CN109470352A - Distributed optical fiber pipeline safety monitoring algorithm based on adaptive threshold - Google Patents

Distributed optical fiber pipeline safety monitoring algorithm based on adaptive threshold Download PDF

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CN109470352A
CN109470352A CN201811220973.0A CN201811220973A CN109470352A CN 109470352 A CN109470352 A CN 109470352A CN 201811220973 A CN201811220973 A CN 201811220973A CN 109470352 A CN109470352 A CN 109470352A
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adaptive threshold
alarm
optical fiber
data
point
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CN109470352B (en
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邢陆雁
张妮娜
曹峰
纪圣华
王秀亮
张玉超
刘顺
王鹏耀
王炜
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Northeast Part Of China Weihai Optoelectronic Information Technical Concern Co
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01HMEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H9/00Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by using radiation-sensitive means, e.g. optical means
    • G01H9/004Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by using radiation-sensitive means, e.g. optical means using fibre optic sensors

Abstract

The present invention provides a kind of distributed optical fiber pipeline safety monitoring algorithm based on adaptive threshold, the algorithm include: to acquire original signal from optical fibre vibration sensor and pre-processed;Multi-level Wavelet Transform packet decomposition is carried out on time domain direction to original signal, a certain details coefficients is chosen and signal is reconstructed;Spatial signature vectors are calculated according to reconstruction signal;Adaptive threshold is calculated according to spatial signature vectors and judges alert locations;The comparison of type of alarm library is finally carried out, corresponding type of alarm is exported.Distributed optical fiber pipeline safety monitoring algorithm based on adaptive threshold can accurately identify artificial excavation and mechanical excavation event, and can effectively shield the worn-out influence for crossing vehicle, have the characteristics that strong environmental adaptability, system sensitivity are high.

