CN104819382B - Self-adaptive constant false alarm rate vibration source detection method for optical fiber early warning system - Google Patents
Self-adaptive constant false alarm rate vibration source detection method for optical fiber early warning system Download PDFInfo
- Publication number
- CN104819382B CN104819382B CN201510166995.3A CN201510166995A CN104819382B CN 104819382 B CN104819382 B CN 104819382B CN 201510166995 A CN201510166995 A CN 201510166995A CN 104819382 B CN104819382 B CN 104819382B
- Authority
- CN
- China
- Prior art keywords
- false alarm
- alarm rate
- detection
- self
- threshold value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 59
- 239000013307 optical fiber Substances 0.000 title claims abstract description 15
- 238000000034 method Methods 0.000 claims abstract description 17
- 238000005070 sampling Methods 0.000 claims abstract description 7
- 238000012360 testing method Methods 0.000 claims description 12
- 238000012217 deletion Methods 0.000 claims description 5
- 230000037430 deletion Effects 0.000 claims description 5
- RVRCFVVLDHTFFA-UHFFFAOYSA-N heptasodium;tungsten;nonatriacontahydrate Chemical compound O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.[Na+].[Na+].[Na+].[Na+].[Na+].[Na+].[Na+].[W].[W].[W].[W].[W].[W].[W].[W].[W].[W].[W] RVRCFVVLDHTFFA-UHFFFAOYSA-N 0.000 claims description 5
- 230000003044 adaptive effect Effects 0.000 claims description 4
- 239000011159 matrix material Substances 0.000 claims description 3
- ASNHGEVAWNWCRQ-UHFFFAOYSA-N 4-(hydroxymethyl)oxolane-2,3,4-triol Chemical compound OCC1(O)COC(O)C1O ASNHGEVAWNWCRQ-UHFFFAOYSA-N 0.000 claims 1
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 4
- 238000012544 monitoring process Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 239000000835 fiber Substances 0.000 description 2
- 239000007789 gas Substances 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 239000003345 natural gas Substances 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 210000001367 artery Anatomy 0.000 description 1
- 238000005452 bending Methods 0.000 description 1
- 238000002485 combustion reaction Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- 239000011800 void material Substances 0.000 description 1
Abstract
The invention provides a signal detection method based on self-adaptive constant false alarm rate (GO/SO-CFAR), which is used for realizing vibration source detection in a pipeline optical fiber early warning system and comprises the following steps: continuously receiving N pulse echo data to obtain a frame of echo data, sequentially selecting a distance unit from M distance units in the nth pulse echo data as a detection unit, removing an interference target from a reference unit sequence, calculating a detection threshold value based on the processed reference unit sampling sequence, and finally comparing the detection unit with the detection threshold value to obtain a detection result. The invention utilizes the self-adaptive constant false alarm rate detector to detect and process the optical fiber vibration signal, and the method can ensure constant false alarm rate and also can show stronger detection capability under different backgrounds.
Description
Technical field
The present invention relates to a kind of self-adaption constant false alarm rate vibration source detection method for optical fiber early warning system, belong to optical fiber and shake
Dynamic signal detection technique field.
Background technology
With expanding economy, oil and natural gas has become the most important energy of the national economic development.Long distance pipeline
As most preferable, the economic means of transportation of oil and natural gas, it has been widely used.Oil-gas pipeline is energy transport
Once leaking combustion explosion easily occurs for main artery, pipeline, not only influences the safety in production of pipeline, will also give the country and people group
Many life bring about great losses with property.
The detecting and warning system of application oil-gas pipeline mainly has following several at present：Electronic impulse type fence, microwave wall
Alarm, active infrared alarm, leakage cable type perimeter detection warning system, electret vibration wireline warning system and optical fiber
Sensor perimeter alarm system.Compared with electric transducer warning system, fibre optical sensor has very in sensing network application
Obvious technical advantage：It can be provided up to 100 kilometers in the case where not needing any outdoor active device (being not required to power supply)
The safety monitoring of distance, is not limited by terrain environments such as the height of landform, complications, turning, bendings, has broken infrared ray, microwave
Wall etc. is only applicable to the limitation that sighting distance and flat site are used.Therefore ground using optical fiber measurement vibration as pipeline pre-warning system
The main method studied carefully.But how rationally effective analysis is carried out to fiber laser arrays signal, set up which type of event model
More effectively, as the big focus and difficult point in research.
