Abstract: Aiming at the problems of alarm flooding, high false alarm rate, and delayed response in industrial centralized control centers, this paper proposes a monitoring and alarm method based on state estimation and intelligent linkage. Early fault perception and sensor anomaly identification are achieved through system state estimation and chi-square testing; redundant alarms are filtered using causal inference and alarm suppression strategies; a multi-factor dynamic risk assessment model is established to prioritize alarm events in real time; and hierarchical, multi-modal alarm outputs are triggered according to risk levels, with automatic association and push of standardized handling procedures. Experiments show that the proposed method can significantly reduce the false alarm rate, suppress alarm storms, improve emergency response efficiency and system security, providing reliable technical support for the intelligent monitoring and operation of industrial centralized control centers.