Description

Distributed optical fiber pipeline safety monitoring algorithm based on adaptive threshold
Technical field:
The present invention relates to distribution type fiber-optics to the safety-security area of oil and gas pipeline vibration monitoring, specifically a kind of base In the distributed optical fiber pipeline safety monitoring algorithm of adaptive threshold.
Background technique:
In petrochemical industry, it is artificial broken that oil and gas pipeline often occurs pipeline construction, steals oily stolen etc. Bad thing part, oil gas is once serious threat will be brought to the security of the lives and property of the people by revealing, therefore pipe safety is supervised Survey technology is increasingly taken seriously and is rapidly developed.Compared to the monitoring such as traditional artificial line walking and conduit fluid pressure detection Means are compared, and distributed optical fiber vibration monitoring technology is with the response time is fast, monitoring distance is long, accurate positioning, the operation is stable, resists The advantages such as electromagnetic interference capability is strong, especially system can provide early warning before destructive insident occurs, this largely may be used To avoid the generation of event.Currently, distributed optical fiber vibration sensing technology has been increasingly used in oil and gas pipeline Safety monitoring on, and just gradually development be mainstream monitoring mode.
The performance of distributed optical fiber pipeline safety monitoring system is influenced by a key factor --- and vibration monitoring is calculated Method, vibration monitoring algorithm are the key that realize that real-time high-efficiency accurately identifies pipeline damage event.All can in current most of algorithms The judgement of vibration alarming is carried out by the way of static threshold, this mode needs constantly to adjust static threshold according to actual environment It is optimal it in actual use, entire to adjust ginseng process complicated, the environmental suitability of algorithm is poor.Light is determined by spike point The problems such as mode of fine vibration position can overcome static threshold bring tune ginseng difficult, but this mode is easy by noise It interferes and generates erroneous judgement, and with the increase of Monitoring Pinpelines distance, the energy of light can generate decaying, and spike point is caused to be weakened To can not promptly and accurately detect the position of fiber-optic vibration, it is then unable to monitor pipeline damage event.
Summary of the invention:
For the above technical problems, the present invention provides a kind of distributed optical fiber pipelines based on adaptive threshold Safety monitoring algorithm, the algorithm incorporate spatial positional information in threshold value, make algorithm determine to report in conjunction with relaxation factor It can not be influenced by optical fiber itself and optical fiber local environment when alert, greatly improve the environmental suitability of system, simultaneously should Algorithm can play compensating action to the optical attenuation that long-distance pipe monitors, and effectively raise the sensitivity of system.Detailed technology Scheme is as described below:
A kind of distributed optical fiber pipeline safety monitoring algorithm based on adaptive threshold, it is characterised in that including following step It is rapid:
Step 1: the data for reading optical fibre vibration sensor acquisition are gone forward side by side line number Data preprocess;
Step 2: WAVELET PACKET DECOMPOSITION being carried out on time domain direction to pretreated data, and reconstructs a certain details coefficients;
Step 3: sub-frame processing being carried out to the reconstruction signal of each location point, and prominent according to the vibration that frame energy seeks signal The vibration mutation amount of variable, each location point forms spatial signature vectors;
Step 4: calculating the adaptive threshold of spatial signature vectors, the position of destructive insident generation is acquired by threshold decision It sets, and location status is set.
Step 5: the state that the position of destructive insident will be present is cached, by the position of caching in regular hour segment It sets state group to be compared with type of alarm library, determines type of alarm and export.
In above-mentioned steps 1 of the present invention, the data of fibre optical sensor acquisition are two-dimensional matrix Dm×n=(dij)m×n, (i=1, 2...,m;J=1,2..., n), wherein m indicates time dimension, i.e., the data points acquired in one second, in one second time The data acquired at j-th of position of optical fiber can be expressed as (d1j,d2j,...,dmj)T;N representation space dimension, i.e., a certain moment The data of whole optical fiber of acquisition, the data that a certain moment i optical fiber acquires at all positions can be expressed as (di1, di2,...,din)。
Data prediction is that collected initial data is carried out longitudinal sliding motion average filter, for filtering out jump signal It influences, concrete implementation step are as follows:
Step 1: choosing rectangular window Wl×nAnd determine the length l of the rectangular window and step-length s of movement;
Step 2: the signal mean value in rectangular window is calculated on time dimensionCalculation formula are as follows:
Wherein, i indicates that window function is located at longitudinal initial position of initial data, initial value 1.
Step 3: window function being moved into s along the longitudinal direction of initial data, repeats step 2, i=i+s at this time.Until window Function stops calculating when sliding into the end of initial data longitudinal direction, will obtain pretreated data at this time
In above-mentioned steps 2 of the present invention, the vertical WAVELET PACKET DECOMPOSITION and reconstruct refer to pretreated signal progress three Layer 2-d wavelet packet decomposes, and the vertical component for extracting third layer carries out signal reconstruction.
In above-mentioned steps 3 of the present invention, the vibration mutation amount is used to indicate that signal suddenly change when optical fiber vibrates Severe degree, obtain vibration mutation amount realization step are as follows:
Step 1;To the sub-frame processing in vertical direction of the signal after wavelet reconstruction, and calculate the energy of each frame;
Step 2: the energy differences between consecutive frame are calculated, and are normalized:
E‘i=(Ei+1-Ei)/Ei, i=1,2 ..., p-1
Wherein, E is frame energy, and p is the number of frame, E 'iFor i-th of normalized frame energy differences.
Step 3: all frame energy differences maximizings that step 2 calculates are required vibration mutation amount.
