CN106764468A - A kind of seepage early warning system and adaptive spectrum noise-eliminating method - Google Patents
A kind of seepage early warning system and adaptive spectrum noise-eliminating method Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
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Abstract
The invention discloses a kind of seepage early warning system and adaptive spectrum noise-eliminating method, including：Power supply unit, collecting unit, plus and blowup unit, processor unit and GPRS transmission unit, processor unit include：To noise information sample conversion, the ADC subelements of discrete sampling data are obtained；FFT (fast Fourier) conversion is carried out to each acquisition noise sample, noise frequency domain information is obtained, and calculate the FFT subelement of amplitude spectrum；Environmental Noise Influence is eliminated by adaptive spectrum denoising algorithm in frequency domain, retains the self-adapted noise elimination subelement of metastable seepage noise information；Frequency spectrum correlation analysis subelement；Early warning analysis subelement and GPRS transmission unit.When alert occur when, transmitting warning information to server or designated mobile phone.It is poor that the present invention can solve current seepage prior-warning device interference free performance, can only be used at night, it is impossible to monitors within 24 hours, there is rate of false alarm and the universal technical problem such as higher of rate of failing to report.
Description
Technical field
The present invention relates to Signal and Information Processing technical field, in particular it relates to a kind of seepage method for early warning and equipment.
Background technology
Seepage source of early warning is to be mainly used in water supply network, is typically placed on well interior conduit, by seismoelectric sensor
Pickup duct noise, by the intensity and spectrum distribution feature of Analyze noise, judges whether seepage occurs, and in time believe early warning
Breath is sent to Surveillance center or chief leading cadre's mobile phone.
When pipe leakage, fluid media (medium) at a high speed pass through seepage space, due to vibrations, friction, slow down, expansion, clash into etc.,
Fluid produces eddy stress or shearing force, forms seepage noise.Seepage sound wave is with the plane wave of non-frequency dispersion and the high-order sound of frequency dispersion
Mode form is propagated in pipeline fluid, is influenceed by fluid and pipeline attenuation characteristic, and as propagation distance increases, noise intensity is fast
Speed weakens.This feature of generation noise during using pipe leakage, has now been developed that various pipeline leakages detection/monitorings set both at home and abroad
For (for example：Leakage measuring instrument by sonic, correlator, seepage early warning etc.), noise detecting method is also the current main application method of water supply network.
Because seepage noise is extremely faint, easily by ambient noise interference, how seepage noise is correctly recognized, it is necessary to seepage
Noise and ambient noise each the characteristics of be analyzed.Theoretical and actual test is proved：Pipeline environment noise belongs to Gauss white noise
Sound, spectral power distribution meets Gaussian Profile；Versus environmental noise, any point in seepage noise transmission approach, seepage noise
With relatively stable noise intensity and spectrum distribution feature.Wherein noise intensity feature decision method is also that current foreign countries' main flow is oozed
Leakage early warning product is (for example：Britain is bold and generous) method that generally uses.
Using noise intensity feature decision method, night 2 is typically chosen in:00~4:00 period acquisition noise data, it is main
Syllabus is, in order to reduce ambient noise interference, to reduce wrong report and miss probability.During collection, according to fixed intervals T seconds (general 5
~10 seconds, different manufacturers were slightly different) acquisition noise data and noise intensity is calculated, gather total degree n times (General N>=1000
It is secondary), for the ease of analysis, the noise intensity discrete series that n times are collected is defined as F (t), while defining strength range threshold value
(general to set 20db or so, different manufacturers are omited for Vth (general to set 5db or so, different manufacturers are slightly different) and excision value Vcut
Have difference), seepage decision method：
1st, as MIN (F (t)>=Vcut and MAX (F (t))-MIN (F (t))<During=Vth, there is seepage；
2nd, as MIN (F (t)<Vcut or MAX (F (t))-MIN (F (t))>During Vth, ne-leakage occurs；
Wherein MAX () takes sequence maximum, and MIN () takes sequence minimum value.This method Main Basiss seepage noise intensity
Stablize relatively, and the stronger feature of ambient noise randomness judges whether seepage occurs.