Also there is substantially deficiency in current fiber-optic vibration signal transacting, its mentality of designing is usually the method directly detected, will
The signal detected is directly sent to display, and the changes in amplitude of clutter and noise is shown simultaneously, the inspection to echo signal
Survey ability is determined by operator to the monitoring of display.But, monitoring result error obtained by this processing method is big, report
Police is inaccurate, the detection of long range complex vibration is faced a severe challenge, and is badly in need of carrying out long-distance optical fiber vibration inspection on this basis
Survey technique study.
The subject matter that existing research is present is not set up suitable model, particularly do not set up suitable signal
Detection model, so that the long range early warning system that production has been put into is less efficient or even lies idle, optical fiber early warning
Signal transacting link in system turns into the main bottleneck of system and industry development.It is, therefore, desirable to provide a kind of suitable
Model realizes the detection of vibration event, to improve the stability of detection probability and false-alarm probability.
The content of the invention
The present invention is based on the optical fiber early warning system vibration source detection algorithm of self-adaption constant false alarm rate (GO/SO-CFAR) to solve
The problem of to fiber-optic vibration signal detection.
Optical fiber early warning system vibration source detection method based on self-adaption constant false alarm rate (GO/SO-CFAR), it includes：To light
Fine vibration signal is acquired；Set up self-adaption constant false alarm rate (GO/SO-CFAR) detector model；Signal without friction is carried out
GO/SO-CFAR is detected, compares the relation of actual false alarm rate and setting false alarm rate, it is ensured that detector normal work；To homogeneous background
Signal is detected, obtains the detection performance under homogeneous background；Multiple target background signal is detected, obtained in multiple target
Detection performance under background；Adjust signal to noise ratio in both environments respectively, then carry out GO/SO-CFAR detections, draw detection performance
Curve.
Wherein, detection threshold value meets following relation：
S=TZ
Wherein, S is threshold value；Z is power level, and its value is divided into two kinds of situation considerations, if it is determined that result k values are more than with reference to single
The half of member, i.e. k>During R/2,OtherwiseT is the normalized factor, and its value is according to false-alarm probability Pfa
Try to achieve,
Self-adaption constant false alarm rate (GO/SO-CFAR) detector set up by such scheme, ensure that the false-alarm of detection
Rate is constant, and has stronger detectability under homogeneous background and multiple target background.
According to an aspect of the invention, there is provided a kind of signal detecting method based on self-adaption constant false alarm rate, it is special
Levy and be to include：
Set up adaptive false alarm rate detector；
Experimental data without friction is detected by self-adaption constant false alarm rate detector, it is ensured that actual false alarm rate and the void of setting
Alert rate is extremely approached, i.e., absolute error ensures in the 1e-4 orders of magnitude；
The signal under homogeneous background and under multiple target background is detected respectively with self-adaption constant false alarm rate, respectively
Its detection probability is tried to achieve, and changes the signal to noise ratio of data, multi-group data is detected, the detection under different signal to noise ratio is obtained
Probability, shows it and detects performance.
Brief description of the drawings
The implementation of Fig. 1 the inventive method and verification process；
Fig. 2 self-adaption constants false alarm rate (GO/SO-CFAR) detector block diagram；
Fig. 3 self-adaption constants false alarm rate (GO/SO-CFAR) deletion algorithm flow chart；
Self-adaption constant false alarm rate (GO/SO-CFAR) testing result under Fig. 4 homogeneous backgrounds；
Self-adaption constant false alarm rate (GO/SO-CFAR) testing result under Fig. 5 multiple target backgrounds；
Detection performance curve of Fig. 6 self-adaption constants false alarm rate (GO/SO-CFAR) under homogeneous background；
Detection performance curve of Fig. 7 self-adaption constants false alarm rate (GO/SO-CFAR) under multiple target background；
Specific embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation is described.Obviously, described embodiment is only a part of embodiment of the invention, rather than whole embodiments.Based on this
Embodiment in invention, for those of ordinary skill in the art, on the premise of not paying creative work, can be with root
Other embodiment is obtained according to these accompanying drawings, the scope of protection of the invention is belonged to.