Through the above steps, each spatial position point can acquire a vibration mutation amount, all vibration mutation amounts One group of spatial signature vectors is formed in the horizontal direction.
In above-mentioned steps 4 of the present invention, the calculating process of the adaptive threshold is as follows:
Step 1: calculating the peak point of spatial signature vectors.The second differnce amount of each value in feature vector is calculated first, Calculation formula are as follows:
Δ2Fi=Fi+2-2Fi+1+Fi, i=1,2 ..., n-2
Wherein, F is spatial signature vectors group, and n is the dimension of vector.Secondly, judging Δ2Fi< 0 value is peak point, All peak points constitute peak value point set S={ s1,s2,……,sq}。
Step 2: traversal peak value point set S calculates the adaptive threshold of each peak point, specific calculation formula is as follows:
Wherein, thiFor peak point SiAdaptive threshold, σ is relaxation factor, for controlling the sensitivity of adaptive threshold, σ is bigger, sensitiveer, the μ of adaptive threshold judgement alarmiFor peak point SiThe mean value of relevant position in spatial signature vectors group F, If SiPosition in F is j, then μiCalculation formula it is as follows:
Corresponding peak point is compared by adaptive threshold after calculating, if peak point is greater than adaptive thresholding Value, then the corresponding position of peak point is considered as alarm point, is otherwise not considered as alarm point.
In above-mentioned steps 5 of the present invention, it is described output type of alarm the realization process includes:
Step 1: establishing type of alarm library.Under acquisition is artificially excavated respectively, machinery excavates, vehicle passes through and quiet environment Data calculate location status group of each type of data in certain time segment, and carry out class to each location status group Type mark, all location status groups constitute type of alarm library;
Step 2: for the alarm point in step 4 of the present invention, calculating the location status group of alarm point, and by itself and alarm class Type library is compared, and exports corresponding type of alarm.
Detailed description of the invention:
Attached drawing 1 is this algorithm overview flow chart
The spatial position waveform diagram that attached drawing 2 is the 1st millisecond
Attached drawing 3 is noiseless, artificial excavation, mechanical excavation, vehicle by the time domain at 2000 meters of positions in the case of four kinds Waveform diagram.
Attached drawing 4 is noiseless, artificial excavation, mechanical excavation, vehicle by small echo weight at 2000 meters of positions in the case of four kinds Waveform diagram after structure.
Attached drawing 5 is indicatrix schematic diagram in the embodiment of the present invention.
Specific embodiment:
In order to keep technical solution of the present invention and algorithm advantage clearer, we are detailed referring to attached drawing further progress below Thin description, but the protection scope of present aspect is not limited to following implementation, is limited with claim.
Embodiment one:
Fig. 1 is the master-plan flow chart of inventive algorithm, in conjunction with flow chart, specific implementation steps are as follows:
Step 1: acquisition data: sample frequency 1KHZ, the distance of fiber-optic monitoring pipeline are 20 kilometers, acquisition each second Size of data be 1000*20000, the 1st millisecond acquisition fiber data as shown in Fig. 2, because sample frequency be 1KHZ, 1000 fiber datas can be acquired in 1 second.Fig. 3 is indicated to acquire 1 second data at 2000 meters of position, is schemed (a) and represent without dry The data disturbed, figure (b) represent the data artificially excavated, and figure (c) represents the data of mechanical excavation, and figure (d) represents vehicle process Data;
Step 2: vertical WAVELET PACKET DECOMPOSITION and reconstruct: 3 grades of WAVELET PACKET DECOMPOSITIONs are carried out to current frame signal, take third layer Vertical component is reconstructed, and the time-domain signal after reconstruct in 2000 meters is as shown in Figure 4;
Step 3: spatial signature vectors are calculated: firstly, dividing 10 on time domain direction to the signal after Wavelet decomposing and recomposing Frame, frame length 100;Secondly, calculating the energy difference between the energy and consecutive frame of every frame, and normalizing is carried out with the energy of former frame Change processing, can obtain 9 framing energy differences by above-mentioned calculating on each position;9 framing energy are finally chosen on each position The maximum value of difference is measured as feature in this position, the feature of all positions constitutes spatial signature vectors, indicatrix such as Fig. 5 institute Show;
Step 4: it calculates adaptive threshold, judge alert locations: firstly, calculating the peak point of spatial signature vectors, and counting The adaptive threshold for calculating each peak point, as shown in the red line in Fig. 5;Secondly, judging the big of peak point and adaptive threshold Small, as indicated in Fig. 5 Green frame, peak point at this location is more than red threshold line, then the position belongs to alarm point, Peak point at other positions is less than red threshold line and is then determined as that other positions are not belonging to alarm point;Finally, to all Spatial position clicks through line position and sets status indicator, i.e. the state in the presence of the location point of alarm at current time is designated as 1, and there is no alarms State of the location point at current time be designated as 0.
Step 5: compare type of alarm library, output alarm: firstly, establish type of alarm library, typelib be by 700 not The location status group of same type forms, each state group is obtained by 6 seconds location status of caching, wherein belonging to artificial Excavate state group totally 300 of type, machinery excavates state group totally 200 of type, and vehicle passes through the state group of type totally 200 Item;Then, alarm 6 seconds state groups of point cache are compared with type of alarm library in real-time calculate, it finally will be with it The type for the state group matched is exported as the type of alarm of the alarm point.
Distributed optical fiber pipeline safety monitoring algorithm proposed by the present invention is that alarm is determined by way of adaptive threshold Position, this identification method is no longer limited to judgement a little, but a certain range of fiber-optic signal is taken into account, so that part Vibration is more prominent, this largely solves algorithm adaptability problem in different environments and remote monitoring is sensitive The problem of degree reduces, the position that the targeted duct destructive insident of system more precise and high efficiency can be made to occur and the corresponding report of output Alert type.

