REN Qiubing, SHEN Yang, LI Mingchao, KONG Rui, LI Minghao

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 monitoring (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 summarizes the features of safety monitoring big data, introduces long-term short-term memory (LSTM), and proposes 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 rolling 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 typical 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 provide decision support for the safe operation of hydraulic structures.

Keywords: hydraulic structure, safety monitoring, deep learning, long short-term memory networks, intelligent analysis