在核电厂中，反应堆冷却剂泵（简称“主泵”）是反应堆冷却剂系统的核心设备之一，实时监测其运行状态对于确保核电厂的安全、稳定、高效以及经济运行至关重要。为了提升传统多元状态估计方法处理时变系统的能力，实现实时跟踪系统状态变化，提出了一种动态多元状态估计技术（dynamic multivariate state estimation technique, DMSET）算法。该方法利用最近邻方法（nearest neighbors, NN）重构数据，通过重构结果与实际值之间的残差来计算异常程度，并基于设定的阈值以识别异常。在记忆矩阵更新中，DMSET使用孤立森林方法（isolation forest, IF）评估样本的重要性，确保记忆矩阵包含最新且具代表性的信息。在核电厂主泵运行数据集上进行实验结果表明，DMSET能够有效检测到核电真实数据中的异常，尤其在实时数据流中表现突出，能够在变化环境中保持较高的稳定性。检测精确率和召回率均高于90%，检测性能相对主成分分析法（principal component analysis, PCA）和MSET等经典异常检测算法具有优势，具备一定的实用价值和研究意义。

In a nuclear power plant, the reactor coolant pump (referred to as the "main pump") is one of the core components of the reactor coolant system. Real-time monitoring of its operational status is critical for ensuring the safe, stable, efficient, and economic operation of the nuclear power plant. To enhance the ability of traditional multivariate state estimation methods in handling time-varying systems and to achieve real-time tracking of system state changes, a dynamic multivariate state estimation technique (DMSET) algorithm was proposed. This method used the nearest neighbors (NN) algorithm to reconstruct data, calculates the anomaly degree based on the residuals between the reconstruction results and actual values, and identifies anomalies using a set threshold. In the memory matrix update, DMSET utilized the isolation forest (IF) method to assess the importance of samples, ensuring that the memory matrix contains the most recent and representative information. Experimental results on a nuclear power plant main pump operational dataset show that DMSET can effectively detect anomalies in real nuclear data, particularly excelling in real-time data streams, maintaining high stability in changing environments. The detection accuracy and recall rate are both above 90%, and the detection performance outperforms traditional anomaly detection algorithms such as principal component analysis (PCA) and MSET, demonstrating practical value and research significance.