Claims (7)

1. a kind of distributed optical fiber pipeline safety monitoring algorithm based on adaptive threshold, it is characterised in that the following steps are included:
Step 1: the data for reading optical fibre vibration sensor acquisition are gone forward side by side line number Data preprocess;
Step 2: WAVELET PACKET DECOMPOSITION being carried out on time domain direction to pretreated data, and reconstructs a certain details coefficients;
Step 3: sub-frame processing being carried out to the reconstruction signal of each location point, and seeks the vibration mutation of signal according to frame energy The vibration mutation amount of amount, each location point forms spatial signature vectors;
Step 4: the adaptive threshold of spatial signature vectors is calculated, the position of destructive insident generation is acquired by threshold decision, and Location status is set;
Step 5: the state that the position of destructive insident will be present is cached, by the position shape of caching in regular hour segment State group is compared with type of alarm library, is determined type of alarm and is exported.
2. a kind of distributed optical fiber pipeline safety monitoring algorithm based on adaptive threshold according to claim 1, special Sign is in above-mentioned steps 1 that the data of fibre optical sensor acquisition are two-dimensional matrix Dm×n=(dij)m×n, (i=1,2 ..., m;J= 1,2 ..., n), wherein m indicates time dimension, i.e., the data points acquired in one second, at optical fiber j-th in one second time The data of the place's of setting acquisition can be expressed as (d1j,d2j,…,dmj)T;N representation space dimension, i.e., whole light of a certain moment acquisition Fine data, the data that a certain moment i optical fiber acquires at all positions are expressed as (di1,di2,…,din)。
3. a kind of distributed optical fiber pipeline safety monitoring algorithm based on adaptive threshold according to claim 1, special Sign is that data prediction is that collected initial data is carried out longitudinal sliding motion average filter, for filtering out the shadow of jump signal It rings, concrete implementation step are as follows:
Step 1: choosing rectangular window Wl×nAnd determine the length l of the rectangular window and step-length s of movement;
Step 2: the signal mean value in rectangular window is calculated on time dimensionCalculation formula are as follows:
Wherein, i indicates that window function is located at longitudinal initial position of initial data, initial value 1;
Step 3: window function being moved into s along the longitudinal direction of initial data, repeats step 2, i=i+s at this time, until window function Stop calculating when sliding into the end of initial data longitudinal direction, pretreated data will be obtained at this time
4. a kind of distributed optical fiber pipeline safety monitoring algorithm based on adaptive threshold according to claim 1, special Sign be in above-mentioned steps 2, the vertical WAVELET PACKET DECOMPOSITION and reconstruct refer to by pretreated signal carry out three layers of two dimension it is small Wave packet decomposes, and the vertical component for extracting third layer carries out signal reconstruction.
5. a kind of distributed optical fiber pipeline safety monitoring algorithm based on adaptive threshold according to claim 1, special Sign is in above-mentioned steps 3 that the vibration mutation amount is used to indicate that the violent journey of signal suddenly change when optical fiber vibrates Degree obtains the realization step of vibration mutation amount are as follows:
Step 1;To the sub-frame processing in vertical direction of the signal after wavelet reconstruction, and calculate the energy of each frame;
Step 2: the energy differences between consecutive frame are calculated, and are normalized:
E‘i=(Ei+1-Ei)/Ei, i=1,2 ..., p-1
Wherein, E is frame energy, and p is the number of frame, E 'iFor i-th of normalized frame energy differences;
Step 3: all frame energy differences maximizings that step 2 calculates are required vibration mutation amount;Through the above steps, Each spatial position point can acquire a vibration mutation amount, and all vibration mutation amounts form one group of sky in the horizontal direction Between feature vector.
6. a kind of distributed optical fiber pipeline safety monitoring algorithm based on adaptive threshold according to claim 1, special Sign is in above-mentioned steps 4 that the calculating process of the adaptive threshold is as follows:
Step 1: calculating the peak point of spatial signature vectors, the second differnce amount of each value, calculates first in calculating feature vector Formula are as follows:
Δ2Fi=Fi+2-2Fi+1+Fi, i=1,2 ..., n-2
Wherein, F is spatial signature vectors group, and n is the dimension of vector.Secondly, judging Δ2Fi< 0 value is peak point, is owned Peak point constitute peak value point set S={ s1,s2,……,sq};
Step 2: traversal peak value point set S calculates the adaptive threshold of each peak point, specific calculation formula is as follows:
Wherein, thiFor peak point SiAdaptive threshold, σ is relaxation factor, and for controlling the sensitivity of adaptive threshold, σ is got over Greatly, sensitiveer, the μ of adaptive threshold judgement alarmiFor peak point SiThe mean value of relevant position in spatial signature vectors group F, if Si Position in F is j, then μiCalculation formula it is as follows:
Corresponding peak point is compared by adaptive threshold after calculating, if peak point is greater than adaptive threshold, Then the corresponding position of peak point is considered as alarm point, is otherwise not considered as alarm point.
7. a kind of distributed optical fiber pipeline safety monitoring algorithm based on adaptive threshold according to claim 1, special Sign be in above-mentioned steps 5, it is described output type of alarm the realization process includes:
Step 1: establishing type of alarm library.Number under acquisition is artificially excavated respectively, machinery excavates, vehicle passes through and quiet environment According to calculating location status group of each type of data in certain time segment, and carry out type to each location status group Mark, all location status groups constitute type of alarm library;
Step 2: for the alarm point in step 4 of the present invention, calculating the location status group of alarm point, and by itself and type of alarm library It is compared, exports corresponding type of alarm.
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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111181634A (en) * 2019-12-18 2020-05-19 东南大学 Distributed optical fiber vibration signal rapid positioning method
CN111537160A (en) * 2020-05-09 2020-08-14 深圳市行健自动化股份有限公司 High-energy pipeline leakage monitoring method based on distributed optical fiber
CN111539394A (en) * 2020-07-08 2020-08-14 浙江浙能天然气运行有限公司 Pipeline along-line third-party construction early warning method based on time domain characteristics and space-time information
CN112187349A (en) * 2020-10-15 2021-01-05 上海欣诺通信技术股份有限公司 Optical time domain reflection-based optical fiber data identification method and storage medium

Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2012037480A (en) * 2010-08-11 2012-02-23 Ihi Inspection & Instrumentation Co Ltd Ae measuring method and equipment by optical fiber sensor using wide band light
CN103134580A (en) * 2011-11-22 2013-06-05 上海华魏光纤传感技术有限公司 Signal processing method for distributed optical fiber vibration measurement system based on wavelet analysis
CN103244829A (en) * 2013-04-27 2013-08-14 天津大学 Classified warning method for pipeline safety incidents based on distributed fiber-optic sensor
CN104132250A (en) * 2014-07-14 2014-11-05 上海师范大学 Pipeline leakage feature vector extraction method based on improved wavelet packet
CN105095624A (en) * 2014-05-15 2015-11-25 中国电子科技集团公司第三十四研究所 Method for identifying optical fibre sensing vibration signal
CN105575024A (en) * 2015-12-30 2016-05-11 杭州安远科技有限公司 Anti-interference optical fiber perimeter protection system and method
CN106874833A (en) * 2016-12-26 2017-06-20 中国船舶重工集团公司第七0研究所 A kind of mode identification method of vibration event
CN107393555A (en) * 2017-07-14 2017-11-24 西安交通大学 A kind of detecting system and detection method of low signal-to-noise ratio abnormal sound signal
CN107576380A (en) * 2017-09-20 2018-01-12 北京邮电大学 A kind of three-dimensional vibrating Modulation recognition method towards Φ OTDR techniques
CN107633222A (en) * 2017-09-14 2018-01-26 浙江亨特科技有限公司 A kind of optical fiber vibration sensing method and system with adaptive learning function
CN108061759A (en) * 2017-11-23 2018-05-22 河海大学 A kind of Reason of Hydraulic Structural Damage recognition methods based on piezoelectric ceramics
CN108181059A (en) * 2017-12-27 2018-06-19 钦州学院 Multiphase flow pipeline leakage acoustic signals recognition methods based on small echo signal
CN108280950A (en) * 2017-12-12 2018-07-13 威海北洋光电信息技术股份公司 A kind of defence area type optical fiber perimeter protection algorithm based on high-frequency energy distribution

