摘要
随着物联网技术的快速发展,传感器数据的异常检测成为保障系统安全稳定运行的关键。传统的异常检测方法在面对复杂时序数据时,存在检测精度不足和实时性差等问题。对此,提出一种基于改进Transformer的物联网传感器数据异常检测算法,通过优化位置编码、自注意力机制以及引入门控循环单元(Gated Recurrent Unit,GRU)来提高模型的时序建模能力和计算效率。实验结果表明,改进后的算法在多个指标上优于传统方法,特别是在处理复杂数据模式和大规模数据流时,能够有效提升异常检测的准确性和实时性。
With the rapid development of the Internet of Things technology,anomaly detection in sensor data has become crucial for ensuring system security and stable operation.Traditional anomaly detection methods face challenges such as insufficient detection accuracy and poor real-time performance when dealing with complex time-series data.To address these issues,this paper proposes an improved Transformer-based anomaly detection algorithm for Internet of Things sensor data.By optimizing positional encoding,self-attention mechanisms,and incorporating Gated Recurrent Unit(GRU),the proposed method enhances the model’s temporal modeling capability and computational efficiency.Experimental results demonstrate that the improved algorithm outperforms traditional methods across multiple metrics,particularly in handling complex data patterns and large-scale data streams,significantly improving anomaly detection accuracy and real-time performance.
作者
张欢欢
ZHANG Huanhuan(Shandong Huayu Institute of Technology,Dezhou,Shandong 253034,China)
出处
《智能物联技术》
2025年第4期33-37,共5页
Technology of Io T& AI
