CN107067605A - A kind of distributed optical fiber fence vibrating intruding recognition methods - Google Patents
A kind of distributed optical fiber fence vibrating intruding recognition methods Download PDFInfo
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- CN107067605A CN107067605A CN201611266595.0A CN201611266595A CN107067605A CN 107067605 A CN107067605 A CN 107067605A CN 201611266595 A CN201611266595 A CN 201611266595A CN 107067605 A CN107067605 A CN 107067605A
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- G—PHYSICS
- G08—SIGNALLING
- 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 present invention discloses a kind of distributed optical fiber fence vibrating intruding recognition methods, measures and storing fiber optic fence vibration signal；Utilize zero-crossing rate threshold method, fiber-optic vibration abnormal signal block of the interception more than threshold parameter；Then, five groups of characteristic parameters of abnormal signal block are calculated, finally, characteristic parameter is trained using Artificial Neural Network, the vibration signal that unknown security protection is invaded is identified.The present invention has used two-stage intrusion behavior recognition mechanism in fiber fence safety-protection system, first abnormal vibration event is intercepted, artificial network's identification is carried out to anomalous event data again, so as to avoid the calculating that Intelligent Recognition is carried out to vibration normal data section, make fence security protection intrusion event identification process more targeted, improve the operating efficiency of fiber fence intrusion alarm system, in addition, the present invention can be effectively reduced interference of the strong wind and heavy rain to fence security protection invasive biology, the main intrusion event of fine-resolution fence.
Description
Technical field
The present invention relates to fiber fence safety-security area, more particularly to a kind of distributed optical fiber fence vibrating intruding identification side
Method.
Background technology
Fiber fence using fiber-optic vibration as sensing objects, with monitoring range is wide, sensitivity is high, good environmental adaptability,
The many-side advantage such as strong antijamming capability, has in safety-security area and widely applies.
The operation principle of fiber fence safety alarm system is：(such as climb, trample, shaking when fence intrusion behavior is produced
Shake, extrude etc.), the optical fiber on fence can be made to produce microvibration；Above-mentioned vibration signal is adopted in real time using sensor fibre
Collection, with reference to advanced signal transacting and pattern-recognition means, fence intrusion behavior is identified, and by intrusion alarm information
(including alert locations, type of alarm, duration etc.) is real-time, reliably report security-protection management system.Safety alarm system
Key be invasive biology algorithm.
Existing most of fiber fence safety-protection systems are carried out by energy threshold or zero-crossing rate threshold mode to intrusion event
Identification, preferably resolves intrusion behavior alarm problem, but it is unable to accurate recognition pin and the specifically intrusion behavior such as kicks, climb.With
This simultaneously, the fiber-optic vibration under the extreme natural environment such as strong wind heavy rain is also easily identified as intrusion behavior, causes a large amount of wrong reports
The generation of phenomenon.
Although the fiber fence safety-protection system with pattern recognition function can precisely recognize that fence invades concrete behavior event,
But the system has bulk redundancy calculating, i.e., carry out pattern recognition process to all fiber-optic vibration signals, lacks to normally shaking
The differentiation of dynamic data and abnormal data, causes the problem of fiber fence safety-protection system data processing amount is big.
In fact, the interference of the harmless event such as shielding strong wind and heavy rain, invades fence main matter (such as cutting net, climbing)
Precisely recognized, it has also become fiber fence safety-protection system practical application is badly in need of two major issues solved.The present invention is proposed
A kind of new distributed optical fiber vibration invasive biology method.
The content of the invention
The technical problems to be solved by the invention are to provide a kind of distributed optical fiber vibration invasive biology method, and it passes through letter
Single fiber fence vibration measurement, fiber-optic vibration abnormal data block is intercepted using zero-crossing rate threshold method；Then using spy
Extractive technique is levied, characteristic parameter is extracted from abnormal vibration data block；Finally using artificial neural network to vibrating intruding thing
Part is identified.
