Dam safety monitoring data completion method based on space-time multi-view fusion

Abstract

The invention discloses a dam safety monitoring data completion method based on space-time multi-view fusion, which comprises the following steps: abstracting view models on a global space view, a global time view, a local space view and a local time view respectively according to the characteristics of dam safety monitoring data; fusing the four models by using lasso regression to generate a space-time multi-view fusion model; and generating complemented data by using the space-time multi-view fusion model. Under the condition of strong correlation of spatial-temporal characteristics, the method can well solve the problems of block missing, local missing and the like in dam safety monitoring data, and through verification on real dam safety monitoring data, the method has smaller errors and better completion effects compared with a conventional classical algorithm and a conventional spatial-temporal model.

Description

technical field [0001] The invention relates to a missing data complement method, in particular to a dam safety monitoring data complement method based on spatio-temporal multi-view fusion. Background technique [0002] With the maturity of Internet technology and the rapid development of data collection and storage capabilities, big data technology has completely penetrated into the field of data information. However, due to the lack of real data, the models and methods based on the ideal data set can no longer meet the real needs of data mining. In order to mine reliable information and establish a more effective application data mining model, it is necessary to complete missing data. He et al. reconstructed the missing data based on the framework of deep learning to facilitate the analysis of time series. The framework is based on the time series of observed data, based on the collection of multiple forecasting models, and the coupling between forecasting modules is com...

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Problems solved by technology

However, this kind of proposed method cannot deal with problems such as continuous missing of mixed classification. The method proposed by Vincent based on DAE also needs to have better accuracy under the premise of complete data, so the effect is not ideal for incomplete data.

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