CN104269006B - Mode identification method for optical fiber early warning system - Google Patents
Mode identification method for optical fiber early warning system Download PDFInfo
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- G—PHYSICS
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- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B13/00—Burglar, theft or intruder alarms
- G08B13/02—Mechanical actuation
- G08B13/12—Mechanical actuation by the breaking or disturbance of stretched cords or wires
- G08B13/122—Mechanical actuation by the breaking or disturbance of stretched cords or wires for a perimeter fence
- G08B13/124—Mechanical actuation by the breaking or disturbance of stretched cords or wires for a perimeter fence with the breaking or disturbance being optically detected, e.g. optical fibers in the perimeter fence
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Abstract
The invention discloses an optical fiber early warning system and a mode identification method, which relate to the field of pipeline monitoring.A signal acquisition and upper computer module amplifies and filters an electric signal, and converts the electric signal into a digital signal in an analog-to-digital manner, and then the digital signal is processed and analyzed; meanwhile, continuous light is generated in the Raman light source, the continuous light passes through a 2X2 branching unit and is divided into 2 beams of light which respectively enter a first wavelength division multiplexer and a second wavelength division multiplexer, the light is respectively injected into the sensing optical fiber from the forward direction and the reverse direction, and the light pulse generated by the laser light source is subjected to distributed amplification through a Raman scattering effect, so that the signal intensity along the sensing optical fiber is ensured; and finally, rearranging signals obtained in the multiple pulse processes in a signal acquisition and upper computer module to obtain two-dimensional signals related to space and time for subsequent use. The invention can effectively identify the events of people walking, manual excavation and vehicle passing, and carry out event positioning, thereby effectively reducing the false alarm rate of the early warning system.
Description
Technical field
The present invention relates to Monitoring Pinpelines field, particularly relate to a kind of predispersed fiber alarm system and mode identification method.
Background technology
Distribution type fiber-optic early warning system based on relevant Ruili scattering (Φ-OTDR) technology is the light intensity of the rear orientation light returned from the scattering of optical fiber each several part by detection, and it is also positioned by detection extraneous vibration signal.Φ-OTDR is using general single mode fiber as optical transport and sensing carrier, monitoring in real time and being accurately positioned of distance can be realized, it is simple to laying, anti-electromagnetic interference capability is strong, it is prone to through engineering approaches, is usually used in the fields such as the safety detection of engineering structure, optical fiber perimeter protection and oil-gas pipeline safety early warning.
In predispersed fiber alarm system, the Classification and Identification of vibration signal is most important, if producing wrong report, is not only likely to result in the waste in manpower and materials, and serious may be delayed process time threat to life property safety.The most how to accurately identify intrusion event kind, and alarm, reduce wrong report, it is to avoid the unnecessary wasting of resources is the critical problem of predispersed fiber alarm system research all the time.Simultaneously as fiber nonlinear effect and fiber Rayleigh scattering coefficient are little, the distance sensing of coherent rayleigh scatter-type OTDR is the longest so that it is application is restricted.
In terms of fixation and recognition, existing method first locating events signal position on spatial domain, then extract the time-domain signal of this position, one-dimensional time-domain signal is carried out feature extraction, then carries out Classification and Identification.
Inventor, during realizing the present invention, finds at least to suffer from the drawback that in prior art and not enough:
This method first positions due to needs, therefore considerably increases the time of identification, and such as multipoint positioning, operand is very big.In addition, the accuracy requirement for location is the highest, and once Wrong localization then can not correctly identify kind of event, easily produce wrong report, it is impossible to enough meet demand.Therefore being badly in need of a kind of efficient, mode identification method is applied to predispersed fiber alarm system accurately.