In order to solve the above problems, the present invention is achieved by the following scheme：A kind of distributed optical fiber vibration invasion is known
Other method, comprises the following steps：
(1) distributed optical fiber vibration sensing system is utilized, fence linked network fiber-optic vibration signal is picked up；
(2) sub-frame processing is carried out to the fiber-optic vibration signal of collection, and calculates the zero-crossing rate of framing fiber-optic vibration signal；
(3) zero-crossing rate threshold value is set, the fiber-optic vibration abnormal signal block more than zero-crossing rate threshold parameter is intercepted, with
Obtain abnormal vibrations block；
(4) five groups of characteristic parameters of the abnormal abnormal vibrations block of zero-crossing rate are extracted respectively, and they are respectively：Zero-crossing rate is total
Number, short-time energy, duration, maximum vibration rising edge angle and end extreme value trailing edge slope；
(5) Artificial Neural Network is finally utilized, five groups of characteristic parameters of known intrusion behavior are trained, and it is right
The fiber-optic vibration signal of unknown intrusion behavior is identified.
Distributed optical fiber vibration sensing system described in above-mentioned steps (1) utilizes M-Z principle of interferences, and distribution type fiber-optic shakes
Dynamic sensor-based system includes system host, light trunk module and sensing optic cable.
Sliding window sub-frame processing, described sliding window framing are carried out to the fiber-optic vibration signal of collection in above-mentioned steps (2)
It is hamming window to handle the window function used.
Framing fiber-optic vibration signal x in above-mentioned steps (2)n(m) zero-crossing rate ZnComputational methods be：
Wherein, sgn [] is sign function, i.e.,：
Short-time energy method described in above-mentioned steps (4) is：
Wherein, N is signal frame length.
Being defined as maximum vibration rising edge angle described in above-mentioned steps (4)：
Assuming that the starting vibrational coordinate point of abnormal vibrations block is (x1,y1), the coordinate in amplitude maximum oscillation point is (x2,y2),
Then maximum vibration rising edge angle, θ is
θ=argtan ((y2-y1)/(x2-x1))
Being defined as end extreme value trailing edge slope described in above-mentioned steps (4)：
Assuming that the vibrational coordinate point of first maximum point of abnormal vibrations block is (x3,y3), the coordinate of cut off is (x4,
y4), then end extreme value trailing edge slope k is：
K=(y4-y3)/(x4-x3)。
Artificial neural network described in above-mentioned steps (5) uses Multilayer Feedforward Neural Networks, i.e. BP neural network.
Artificial neural network described in above-mentioned steps (5) selects three layer perceptron network, input using BP neural network
Layer unit number is 5,5 feature extraction parameters of correspondence；Output layer unit number is 4, and correspondence cuts net, climbing, wind and rain and slap 4
Individual intrusion behavior event；Hidden layer neural unit data are taken between 8~10.
Compared with prior art, the present invention has following features：
1. solution is unable to accurate recognition by energy threshold or zero-crossing rate threshold value recognition methods and cuts the tool such as net, climbing, wind and rain
The deficiency of body intrusion behavior；
2. having used two-stage intrusion behavior recognition mechanism in fiber fence safety-protection system, i.e., first abnormal vibration event is entered
Row interception, then artificial network's identification is carried out to anomalous event data, intelligent knowledge is carried out to vibration normal data section so as to avoid
It is other to calculate, make fence security protection intrusion event identification process more targeted, improve fiber fence intrusion alarm system
Operating efficiency.
Brief description of the drawings
Fig. 1 is a kind of distributed optical fiber vibration invasive biology method flow diagram.
