CN108344692A - Using the COD online automatic monitors of technology of Internet of things - Google Patents
Using the COD online automatic monitors of technology of Internet of things Download PDFInfo
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- CN108344692A CN108344692A CN201810141377.7A CN201810141377A CN108344692A CN 108344692 A CN108344692 A CN 108344692A CN 201810141377 A CN201810141377 A CN 201810141377A CN 108344692 A CN108344692 A CN 108344692A
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/3103—Atomic absorption analysis
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Abstract
The present invention provides a kind of COD online automatic monitors using technology of Internet of things, the monitor is used to monitor the COD value of oilfield drilling waste, the oilfield drilling waste includes that the mass percent of soil property and liquid and liquid and earthen matter is more than 89% and is less than 95%, which includes：Oilfield drilling waste sampling unit is handled soil property sample contents for carrying out oil field soil property sampling and the processing of oilfield liquids substance to selection area；Spectral scan unit is handled spectral signal for carrying out spectral scan to soil property sample contents；Comparing unit, for the signal after the spectral scan cell processing to be transferred to server by Internet of Things, it is compared with given threshold in server, and early warning is carried out to the signal value more than threshold value, the threshold value is the COD preset values of the waste obtained by statistical.
Description
Technical field
The invention belongs to field of spectral analysis technology, and in particular to a kind of to use Internet of Things skill using spectral analysis technique
The COD online automatic monitors of art.
Background technology
It is especially main to two kinds of organic matter in soil property and full nitrogen at home and abroad being studied at present about soil property component monitoring
Component content research in, it is most of to have obtained preferable achievement in research.On this basis, Many researchers also use light
Some metal element contents in spectral analysis technology quantitative analysis soil property, it is pre- although also obtaining certain achievement for monitoring
Effect is surveyed but not as good as apparent to the prediction effect of organic matter and total nitrogen content.
Attempt for the first time by spectral technique be introduced into soil property detection be American scientist Browsers and Hanks, 1965
Find that the organic matter in soil property has relevant feature line mark near infrared spectrum region and organic matter year.Then in 1970, with
Afterwards in 1970, Salisbury and Hunt are equally found that certain minerals in soil property near infrared spectrum there is also phases
The feature line mark of pass.The achievement of the above several researchers has started the new method for soil property composition detection, while in theory
On demonstrate using spectral analysis technique in soil property Cucumber or ingredient carry out qualitative or quantitative analysis possibility.
At home and abroad in the result of study of scholar it is recognised that at present for soil property ingredient detection in, organic matter and
The prediction effect of full nitrogen is generally forged a good relationship, and, that has not consistent in each document report for the prediction effect of metallic element
The prediction effect of other scholar is preferable, and some is then not fully up to expectations, and at every in report, the processing for soil property sample contents
It is not quite similar, some researchers the operations such as can be dried, be ground up, sieved to collected sample early period in experiment, experiment
As a result show that the prediction effect of spectrum analysis can be improved by carrying out processing appropriate to soil property sample contents.Report proves sample
The physical properties such as water content, particle diameter precision of prediction can all be had an impact, and testing conditions such as sample whether rotate,
The factors such as detection height, angle also can all have an impact prediction effect.But the experiment conducted in having been reported that is adopted close red
External spectrum instrument carries out, and tests and carry out in laboratory environments mostly, rarely has the scene of field progress in the wild is practical to measure
Report.
When analyzing soil property ingredient using spectral analysis technique, the performance of spectral instrument has a fixing for prediction effect
It rings, and different prediction model method for building up has also embodied extremely important effect during prediction.Even if adopting sometimes
With the spectral instrument of same model, the method that prediction model is established is different, and obtained detection result is also different, wherein mainly adopting
With the methods of principle component regression, offset minimum binary and artificial neural network.Also have many scholars for a variety of data at
Reason method is compared, and as a result shows that each method has the characteristics that itself, but which kind of specifically used data processing method into
The testing result that row modeling can most be had, does not obtain an exact conclusion in current achievement in research.
