CN107911122A - Based on the distributed optical fiber vibration sensing data lossless compression method for decomposing compression - Google Patents
Based on the distributed optical fiber vibration sensing data lossless compression method for decomposing compression Download PDFInfo
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- 230000006835 compression Effects 0.000 title claims abstract description 51
- 238000007906 compression Methods 0.000 title claims abstract description 51
- 238000000034 method Methods 0.000 title claims abstract description 30
- 239000013307 optical fiber Substances 0.000 title claims abstract description 27
- 230000015556 catabolic process Effects 0.000 abstract description 2
- 238000006731 degradation reaction Methods 0.000 abstract description 2
- 238000000253 optical time-domain reflectometry Methods 0.000 description 10
- 238000012913 prioritisation Methods 0.000 description 7
- 238000005516 engineering process Methods 0.000 description 4
- 238000012544 monitoring process Methods 0.000 description 4
- 230000035945 sensitivity Effects 0.000 description 4
- 238000011161 development Methods 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000005056 compaction Methods 0.000 description 1
- 239000012141 concentrate Substances 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 230000007123 defense Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 229910052500 inorganic mineral Inorganic materials 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 239000011707 mineral Substances 0.000 description 1
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- 238000011160 research Methods 0.000 description 1
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Classifications
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- H—ELECTRICITY
- H03—ELECTRONIC CIRCUITRY
- H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
- H03M7/00—Conversion of a code where information is represented by a given sequence or number of digits to a code where the same, similar or subset of information is represented by a different sequence or number of digits
- H03M7/30—Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction
- H03M7/60—General implementation details not specific to a particular type of compression
- H03M7/6011—Encoder aspects
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H9/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by using radiation-sensitive means, e.g. optical means
- G01H9/004—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by using radiation-sensitive means, e.g. optical means using fibre optic sensors
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B10/00—Transmission systems employing electromagnetic waves other than radio-waves, e.g. infrared, visible or ultraviolet light, or employing corpuscular radiation, e.g. quantum communication
- H04B10/25—Arrangements specific to fibre transmission
Abstract
The invention discloses a kind of based on the distributed optical fiber vibration sensing data lossless compression method for decomposing compression, distributed optical fiber vibration sensing data are carried out discrete cosine transform by the present invention, the energy main part of discrete cosine coefficient is extracted, after inverse discrete cosine transform with initial data difference, energy main body coefficient and difference are subjected to linear predictive coding respectively again, finally carry out entropy coding.Data are decomposed into data of the two parts with different characteristic and are compressed again by the present invention, and the strategy that would detract from compression is incorporated in the frame of lossless compression, and compressed capability is improved on the premise of no data degradation.
Description
Technical field
It is particularly a kind of based on the distributed optical fiber vibration sensing data for decomposing compression the present invention relates to technical field of optical fiber
Lossless compression method.
Background technology
Strengthen border and take precautions against, improve energy security, improving social security etc. be social stability, rapid economic development it is basic
It is required that.The perimeter security monitoring of some important base facilities of the multiple fields such as military and national defense, large-scale industrial and mineral, civilian security protection, is
Avoid causing heavy economic losses, the effective means for development of maintaining social stability.With the continuous development of society, the security protection of people
Consciousness is continuously improved, and various safety monitoring technologies are also evolving.Fiber optic intrusion sensing based on optical time domain reflectometer OTDR
Device system, have distribution, high sensitivity, monitoring range it is wide, can it is hidden, from advantages such as topography and geomorphology limitations, in circumference peace
Great potential in terms of anti-intrusion monitoring, it has also become the research hotspot of people.
Phase sensitivity optical time domain reflectometer (Phase-sensitive Optical Time Domain Reflectometry,
Φ-OTDR) grow up on the basis of original OTDR distributed sensors.It is a kind of typical distributed optical fiber sensing
Technology, high sensitivity is whole passive, can continuously perceive strained in transmission path, vibrate when dynamic parameter spatial distribution and when
Between change information.
Φ-OTDR are in actual vibration measurement, the characteristics of due to its high sensitivity, fast response time, often produce
Substantial amounts of sensing data.In typical Φ-OTDR systems, it is assumed that it is 100MSP/s that it, which counts and adopts the sample rate of module, translation bit
Number is 14bit, then the data traffic of the system is 100MSP/s × 14bit=175MB/s.So huge data are not easy to
Transmission and preservation, it is therefore desirable to be compressed to it.Current technology is mostly to use discrete cosine transform or wavelet transform
Lossy compression method, lossy compression method often obtains higher compression ratio, but can cause the part loss of initial data.And
In actual engineer application, complete accurate data acquisition is necessary.
The content of the invention
The technical problems to be solved by the invention are overcome the deficiencies in the prior art and provide and a kind of compressed based on decomposing
Distributed optical fiber vibration sensing data lossless compression method, initial data is decomposed into two parts by this method has different characteristic
Data are compressed again, and the strategy that would detract from compression is incorporated in the frame of lossless compression, is carried on the premise of no data degradation
Rise compressed capability.
