基于不完全信息补全的产品全生命周期评价方法

doi:10.3901/JME.2025.11.311

摘要/Abstract

摘要： 针对机电产品生命周期清单中存在的混合型不完全信息问题，提出一种基于不完全信息补全的全生命周期评价方法。首先，在界定全生命周期环境影响评价概念和评价体系的基础上，分析了机电产品全生命周期各阶段中的清单信息不完全问题。在建立生命周期评价(Life cycle assessment，LCA)案例数据库的基础上，当实时LCA清单信息表中存在不完全但在LCA案例数据库中已有相应数据时，采用基于BP神经网络的匹配填充算法，该算法利用文本和数值相似度计算零部件各参数之间的相似程度，并通过输出层计算与缺失数据相似度达到100%的零部件，从而实现对缺失数据的匹配填充；当实时LCA清单信息表中存在不完全且LCA案例数据库中无相应数据时，运用支持向量机模型预测缺失的分类型变量，并利用基于线性回归的多重插补法填补缺失的连续型变量，实现对混合型不完全信息的有效补全。最后，以YDE3120CNC滚齿机为案例对象，通过目的与范围分析、特征化和标准化等步骤，对其进行了全生命周期环境影响评价，确定全生命周期各阶段中的薄弱环节，并给出相应改善建议。

关键词: 生命周期评价, 不完全信息补全, BP神经网络, 支持向量机, 多重插补法

Abstract: Regarding the mixed incomplete information issues in the life cycle inventory of electromechanical products, a comprehensive life cycle assessment(LCA) method based on incomplete information imputation is proposed. Firstly, based on defining the concept of life cycle environmental impact assessment and its structure, the incomplete inventory information problems in each stage of the electromechanical product life cycle are analyzed. Building on an existing LCA case database, when there are incomplete data in the real-time life cycle inventory but corresponding data exist in the LCA case database, a matching filling algorithm based on BP neural network is adopted. This algorithm calculates the similarity levels between various parameters of the components using both text and numerical similarity, and through the output layer, identifies components with a similarity of 100% to fill the missing data. When there are data missing in the real-time life cycle inventory and no corresponding data in the LCA case database, the support vector machine model is employed to predict missing categorical variables, while multiple imputation based on linear regression is used to fill missing continuous variables, effectively completing the incomplete mixed-type information. Finally, taking YDE3120 CNC gear hobbing machine as a case object, the entire lifecycle environmental impact assessment is conducted through steps such as purpose and scope analysis, characterization, and standardization, etc., to identify weak links in each life cycle stage and provide corresponding improvement suggestions.

Key words: life cycle assessment, incomplete information, BP neural network, support vector machine, multiple imputation

中图分类号:

U270

杨晨, 李涛, 王明宇, 杨东东, 闫萌. 基于不完全信息补全的产品全生命周期评价方法[J]. 机械工程学报, 2025, 61(11): 311-325.

YANG Chen, LI Tao, WANG Mingyu, YANG Dongdong, YAN Meng. Product Life Cycle Assessment Method Based on Incomplete Information Imputation[J]. Journal of Mechanical Engineering, 2025, 61(11): 311-325.

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链接本文: http://www.cjmenet.com.cn/CN/10.3901/JME.2025.11.311

http://www.cjmenet.com.cn/CN/Y2025/V61/I11/311

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