电子学报2025，Vol.53Issue(9)：3047-3059,13.DOI:10.12263/DZXB.20250473
基于大语言模型的时空数据零样本插补
LLM-Based Zero-Shot Imputation of Spatiotemporal Data
摘要
Abstract
Internet of things(IoT)sensing data commonly suffers from data sparsity issues due to multiple factors in-cluding deployment costs,environmental constraints,and equipment failures,severely limiting the overall performance of intelligent sensing systems.Most existing imputation methods rely on labeled data for supervised training,resulting in se-verely insufficient generalization capabilities when facing"cold start"scenarios in new environments,failing to meet the practical demands of rapid IoT deployment and cross-domain applications.This paper introduces,for the first time,the in-trinsic reasoning capabilities of large language models(LLM)into the spatiotemporal data imputation domain,proposing the ZeroImpute framework based on multi-agent collaborative reasoning that achieves a paradigmatic shift from traditional"data-driven learning"to"knowledge-driven reasoning."The core innovation of this method lies in constructing a collabora-tive reasoning system comprising specialized task-oriented LLM agents:the temporal analysis agent is responsible for se-mantic understanding and reasoning of complex temporal dependencies,capturing forward evolutionary trends and back-ward constraint conditions through bidirectional sequence modeling;the spatial analysis agent focuses on modeling and parsing dynamic spatial relationships,achieving precise identification of time-varying spatial correlations through temporal context guidance;the imputation decision agent integrates multi-source semantic knowledge and employs adaptive weight fusion algorithms to complete final intelligent imputation decisions.Each agent achieves deep understanding of complex spatiotemporal patterns through semantic knowledge representation and logical reasoning,transforming traditional numeri-cal computation problems into semantic reasoning tasks that can be collaboratively processed by multiple agents,thereby overcoming the limitations of single models in handling complex spatiotemporal relationships.The framework possesses significant technical advantages:first,it achieves true zero-shot generalization capability,enabling direct deployment with-out requiring any domain-specific training data;second,through multi-agent specialization,it enhances the identification ac-curacy and reasoning quality of complex spatiotemporal patterns;third,it exhibits excellent interpretability with transparent agent reasoning processes,enhancing system trustworthiness;finally,plug-and-play deployment substantially reduces tech-nical barriers and deployment costs for practical applications.Comprehensive evaluations on three real-world IoT datasets demonstrate that ZeroImpute achieves at least a 4.5%performance improvement in MAE compared to the best-performing specialized deep learning models under strictly zero-shot,zero-training settings.Moreover,the method exhibits robustness across different missing rate scenarios,effectively addressing critical practical challenges including rapid deployment in new regions,cross-domain data imputation generalization,and efficient deployment in resource-constrained environments.This research pioneers a new paradigm of multi-agent collaborative reasoning for spatiotemporal computation,providing novel technical pathways for the spatiotemporal data imputation field and offering crucial technical support and theoretical foundations for advancing IoT technology adoption across broader industrial applications.关键词
物联网感知/数据稀疏/冷启动/时空数据插补/大语言模型/零样本Key words
internet of things sensing/data sparsity/cold start/spatiotemporal data imputation/large language mod-els/zero-shot分类
信息技术与安全科学引用本文复制引用
梅雅欣,秦慧玲,梁玉珠,张广学,王田..基于大语言模型的时空数据零样本插补[J].电子学报,2025,53(9):3047-3059,13.基金项目
国家自然科学基金(No.62372047) （No.62372047）
北京市自然科学基金(No.4232028) （No.4232028）
珠海市产学研项目(No.2220004002686,No.2320004002812) （No.2220004002686,No.2320004002812）
珠海市基础与应用共础课题研究项目(No.2220004002619) （No.2220004002619）
珠海市社会发展领域科技计划项目(No.2320004000213) National Natural Science Foundation of China(No.62372047) （No.2320004000213）
Natural Science Foundation of Beijing(No.4232028) （No.4232028）
Zhuhai Industry-University-Research Project(No.2220004002686,No.2320004002812) （No.2220004002686,No.2320004002812）
Zhuhai Ba-sic and Applied Basic Research Project(No.2220004002619) （No.2220004002619）
Zhuhai Social Development Field Science and Technology Plan Project(No.2320004000213) （No.2320004000213）