Summary of the invention
The invention provides a kind of predispersed fiber alarm system and mode identification method, the present invention can identify event type the most accurately, it is to avoid unnecessary waste, and the generation of peril, described below:
A kind of predispersed fiber alarm system, including: LASER Light Source and Raman light source, the produced continuous light of described LASER Light Source, it is modulated being converted to light pulse via acousto-optic/electrooptic modulator, wherein said acousto-optic/electrooptic modulator and driver thereof are write the programme-control break-make of FGPA by the upper computer module in signals collecting and upper computer module, make continuous light form pulsed light；
Described pulsed light is after image intensifer amplifies, injected in sensor fibre by the first optical fiber circulator, the second optical fiber circulator, described pulsed light propagates scattered light dorsad produced by communication process and reflection light can be propagated in described sensor fibre along the direction in opposite direction with light pulse propagation, enters photodetector via the second optical fiber circulator and forms the signal of telecommunication；
After the described signal of telecommunication is amplified through described signals collecting and upper computer module, filters, analog digital conversion is digital signal, complete process and the analysis of digital signal；
Simultaneously, described Raman light source produces continuous light, through 2X2 shunt, it is divided into 2 bundle light, enter first wave division multiplexer, the second wavelength division multiplexer respectively, be injected separately into described sensor fibre from forward and inverse direction, pass through Raman scattering effect, the light pulse produced by described LASER Light Source is carried out distributed air-defense, it is ensured that at the signal intensity that described sensor fibre is along the line；
Finally, in described signals collecting and upper computer module, the signal obtained during multiple pulses is reset, obtain the 2D signal about room and time, for follow-up.
Described mode identification method comprises the following steps:
Use the method calculating scatter matrix that 10 Expressive Features of image are selected, using the feature chosen as the input of grader, carry out Classification and Identification；
By gaussian kernel function and man-to-man many categorised decisions, all training samples are tested, obtains final classification results；
As an entirety and select the method for 5-folding cross validation that accuracy rate is evaluated multiple graders；Draw Average Accuracy and recognition efficiency；
The grader designed is applied to described predispersed fiber alarm system scene intrusion event is monitored in real time identification.
Described 10 Expressive Features particularly as follows:
There is the quantity and barycenter minimum range, shape facility and shape dress coefficient 10 features of composition that the oval eccentricity of identical second moment and region have the oval major axis of identical second moment and region to have the oval short axle of identical second moment and region to have the quantity of object in diameter of a circle of the same area, region to deduct these object Holes in pixel count in region convex hull pixel count, region and region.
The technical scheme that the present invention provides provides the benefit that: this method is identified classification by the two-dimensional digital image being formed system acquisition signal and processes, can effectively identify people's walking, hand digging and cross car event, and carry out state event location, effectively reduce the rate of false alarm of early warning system.On the other hand, common coherent rayleigh scattering system distance sensing only has 20 kilometers, and detection light is persistently amplified by this programme by raman pump light, and distance sensing increases to more than 50 kilometers.
Accompanying drawing explanation
Fig. 1 is the light path schematic diagram of predispersed fiber alarm system；
Fig. 2 is three kinds of event 2D signal space-time diagrams；
Wherein, (a) behaves；B () is hand digging；C () is that cart is passed by.
Fig. 3 is image after three kinds of event flags；
Wherein, (a) behaves；B () is hand digging；C () is that cart is passed by.
Fig. 4 is regional quality distance schematic diagram in the heart；
Fig. 5 is region shape schematic diagram；
Fig. 6 is the stroll criterion matrix value schematic diagram of 10 eigenvalues；
Fig. 7 is RVM classifier training process schematic；
Fig. 8 is RVM grader identification process schematic.
In accompanying drawing, the list of parts representated by each label is as follows:
1: LASER Light Source；2: acousto-optic/electrooptic modulator and driver thereof；
3: image intensifer；4-1: the first optical fiber circulator；
4-2: the second optical fiber circulator；5: Raman light source；
6:2X2 shunt；7-1: first wave division multiplexer；
7-2: the second wavelength division multiplexer；8: photodetector；
9: signals collecting and upper computer module；10: sensor fibre.
Detailed description of the invention
For making the object, technical solutions and advantages of the present invention clearer, below embodiment of the present invention is described further in detail.