Shown in Figure 1, Fig. 1 is the optical fiber early warning system based on self-adaption constant false alarm rate (GO/SO-CFAR) of the present invention
The flow chart of system vibration source detection algorithm.As shown in figure 1, disclosed in the present embodiment based on self-adaption constant false alarm rate (GO/SO-
CFAR optical fiber early warning system vibration source detection algorithm) includes：
S101：Fiber-optic vibration signal is acquired；
S102：Set up self-adaption constant false alarm rate (GO/SO-CFAR) detector model；
S103：Carry out self-adaption constant false alarm rate (GO/SO-CFAR) detection to signal without friction, relatively actual false alarm rate with
Set the relation of false alarm rate, it is ensured that detector normal work；
S104：Homogeneous background signal is detected, the detection performance under homogeneous background is obtained；
S105：Multiple target background signal is detected, the detection performance under multiple target Beijing is obtained；
S106:Adjust signal to noise ratio in both environments respectively, then carry out self-adaption constant false alarm rate (GO/SO-CFAR) and detect,
Draw detection performance curve.
The signal of fiber-optic vibration system is acquired in S101, N number of pulse echo data is continuously received and obtains one
Frame echo data, and each pulse echo data are handled and sampled.Wherein, M are included in each pulse echo data
According to echo time tactic range cell from left to right, different columns represent echo position.Specifically, it will receive
Each pulse echo data handled and sampled, form the signal that is arranged in matrix.
In S102, self-adaption constant false alarm rate (GO/SO-CFAR) detector model, self-adaption constant false alarm rate (GO/ are set up
SO-CFAR) detector model to the data for having formed matrix as shown in Fig. 2 carry out part deletion, then obtain threshold value, finally
Detection unit is compared with threshold value, testing result is drawn.Intermediate portions therein are deletion processes, as shown in figure 3, it is taken off
The method shown includes：
S301：To reference unit sampling sequence x(1)≤x(2)≤…≤x(R)；
S302：Then the orderly sampling relatively low to k is summed
Wherein, x is the value of reference unit；ZkIt is preceding k reference unit clutter power；
S303：The normalized factor T of threshold value is obtained according to given wrong probability of erasurek, the mistake for deleting process kth step deletes
Except probability PFCIt is defined as
PFC=Pr { Dk>0|HN} (1)
Wherein, PFCFor wrong probability of erasure, HNSampled for weak clutter, DkFor test statistics,
Dk=x(k+1)-TkZk (2)
Wherein, x(k+1)For (k+1) item sampled value, TkFor the kth normalized factor of threshold value；
Mistake probability of erasure PFCIt can be write as integrated form,
Wherein,For in HNAssuming that lower DkMoment generating function, definition is
(2) formula is substituted into (4) formula, obtained
Ordered Statistic D(1),D(2)... D(k+1)Joint moment generating function be defined as
Compare (5), (6) two formulas, it can be deduced that
w1=w2=...=wk=-Tkw
And
wk+1=w
Therefore
Wherein, R is reference unit number, and wushu (7) substitutes into formula (3), obtained
According to formula (8) the normalized factor of threshold value can be obtained in the case of given wrong probability of erasure；
S304：Try to achieve adaptive threshold TkZk,+1 reference unit value of kth is compared with adaptive threshold；
S305：If x(k+1)Less than TkZk, then above-mentioned steps are repeated from increasing 1 to k, otherwise directly stop circulation, i.e.,
Illustrate x(k+1),…,x(R)Belong to strong clutter area sampling；
S306：Then k values are compared with R/2 values；
S307：If k>R/2, then judge that detection unit is in weak clutter area, by x(1),…,x(k)Clutter power Z is formed, i.e.,Its test statistics D moment generating function definition is
The definition of false-alarm probability is
Pfa=Φ (T) (10)
Wherein, PfaIt is false-alarm probability, then simultaneous formula (9), formula (10), are obtained
S308：If k<R/2, then judge that detection unit is in strong clutter area, by x(k+1),…,x(R)Forming clutter power Z isIts test statistics D moment generating function definition is
Equally by formula (12) and false-alarm definition (10) simultaneous
In summary, normalized factor T is according to false-alarm probability PfaTry to achieve, such as following formula
Finally calculate threshold value
S=TZ
Wherein, S is threshold value.