Li Jie of China Agricultural Machinery Science Research Academy in 2012 et al. is complete to the soil property sample contents near 72 Beijing
Nitrogen, full potassium, the content of organic matter and COD value are analyzed, and are modeled by Partial Least Squares, prediction result and actual value
With good consistency, the coefficient of determination reaches as high as 0.9554.What of Zhejiang University brave et al. has studied variable grain diameter
With different modeling methods for the image of soil property content of organic matter near-infrared spectral analytical method, sample is divided into particle in research
A diameter of 0.169-2mm and be less than two sample sets of 0.169mm, establish three kinds of prediction models, the results showed that work as particle diameter
When between 0.169-2mm, prediction related coefficient is all higher than 0.84, and predicted root mean square error is respectively less than 0.20；And when particle is straight
When diameter is less than 0.169mm, prediction related coefficient is respectively less than 0.71, and predicted root mean square error is all higher than 0.23.
From the point of view of development present situation, spectra methods is generally defined as a kind of completely new soil property into sorting at present
Quick, the loseless method surveyed, and new development trend from now on, for solving the traditional chemical in soil property composition detection at present
The efficiency of monitoring method is low, it is of high cost, can not the drawbacks such as Fields detection.According to current achievement in research, spectral analysis technique for
The prediction effect of soil property organic matter and total nitrogen content is generally preferable, and the related coefficient of prediction result and chemical detection can generally reach
0.9 or more is even higher.The size of soil property particle diameter has a certain impact for prediction effect, but less, soil property water content
Also prediction result can be produced bigger effect, influence of the data processing method used by prediction mode to prediction result is little.
And generally it is not so good as the prediction effect of organic matter and total nitrogen content for the prediction effect of the metal components content such as phosphorus, potassium, calcium, magnesium
It is good, but can also reach 0.8 in some studies, what effect was relatively good in individual researchs can reach 0.9 or more, this just needs building
Suitable data processing method is selected when vertical prediction model.
Invention content
In view of the above analysis, it is on-line automatic using the COD of technology of Internet of things that the main purpose of the present invention is to provide a kind of
Monitor, the monitor are used to monitor the COD value of oilfield drilling waste, and the oilfield drilling waste includes soil property and liquid
State substance liquid and the mass percent of earthen matter are more than 89% and are less than 95%, which includes：
Oilfield drilling waste sampling unit, for being carried out at oil field soil property sampling and oilfield liquids substance to selection area
Reason, handles soil property sample contents；
Spectral scan unit is handled spectral signal for carrying out spectral scan to soil property sample contents；Compare list
Member, for the signal after the spectral scan cell processing to be transferred to server by Internet of Things, server by it with set
Determine threshold value to be compared, and early warning is carried out to the signal value more than threshold value, the threshold value is discarded by what statistical obtained
The COD preset values of object.
Further, the oilfield drilling waste sampling unit includes：
Number of samples determination subelement, for determining number of samples P before sampling, by variance yields and average value and true value
Mathematic interpolation number of samples, and P be >=19 natural number；
Sampling processing subelement, for the size according to sampling area, using snakelike sampling, sampling depth is selected as
30CM is sampled according to calculated number of samples, when sampling, is first produced a sampling section, is carried out being parallel to section
Sampling, adopts the earthwork to perpendicular to ground, the soil property after sampling is mixed, and humidity that soil property contains is passed through the side that is evaporated
Formula is adjusted to 35~38%, then mixing thickness soil property sample contents are crushed, and removal stone, root system of plant mix and transform into four sides
Shape divides diagonal line and is divided into four parts, diagonal two parts therein is taken, after being repeated a number of times, by the weight control of soil property sample contents
The weight in needs is made, then to handling sample contents through hydrochloric acid, nitric acid, perchloric acid；
Spectral scan unit includes：
Rotation sweep subelement, for soil property sample contents to be positioned in sample container, shake makes its upper surface put down substantially
It is distributed, sample is positioned on quartz window smoothly then, in measurement process, soil property sample contents can be revolved with sample container instrument
Turn to obtain the averaged spectrum obtained after multiple rotary scanning analysis, the rotary speed of sample container is 4cm/s, soil property sample contents
It can be scanned in 40s 64 times, the spectrum after arithmetic mean is as a sampling spectrum；Each sample, which is positioned over different samples, to be held
It in device, measures twice, the spectroscopic data after being averaged again is used for signal processing；
Signal processing subelement, wherein the signal processing is based on the signal processing subelement, signal processing is single