See Fig. 1, predispersed fiber alarm system by: LASER Light Source 1, acousto-optic/electrooptic modulator and driver 2 thereof, image intensifer the 3, first optical fiber circulator 4-1, the second optical fiber circulator 4-2, Raman light source 5,2X2 shunt 6, first wave division multiplexer 7-1, the second wavelength division multiplexer 7-2, photodetector 8, signals collecting and upper computer module 9, sensor fibre 10 are constituted.
nullBy the produced continuous light of LASER Light Source 1，It is modulated being converted to light pulse via acousto-optic/electrooptic modulator 2，Wherein acousto-optic/electrooptic modulator and driver 2 thereof are by the programme-control break-make of upper computer module write FGPA，Continuous light is made to form pulsed light，Light pulse is after image intensifer 3 amplifies，By the first optical fiber circulator 4-1、Second optical fiber circulator 4-2 injects in sensor fibre 10，Propagate scattered light produced by its communication process dorsad and reflection light can be propagated along the direction in opposite direction with light pulse propagation in sensor fibre 10，Enter photodetector 8 via the second optical fiber circulator 4-2 and form the signal of telecommunication，It is amplified through signals collecting and upper computer module 9、Filtering、After analog digital conversion is digital signal，Complete process and the analysis of digital signal，It is hereby achieved that based on principle of interference and the test result of the distributed fiberoptic sensor of Rayleigh scattering principle.
Simultaneously, Raman light source 5 produces continuous light, through 2X2 shunt 6, it is divided into 2 bundle light, enter first wave division multiplexer 7-1, the second wavelength division multiplexer 7-2 respectively, be injected separately into sensor fibre 10 from forward and inverse direction, pass through Raman scattering effect, the pulsed light produced by LASER Light Source 1 is carried out distributed air-defense, it is ensured that predispersed fiber alarm system is at sensor fibre 10 signal intensity along the line.Finally, in signals collecting and upper computer module 9, the signal obtained during multiple pulses is reset, obtain the 2D signal about room and time, for follow-up.
A kind of security incident mode identification method based on predispersed fiber alarm system, said method comprising the steps of:
1, vibration signal characteristics extracts process
1) 2D signal that Φ-OTDR predispersed fiber alarm system collects is exported in the form of images, utilize image procossing
Technology carries out image segmentation to 2D signal, is separated with background area 2D signal event generation area；
The embodiment of the present invention selects threshold segmentation method based on Ostu.The method utilizing medium filtering removes the noise in image, selects the method for image expansion to eliminate the cavity in region, is then marked event area in image with different colours.
2) people's walking, hand digging and car excessively are the event types that system is mainly differentiated, three kinds of events are because active force is different, the amplitude of the two-dimension vibration signal therefore obtained is different, utilizes the primary signal amplitude (Amplitude) of different marked region as an eigenvalue in characteristic vector.
3) different events is under identical sample rate, and case point interval in time domain is different.First calculating the barycenter of regional, barycenter is usedRepresenting, wherein n represents the number in region.The distance of the barycenter being then adjacent for each centroid calculation also seeks minimum range.
dmin1=min (| ci-ci-1|,|ci-ci+1|), i ∈ (2 ..., n-1) (1)
If image only having a characteristic area, it is impossible to when obtaining barycenter spacing, then give the bigger value in one, this feature region and represent distance.
It can also be observed that the region shape of different event there is also difference from image, utilize shape (Shap) feature to increase the multiformity of feature.First the border b in each region is calculatedi, then calculate border according to formula (3) and arrive a little the distance of barycenter.
Dik=| bik-ci|, k ∈ (1 ..., K) (2)
In formula, K is the number of each zone boundary point, asks each zone boundary point to ultimate range D of barycenterimaxWith minimum range Dimin, formula (4) calculate the form factor S in each region.
Si=| Dimax-Dimin|(3)
In order to be described more fully with provincial characteristics, this method utilizes general region description to join in characteristic vector.Region description subcategory is a lot, the most mostly concerned 7 of feature selection according to security incident region join in characteristic vector, including: region convex hull pixel count ConvesArea, pixel count Area in region, eccentricity Eccentricity of the ellipse of identical second moment is had with region, the major axis MajorAxisLength of the ellipse of identical second moment is had with region, the short axle MinorAxisLength of the ellipse of identical second moment is had with region, diameter of a circle EquivDiameter of the same area is had with region, EulerNumber is the quantity that in region, the quantity of object deducts these object Holes.Above 7 features and barycenter minimum range, shape facility and shape dress coefficient 10 features of composition, as the Expressive Features amount of image, be input to next step and analyze.