In S103, the data without friction arrived to fiber-optic vibration system acquisition carry out GO/SO-CFAR detections, parameter setting
And the false-alarm probability after detection is as shown in table 1.
Table 1
It can be seen that the error between actual false-alarm probability and setting false-alarm probability is in 1e-4 grades, illustrate that self-adaption constant is empty
Alert rate (GO/SO-CFAR) detector normal work.
In S104, the data under same group of homogeneous background are carried out with the repetition of self-adaption constant false alarm rate (GO/SO-CFAR)
10000 detections, parameter setting is as shown in table 2,
Table 2
16 width testing result figures are taken respectively, as shown in figure 4, as shown in table 3, being said by the record of testing result data again
The detection performance of bright self-adaption constant false alarm rate (GO/SO-CFAR) detector under homogeneous background.
Table 3
In S105, self-adaption constant false alarm rate (GO/SO-CFAR) is carried out respectively to the data under one group of multiple target background
10000 detections are repeated, parameter setting is as shown in table 4
Table 4
16 width testing result figures are taken respectively, as shown in figure 5, again by the record of testing result data, as shown in table 5, more
The detection performance of self-adaption constant false alarm rate (GO/SO-CFAR) detector under multiple target background can be illustrated.
Table 5
| Detection mode | Average detected probability |
| Self-adaption constant false alarm rate (GO/SO-CFAR) | 0.9950 |
In S106, the data to different signal to noise ratio are detected respectively, you can obtained in the case where signal to noise ratio is different,
The detection probability value of self-adaption constant false alarm rate (GO/SO-CFAR) detector, that is, detect performance curve, under homogeneous background and many
Detect performance respectively as shown in Figure 6,7 under target background.As can be seen that the detection energy of self-adaption constant false alarm rate (GO/SO-CFAR)
Power is stronger.
It is of the invention that there is advantages below compared with existing detection method
(1), this method can be reached to fiber-optic vibration signal detection；
(2), constant false alarm rate detection method can guarantee that false-alarm probability is constant, and performance is stable；
(3), self-adaption constant false alarm rate (GO/SO-CFAR) all shown under homogeneous background and under multiple target background compared with
High detection probability, i.e. self-adaption constant false alarm rate (GO/SO-CFAR) method can guarantee that the detectability under different background.
Claims (3)
1. the signal detecting method based on self-adaption constant false alarm rate, it is characterised in that including：
Set up adaptive false alarm rate detector；
Experimental data without friction is detected by self-adaption constant false alarm rate detector, it is ensured that actual false alarm rate and the false alarm rate of setting
Extremely approach, i.e., absolute error ensures in the 1e-4 orders of magnitude；
The signal under homogeneous background and under multiple target background is detected respectively with self-adaption constant false alarm rate, tried to achieve respectively
Its detection probability, and the signal to noise ratio of data is changed, multi-group data is detected, the detection obtained under different signal to noise ratio is general
Rate, shows it and detects performance；
Self-adaption constant false alarm rate detector model is set up, including part deletion is carried out to the data for having formed matrix, then is obtained
Threshold value, is finally compared to detection unit with threshold value, draws testing result,
Wherein described part, which is deleted, to be included：
(1), to the i-th row sampling sequence x of reference unit(1)≤x(2)≤…≤x(R), wherein x is reference unit value, and R is total
Reference unit number；
(2) x, is used(1)Clutter sampling is represented, compares x(2)And T1x(1), wherein T1It is to meet given wrong probability of erasure PFCThreshold
It is worth the normalized factor, if x(2)<T1x(1), judge x(1)And x(2)It is same profile samples, then carries out next step；If x(2)>
T1x(1), judge x(2),…,x(R)It is the echo-signal in strong clutter region, by that analogy, all dividing elements of the i-th row is gone out strong miscellaneous
Ripple area and weak clutter area two parts；
(3), assume that it is k to divide position, if k>R/2, wherein R are reference unit number, then judge that detection unit is in weak clutter
Area, by x(1),…,x(k)Form clutter power Z；If k<R/2, then judge that detection unit is in strong clutter area, by x(k+1),…,x(R)
Form clutter power Z；
(4) threshold value, is obtained according to S=TZ, wherein S is threshold value, and T is the normalized factor, should when detection unit is more than threshold value S
Detection unit should be judged to noise, i.e., the signal should be alarmed in optical fiber early warning.