Patent Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2012037480A (en) * 2010-08-11 2012-02-23 Ihi Inspection & Instrumentation Co Ltd Ae measuring method and equipment by optical fiber sensor using wide band light
CN103134580A (en) * 2011-11-22 2013-06-05 上海华魏光纤传感技术有限公司 Signal processing method for distributed optical fiber vibration measurement system based on wavelet analysis
CN103244829A (en) * 2013-04-27 2013-08-14 天津大学 Classified warning method for pipeline safety incidents based on distributed fiber-optic sensor
CN105095624A (en) * 2014-05-15 2015-11-25 中国电子科技集团公司第三十四研究所 Method for identifying optical fibre sensing vibration signal
CN104132250A (en) * 2014-07-14 2014-11-05 上海师范大学 Pipeline leakage feature vector extraction method based on improved wavelet packet
CN105575024A (en) * 2015-12-30 2016-05-11 杭州安远科技有限公司 Anti-interference optical fiber perimeter protection system and method
CN106874833A (en) * 2016-12-26 2017-06-20 中国船舶重工集团公司第七0研究所 A kind of mode identification method of vibration event
CN107393555A (en) * 2017-07-14 2017-11-24 西安交通大学 A kind of detecting system and detection method of low signal-to-noise ratio abnormal sound signal
CN107633222A (en) * 2017-09-14 2018-01-26 浙江亨特科技有限公司 A kind of optical fiber vibration sensing method and system with adaptive learning function
CN107576380A (en) * 2017-09-20 2018-01-12 北京邮电大学 A kind of three-dimensional vibrating Modulation recognition method towards Φ OTDR techniques
CN108061759A (en) * 2017-11-23 2018-05-22 河海大学 A kind of Reason of Hydraulic Structural Damage recognition methods based on piezoelectric ceramics
CN108280950A (en) * 2017-12-12 2018-07-13 威海北洋光电信息技术股份公司 A kind of defence area type optical fiber perimeter protection algorithm based on high-frequency energy distribution
CN108181059A (en) * 2017-12-27 2018-06-19 钦州学院 Multiphase flow pipeline leakage acoustic signals recognition methods based on small echo signal

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
何志勇: "基于小波包分解的光纤振动特征提取方法", 《现代计算机》 *
曲志刚等: "油气管道安全分布式光纤预警系统研究", 《压电与声光》 *
郑印等: "相位敏感光时域反射计识别入侵事件算法", 《光子学报》 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111181634A (en) * 2019-12-18 2020-05-19 东南大学 Distributed optical fiber vibration signal rapid positioning method
CN111181634B (en) * 2019-12-18 2021-04-13 东南大学 Distributed optical fiber vibration signal rapid positioning method
CN111537160A (en) * 2020-05-09 2020-08-14 深圳市行健自动化股份有限公司 High-energy pipeline leakage monitoring method based on distributed optical fiber
CN111539394A (en) * 2020-07-08 2020-08-14 浙江浙能天然气运行有限公司 Pipeline along-line third-party construction early warning method based on time domain characteristics and space-time information
CN112187349A (en) * 2020-10-15 2021-01-05 上海欣诺通信技术股份有限公司 Optical time domain reflection-based optical fiber data identification method and storage medium

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