水工建筑物安全监控深度分析模型及其优化研究

随着水工建筑物安全管理自动化技术的发展,以丰富性、多样性、复杂性为特点的大数据逐渐成为水工建筑物安全监控体系的显著特征.常用安全监控数学模型(三大常规模型、浅层学习算法)难以从大量数据中自动提取深层次潜在信息,即浅层模型与大数据挖掘分析不相适应.深度学习算法由多重非线性映射层构成,能够逐层学习输入数据本质特征并完成高级...

Safety monitoring model of hydraulic structures and its optimization based on deep learning analysis

With the development of automation technology for safety management of hydraulic structures, big data characterized by richness, diversity and complexity has gradually become a significant feature of safety monitoring system of hydraulic structures. The commonly used mathematical models of safety monitor- ing (three conventional models and shallow learning algorithms) are difficult to extract the deep underlying information automatically from large amounts of data, i.e. the shallow model is incompatible with big data mining and analysis. Deep learning algorithm is composed of multiple nonlinear mapping layers, which can learn the essential characteristics of input data layer by layer and complete the high-level abstraction, but it also has some problems such as poor engineering applicability. To address this issue, this paper summa- rizes the features of safety monitoring big data,introduces long-term short-term memory (LSTM),and pro- poses an optimized deep analysis model for safety monitoring of different types of hydraulic structures. The model takes competitive learning mechanism as the core, adopts digital filtering, limited interval and roll- ing iteration to improve LSTM from three aspects of front-end processing, network structure and epitaxial prediction. It also achieves optimization modeling through random search and step verification. Combining with engineering projects,several groups of measured data of different effect quantities were selected as typ- ical application scenarios, and the effectiveness of the proposed method has been verified and evaluated through simulation and comparison experiments. The results indicate that compared with the shallow model, the deep model is more suitable for safety monitoring big data processing in most scenarios, so as to pro- vide decision support for the safe operation of hydraulic structures.