The present invention uses following technical scheme to solve above-mentioned technical problem：
A kind of distributed optical fiber vibration sensing data lossless compression method based on decomposition compression proposed according to the present invention,
Comprise the following steps：
Step 1: one group of initial data m is carried out discrete cosine transform, one group of discrete cosine coefficient M is obtained；Wherein, m=
{mi| i is integer and 1≤i≤N }={ m1,m2,...,mN, miIt is i-th of data in m, N is the data amount check included in m, M=
{Mj| j is integer and 1≤j≤N }={ M1,M2,...,MN, MjIt is j-th of data in M；
Step 2: the gross energy of M isThen h is in [1, N] section and meets the minimum integer of following formula：
Wherein, r is percentage of energy；
Obtain frequency domain energy main body coefficient X, X=X1,X2,...,XN,XjIt is j-th of number in frequency domain energy main body coefficient X
According to,
Step 3: carrying out inverse discrete cosine transform to X, time domain energy main body coefficient x, x=x are obtained1,x2,...,xN, xi
It is i-th of data in x；
Step 4: m and x are made calculus of differences, difference d, d are obtainedi=mi-xi, wherein, diIt is i-th of data in d；
Step 5: carrying out linear predictive coding to X, the prediction remainder PX of X is obtained；Linear predictive coding is carried out to d, obtains d's
Predict remainder Pd；
Wherein, PXjIt is j-th of data in PX；
Wherein, PdiIt is i-th of data in Pd；
Step 6: carrying out entropy coding to PX, the entropy coding compressed data EX of PX is obtained；Entropy coding is carried out to Pd, obtains the entropy of Pd
Coded compressed data Ed.
As of the present invention a kind of based on the distributed optical fiber vibration sensing data lossless compression method for decomposing compression
Further prioritization scheme, decodes the compressed data that step 6 obtains, comprises the following steps that：
Step A, entropy decoding is carried out to EX, obtains PX；Entropy decoding is carried out to Ed, obtains Pd；
Step B, linear prediction decoding is carried out to PX, obtains X；Linear prediction decoding is carried out to Pd, obtains d；
Step C, inverse discrete cosine transform is carried out to X, obtains x；
Step D, x is added with d, obtains m.
As of the present invention a kind of based on the distributed optical fiber vibration sensing data lossless compression method for decomposing compression
Further prioritization scheme, the formula of discrete cosine transform is in step 1：
As of the present invention a kind of based on the distributed optical fiber vibration sensing data lossless compression method for decomposing compression
Further prioritization scheme, initial data is original distribution formula optical fiber vibration sensing data in step 1.
As of the present invention a kind of based on the distributed optical fiber vibration sensing data lossless compression method for decomposing compression
Further prioritization scheme, r is 95 in step 2.
As of the present invention a kind of based on the distributed optical fiber vibration sensing data lossless compression method for decomposing compression
Further prioritization scheme, inverse discrete cosine transform formula is in step 3：
As of the present invention a kind of based on the distributed optical fiber vibration sensing data lossless compression method for decomposing compression
Further prioritization scheme, the linear predictive coding that step 5 uses is 2 rank linear predictions.
As of the present invention a kind of based on the distributed optical fiber vibration sensing data lossless compression method for decomposing compression
Further prioritization scheme, the entropy coding that step 6 uses is the coding that counts.
The present invention compared with prior art, has following technique effect using above technical scheme：
(1) the solution of the present invention is lossless compression, will not lose any information, can intactly rebuild distribution type fiber-optic and shake
The initial data of dynamic sensing, improves the accuracy and adaptability of distributed optical fiber vibration sensing system；
(2) present invention enhances compression effectiveness on the premise of lossless, alleviates distributed light using Compression Strategies are decomposed
Data transfer and the pressure preserved in fine vibration sensing system.
Brief description of the drawings
Fig. 1 is based on the lossless compression principle framework for decomposing compression；Wherein, (a) is cataloged procedure, and (b) is decoding process.
Fig. 2 is Φ-OTDR vibration signals.
Fig. 3 is the discrete cosine coefficient of vibration signal.
Embodiment
Technical scheme is described in further detail below in conjunction with the accompanying drawings：
As shown in (a) in Fig. 1, cataloged procedure of the invention comprises the steps of：
Step 1, one group of initial data m is subjected to discrete cosine transform, obtains one group of discrete cosine coefficient M；Wherein, m=
{mi| i is integer and 1≤i≤N }={ m1,m2,...,mN, miIt is i-th of data in m, N is the data amount check included in m, M=
{Mj| j is integer and 1≤j≤N }={ M1,M2,...,MN, MjIt is j-th of data in M；
The formula of discrete cosine transform is：
Fig. 2 and Fig. 3 respectively show the data and curves before and after discrete cosine transform, before the energy main body after conversion concentrates on
Face is a bit of, indicates the outstanding energy compaction characteristic of discrete cosine transform.