Although seepage strength characteristic distinguished number has, algorithm is simple, the low advantage of hardware cost, and interference free performance is poor,
Can only be used at night, there is rate of false alarm and rate of failing to report generally higher etc. not enough.
The content of the invention
It is an object of the invention to provide a kind of seepage early warning system and adaptive spectrum noise-eliminating method, to solve current seepage
Prior-warning device interference free performance is poor, can only be used at night, it is impossible to monitors within 24 hours, there is rate of false alarm and rate of failing to report is generally higher
Etc. technical problem.
For achieving the above object, the technical solution adopted in the present invention is as follows：
A kind of seepage early warning system, including：
1) power supply unit, for equipment provides electric power safeguard；
2) collecting unit, the seepage noise sample for picking up pipeline weak；
3) plus and blowup unit, for the faint duct noise plus and blowup to collecting, and delivers in processor
Putting ADC carries out data acquisition；
4) processor unit：It is responsible for Data Management Analysis and early warning output；The unit includes following several subelements：
A) ADC subelements：To noise information sample conversion, discrete sampling data are obtained；
B) FFT subelement：FFT is carried out to acquisition noise sample, noise frequency domain information is obtained, and calculate amplitude
Spectrum；
C) self-adapted noise elimination subelement：Once complete gatherer process, i.e. acquisition interval+collection duration is defined, is one and is adopted
In the collection cycle, continuous m collection period is defined as a de-noising cycle, is that each frequency defines a Variable Learning, study becomes
The position for indicating current frequency regulation coefficient is measured, FFT is performed to each gathered data, and obtain input range spectrum,
Definition study amplitude spectrum, in each collection period, to input range spectrum, frequency is compared one by one with study amplitude spectrum, to retain
Metastable seepage noise information；Including：
Comparing unit, compares for being composed to input range and learning amplitude spectrum, obtains spectral change state；
Unit, for learning to the Variable Learning, tracks spectral change state；
Adjustment unit, for being adjusted to the study amplitude spectrum, obtains de-noising amplitude spectrum；
D) frequency spectrum correlation analysis subelement：Data carry out frequency spectrum related operation by frequency spectrum correlation analysis algorithm after de-noising,
Obtain noise correlation coefficients value；Including：
Receiving unit, for receiving described amplitude spectrum；
Arithmetic element：Related operation is carried out for the amplitude spectrum to receiving and previous de-noising periodic amplitude spectrum, phase is obtained
Coefficient values；
Memory cell：For storing the amplitude spectrum for receiving；
E) early warning analysis subelement：Comprehensive statistics analysis are carried out to correlated results, confirms whether seepage occurs；Including：
Receiving unit, for receiving relevance degree；
Statistic unit：For carrying out statistical analysis to the relevance degree for receiving；
Judging unit：For judging whether seepage occurs according to statistics；
5) GPRS transmission unit：When alert occur when, transmitting warning information to server or designated mobile phone.
