Collaborative design of complex product lifecycle value chain by fusing multi-agent demand frequency characteristics

doi:10.3785/j.issn.1006-754X.2023.03.207

Chinese Journal of Engineering Design

2024, Vol. 31

Issue (1): 1-9 DOI: 10.3785/j.issn.1006-754X.2023.03.207

Product Innovation Design

Collaborative design of complex product lifecycle value chain by fusing multi-agent demand frequency characteristics

Zhou HE^1,²(

),Yang WANG³,Xiangyu JIANG⁴,Zhaoxi HONG^4,⁵(

),Lili HE³,Yixiong FENG^4,⁶

^1.Sussex Artificial Intelligence Institute, Zhejiang Gongshang University, Hangzhou 310012, China
^2.Hangzhou Zhouli Data Technology Co. , Ltd. , Hangzhou 310019, China
^3.School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
^4.State Key Laboratory of Fundamental Components of Fluid Power and Mechatronic systems, Zhejiang University, Hangzhou 310058, China
^5.Ningbo Innovation Center, Zhejiang University, Ningbo 315100, China
^6.State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China

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Abstract

Complex products with high turnover and short shelf life are characterized by a higher order frequency. Traditional value chain design is mostly based on the time series of order data of complex products and the impact of sales volume on sales cycle, while ignoring the detail information contained in the order frequency, which is difficult to accurately capture the rapidly changing supply and demand relationship between multi-agents. In order to solve this problem, a collaborative design method of complex product lifecycle value chain fusing multi-agent demand frequency characteristics was proposed. Firstly, the frequency sequence extraction method of gated convolution was used to identify the multi-agent requirement; Secondly, the Transformer time series prediction model based on frequency segmentation was integrated into the order frequency information, and the lifecycle value chain was built according to the improved time series frequency multi-head self-attention (seq-fre multi-head attention) structure. The time series and frequency characteristics of different segments corresponded to different attention heads to realize the fusion of multi-stage time series and frequency features; Finally, the new value chain collaborative design method was applied to the multi-agent demand prediction problem of a complex product, and experimental verification was carried out. The result showed that the proposed value chain collaborative design method fusing demand frequency features has high prediction accuracy and good application prospects.

Key words： value chain collaborative design time series prediction Transformer frequency feature

Received: 13 October 2023 Published: 04 March 2024

CLC:

TP 24

Corresponding Authors: Zhaoxi HONG E-mail: zh277@sussex.ac.uk;hzhx@zju.edu.cn

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	Zhou HE
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	Lili HE
	Yixiong FENG

Cite this article:

Zhou HE,Yang WANG,Xiangyu JIANG,Zhaoxi HONG,Lili HE,Yixiong FENG. Collaborative design of complex product lifecycle value chain by fusing multi-agent demand frequency characteristics. Chinese Journal of Engineering Design, 2024, 31(1): 1-9.

URL:

https://www.zjujournals.com/gcsjxb/10.3785/j.issn.1006-754X.2023.03.207 OR https://www.zjujournals.com/gcsjxb/Y2024/V31/I1/1

融合多主体需求频率特征的复杂产品全生命周期价值链协同设计

高周转率和短保质期的复杂产品具有较高订单频率的特征。传统的价值链设计大多基于复杂产品订单数据的时序和销量对销售周期的影响，忽略了其订单频率中蕴含的细节信息，因而难以准确捕捉多主体间快速变化的供需关系。为了解决这一问题，提出了一种融合多主体需求频率特征的复杂产品全生命周期价值链协同设计方法。首先，采用门控卷积的频率序列提取方法识别多主体需求；其次，将基于频率分段的Transformer时序预测模型融合于订单频率信息，根据改进的时序-频率多头自注意力（seq-fre multi-head attention）结构建立全生命周期价值链，不同分段的时序和频率特征对应不同的注意力头，以实现多段时序和频率特征的融合；最后，将新型价值链协同设计方法应用于某复杂产品多主体需求预测问题，进行实验验证。研究表明，所提出的融合需求频率特征的价值链协同设计方法预测准确度较高，具有很好的应用前景。

关键词： 价值链, 协同设计, 时序预测, Transformer, 频率特征

Fig.1 Basic path of value chain evolution and upgrading for complex product manufacturing enterprise

Fig.2 Structure of SFTransformer model

Fig.3 Structure of one-dimensional gated convolution

Fig.4 Input network for feature data sequences

Fig.5 Structure of timing-frequency multi-head self-attention module

Fig.6 Sales dataset of a complex product manufacturing enterprise

Table 1 SFTransformer model parameter settings

Table 2 Comparison of prediction performance of different models

Fig.7 Prediction results of product sales volume by each model


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