Embodiment
As illustrated, a kind of distributed optical fiber vibration invasive biology method, comprises the following steps：
(1) fence linked network fiber-optic vibration picking up signal, using distributed optical fiber vibration sensing system, to fence linked network optical fiber
Vibration signal is picked up；
(2) signal framing and zero-crossing rate are calculated, and carry out sub-frame processing to the fiber-optic vibration signal of collection, and calculate framing light
The zero-crossing rate of fine vibration signal；
(3) abnormal vibrations block signal is intercepted, and sets zero-crossing rate threshold value, different to the fiber-optic vibration more than zero-crossing rate threshold parameter
Regular signal block is intercepted, to obtain abnormal vibrations block；
(4) feature extraction, extracts five groups of characteristic parameters of abnormal vibrations block, they are respectively respectively：Zero-crossing rate is total, short
Shi Nengliang, duration, maximum vibration rising edge angle and end extreme value trailing edge slope；
(5) artificial neural network is recognized, using Artificial Neural Network, to five groups of characteristic parameters of known intrusion behavior
It is trained, and the fiber-optic vibration signal of unknown intrusion behavior is identified.
Distributed optical fiber vibration sensing system described in above-mentioned steps (1) utilizes M-Z principle of interferences, and distribution type fiber-optic shakes
Dynamic sensor-based system includes system host, light trunk module and sensing optic cable.
Sliding window sub-frame processing, described sliding window framing are carried out to the fiber-optic vibration signal of collection in above-mentioned steps (2)
It is hamming window to handle the window function used.
Framing fiber-optic vibration signal x in above-mentioned steps (2)n(m) zero-crossing rate ZnComputational methods be：
Wherein, sgn [] is sign function, i.e.,：
Short-time energy method described in above-mentioned steps (4) is：
Wherein, N is signal frame length.
Being defined as maximum vibration rising edge angle described in above-mentioned steps (4)：
Assuming that the starting vibrational coordinate point of abnormal signal block is (x1, y1), the coordinate in amplitude maximum oscillation point is (x2,y2),
Then maximum vibration rising edge angle, θ is
θ=argtan ((y2-y1)/(x2-x1))
Being defined as end extreme value trailing edge slope described in above-mentioned steps (4)：
Assuming that the vibrational coordinate point of first maximum point of abnormal signal block is (x3,y3), the coordinate of cut off is (x4,
y4), then end extreme value trailing edge slope k is：
K=(y4-y3)/(x4-x3)。
Artificial neural network described in above-mentioned steps (5) uses Multilayer Feedforward Neural Networks, i.e. BP neural network.
Artificial neural network described in above-mentioned steps (5) selects three layer perceptron network, input using BP neural network
Layer unit number is 5,5 feature extraction parameters of correspondence；Output layer unit number is 4, and correspondence cuts net, climbing, wind and rain and slap 4
Individual intrusion behavior event；Hidden layer neural unit data are taken between 8~10.
The present invention has used two-stage intrusion behavior recognition mechanism in fiber fence safety-protection system, i.e., first to abnormal vibration thing
Part is intercepted, then carries out artificial network's identification to anomalous event data, and intelligence is carried out to vibration normal data section so as to avoid
The calculating that can be recognized, makes fence security protection intrusion event identification process more targeted, improves fiber fence intrusion alarm system
The operating efficiency of system, in addition, the present invention can be effectively reduced interference of the strong wind and heavy rain to fence security protection invasive biology, fine-resolution
The main intrusion event of fence.
Above content is to combine specific preferred embodiment further description made for the present invention, it is impossible to assert
Present invention specific implementation is confined to these above-mentioned explanations.For general technical staff of the technical field of the invention,
Without departing from the inventive concept of the premise, some simple deduction or replace can also be made, the present invention should be all considered as belonging to
Protection domain.
Claims (4)
1. a kind of distributed optical fiber vibration invasive biology method, its feature comprises the following steps：
(1) distributed optical fiber vibration sensing system is utilized, fence linked network fiber-optic vibration signal is picked up；
(2) sub-frame processing is carried out to the fiber-optic vibration signal of collection, and calculates the zero-crossing rate of framing fiber-optic vibration signal；
(3) zero-crossing rate threshold value is set, the fiber-optic vibration abnormal signal block more than zero-crossing rate threshold parameter is intercepted, to obtain
Abnormal vibrations block；
(4) five groups of characteristic parameters of the abnormal abnormal vibrations block of zero-crossing rate are extracted respectively, and they are respectively：Zero-crossing rate is total, short
Shi Nengliang, duration, maximum vibration rising edge angle and end extreme value trailing edge slope；
(5) Artificial Neural Network is finally utilized, characteristic parameter known to intrusion behavior is trained, unknown security protection is entered
The fiber-optic vibration signal for invading event is identified.
