系统管理学报2026，Vol.35Issue(2)：380-393,14.DOI:10.3969/j.issn2097-4558.2026.02.006
信息不完全下基于关联匹配的工程物资需求预测
Project Material Demand Forecasting Based on Correlation Matching Under Incomplete Information
摘要
Abstract
Project material demand forecasting is a critical component of project management,playing an important role in ensuring the smooth progress of projects and reducing procurement costs.Traditional methods heavily rely on complete project information;however,in the early stages of engineering projects,information incompleteness constrains the forecasting lead time,reduces procurement flexibility,and poses significant challenges to demand planning.Moreover,project material procurement data are characterized by high dimensionality,sparsity,and heavy-tailed distributions,for which existing forecasting models exhibit limited predictive performance on extremely imbalanced datasets.To address these issues,this paper proposes a long-term project material demand forecasting method under conditions of incomplete information.By constructing an association graph structure,the method leverages complete information from similar projects to enhance prediction capabilities for early-stage projects,thereby significantly extending the forecasting lead time.In addition,to cope with data imbalance,a dedicated matching module and customized loss function are designed to improve prediction accuracy.Empirical analysis based on real project data from the State Grid Corporation of China spanning 2015～2023 demonstrates that transferring source-domain information from projects with complete information to early-stage projects with incomplete information can effectively enhance forecasting performance.Furthermore,when detailed project information is lacking in the early stages,standardized and fine-grained material coding schemes play a particularly important role in demand forecasting,with the precision contribution of a single indicator exceeding 20％.The proposed method not only achieves strong predictive accuracy in the early stages of projects but also outperforms traditional demand forecasting models overall.This paper provides an effective tool for project material demand forecasting and contributes to the enhancement of digital and intelligent operational management of engineering projects.关键词
需求预测/工程物资/不完全信息/关联匹配/图网络模型Key words
demand forecasting/project materials/incomplete information/correlation matching/graph neural network model分类
管理科学引用本文复制引用
李扬,肖勇波,辛诚,刘镓铭,柏扬..信息不完全下基于关联匹配的工程物资需求预测[J].系统管理学报,2026,35(2):380-393,14.基金项目
国家电网有限公司科技项目(5108-202218280A-2-433-XG) （5108-202218280A-2-433-XG）
国家自然科学基金资助项目(72125002,72293561) （72125002,72293561）