The most comprehensively describing provincial characteristics will necessarily make feature vector dimension too much, not only increases complexity, and too much feature can increase the dependency between feature, causes classification error.Therefore, utilize the method for feature selection to carry out dimensionality reduction and can not only improve nicety of grading, and be substantially reduced recognition efficiency.The present invention uses the method calculating scatter matrix to carry out feature selection.Calculate in the class of three class event samples scatter matrix between scatter matrix and class first against 10 iamge description characteristic quantities mentioned above respectively, utilize separability criterion formula (4) to carry out evaluating characteristic performance.
J=tr{Sω -1Sm}(4)
Wherein SωIt is scatter matrix in class, SmFor mixing scatter matrix:
Sm=Sω+Sb(5)
Wherein, SbIt it is scatter matrix between class
In l dimension space, the sample of each class the most well clusters around average, and when inhomogeneity is completely separate, this formula value of calculation is big.Calculate the separability criterion value of each feature according to formula (4), select the bigger feature of criterion value and carry out Classification and Identification.As shown in Figure 6, calculating the criterion value of each feature respectively, the criterion value of the 2nd, 3,4, No. 6 features is the most notable compared with the criterion value of other features, shows that it is more suitable for classification foundation, in grader later during application, these 4 features will be chosen and input as grader.
2. security event classification identification
(1), after selecting the most significant feature of criterion value, feature is inputted grader and carries out Classification and Identification.The present invention uses the method for Method Using Relevance Vector Machine RVM (RelevanceVectorMachine) to carry out the design of grader.Grader Selection of kernel function gaussian kernel function.Carry out many categorised decisions time, many categorised decisions of " one to one " that selection sort precision is the highest, i.e. use 3 RVM that all samples carry out following test respectively: whether to be that people walks or excavated by manual work office, if be that people walks or car passes through, if to be hand digging or car passes through；Judge event type identical in 3 RVM results afterwards, be final classification results.Before using grader, first with the training sample gathered in advance, grader is trained, and the performance of the grader trained with the test of ready test sample.
(2) as an entirety and select the method [1] of 5-folding cross validation that accuracy rate is evaluated multiple graders.Draw Average Accuracy and the recognition efficiency of algorithm.
(3) grader designed is applied to Φ-OTDR distribution type fiber-optic early warning system scene intrusion event is monitored in real time identification.
The present invention is further detailed explanation with detailed description of the invention below in conjunction with the accompanying drawings:
(1) Fig. 1 show the System Working Principle schematic diagram of predispersed fiber alarm system.System is made up of LASER Light Source 1, acousto-optic/electrooptic modulator 2, image intensifer 3, optical fiber circulator 4, Raman light source 5,2X2 shunt 6, wavelength division multiplexer 7, photodetector 8, signals collecting and upper computer module 9, sensor fibre 10.By the produced continuous light of LASER Light Source 1, it is modulated being converted to light pulse via acousto-optic/electrooptic modulator 2, light pulse is by the first optical fiber circulator 4-1, second optical fiber circulator 4-2 injects in sensor fibre 10, propagate scattered light produced by its communication process dorsad and reflection light can be propagated along the direction in opposite direction with light pulse propagation in sensor fibre 10, the signal of telecommunication entering photodetector 8 formation via the second optical fiber circulator 4-2, it is amplified through signals collecting and upper computer module 9, filtering, after analog digital conversion is digital signal, send into process and the analysis completing digital signal in host computer, it is hereby achieved that based on principle of interference and the test result of the distributed fiberoptic sensor of Rayleigh scattering principle.Simultaneously, Raman light source 5 produces continuous light, through 2X2 shunt 6, it is divided into 2 bundle light, respectively enter first wave division multiplexer 7-1 and the second wavelength division multiplexer 7-2, be injected separately into sensor fibre 10 from forward and inverse direction, pass through Raman scattering effect, the pulsed light produced by LASER Light Source 1 is carried out distributed air-defense, it is ensured that predispersed fiber alarm system is at sensor fibre 10 signal intensity along the line.Finally, in host computer, the signal obtained during multiple pulses is reset, obtain the 2D signal about room and time, for follow-up.
