【摘要】 通过大数据预处理和分析技术，对位移、应变、风速、温度、湿度和索力等桥梁监测数据进行分析。首先使用傅里叶变换和小波变换，分析了不同监测数据类型之间的相关性，之后通过Bi-LSTM多源预测模型验证了相关性分析的结论。结果表明：位移信号与温度信号之间具有负相关性、位移信号与湿度信号具有正相关性，引入相关性强的桥梁监测数据建立多源预测模型能有效提高预测精度。研究结果对桥梁结构健康监测数据的关联分析与挖掘有参考价值，可为桥梁的日常养护、监测运营和应急管理提供决策依据。更多还原

【Abstract】 Through big data preprocessing and analysis technology,the displacement,strain,wind speed,temperature,humidity and cable stress of bridge monitoring data were analyzed.Firstly,Fourier transform and wavelet transform were used to analyze the correlation between different monitoring data types,and then BI-LSTM multi-source prediction model was used to verify the correctness of the conclusion of correlation analysis.The results show that there is a negative correlation between the displacement signal and the temperature signal,and a positive correlation between the displacement signal and the humidity signal.Introducing the bridge monitoring data with strong correlation to establish the multi-source prediction model can effectively improve the prediction accuracy.The research results of this paper have reference value for the correlation analysis and mining of bridge structural health monitoring data,and provide decision-making basis for the daily maintenance,monitoring operation and emergency management of bridges.更多还原