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.