A kind of adaptive spectrum noise-eliminating method of new seepage early warning system, the system includes：Power supply unit, collection are single
Unit, plus and blowup unit, processor unit and GPRS transmission unit；The processor unit includes：Noise information is sampled
Conversion, obtains the ADC subelements of discrete sampling data；FFT is carried out to acquisition noise sample, noise frequency domain information is obtained,
And calculate the FFT subelement of amplitude spectrum；Environmental Noise Influence is eliminated by adaptive spectrum Denoising Method in frequency domain, retains phase
To the self-adapted noise elimination subelement of the seepage noise information of stabilization；Data carry out frequency spectrum by frequency spectrum correlation analysis algorithm after de-noising
Related operation, obtains the frequency spectrum correlation analysis subelement of noise correlation coefficients value；Comprehensive statistics analysis are carried out to correlated results, really
Recognize the early warning analysis subelement whether seepage occurs；Wherein self-adapted noise elimination is suddenly as follows：
Double acquisition interval is defined for TS；A length of TL when defining single acquisition；Once complete gatherer process is defined, i.e.,
Acquisition interval+collection duration, is a collection period TP, then TP=TS+TL；
Continuous m collection period is defined as a de-noising cycle T F；Define regulation coefficient array G [n], wherein n=2*m
+ 1, make G [n]=- 1/2, -1/4 ... ..-1/2m, 0,1/2m ... ..1/4,1/2；
For each frequency defines a Variable Learning g, and 0≤g<N, Variable Learning g are used to indicate current frequency adjustment system
Several positions, corresponding to the subscript of G [n] array, and Variable Learning g is initialized as m；
FFT is performed to each gathered data, and it is Vi to obtain input range spectrum；Definition study amplitude spectrum Vb；Every
To input range spectrum Vi and study amplitude spectrum Vb, frequency is compared individual collection period one by one, and is adjusted as follows:
First by frequency regularized learning algorithm variable:
Then, by frequency regularized learning algorithm amplitude spectrum:Vb (n)=Vb (n) * (1+G [g (n)])；
The present invention eliminates the influence of ambient noise using adaptive spectrum noise cancellation apparatus；It is comprehensive using frequency spectrum related algorithm device
Assessment noise intensity and spectrum distribution feature are closed, so the present invention has preferable interference free performance, extremely low wrong report and fails to report
Rate, it is adapted to continue to monitor for 24 hours, the features such as meet seepage early warning requirement of real-time.
Brief description of the drawings
Fig. 1 is inventive algorithm schematic flow sheet；
Fig. 2 is the time diagrams such as collection period of the invention, de-noising cycle；
Fig. 3 is frequency spectrum correlation timing schematic diagram of the invention；
Fig. 4 is adaptive spectrum denoising algorithm schematic diagram of the present invention；
Fig. 5 is the example adaptive spectrum denoising algorithm flow chart of embodiments of the present invention；
Fig. 6 is the example frequency spectrum related algorithm flow chart of embodiments of the present invention；
Fig. 7 is the example early warning analysis algorithm flow chart of embodiments of the present invention.
Fig. 8 is the circuit theory of constitution block diagram of invention.
Fig. 9 is algorithm process schematic flow sheet of the invention；
Figure 10 is interrupt processing flow chart of the invention；
Figure 11 is the example adaptive spectrum denoising algorithm flow chart of embodiments of the present invention；
Figure 12 is the example frequency spectrum related algorithm flow chart of embodiments of the present invention；
Figure 13 is the example early warning analysis algorithm flow chart of embodiments of the present invention.
Specific embodiment
Specific embodiment of the invention is described in detail below in conjunction with accompanying drawing.It should be appreciated that this place is retouched
The specific embodiment stated is merely to illustrate and explain the present invention, and is not intended to limit the invention.
Influenceed by aqueous fluid and pipeline attenuation characteristic, seepage noise will be far above low in communication process high frequency attenuation speed
The frequency rate of decay, therefore seepage noise frequency can be gathered be mainly distributed in the range of 50~2Khz, according to sampling thheorem, sampling frequency
Rate should be higher than that 4Khz, and typically 8Khz sample frequencys are taken in practical application.
According to seepage noise behavior：With metastable noise intensity and spectrum distribution feature.This algorithm synthesis foundation
Noise intensity and spectrum distribution feature, are processed and are analyzed from frequency domain to noise data, and algorithm process flow is as shown in Figure 1；
Each acquisition noise data are converted through FFT (fast Fourier) first, obtain noise frequency domain information；By " adaptive
Answer frequency spectrum de-noising " algorithm elimination Environmental Noise Influence, retain metastable seepage noise information；Data are by " frequency spectrum after de-noising
Correlation analysis " algorithm is carrying out frequency spectrum correlation operation according to the de-noising cycle, obtains degree of relevancy on noise temporal；Correlation knot
Fruit carries out statistical analysis by " early warning analysis " module, is confirmed whether that seepage occurs.
