Method for identifying disturbance event in distributed type optical fiber pipeline security early-warning system

Abstract

The invention discloses a method for identifying a disturbance event in a distributed type optical fiber pipeline security early-warning system. When the disturbance event exists, wavelet de-noising is conducted on two routes of sampling signals. The characteristic values of one sampling signal where wavelet de-noising is conducted are extracted, wherein the characteristic values include the vibration fragment length, the time domain energy, the k-order original point distance, the k-order center distance, the skewness, the kurtosis and low frequency wavelet coefficient energy Ej, obtained through wavelet decomposition, of all layers, and j ranges from 1 to 7. The thirteen extracted characteristic values are sent to a decision tree classification device, and the type of the disturbance event is obtained through the decision tree classification device. Man-machine interaction incremental learning is achieved by changing the type of the disturbance event stored in a database under the condition that a new type of the disturbance event appears or the type, obtained through the decision tree classification device, of the disturbance event is wrong, and online training is conducted on the decision tree classification device according to the modified type of the disturbance event. By means of the method, the type of the disturbance event can be accurately obtained.

Description

technical field [0001] The invention relates to a disturbance event identification method in a distributed optical fiber pipeline safety early warning system, belonging to the technical field of optical fiber sensing. Background technique [0002] With the development of society and the advancement of science and technology, the protection of various social properties and public facilities has gradually received attention, especially for defense targets with fixed locations and wide geographical ranges such as transportation lines, energy pipelines, and communication lines. The requirement is that the safety early warning system has the ability to intelligently identify disturbance event patterns in the case of ultra-long-distance early warning. Intelligent identification of event patterns, that is, intelligent disturbance information processing and identification; 3. It has the ability to perform incremental learning in response to changes in the environment and the emergen...

AI Technical Summary

Problems solved by technology

The disadvantage of this type of method is that the analysis of eigencomponents is more complicated, and disturbance events that require a combination of multiple eigenvalues ​​to be able to distinguish cannot be effectively distinguished, and the classification accuracy cannot be guaranteed; it can only be applied in a specified environment, and the specified disturbance Event classification and distinction, can no longer carry out incremental learning to deal with changes in the environment and the emergence of new types of disturbance events, lack of versatility

The disturbance event identification method in the existing distributed optical fiber pipeline safety early warning system also has a classifier using a support vector machine. The classifier using a support vector machine is more dependent on the accuracy of the training sample data. When a certain sample error is large, it will It has a great impact on the overall training effect and further affects the classification accuracy

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