2. signal detecting method according to claim 1, it is characterised in that further comprise：
The normalized factor T of threshold value is obtained according to given wrong probability of erasurek, the wherein normalized factor T of threshold valuekAccording to following formula meter
Obtain
Wherein, R is reference unit number, PFCThe wrong probability of erasure of deletion process kth step
PFC=Pr { Dk>0|HN} (2)
Wherein, PFCFor wrong probability of erasure, HNSampled for weak clutter, DkFor test statistics,
Dk=x(k+1)-TkZk (3)
Wherein, x(k+1)For (k+1) item sampled value, TkFor the kth normalized factor of threshold value, ZkFor first k in order sampling and.
3. signal detecting method according to claim 1, it is characterised in that normalized factor T is according to false-alarm probability PfaTry to achieve,
Such as following formula
。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510166995.3A CN104819382B (en) | 2015-04-09 | 2015-04-09 | Self-adaptive constant false alarm rate vibration source detection method for optical fiber early warning system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510166995.3A CN104819382B (en) | 2015-04-09 | 2015-04-09 | Self-adaptive constant false alarm rate vibration source detection method for optical fiber early warning system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104819382A CN104819382A (en) | 2015-08-05 |
| CN104819382B true CN104819382B (en) | 2017-08-01 |
Family
ID=53729775
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510166995.3A Expired - Fee Related CN104819382B (en) | 2015-04-09 | 2015-04-09 | Self-adaptive constant false alarm rate vibration source detection method for optical fiber early warning system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104819382B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108123750A (en) * | 2016-11-30 | 2018-06-05 | 光子瑞利科技（北京）有限公司 | One kind is beneficial to clutter detection fiber circumference prior-warning device, system |
| CN113819401A (en) * | 2021-11-17 | 2021-12-21 | 西南石油大学 | Desert buried pipeline monitoring system and method based on optical fiber vibration and temperature test |
| CN117520790A (en) * | 2024-01-08 | 2024-02-06 | 南京信息工程大学 | False alarm control method and system for optical fiber vibration source detection under non-stationary interference |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101329400A (en) * | 2008-07-30 | 2008-12-24 | 电子科技大学 | Constant false alarm detection method of radar target based on goodness-of-fit test |
| CN101872014A (en) * | 2010-06-18 | 2010-10-27 | 深圳麒景雷信科技有限公司 | Target signal detection method based on improved COSGO (Average Order Statistics Greatest of)-CFAR (Constant False Alarm Rate) |
| CN102563360A (en) * | 2012-01-16 | 2012-07-11 | 北方工业大学 | Vibration event detection method of pipeline safety early warning system based on sequential probability ratio detection |
| CN103558595A (en) * | 2013-11-11 | 2014-02-05 | 上海航天测控通信研究所 | Universal radar constant false alarm rate detector and data sorting method thereof |
| CN103760543A (en) * | 2014-01-10 | 2014-04-30 | 杭州电子科技大学 | MM-CFAR target detection method |
| CN103760542A (en) * | 2014-01-10 | 2014-04-30 | 杭州电子科技大学 | MMVI-CFAR target detection method |
-
2015
- 2015-04-09 CN CN201510166995.3A patent/CN104819382B/en not_active Expired - Fee Related
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101329400A (en) * | 2008-07-30 | 2008-12-24 | 电子科技大学 | Constant false alarm detection method of radar target based on goodness-of-fit test |
| CN101872014A (en) * | 2010-06-18 | 2010-10-27 | 深圳麒景雷信科技有限公司 | Target signal detection method based on improved COSGO (Average Order Statistics Greatest of)-CFAR (Constant False Alarm Rate) |
| CN102563360A (en) * | 2012-01-16 | 2012-07-11 | 北方工业大学 | Vibration event detection method of pipeline safety early warning system based on sequential probability ratio detection |