Step 2, the gross energy of M isThen h is in [1, N] section and meets the minimum integer of following formula：
Wherein, r is percentage of energy；
Obtain frequency domain energy main body coefficient X, X=X1,X2,...,XN,XjIt is j-th of number in frequency domain energy main body coefficient X
According to,
Step 3, inverse discrete cosine transform is carried out to X, obtains time domain energy main body coefficient x, x=x1,x2,...,xN, xiIt is
I-th of data in x；
Inverse discrete cosine transform formula is：
Step 4, m and x are made into calculus of differences, obtains difference d, di=mi-xi, wherein, diIt is i-th of data in d；
Step 5, linear predictive coding is carried out to X, obtains the prediction remainder PX of X；Linear predictive coding is carried out to d, obtains the pre- of d
Survey remainder Pd；
Wherein, PXjIt is j-th of data in PX；
Wherein, PdiIt is i-th of data in Pd；
Step 6, entropy coding is carried out to PX, obtains the entropy coding compressed data EX of PX；Entropy coding is carried out to Pd, the entropy for obtaining Pd is compiled
Code compressed data Ed.
As shown in (b) in Fig. 1, decoding process of the invention comprises the steps of：
Step 1, entropy decoding is carried out to EX, obtains PX；Entropy decoding is carried out to Ed, obtains Pd；
Step 2, linear prediction decoding is carried out to PX, obtains X；Linear prediction decoding is carried out to Pd, obtains d；
Step 3, inverse discrete cosine transform is carried out to X, obtains x；
Step 4, x is added with d, obtains m.
The performance of one lossless compression method is typically to be evaluated by compression ratio.Compression ratio (CR) is defined by the formula：
Wherein, ScIt is the size of compressed data, SoIt is the size of initial data.
It is the compression ratio of the Φ-OTDR data of different type difference group as shown in table 1, using the solution of the present invention to not
The compression ratio that the Φ-OTDR data of same type difference group are compressed and produce, it can be seen that the present invention program effectively presses
Contracted Φ-OTDR data.
Table 1
Above content is that a further detailed description of the present invention in conjunction with specific preferred embodiments, it is impossible to is assert
The specific implementation of the present invention is confined to these explanations.For general technical staff of the technical field of the invention,
On the premise of not departing from present inventive concept, some simple deductions can also be made or substituted, should all be considered as belonging to the present invention's
Protection domain.
Claims (8)
1. it is a kind of based on decompose compression distributed optical fiber vibration sensing data lossless compression method, it is characterised in that including with
Lower step：
Step 1: one group of initial data m is carried out discrete cosine transform, one group of discrete cosine coefficient M is obtained；Wherein, m={ mi|i
For integer and 1≤i≤N }={ m1,m2,...,mN, miIt is i-th of data in m, N is the data amount check included in m, M={ Mj|j
For integer and 1≤j≤N }={ M1,M2,...,MN, MjIt is j-th of data in M；
Step 2: the gross energy of M isThen h is in [1, N] section and meets the minimum integer of following formula：
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Step 3: carrying out inverse discrete cosine transform to X, time domain energy main body coefficient x, x=x are obtained1,x2,...,xN, xiIt is in x
I-th of data；
Step 4: m and x are made calculus of differences, difference d, d are obtainedi=mi-xi, wherein, diIt is i-th of data in d；
Step 5: carrying out linear predictive coding to X, the prediction remainder PX of X is obtained；Linear predictive coding is carried out to d, obtains the prediction of d
Remainder Pd；
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Compressed data Ed.
It is 2. according to claim 1 a kind of based on the distributed optical fiber vibration sensing lossless date-compress side for decomposing compression
Method, it is characterised in that decode, comprise the following steps that to the compressed data that step 6 obtains：
Step A, entropy decoding is carried out to EX, obtains PX；Entropy decoding is carried out to Ed, obtains Pd；
Step B, linear prediction decoding is carried out to PX, obtains X；Linear prediction decoding is carried out to Pd, obtains d；
Step C, inverse discrete cosine transform is carried out to X, obtains x；
Step D, x is added with d, obtains m.
It is 3. according to claim 1 a kind of based on the distributed optical fiber vibration sensing lossless date-compress side for decomposing compression
Method, it is characterised in that the formula of discrete cosine transform is in step 1：
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Method, it is characterised in that initial data is original distribution formula optical fiber vibration sensing data in step 1.
It is 5. according to claim 1 a kind of based on the distributed optical fiber vibration sensing lossless date-compress side for decomposing compression
Method, it is characterised in that r is 95 in step 2.
It is 6. according to claim 1 a kind of based on the distributed optical fiber vibration sensing lossless date-compress side for decomposing compression
Method, it is characterised in that inverse discrete cosine transform formula is in step 3：
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It is 7. according to claim 1 a kind of based on the distributed optical fiber vibration sensing lossless date-compress side for decomposing compression
Method, it is characterised in that the linear predictive coding that step 5 uses is 2 rank linear predictions.
It is 8. according to claim 1 a kind of based on the distributed optical fiber vibration sensing lossless date-compress side for decomposing compression
Method, it is characterised in that the entropy coding that step 6 uses is the coding that counts.
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| 黄庆卿: ""机械振动无线传感器网络同步采集与数据压缩方法研究"", 《中国博士学位论文全文数据库工程科技II辑》 * |
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