Member acquires measured signal, obtains original absorbance spectrum, determine correction reference data wavelength, utilize school for acquiring reference signal
Positive reference data wavelength calibration absorption spectrum；The correction reference data wavelength by being calculated as follows：
Spectrum matrix is denoted as X (M*K), wherein i-th of wavelength variable represents i-th of column vector in spectrum matrix X, remembers N=
Min (m-1, k), the n=1 before carrying out first time interative computation, the arbitrary a certain row for selecting spectrum matrix, are denoted as variable xk(0)；
The column vector location sets not being selected into are denoted as T,
Calculate separately xk(n-1)To remaining the projection of following variables, it is denoted as：
Note k (n-1)=arg (max (| | Pj||),j∈T)；N=n+1 enables x if n=Nk(n-1)As the row being selected into
Variable, returns to above-mentioned column vector position and calculates step and calculated again, using multiple linear regression method to the variables set that newly selects
It closesIt is assessed, obtains correction reference data wavelength value；
After the spectral scan output valve for obtaining soil property sample contents, in order to remove the soil property that background interference obtains more high s/n ratio
Spectroscopic data is needed with improving the robustness and predictive ability of the soil property ingredient prediction model that the later stage is established to P sample
Data are handled as follows：
Calculate the spectrum average of sample contents：
To unknown sample spectrum x (1 × m), pass through the following formula spectrum x that obtains that treatedcentered：
Then spectrum standard deviation is calculated:
Spectrum sample value is handled as follows：
Entire spectrum sample signal value is handled as follows, value X after being handled：
Wherein, n is sample number, is counted for wavelength, and the spectrum that Xn corresponds to the sample contents as n-th sample is equal
Value；
After obtaining spectral signal, the spectral signal processing further includes that spectrum sample, carries out 2-d wavelet change at equal intervals
It changes, then carries out matrix of wavelet coefficients stationary window transversal scanning, obtain nonstationary noise variance evaluation vector sum signal variance
Estimate vector adjusts window width, then carries out rescan point by point, and it is accurate to obtain accurate noise variance evaluation vector sum
Signal variance estimate vector, then by Bayes's threshold value shrink denoising, finally carry out 2-d wavelet contravariant exchange for central row to
Amount is used as signal value output；
It is described to include to the point-by-point adjustment of window width progress：The Noise Variance Estimation of window is fixed first, definition is solid
Determine window Wm×n, width m is set as empirical value, and height n is coefficient matrix height, by window width diagonally high frequency coefficient matrix
Center row vectorOn slide laterally point by point, whereinUsing in wavelet coefficient estimation in window
The noise variance vector of heart row, according to the change rate of noise variance vector, dynamic adjusts window width, and specially basis is made an uproar
The linear phase property of the sound property that approaches uniformity is distributed in all wavelet coefficients and wavelet basis, high-rise wavelet coefficient
Noise variance vector can be by being averaging to obtain, to signal variance vector in the noise variance vector of last layer by two neighboring variance
It is estimated, evaluation method is similar with above-mentioned noise variance vector evaluation method, but since to concentrate on part small for signal variance
In wave system number, there is larger difference on each wavelet decomposition layer, need to carry out layering estimation, in all directions coefficient square of the layer calculated
It is slid laterally respectively in the center row vector of battle array, using all wavelet coefficients respectively decomposed in this layer of window on direction, estimation should
The signal variance vector of layer, is set dynamically according to the change rate of signal variance vector, resets serial ports width, setting signal window
Mouth adjustment threshold value, every layer of signal variance vector is estimated using new window again, then acquires the threshold value of every layer of matrix of wavelet coefficients
Vector uses threshold for every a line of high frequency transverse direction coefficient matrix, longitudinal high frequency coefficient matrix, diagonal high frequency coefficient matrix
Value vector carries out contraction noise reduction, and spectrum matrix is changed in the wavelet coefficient contravariant after noise reduction, takes the center row vector of spectrum matrix
As the spectral signal after noise reduction；By treated, spectrum matrix signal is compared with given threshold, and to more than threshold value
Signal value carries out early warning, and the threshold value is the COD preset values of the waste obtained by statistical.
Technical scheme of the present invention has the following advantages：
A kind of COD online automatic monitors using technology of Internet of things of the present invention, and the comprehensive oil drilling that proposes gives up
The solid monitoring method of gurry COD value and the treatment mechanism of subsequent spectral signal have especially carried out spectral signal more
It handling, and it is interfered and noise has carried out fusion treatment again so that the spectral signal value of acquisition disclosure satisfy that prediction effect,
Confidence level is higher, overcomes not good to soil property tenor prediction effect in the prior art, cannot achieve soil property tenor
The defect of monitoring.