| CN103558595A (en) * | 2013-11-11 | 2014-02-05 | 上海航天测控通信研究所 | Universal radar constant false alarm rate detector and data sorting method thereof |
| CN103760543A (en) * | 2014-01-10 | 2014-04-30 | 杭州电子科技大学 | MM-CFAR target detection method |
| CN103760542A (en) * | 2014-01-10 | 2014-04-30 | 杭州电子科技大学 | MMVI-CFAR target detection method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104819382A (en) | 2015-08-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10962437B1 (en) | Aggregate leak indicator display systems and methods | |
| US10948471B1 (en) | Leak detection event aggregation and ranking systems and methods | |
| CN113963514B (en) | Integrated monitoring and early warning system for oil gasification pipeline | |
| CN105527650B (en) | Microseismic signals and p ripple first arrival automatic identification algorithms under a kind of engineering yardstick | |
| US20150308919A1 (en) | Intelligent adaptive system and method for monitoring leakage of oil pipeline networks based on big data | |
| CN102563360B (en) | Vibration event detection method of pipeline safety early warning system based on sequential probability ratio detection | |
| CN111259335B (en) | Tunnel underpass road construction risk analysis system and method | |
| CN102928877B (en) | Based on the seismic attribute fusion method of D-S evidence theory | |
| CN104819382B (en) | Self-adaptive constant false alarm rate vibration source detection method for optical fiber early warning system | |
| CN106323385A (en) | Online detection of storage tank, assessment method and device | |
| CN110335430A (en) | Monitoring pipeline safety system, method and apparatus based on deep learning | |
| CN105954797A (en) | Fracture identification method and fracture identification device of seismic data | |
| CN106706119B (en) | Vibration source identification method and system based on signal frequency domain characteristics | |
| CN114693114A (en) | Monitoring method and device for underground space structure, computer equipment and storage medium | |
| CN108682101A (en) | Vibration optical fiber intrusion event detection method based on double threshold method | |
| Iaccarino et al. | Onsite earthquake early warning: predictive models for acceleration response spectra considering site effects | |
| CN104964736B (en) | Optical fiber invasion vibration source identification method based on time-frequency characteristic maximum expected classification | |
| CN105222885B (en) | Optical fiber vibration detection method and device | |
| CN114239215A (en) | Intelligent pipeline on-line monitoring system | |
| CN105046859A (en) | Optical fiber intrusion detection method based on vibration signal space-time two-dimensional sparse representation K-S detection | |
| CN107784148B (en) | Failure rate evaluation method and device for gathering and transportation pipeline | |
| CN116186642A (en) | Distributed optical fiber sensing event early warning method based on multidimensional feature fusion | |
| CN103777244A (en) | Method for quantitatively analyzing earthquake crack attribute volume | |
| CN108961638A (en) | Vibration optical fiber intrusion event detection method based on wavelet coefficient energy and algorithm | |
| Wang et al. | Cumulative absolute velocity prediction for earthquake early warning with deep learning |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| EXSB | Decision made by sipo to initiate substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB03 | Change of inventor or designer information | ||
| CB03 | Change of inventor or designer information |
Inventor after: Qu Hongquan Inventor after: Feng Chong Inventor after: Bi Fukun Inventor after: Du Xu Inventor after: Xue Xiaopeng Inventor before: Qu Hongquan Inventor before: Zheng Tong Inventor before: Bi Fukun Inventor before: Du Xu Inventor before: Xue Xiaopeng |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20170801 |