大数据2026，Vol.12Issue(5)：172-189,18.DOI:10.11959/j.issn.2096-0271.BDR26030
电力物资供应链电子数据信任强度自适应分级方法
Adaptive grading method of trust intensity for electronic data in the electric power material supply chain
摘要
Abstract
The management of electronic data in power material supply chains is characterized by complex requirements,while traditional trust strength classification methods are constrained by static rules and single evaluation dimensions,and lack sufficient domain knowledge guidance,making it difficult to meet the demands of fine-grained and differentiated applications.To address this issue,an adaptive trust strength classification method for electronic data in power material supply chains,named Adap-LLM,was proposed.In this method,a multi-dimensional scoring system and classification rules constructed by domain experts were injected into a large language model(LLM)in the form of structured prompts,and parameter-efficient fine-tuning was integrated to achieve deep fusion of domain knowledge,thereby enabling adaptive classification of trust strength for various types of power documents.Experimental results demonstrated that Adap-LLM outperforms the compared models across all evaluation metrics on both private power supply chain dataset(PPSCD)and a cross-domain evaluation dataset constructed from public data sources(CDE-PDS),which fully validates its effectiveness and advancement in the hierarchical management of big data in power material supply chains,and exhibits promising engineering application value.关键词
供应链电子数据分级/大语言模型/领域知识/提示词工程/参数高效微调Key words
supply chain electronic data grading/LLM/domain knowledge/prompt engineering/parameter-efficient fine-tuning分类
信息技术与安全科学引用本文复制引用
俞春华,郭庆雷,杨珂,柏能,黄宜华..电力物资供应链电子数据信任强度自适应分级方法[J].大数据,2026,12(5):172-189,18.基金项目
国家电网公司总部科技项目(No.5700-202418240A-1-1-ZN) State Grid Corporation of China Headquarters Science and Technology Project(No.5700-202418240A-1-1-ZN) （No.5700-202418240A-1-1-ZN）
