Early warning method for third-party construction of oil and gas pipelines based on emd decomposition and lstm
A technology for oil and gas pipelines and classification models, applied in neural learning methods, instruments, biological neural network models, etc., can solve problems such as waveform data interference, low precision, and inability to accurately identify third-party construction, and achieve classification errors, The effect of rapid classification recognition, strong universality and portability
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Embodiment 1
[0044] In this embodiment, a distributed optical fiber sensor is laid along the long-distance oil and gas pipeline, which is buried about 3-5 meters deep underground like the oil and gas pipeline. The back Rayleigh scattered light intensity along the axial direction in the optical fiber is detected by technology, and the specific position of the disturbance event is located according to the coherent interference result of the returned back Rayleigh scattered light, and according to the difference of the interference waveform, the Accurate classification and identification of vibration sources.
[0045] Specifically, such as figure 1 As shown, in this example the The distributed optical fiber system consists of three major parts, including distributed optical fiber vibration sensors, data acquisition modules and computers.
[0046] Distributed optical fiber vibration sensors specifically include: ultra-narrow linewidth lasers, acousto-optic modulators, erbium-doped optical ...
Embodiment 2
[0115] The difference between the third-party construction early warning method for oil and gas pipelines based on EMD decomposition and LSTM in this embodiment and Embodiment 1 is that:
[0116] The threshold for early warning triggering is not limited to the three consecutive forecast results described in Example 1, which are bands of third-party construction danger signals such as excavators. It can also be only once, or twice, four times, or five times to meet different application needs.
[0117] Other steps can refer to embodiment 1.
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