(2) 2D signal that predispersed fiber alarm system collects people walking, hand digging and cart are passed by after filtering after image as shown in Figure 2.
(3) method of Threshold segmentation in image processing techniques is utilized event area to be extracted from background image, and it is carried out medium filtering removal noise, the method utilizing image expansion eliminates the hole in region, is marked the event area in image, as shown in Figure 3.
(4) utilize morphologic feature extracting method that the region in three types occurrence diagram picture is carried out feature extraction, including event amplitude, interregional every, region shape and region description, as shown in Figure 4 and Figure 5, utilizing the method calculating scatter matrix characteristic vector is carried out screening to remove redundancy feature after feature extraction, bigger front four features of selection criteria values, as characteristic vector, calculate scatter matrix result such as Fig. 6.
(5) being input to features described above vector in RVM grader be trained grader, the present invention uses man-to-man many categorised decisions, and event to be sorted has people to walk, hand digging and calling a taxi is passed by, therefore needs three RVM graders.Training flow process and identification process such as Fig. 7, grader RVM1 is responsible for distinguishing people and walks about and hand digging event, grader RVM2 is responsible for distinguishing hand digging and event passed by by cart, grader RVM3 be responsible for distinguish people walk about and cart pass by event, use ready data sample that three graders are carried out the classification based training of corresponding event respectively.Identification process is as shown in Figure 8, the characteristic quantity of event to be identified is simultaneously fed in RVM1, RVM2 and RVM3, three graders all provide classification results afterwards, and wherein the result of 2 graders is corresponding real event type, finally with the result of the selected final classification of majority principle.
(6) 100 samples of every kind of event select 20 samples classifier performance is tested, and utilize the method evaluation test result of cross validation.
(7) during signal characteristic subsequently enters the grader trained, the result obtained is the grader result to intrusion event identification, and this result is sent to host computer and shows.
List of references
[1] military graceful. Classifier Performance Evaluation research [D]. Beijing Jiaotong University, 2010.
It will be appreciated by those skilled in the art that accompanying drawing is the schematic diagram of a preferred embodiment, the invention described above embodiment sequence number, just to describing, does not represent the quality of embodiment.
The foregoing is only presently preferred embodiments of the present invention, not in order to limit the present invention, all within the spirit and principles in the present invention, any modification, equivalent substitution and improvement etc. made, should be included within the scope of the present invention.
Claims (2)
1. for a mode identification method for predispersed fiber alarm system, described predispersed fiber alarm system, including: LASER Light Source and Raman light source,
The produced continuous light of described LASER Light Source, it is modulated being converted to light pulse via acousto-optic/electrooptic modulator, wherein said acousto-optic/electrooptic modulator and driver thereof are write the programme-control break-make of FGPA by the upper computer module in signals collecting and upper computer module, make continuous light form pulsed light；
Described pulsed light is after image intensifer amplifies, injected in sensor fibre by the first optical fiber circulator, the second optical fiber circulator, described pulsed light propagates scattered light dorsad produced by communication process and reflection light can be propagated in described sensor fibre along the direction in opposite direction with light pulse propagation, enters photodetector via the second optical fiber circulator and forms the signal of telecommunication；
After the described signal of telecommunication is amplified through described signals collecting and upper computer module, filters, analog digital conversion is digital signal, complete process and the analysis of digital signal；
Simultaneously, described Raman light source produces continuous light, through 2X2 shunt, it is divided into 2 bundle light, enter first wave division multiplexer, the second wavelength division multiplexer respectively, be injected separately into described sensor fibre from forward and inverse direction, pass through Raman scattering effect, the light pulse produced by described LASER Light Source is carried out distributed air-defense, it is ensured that at the signal intensity that described sensor fibre is along the line；
Finally, in described signals collecting and upper computer module, the signal obtained during multiple pulses is reset, obtain the 2D signal about room and time, for follow-up；
It is characterized in that, described mode identification method comprises the following steps:
Use the method calculating scatter matrix that 10 Expressive Features of image are selected, using the feature chosen as the input of grader, carry out Classification and Identification；
By gaussian kernel function and man-to-man many categorised decisions, all training samples are tested, obtains final classification results；
As an entirety and select the method for 5-folding cross validation that accuracy rate is evaluated multiple graders；Draw Average Accuracy and recognition efficiency；
The grader designed is applied to described predispersed fiber alarm system scene intrusion event is monitored in real time identification.