The algorithm course of work, defines double acquisition interval for TS for convenience of description, defines a length of during single acquisition
TL, once complete gatherer process (acquisition interval+collection duration) is a collection period TP for definition, then TP=TS+TL.Will be continuous
M collection period be defined as de-noising a cycle T F, such as Fig. 2 (collection period of the invention, de-noising cycle time diagram,
Note：M=5 in figure) shown in:
Each collection period TP gathers a noise data, gathers duration TL, and gathered data is obtained first through FFT computings
Input range is composed, then by " adaptive spectrum de-noising " algorithm process eliminating environmental disturbances noise；Often by a de-noising week
Phase TF, " frequency spectrum correlation analysis " computing is performed by current de-noising amplitude spectrum and previous de-noising periodic amplitude spectrum, obtains noise phase
Relation number r (i).Such as Fig. 3 (frequency spectrum correlation timing schematic diagram of the invention, note：M=5 in figure) shown in.
The correlation coefficient r (i) for calculating carries out statistical disposition by " early warning analysis " module, judges whether seepage occurs.
(note：Actual test shows：When TS >=5 second；TL >=1 second；During m >=5, performance is optimal.)
In order to better illustrate the present invention, various pieces are described in detail below：
Adaptive spectrum de-noising part：
Theory and practice is proved:Pipeline environment noise belongs to white Gaussian noise, and spectral power distribution meets Gaussian Profile；And
Seepage noise has relatively stable noise intensity and relatively stable spectrum distribution.For above-mentioned ambient noise and the spy of seepage noise
Point, design self-adapted noise elimination model as shown in Fig. 4 (adaptive spectrum denoising algorithm schematic diagram).
In order to illustrate the operation principle of self-adapted noise elimination, regulation coefficient array G [n], wherein n=2*m+1, order are defined first
G [n]=- 1/2, -1/4 ... ..-1/2m, 0,1/2m ... ..1/4,1/2 }；If setting m=5, i.e., 5 collection period definition
Be a filtering cycle, then regulation coefficient array G (n)={ -1/2, -1/4, -1/8, -1/16, -1/32,0,1/32,1/16,1/
8,1/4,1/2 }.For each frequency defines a Variable Learning g, and 0≤g<N, Variable Learning g are used to indicate current frequency adjustment
The position of coefficient, corresponding to the subscript of G [n] array, during initial acquisition, Variable Learning g is set to m.Each gathered data is held
Row FFT, and it is Vi to obtain input range spectrum；Definition study amplitude spectrum Vb, study amplitude spectrum Vb are used for tracking study input
Noise changes, and according to the strong feature of ambient noise time randomness, eliminates Environmental Noise Influence, finally gives the noise after de-noising
Amplitude spectrum.
During work, gathered data is converted through FFT (fast Fourier) and calculates acquisition input range spectrum Vi, by input range
Frequency is compared spectrum Vi and study amplitude spectrum Vb one by one, and computing in the following manner：
First by frequency regularized learning algorithm variable:
Then, by frequency regularized learning algorithm amplitude spectrum:Vb (n)=Vb (n) * (1+G [g (n)])；
Fig. 5 is example adaptive spectrum denoising algorithm flow chart according to the embodiment of the present invention, first to collection number
According to execution FFT, and calculate acquisition input range spectrum Vi；Then input range spectrum Vi and study amplitude spectrum are compared by frequency
Vb, obtains spectral change state；According to the spectral change state for obtaining, regularized learning algorithm variable g and study amplitude spectrum Vb；Work as completion
In one de-noising cycle, study amplitude spectrum Vb is passed into next stage and is processed.
Frequency spectrum correlation analysis part：
We select simple correlation coefficient algorithm in frequency spectrum correlation analysis, also known as Pearson correlation coefficient or " Pearson came product
Square coefficient correlation ", the tightness degree that it is contacted between describing two spacing variables, is a kind of linearly dependent coefficient.Pearson's phase
Relation number is that, for reflecting two statistics of linear variable displacement degree of correlation, coefficient correlation is represented with r.