Especially it is noted that this monitoring device has fully taken into account shadow of the liquid for the tenor of soil property
It rings, monitored results need not additionally monitor the tenor in the liquid in oilfield drilling waste again, it will be able to have
Effect ground reflects the tenor in waste, and reliable and quick reference is given for environmentally friendly management unit.
Description of the drawings
Fig. 1 shows the composition frame chart of this monitor.
Specific implementation mode
As shown in Figure 1, the COD online automatic monitors using technology of Internet of things of the present invention, for monitoring oil drilling
Metal in waste, the waste include soil property and liquid, and the monitor is for monitoring oilfield drilling waste
COD value, the oilfield drilling waste includes that soil property and liquid liquid and the mass percent of earthen matter are more than
89% and it is less than 95%, which includes：
Oilfield drilling waste sampling unit, for being carried out at oil field soil property sampling and oilfield liquids substance to selection area
Reason, handles soil property sample contents；
Spectral scan unit is handled spectral signal for carrying out spectral scan to soil property sample contents；Compare list
Member, for the signal after the spectral scan cell processing to be transferred to server by Internet of Things, server by it with set
Determine threshold value to be compared, and early warning is carried out to the signal value more than threshold value, the threshold value is discarded by what statistical obtained
The COD preset values of object.
The oilfield drilling waste sampling unit includes：
Number of samples determination subelement, for determining number of samples P before sampling, by variance yields and average value and true value
Mathematic interpolation number of samples, and P be >=19 natural number；
Sampling processing subelement, for the size according to sampling area, using snakelike sampling, sampling depth is selected as
30CM is sampled according to calculated number of samples, when sampling, is first produced a sampling section, is carried out being parallel to section
Sampling, adopts the earthwork to perpendicular to ground, the soil property after sampling is mixed, and humidity that soil property contains is passed through the side that is evaporated
Formula is adjusted to 35~38%, then mixing thickness soil property sample contents are crushed, and removal stone, root system of plant mix and transform into four sides
Shape divides diagonal line and is divided into four parts, diagonal two parts therein is taken, after being repeated a number of times, by the weight control of soil property sample contents
The weight in needs is made, then to handling sample contents through hydrochloric acid, nitric acid, perchloric acid；
Spectral scan unit includes：
Rotation sweep subelement, for soil property sample contents to be positioned in sample container, shake makes its upper surface put down substantially
It is distributed, sample is positioned on quartz window smoothly then, in measurement process, soil property sample contents can be revolved with sample container instrument
Turn to obtain the averaged spectrum obtained after multiple rotary scanning analysis, the rotary speed of sample container is 4cm/s, soil property sample contents
It can be scanned in 40s 64 times, the spectrum after arithmetic mean is as a sampling spectrum；Each sample, which is positioned over different samples, to be held
It in device, measures twice, the spectroscopic data after being averaged again is used for signal processing；
Signal processing subelement, wherein the signal processing is based on the signal processing subelement, signal processing is single
Member acquires measured signal, obtains original absorbance spectrum, determine correction reference data wavelength, utilize school for acquiring reference signal
Positive reference data wavelength calibration absorption spectrum；The correction reference data wavelength by being calculated as follows：
Spectrum matrix is denoted as X (M*K), wherein i-th of wavelength variable represents i-th of column vector in spectrum matrix X, remembers N=
Min (m-1, k), the n=1 before carrying out first time interative computation, the arbitrary a certain row for selecting spectrum matrix, are denoted as variable xk(0)；
The column vector location sets not being selected into are denoted as T,
Calculate separately xk(n-1)To remaining the projection of following variables, it is denoted as：
Note k (n-1)=arg (max (| | Pj||),j∈T)；N=n+1 enables x if n=Nk(n-1)As the row being selected into
Variable, returns to above-mentioned column vector position and calculates step and calculated again, using multiple linear regression method to the variables set that newly selects
Close { xk(0),xk(1),…,xk(n-1)Assessed, obtain correction reference data wavelength value；
After the spectral scan output valve for obtaining soil property sample contents, in order to remove the soil property that background interference obtains more high s/n ratio
