The invention discloses a safety performance monitoring method, system and equipment for an additionally-installed elevator steel structure and a medium, and belongs to the field of steel structures. According to the method, the Kalman filtering algorithm is adopted to predict and track the data, and the model data deviation is automatically reduced, so that the data accuracy of the deformation characteristic and the vibration characteristic of the steel structure is improved. According to the method, a K-means algorithm is used for clustering, the optimal clustering number K = 4 is extracted, and the deformation quantity and deformation degree clustering of the sample points of the accurately added elevator steel structure are obtained and serve as an important basis for determining the deformation degree of the deformation points and needing to be overhauled. According to the method, the K-means clustering method is optimized through the Kalman filtering algorithm, the actual safety of the elevator steel structure is evaluated in real time, and normalized state monitoring and safety diagnosis on the elevator steel structure are accurately achieved.
本发明公开一种加装电梯钢结构的安全性能监测方法、系统、设备及介质,属于钢结构领域。该方法采用Kalman滤波算法可实现对数据的预测及追踪,自动降低模型数据偏差,以提高钢结构形变特征与震动特征的数据精准度。K‑means算法进行聚类,并提取最优聚类数K=4,得出准确加装电梯钢结构样本点形变量及形变程度聚类,作为确定形变点的形变程度及需要进行检修的重要依据。本发明利用Kalman滤波算法来优化K‑means聚类方法,实时评价实际电梯钢结构安全性,精准地实现了对电梯钢结构进行常态化状态监测与安全诊断。
Safety performance monitoring method, system and equipment of additionally-installed elevator steel structure and medium
加装电梯钢结构的安全性能监测方法、系统、设备及介质
22.03.2024
Patent
Elektronische Ressource
Chinesisch
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