After a complete de-noising cycle, by the amplitude modal data of adaptive spectrum de-noising, with previous de-noising week
The amplitude modal data of phase, according to Pearson correlation coefficient formula deployment analysis, obtains the correlation degree of different time seepage noise.
Operational formula：
This kind of method, by amplitude modal data directly as correlation analysis input data deployment analysis, correlated results synthesis
Embody seepage noise intensity and spectrum distribution feature.Noise intensity method is relied on relative to simple, with more preferable accuracy
And objectivity, advantageously reduce seepage misinformation probability.
The frequency spectrum correlation coefficient r of acquisition, span is [- 1,1], r>0 represents positive correlation, r<0 represents negatively correlated, | r |
Illustrate the height of degree of correlation between variable.Distinguishingly, r=1 is referred to as perfect positive correlation, and r=-1 is referred to as perfect negative correlation, r
=0 is referred to as uncorrelated.Theory and practice confirmation, when | r | is more than or equal to 0.8, had both thought that two variables had very strong linear phase
Guan Xing.
Fig. 6 is example frequency spectrum related algorithm flow chart according to the embodiment of the present invention, first to current study amplitude
The spectrum Vb and amplitude spectrum VPREV in previous de-noising cycle does correlation analysis, obtains correlation coefficient r value；Vb is saved in VPREV afterwards
In；The correlation coefficient r value of calculating finally is passed into early warning analysis to be processed.
Early warning analysis principle：
Through frequency spectrum correlation analysis, the correlation coefficient r value of acquisition, the signal for completing certain hour by early warning analysis algorithm is related
Degree statistics and early warning treatment, practical application and actual test according to current seepage early warning show：When continuous time >=60 minute
During r value >=0.8 of all acquisitions, pipe leakage occurs；When the conditions set forth above are not met, pipeline ne-leakage.Using this early warning
Judgment mode relative to tradition only night collection and alarm mode, can accomplish 24 hours monitor, possess more preferable real-time and
Practicality.
Fig. 7 is example early warning analysis algorithm flow chart according to the embodiment of the present invention, the phase obtained to correlation analysis
Relation number r values are counted, if r >=0.8, serial correlation degree variables TC=TC+1；Otherwise TC=0；Judge continuous phase
Close whether degree variables TC exceedes early warning number of times (such as 60 times, early warning number of times=pre-warning time/TF), if it exceeds early warning time
Number, then early warning output.
Hardware components
Practical application and popularization with seepage source of early warning, on the premise of assurance function and perfect performance, more pair set
Standby power consumption and cost propose high requirement, and each restraining factors of choosing comprehensively equipment source of early warning propose design as shown in Figure 8
Structure.
Power supply unit：Comprehensive assessment equipment power dissipation and equipment volume, using two section ER14505 battery parallel power supplies.
Collecting unit：Using the most frequently used piezoelectric ceramic process sensor of water supply industry, piezoelectric ceramics has extremely sensitive
Characteristic, atomic weak mechanical oscillation can be converted into electric signal, can be used for sonar system, meteorological detection, remote measurement environment protect
Shield, household electrical appliance etc..Piezoelectric ceramics makes it can even sense that more than ten meters of outer winged insects pat wings to sky to the sensitivity of external force
The disturbance of gas, piezoelectricity seismic detector is made of it, can accurately measure earthquake intensity, indicates the azimuth-range of earthquake.
Plus and blowup unit：Using three-level low noise, consumption high gain amplifier, amplifier selects LT1492CS8, this amplification
Circuit can be provided>The gain amplifier of 80db.
MSP430 processors：Comprehensive assessment sexual valence when internal resource, this equipment uses MSP430F5418A models.