Spectroscopic data is needed with improving the robustness and predictive ability of the soil property ingredient prediction model that the later stage is established to P sample
Data are handled as follows：
Calculate the spectrum average of sample contents：
To unknown sample spectrum x (1 × m), pass through the following formula spectrum x that obtains that treatedcentered：
Then spectrum standard deviation is calculated:
Spectrum sample value is handled as follows：
Entire spectrum sample signal value is handled as follows, value X after being handled：
Wherein, n is sample number, is counted for wavelength, and the spectrum that Xn corresponds to the sample contents as n-th sample is equal
Value；
After obtaining spectral signal, the spectral signal processing further includes that spectrum sample, carries out 2-d wavelet change at equal intervals
It changes, then carries out matrix of wavelet coefficients stationary window transversal scanning, obtain nonstationary noise variance evaluation vector sum signal variance
Estimate vector adjusts window width, then carries out rescan point by point, and it is accurate to obtain accurate noise variance evaluation vector sum
Signal variance estimate vector, then by Bayes's threshold value shrink denoising, finally carry out 2-d wavelet contravariant exchange for central row to
Amount is used as signal value output；
It is described to include to the point-by-point adjustment of window width progress：The Noise Variance Estimation of window is fixed first, definition is solid
Determine window Wm×n, width m is set as empirical value, and height n is coefficient matrix height, by window width diagonally high frequency coefficient matrix
Center row vectorOn slide laterally point by point, whereinUsing in wavelet coefficient estimation in window
The noise variance vector of heart row, according to the change rate of noise variance vector, dynamic adjusts window width, and specially basis is made an uproar
The linear phase property of the sound property that approaches uniformity is distributed in all wavelet coefficients and wavelet basis, high-rise wavelet coefficient
Noise variance vector can be by being averaging to obtain, to signal variance vector in the noise variance vector of last layer by two neighboring variance
It is estimated, evaluation method is similar with above-mentioned noise variance vector evaluation method, but since to concentrate on part small for signal variance
In wave system number, there is larger difference on each wavelet decomposition layer, need to carry out layering estimation, in all directions coefficient square of the layer calculated
It is slid laterally respectively in the center row vector of battle array, using all wavelet coefficients respectively decomposed in this layer of window on direction, estimation should
The signal variance vector of layer, is set dynamically according to the change rate of signal variance vector, resets serial ports width, setting signal window
Mouth adjustment threshold value, every layer of signal variance vector is estimated using new window again, then acquires the threshold value of every layer of matrix of wavelet coefficients
Vector uses threshold for every a line of high frequency transverse direction coefficient matrix, longitudinal high frequency coefficient matrix, diagonal high frequency coefficient matrix
Value vector carries out contraction noise reduction, and spectrum matrix is changed in the wavelet coefficient contravariant after noise reduction, takes the center row vector of spectrum matrix
As the spectral signal after noise reduction；By treated, spectrum matrix signal is compared with given threshold, and to more than threshold value
Signal value carries out early warning, and the threshold value is the COD preset values of the waste obtained by statistical.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention
All any modification, equivalent and improvement etc., should all be included in the protection scope of the present invention made by within refreshing and principle.
Claims (2)
1. a kind of COD online automatic monitors using technology of Internet of things, which is characterized in that the waste includes soil property and liquid
State substance liquid and the mass percent of earthen matter are more than 89% and are less than 95%, which includes：
Oilfield drilling waste sampling unit, for carrying out oil field soil property sampling and the processing of oilfield liquids substance to selection area,
Soil property sample contents are handled；
Spectral scan unit is handled spectral signal for carrying out spectral scan to soil property sample contents；
Comparing unit is being serviced for the signal after the spectral scan cell processing to be transferred to server by Internet of Things
It is compared by device with given threshold, and carries out early warning to the signal value more than threshold value, and the threshold value is to pass through statistical
The COD preset values of obtained waste.
