Cable Tunnel Intrusion Monitoring Method Based on Optical Fiber Sensor Signal and Abnormal Coding

Abstract

The invention relates to a cable tunnel intrusion monitoring method based on optical fiber sensor signals and abnormal coding. The method can encode the abnormal conditions of the input optical fiber sensor signals, and then obtain a regression model based on coding, and then realize the monitoring of cable tunnel intrusion conditions. monitor. Using the patent of the invention, a regression prediction model based on abnormal codes rather than specific values ​​can be obtained, which can avoid the problem of inability to run stably for a long time caused by the binding of traditional prediction methods with specific experimental values, and is effective in a longer time range Monitor the cable tunnel intrusion.

Description

Technical field: [0001] The invention relates to a cable tunnel intrusion monitoring method based on optical fiber sensor signals and abnormal codes, which is used for monitoring the intrusion situation of cable tunnels, and belongs to the technical field of cable tunnel state safety monitoring. Background technique: [0002] Power cables are an important carrier of power transmission, and cable tunnel failures affect the performance of power grid construction. Criminals often invade cable tunnels, and the internal cross interconnection wires, grounding wires, grounding copper bars, etc. in the tunnel are often stolen and stolen, which not only causes huge economic losses, but also seriously affects the safe and effective operation of the power system. Therefore, It is very necessary to monitor the cable tunnel intrusion. [0003] Currently, there are two ways to monitor the intrusion of cable tunnels: one is to have a special person check the tunnel situation at fixed inte...

AI Technical Summary

Problems solved by technology

Criminals often invade cable tunnels, and the internal cross-connection wires, grounding wires, and grounding copper bars in the tunnel are often stolen, which not only causes huge economic losses, but also seriously affects the safe and effective operation of the power system. Therefore, It is very necessary to monitor the cable tunnel intrusion

[0003] There are currently two ways to monitor cable tunnel intrusions: one is to directly have special personnel inspect the tunnels at fixed intervals. There will be situations where the detection is not timely or missed; the second is to use the optical fiber sensor signal to reflect the situation in the tunnel, and establish a corresponding artificial intelligence model to predict possible intrusions. These models are usually based on laboratory data or cable tunnels. The data obtained at the initial stage is constructed, and its characteristic is that it is bound to a specific value and the accuracy will be very high in the initial stage of system operation; however, due to the fact that tunnels with large regional distribution will inevitably experience settlement and deformation during long-term use, So these numerical-based models will become more and more inaccurate, and the false positive rate will continue to increase after running for a period of time

[0004] Therefore, it is necessary to propose a method that can avoid the problem of binding the traditional method and the specific experimental value, and monitor the cable tunnel intrusion in a longer time range.

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