MSP430 series monolithics be Texas Instruments (TI) start within 1996 to introduce to the market a kind of 16 super low-power consumptions, with essence
The mixed-signal processor (Mixed Signal Processor) of simple instruction set (RISC).With following advantage：
Disposal ability is strong：MSP430 series monolithics are the single-chip microcomputers of 16, employ reduced instruction set computer (RISC)
Structure, with abundant addressing system (7 kinds of source operand addressing, 4 kinds of destination operand addressing), 27 succinct core instructions
And substantial amounts of dummy instruction；Data storage can all participate in various computings in substantial amounts of register and piece；It is also efficient
Table look-up process instruction.These features ensure that can develop efficient source program.
Fast operation：MSP430 series monolithics under the driving of 25MHz crystal, can realize the instruction cycle of 40ns.
The data width of 16, the instruction cycle of 40ns and multi-functional hardware multiplier (can realize multiply-add operation) are engaged, energy
Realize some algorithms (such as FFT) of Digital Signal Processing.
Super low-power consumption：The super low-power consumption performance of MSP430 single-chip microcomputers has been industry undisputable fact.MSP430 series monolithics
The supply voltage of machine uses 1.8-3.6V.Have five kinds of low-power consumption modes (LPM0~LPM4).It is reachable under real-time clock pattern
2.5 μ A are minimum up to 0.1 μ A under RAM holding patterns.
Aboundresources in piece:Each series of MSP430 series monolithics is all integrated with more rich interior peripheral hardware.They divide
Be not house dog (WDT), analog comparator, timer, UART, SPI, I2C, hardware multiplier, liquid crystal driver, 10/12
The various combination of some peripheral modules such as ADC, 16 sigma-delta ADC, DMA, real-time clock (RTC) and USB controllers.10/12
Hardware A/D converter has switching rate higher, reaches as high as 200kbps, disclosure satisfy that most of data acquisition applications.At this
In design, the ADC of 12 throw-over rates high fully meets design requirement, is also beneficial to power consumption and cost control.
GPRS radio-cells (General Packet Radio Service):It is the letter of general packet radio service technology
Claim, it is a kind of available mobile data services of gsm mobile telephone user, belongs to the data transfer skill in Generation Mobile Telecommunication System
Art.Because GPRS is promoted compared with early, coverage rate most wide, signal stabilization, the low advantage of use cost, while considering seepage source of early warning
Amount of communication data is minimum, is less than 5Mbyte within one month, therefore GPRS communication modes are undoubtedly best selection.
Software section：
Software is estimated analysis, new algorithm compositive index noise intensity to pipe leakage using new seepage warning algorithm
With spectrum distribution feature, processed and analyzed from frequency domain to noise data, (example is of the invention for algorithm process flow such as Fig. 9
Algorithm process schematic flow sheet) illustrate：
Acquisition noise data are converted through FFT (fast Fourier) first, obtain noise frequency domain information；By " self adaptation is frequently
Spectrum de-noising " algorithm eliminates Environmental Noise Influence, retains metastable seepage noise information；Data are by " frequency spectrum is related after de-noising
Analysis " algorithm carries out frequency spectrum correlation operation according to the de-noising cycle, obtains degree of relevancy on noise temporal；Correlated results by
" early warning analysis " module carries out Statistic analysis, is confirmed whether that seepage occurs.
Because collection seepage noise frequency is mainly distributed in the range of 50~2Khz, according to sampling thheorem, sample frequency should
Higher than 4Khz, 8Khz sample frequencys are chosen in the design.Consideration equipment actual power loss and operand, gather 100ms data every time,
I.e. 800 valid data samples, obtain 400 frequency spectrums (symmetry of FFT), spectral resolution 10Hz after FFT.In order to
Compaction algorithms amount, and consider that seepage noise is mainly distributed on 50~2Khz scopes, all take preceding 200 frequency spectrums and participate in rear class fortune
Calculate.
