A Method for Evaluation of Single Measuring Point for Dam Safety Monitoring Based on Multiple Correlation Sequences
A related sequence and safety monitoring technology, applied in the direction of instruments, biological neural network models, design optimization/simulation, etc., can solve problems such as instability, noise values and missing values, and affect the accuracy of single-measurement point evaluation, etc., to improve performance effect
Active Publication Date: 2020-10-16
HOHAI UNIV
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In practical applications, the sequence data of a single measurement point often exhibits characteristics such as non-smoothness, instability, noise values and missing values, which will affect the accuracy of single measurement point evaluation
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[0025] Below in conjunction with specific embodiment, further illustrate the present invention, should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various equivalent forms of the present invention All modifications fall within the scope defined by the appended claims of the present application.
[0026] A large number of sensors are deployed in the structure to sense changes in various physical quantities and generate massive monitoring data during production and operation. Since the data change of a single measuring point is often accompanied by the synchronous change of the data of multiple related measuring points, by analyzing the time series data of multiple measuring points, predicting the monitoring value, combining the range of monitoring data changes and the observation information...
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The invention discloses a single measuring point evaluation method for dam safety monitoring based on multi-correlation sequences, which includes three stages: measuring point sequence preprocessing, single measuring point time series data prediction and single measuring point operating state evaluation. Using the single-node assessment model based on Multi-sequence Collaboration (SAM) based on multi-sequence collaboration, the sequence data of the measurement points are preprocessed, and after normalization, the cosine similarity is used to calculate the correlation between the sequences . Select multiple related sequences as input, and add an attention layer to assign attention weights to multiple sequences, then use the LSTM layer for prediction, and finally complete the single-point evaluation through the Softmax layer, thereby increasing the accuracy of single-point evaluation and improving dam safety Performance of single measuring point safety evaluation in monitoring system. The invention utilizes a plurality of relevant measuring point sequences to predict and evaluate the monitoring data of a single measuring point, which is beneficial to improving the safety evaluation performance of a single measuring point in a dam safety monitoring system.
Description
technical field [0001] The invention belongs to the field of engineering safety monitoring, in particular to a method for evaluating a single measuring point of dam safety monitoring based on a multi-correlation sequence. Background technique [0002] During the production and operation of large civil engineering structures, the deployed automated monitoring system will continuously generate new monitoring data. Due to the huge space of the structure and the large number of measuring points, it is difficult to obtain the actual operation of the structure directly from the massive original monitoring data. Therefore, it is necessary to identify, analyze, extract and summarize the monitoring data, combined with the actual structure Judging the operating status to ensure its safe and sustainable operation. [0003] Engineering practice shows that the operating state of structures is reflected by monitoring data, and the analysis of observation data has very important engineeri...
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IPC IPC(8): G06F30/27G06K9/62G06N3/04
CPCG06F30/20G06N3/044G06N3/045G06F18/22
Inventor 毛莺池程杨堃齐海王龙宝平萍
Owner HOHAI UNIV