2. a kind of distributed optical fiber vibration invasive biology method as claimed in claim 1, it is characterized in that：In above-mentioned steps (1)
Described distributed optical fiber vibration sensing system utilizes M-Z principle of interferences, and distributed optical fiber vibration sensing system includes system master
Machine, light trunk module and sensing optic cable.
3. a kind of distributed optical fiber vibration invasive biology method as claimed in claim 1, it is characterized in that：In above-mentioned steps (2)
Sliding window sub-frame processing is carried out to the fiber-optic vibration signal of collection, the window function that described sliding window sub-frame processing is used is hamming
Window.
4. a kind of distributed optical fiber vibration invasive biology method as claimed in claim 1, it is characterized in that：In above-mentioned steps (2)
Framing fiber-optic vibration signal xn(m) zero-crossing rate ZnComputational methods be：
Wherein, sgn [] is sign function, i.e.,：
Short-time energy method described in above-mentioned steps (4) is：
Wherein, N is signal frame length.
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Cited By (7)
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|---|---|---|---|---|
| CN107730800A (en) * | 2017-11-13 | 2018-02-23 | 浙江众盟通信技术有限公司 | Anti-Interference Analysis method based on fiber-optic vibration safety pre-warning system |
| CN108182776A (en) * | 2017-12-28 | 2018-06-19 | 无锡联河光子技术有限公司 | A kind of alarm mode analysis method of distributed optical fiber fence safety pre-warning system |
| CN108682101A (en) * | 2018-05-23 | 2018-10-19 | 重庆科技学院 | Vibration optical fiber intrusion event detection method based on double threshold method |
| CN109064696A (en) * | 2018-08-17 | 2018-12-21 | 成都九洲电子信息***股份有限公司 | The optical fiber perimeter security system realized based on deep learning |
| CN109523731A (en) * | 2018-12-27 | 2019-03-26 | 江苏明江机械制造有限公司 | A kind of data processing method and system of distribution type fiber-optic safe early warning |
| CN110779614A (en) * | 2019-10-31 | 2020-02-11 | 武汉理工光科股份有限公司 | Submarine cable anchor damage monitoring and positioning method and system based on distributed optical fiber sensing |
| US11138869B2 (en) | 2019-04-24 | 2021-10-05 | Carrier Corporation | Alarm system |
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107730800A (en) * | 2017-11-13 | 2018-02-23 | 浙江众盟通信技术有限公司 | Anti-Interference Analysis method based on fiber-optic vibration safety pre-warning system |
| CN108182776A (en) * | 2017-12-28 | 2018-06-19 | 无锡联河光子技术有限公司 | A kind of alarm mode analysis method of distributed optical fiber fence safety pre-warning system |
| CN108682101A (en) * | 2018-05-23 | 2018-10-19 | 重庆科技学院 | Vibration optical fiber intrusion event detection method based on double threshold method |
| CN109064696A (en) * | 2018-08-17 | 2018-12-21 | 成都九洲电子信息***股份有限公司 | The optical fiber perimeter security system realized based on deep learning |
| CN109523731A (en) * | 2018-12-27 | 2019-03-26 | 江苏明江机械制造有限公司 | A kind of data processing method and system of distribution type fiber-optic safe early warning |
| US11138869B2 (en) | 2019-04-24 | 2021-10-05 | Carrier Corporation | Alarm system |
| CN110779614A (en) * | 2019-10-31 | 2020-02-11 | 武汉理工光科股份有限公司 | Submarine cable anchor damage monitoring and positioning method and system based on distributed optical fiber sensing |
| CN110779614B (en) * | 2019-10-31 | 2021-07-06 | 武汉理工光科股份有限公司 | Submarine cable anchor damage monitoring and positioning method and system based on distributed optical fiber sensing |
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