Comprehensive assessment actual application environment and equipment power dissipation and equipment performance, take 60 seconds for a collection period, i.e. TP=
60 seconds；5 collection period are a de-noising cycle, i.e. TF=TP*m=300 seconds, m=5；Continuous 20 correlation analyses result r values
>=0.8, confirm that seepage occurs, i.e., pre-warning time=20*TF=6000 seconds=100 minutes.
In order to simplify realization, shorten process time, raising treatment effeciency, reduction equipment power dissipation, all collections and computing work
Work will be completed in RTC Interruptions, RTC fixed time intervals 1 minute, i.e. configures RTC Interruptions, fixed time interval 1 within TP=60 seconds
Minute, equipment only wakes up CPU, when interruption is exited, reenters low-power consumption mode when RTC is interrupted.Interrupt total handling process
As shown in Figure 10 (example interrupt processing flow chart of the invention)：For apparent explanation algorithm process flow, to key
Algorithm and process step, will deployment analysis one by one.
Adaptive spectrum de-noising is realized：
Adaptive spectrum de-noising realizes that startup ADC12 first simultaneously configures DMA channel, 8K acquisition rates in RTC Interruptions
Continuous acquisition 100ms data；After the completion of to be collected, FFT is carried out to gathered data, and calculate amplitude spectrum；By frequency ratio
Composed compared with input range and study amplitude spectrum, obtain spectral change state；According to spectral change state, regularized learning algorithm variable g；By frequency
Point adjustment bin magnitudes；Into subordinate " frequency spectrum correlation analysis " processing routine.Flow chart such as Figure 11 (embodiment party of the invention
The example adaptive spectrum denoising algorithm flow chart of formula) shown in.
Frequency spectrum correlation analysis is realized：
Correlation analysis is done to the current study amplitude spectrum Vb and amplitude spectrum VPREV in previous de-noising cycle first, obtains related
Coefficient r values；Vb to VPREV is preserved afterwards；Correlation coefficient r value passes to early warning analysis and carries out statistical analysis treatment；Correlation analysis
Complete；Shown in flow chart such as Figure 12 (example frequency spectrum related algorithm flow chart according to the embodiment of the present invention).This kind of method,
Input data by amplitude modal data directly as correlation analysis launches correlation analysis, and correlated results comprehensively embodies seepage
Noise intensity and spectrum distribution feature.Noise intensity method is relied on relative to simple, with more preferable accuracy and objectivity, is had
Beneficial to reduction seepage misinformation probability.
Early warning analysis is realized：
The correlation coefficient r value that statistics " correlation analysis " is obtained, if r >=0.8, serial correlation degree variables TC=TC+
1；Otherwise TC=0；Judge whether serial correlation degree variables TC exceedes associated numbers of times 20 times (100 minutes), if it exceeds related
Number of times, then early warning output；Early warning information is by GPRS teletransmissions to server or designated mobile phone；So far early warning analysis is completed；Flow
Figure is as shown in Figure 13 (example early warning analysis algorithm flow chart according to the embodiment of the present invention).
The preferred embodiment of the present invention is described in detail above in association with accompanying drawing, but, the present invention is not limited to above-mentioned reality
The detail in mode is applied, in range of the technology design of the invention, various letters can be carried out to technical scheme
Monotropic type, these simple variants belong to protection scope of the present invention.
It is further to note that each particular technique feature described in above-mentioned specific embodiment, in not lance
In the case of shield, can be combined by any suitable means.
Additionally, can also be combined between a variety of implementation methods of the invention, as long as it is without prejudice to originally
The thought of invention, it should equally be considered as content disclosed in this invention.
