Hierarchical multi-source data fusion method for pipeline linkage monitoring network

Abstract

The invention discloses a hierarchical multi-source data fusion method for a pipeline linkage monitoring network, which comprises the following steps: carrying out data level preprocessing for various primary linkage detection signals acquired by a sensor at a common node of the monitoring network by using wavelet transformation, and extracting leakage-sensitive characteristic parameters; establishing a characteristic level data fusion model based on an ant colony neural network, processing the leakage characteristic parameters extracted by various sensors on the node, and constructing an elementary probability assignment function of evidence according to the output result of the ant colony neural network; and carrying out evidence synthesis at a cluster-head node according to an evidence combination rule, and making final decisions according to a maximum trust value method. The invention provides the hierarchical multi-source linkage detection data fusion method from the data level and characteristic level to decision level, and solves the multi-source data processing problem of the pipeline linkage monitoring network; and the method utilizes the linkage detection information acquired by various sensors in the network so as to effectively improve the accuracy rate of leakage identification.

Description

technical field [0001] The invention relates to the technical field of pipeline leakage detection, in particular to a multi-source detection data fusion method based on a wavelet neural network and evidence theory in a pipeline leakage monitoring network based on a wireless sensor network. Background technique [0002] The urban natural gas supply system is one of the "lifelines" of modern cities, and its main transmission and distribution method is pipeline transmission. As the natural gas pipeline network becomes more and more complex and the pipelines become longer and longer, major safety hazards become more prominent. Due to factors such as pipeline deterioration, aging, natural disasters and man-made destruction, pipeline leakage and resulting explosion accidents occur frequently, seriously threatening the safety of the urban natural gas supply system. The common causes of pipeline leakage are: the stress caused by the switch of the pump station, the misoperation of t...

AI Technical Summary

Problems solved by technology

[0004] Although some research results have been achieved in pipeline leak detection, there are still some problems as follows: 1) The manual regular inspection method based on a single leak detection instrument has low efficiency and poor real-time performance, and urgently needs networked and automated monitoring; 2 ) Existing monitoring systems are mostly used for long-distance pipeline detection, and it is difficult to apply to urban natural gas pipeline leakage detection under strong background noise and complex working conditions

However, the basic probability assignment is a prerequisite for decision analysis using evidence theory, and its selection directly affects the accuracy and effectiveness of the final fusion results. At present, most of them rely entirely on experts for subjective assignment.

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