摘要
为了提升边坡位移预测结果的可靠性,并为边坡结构的稳定性提供全面的技术支持,引入多头自注意力机制,并以某高陡松散岩质边坡为例,设计并研究其位移预测方法。在边坡的关键位置布置监测点,以收集其短期内的位移数据。对采集到的数据进行预处理,包括滤波处理和去趋势化,以确保采样信号的清晰与准确。将多头自注意力机制应用于边坡位移监测数据的处理中,以捕捉不同位置和时间点的位移数据之间的内在关系与局部动态特征。为了进一步提升预测结果的准确性,采用动态时间规整技术对预测结果进行优化与调整,实现对预测结果偏差的补偿。对比实验结果表明:该方法不仅能够准确预测高陡松散岩质边坡的位移情况,而且预测结果的偏差控制在±1 mm范围内,这充分证明了该方法的有效性和可靠性。
This paper introduces a multi head self attention mechanism and takes a high steep loose rock slope as an example to design and study its displacement prediction method.Install monitoring points at key locations on the slope to collect short-term displacement data.Preprocess the collected data,including filtering and de trending,to ensure the clarity and accuracy of the sampled signal.Applying multi head self attention mechanism to the processing of slope displacement monitoring data to capture the intrinsic relationships and local dynamic features between displacement data at different positions and time points.Dynamic time warping technology is adopted to optimize and adjust the prediction results,and compensate for the deviation of the prediction results.
出处
《工业控制计算机》
2025年第8期70-72,共3页
Industrial Control Computer
关键词
多头自注意力机制
动态时间规整
预测方法
位移
边坡
multi head self attention mechanism
dynamic time regulation
prediction methods
displacement
slope
