing needs. In order to better facilitate the development of different types of products to meet the changing needs of product specifications, it is necessary to analyze the dependencies between product specifications by considering the relationsh...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50G06Q50/04
CPCG06Q50/04Y02P90/30
Inventor 张健李冰冰王星星
Owner SHANTOU UNIV
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