Method the most according to claim 1, it is characterised in that described 10 Expressive Features particularly as follows:
There is the quantity and barycenter minimum range, shape facility and form factor 10 features of composition that the oval eccentricity of identical second moment and region have the oval major axis of identical second moment and region to have the oval short axle of identical second moment and region to have the quantity of object in diameter of a circle of the same area, region to deduct these object Holes in pixel count in region convex hull pixel count, region and region.
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| CN105141477B (en) * | 2015-08-20 | 2018-12-04 | 中国人民解放军西安通信学院 | A kind of optical-fiber network information security monitoring system and monitoring method based on Fibre Optical Sensor |
| CN105698916B (en) * | 2016-03-01 | 2019-07-26 | 深圳艾瑞斯通技术有限公司 | Fiber-optic vibration model determines method and optical fiber prior-warning device, system |
| CN105931402B (en) * | 2016-06-27 | 2018-06-05 | 上海波汇科技股份有限公司 | Optical fiber perimeter intrusion detection method based on image identification |
| CN106441773B (en) * | 2016-11-03 | 2018-09-18 | 北京航天易联科技发展有限公司 | A kind of adjustable vibratory impulse test device |
| CN106504451B (en) * | 2016-11-14 | 2018-08-14 | 中国人民解放军国防科学技术大学 | A kind of demodulating algorithm of the optical fiber perimeter safety signal based on matrix theory |
| CN108133559A (en) * | 2016-11-30 | 2018-06-08 | 光子瑞利科技（北京）有限公司 | Application of the optical fiber end-point detection in circumference early warning system |
| CN108197646A (en) * | 2017-12-28 | 2018-06-22 | 中国电子科技集团公司第五十四研究所 | A kind of target classification identification method for distributed optical fiber sensing system |
| CN109272017B (en) * | 2018-08-08 | 2022-07-12 | 太原理工大学 | Vibration signal mode identification method and system of distributed optical fiber sensor |
| CN110335430A (en) * | 2019-06-19 | 2019-10-15 | 中国科学院自动化研究所 | Monitoring pipeline safety system, method and apparatus based on deep learning |
| CN114268365B (en) * | 2021-12-02 | 2023-07-11 | 国网甘肃省电力公司酒泉供电公司 | Communication optical cable intelligent early warning method and system based on visualization technology |
| CN114166331A (en) * | 2021-12-03 | 2022-03-11 | 武汉理工光科股份有限公司 | Non-threat signal identification method and device, electronic equipment and storage medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0605301A1 (en) * | 1992-12-29 | 1994-07-06 | Ando Electric Co., Ltd. | Optical time domain reflectometer |
| CN104034409A (en) * | 2014-06-27 | 2014-09-10 | 天津大学 | Distributed optical fiber vibration sensing method and system based on pulse code external modulation |
-
2014
- 2014-09-24 CN CN201410494088.7A patent/CN104269006B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0605301A1 (en) * | 1992-12-29 | 1994-07-06 | Ando Electric Co., Ltd. | Optical time domain reflectometer |
| CN104034409A (en) * | 2014-06-27 | 2014-09-10 | 天津大学 | Distributed optical fiber vibration sensing method and system based on pulse code external modulation |
Non-Patent Citations (3)
| Title |
|---|
| Enhancement of SNR and Spatial Resolution in φ-OTDR System by Using Two-Dimensional Edge Detection Method;TaoZhu，Xianghui Xiao，Qian He，Dongmei Diao;《Lightwave Technology》;20130930;SECTIONⅠ-Ⅳ * |
| 基于双光束干涉的相位敏感光时域反射计;安阳等;《光学学报》;20130731;第33卷(第7期);全文 * |
| 基于双向拉曼放大的相位敏感光时域反射仪;王杰等;《物理学报》;20131231;第62卷(第4期);第044212-1至044212-5页 * |
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