吉林大学学报（信息科学版）2026，Vol.44Issue(2)：284-290,7.
基于矩阵分解的管道流动系统递推滤波
Matrix Factorization Based Recursive Filtering for Pipeline Flow Systems
摘要
Abstract
Long-distance pipelines spanning,complex geographical environments and diverse climate zones require monitoring systems to address multiple technical difficulties,while existing theories predominantly rely on mathematical models under idealized conditions.To address the requirements of energy efficiency optimization and security protection for intelligent pipeline monitoring,a collaborative analysis model integrating DCS(Duty Cycle Scheduling)and DoS(Denial of Service)attack characteristics is constructed.This framework innovatively combines MF(Matrix Factorization)technology with a novel recursive filtering algorithm.By establishing a discretized pipeline system model and a filter model incorporating multi-source noise and stochastic nonlinearities,a recursive filtering algorithm derived from solving the Riccati difference equation is proposed.A rigorous analysis of the boundedness of the filtering error covariance is conducted,and the optimal filter gain for the system is derived.Simulation results demonstrate that the proposed method achieves reduced energy consumption in pipeline sensor networks under sparse measurements while maintaining the integrity of output data.It effectively compensates for state estimation deviations caused by noise and stochastic nonlinear factors,enabling precise filtering of pipeline system flow rates and pressures.关键词
天然气管道系统/占空比调度/拒绝服务攻击/矩阵分解/递推滤波Key words
natural gas pipeline systems/duty cycle scheduling/denial-of-service attacks/matrix factorization/recursive filtering分类
信息技术与安全科学引用本文复制引用
高宏宇,胡银鸽,于皓然,蔡金瑞..基于矩阵分解的管道流动系统递推滤波[J].吉林大学学报（信息科学版）,2026,44(2):284-290,7.基金项目
国家自然科学基金资助项目(62103096) （62103096）
黑龙江省自然科学基金资助项目(LH2023F006) （LH2023F006）