Table 1：
Claims (2)
1. a kind of seepage early warning system, it is characterised in that including：
1) power supply unit, for equipment provides electric power safeguard；
2) collecting unit, the seepage noise sample for picking up pipeline weak；
3) plus and blowup unit, for the faint duct noise plus and blowup to collecting, and delivers to the built-in ADC of processor
Carry out data acquisition；
4) processor unit：It is responsible for Data Management Analysis and early warning output；The unit includes following several subelements：
A) ADC subelements：To noise information sample conversion, discrete sampling data are obtained；
B) FFT subelement：FFT is carried out to acquisition noise sample, noise frequency domain information is obtained, and calculate amplitude spectrum；
C) self-adapted noise elimination subelement：Once complete gatherer process, i.e. acquisition interval+collection duration is defined, is a collection week
Phase, continuous m collection period is defined as a de-noising cycle, is that each frequency defines a Variable Learning, Variable Learning is used
In the position for indicating current frequency regulation coefficient, FFT is performed to each gathered data, and obtain input range spectrum, definition
Study amplitude spectrum, in each collection period, to input range spectrum, frequency is compared one by one with study amplitude spectrum, relative to retain
The seepage noise information of stabilization；Including：
Comparing unit, compares for being composed to input range and learning amplitude spectrum, obtains spectral change state；
Unit, for learning to the Variable Learning, tracks spectral change state；
Adjustment unit, for being adjusted to the study amplitude spectrum, obtains de-noising amplitude spectrum；
D) frequency spectrum correlation analysis subelement：Data carry out frequency spectrum related operation by frequency spectrum correlation analysis algorithm after de-noising, obtain
Noise correlation coefficients value；Including：
Receiving unit, for receiving described amplitude spectrum；
Arithmetic element：Related operation is carried out for the amplitude spectrum to receiving and previous de-noising periodic amplitude spectrum, phase relation is obtained
Numerical value；
Memory cell：For storing the amplitude spectrum for receiving；
E) early warning analysis subelement：Comprehensive statistics analysis are carried out to correlated results, confirms whether seepage occurs；Including：
Receiving unit, for receiving relevance degree；
Statistic unit：For carrying out statistical analysis to the relevance degree for receiving；
Judging unit：For judging whether seepage occurs according to statistics；
5) GPRS transmission unit：When alert occur when, transmitting warning information to server or designated mobile phone.
2. a kind of adaptive spectrum noise-eliminating method of new seepage early warning system, the system includes：Power supply unit, collecting unit,
Plus and blowup unit, processor unit and GPRS transmission unit；The processor unit includes：Noise information sampling is turned
Change, obtain the ADC subelements of discrete sampling data；FFT is carried out to acquisition noise sample, noise frequency domain information is obtained, and
Calculate the FFT subelement of amplitude spectrum；Environmental Noise Influence is eliminated by adaptive spectrum Denoising Method in frequency domain, retains relative
The self-adapted noise elimination subelement of the seepage noise information of stabilization；Data carry out frequency spectrum phase by frequency spectrum correlation analysis algorithm after de-noising
Computing is closed, the frequency spectrum correlation analysis subelement of noise correlation coefficients value is obtained；Comprehensive statistics analysis are carried out to correlated results, is confirmed
The early warning analysis subelement whether seepage occurs；Wherein self-adapted noise elimination is suddenly as follows：
Double acquisition interval is defined for TS；A length of TL when defining single acquisition；Once complete gatherer process is defined, that is, is gathered
Interval+collection duration, is a collection period TP, then TP=TS+TL；
Continuous m collection period is defined as a de-noising cycle T F；Definition regulation coefficient array G [n], wherein n=2*m+1,
Make G [n]=- 1/2, -1/4 ... ..-1/2m, 0,1/2m ... ..1/4,1/2；
For each frequency defines a Variable Learning g, and 0≤g<N, Variable Learning g are used to indicate current frequency regulation coefficient
Position, corresponding to the subscript of G [n] array, and Variable Learning g is initialized as m；
FFT is performed to each gathered data, and it is Vi to obtain input range spectrum；Definition study amplitude spectrum Vb；Adopted at each
The collection cycle, frequency was compared one by one to input range spectrum Vi and study amplitude spectrum Vb, and was adjusted as follows:
First by frequency regularized learning algorithm variable:
Then, by frequency regularized learning algorithm amplitude spectrum:Vb (n)=Vb (n) * (1+G [g (n)]).
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