2. a kind of COD online automatic monitors using technology of Internet of things as claimed in claim 1, which is characterized in that
The oilfield drilling waste sampling unit includes：
Number of samples determination subelement, for determining number of samples P before sampling, by the difference of variance yields and average value and true value
Value calculates number of samples, and the natural number that P is >=19；
Sampling processing subelement, for the size according to sampling area, using snakelike sampling, sampling depth is selected as
30CM is sampled according to calculated number of samples, when sampling, is first produced a sampling section, is carried out being parallel to section
Sampling, adopts the earthwork to perpendicular to ground, the soil property after sampling is mixed, and humidity that soil property contains is passed through the side that is evaporated
Formula is adjusted to 35~38%, then mixing thickness soil property sample contents are crushed, and removal stone, root system of plant mix and transform into four sides
Shape divides diagonal line and is divided into four parts, diagonal two parts therein is taken, after being repeated a number of times, by the weight control of soil property sample contents
The weight in needs is made, then to handling sample contents through hydrochloric acid, nitric acid, perchloric acid；
Spectral scan unit includes：
Rotation sweep subelement shakes with making its upper surface substantially flat for soil property sample contents to be positioned in sample container
Distribution, then sample is positioned on quartz window, in measurement process, soil property sample contents can with sample container instrument rotate with
The averaged spectrum obtained after multiple rotary scanning analysis is obtained, the rotary speed of sample container is 4cm/s, and soil property sample contents exist
It can be scanned in 40s 64 times, the spectrum after arithmetic mean is as a sampling spectrum；Each sample is positioned over different sample containers
In, it measures twice, the spectroscopic data after being averaged again is used for signal processing；
Signal processing subelement, wherein the signal processing is based on the signal processing subelement, which uses
In acquisition reference signal, measured signal is acquired, obtains original absorbance spectrum, correction reference data wavelength is determined, is joined using correction
Examine reference wavelength correction absorption spectrum；The correction reference data wavelength by being calculated as follows：
Spectrum matrix is denoted as X (M*K), wherein i-th of wavelength variable represents i-th of column vector in spectrum matrix X, remembers N=min
(m-1, k), the n=1 before carrying out first time interative computation, the arbitrary a certain row for selecting spectrum matrix are denoted as variable xk(0)；Not having
There are the column vector location sets being selected into be denoted as T,
Calculate separately xk(n-1)To remaining the projection of following variables, it is denoted as：
Note k (n-1)=arg (max (| | Pj||),j∈T)；N=n+1 enables x if n=Nk(n-1)As the row variable being selected into,
It returns to above-mentioned column vector position and calculates step and calculated again, using multiple linear regression method to the variables collection that newly selects
{xk(0),xk(1),…,xk(n-1)Assessed, obtain correction reference data wavelength value；
After the spectral scan output valve for obtaining soil property sample contents, in order to remove the soil property spectrum that background interference obtains more high s/n ratio
Data need the data to P sample to improve the robustness and predictive ability of the soil property ingredient prediction model that the later stage is established
It is handled as follows：
Calculate the spectrum average of sample contents：
To unknown sample spectrum x (1 × m), pass through the following formula spectrum x that obtains that treatedcentered：
Then spectrum standard deviation is calculated:
Spectrum sample value is handled as follows：
Entire spectrum sample signal value is handled as follows, value X after being handled：
Wherein, n is sample number, is counted for wavelength, and Xn corresponds to the spectrum average of the sample contents as n-th sample；
After obtaining spectral signal, the spectral signal processing further includes that spectrum sample, carries out two-dimensional wavelet transformation, connect at equal intervals
Carry out matrix of wavelet coefficients stationary window transversal scanning, obtain nonstationary noise variance evaluation vector sum signal variance estimate to
Amount, adjusts window width, then carry out rescan point by point, obtains accurate noise variance evaluation vector sum precise signal side
Then poor estimate vector shrinks denoising by Bayes's threshold value, finally carry out 2-d wavelet contravariant and exchange central row vector conduct for
Signal value output；
It is described to include to the point-by-point adjustment of window width progress：The Noise Variance Estimation of window is fixed first, defines fixed window
Mouth Wm×n, width m is set as empirical value, and height n is coefficient matrix height, by window width diagonally high frequency coefficient matrix center
Row vectorOn slide laterally point by point, whereinCentral row is estimated using wavelet coefficient in window
Noise variance vector, according to the change rate of noise variance vector, dynamic adjustment window width specially exists according to noise
The linear phase property of approaches uniformity is distributed in all wavelet coefficients property and wavelet basis, the noise of high-rise wavelet coefficient
Variance vectors can carry out signal variance vector by being averaging to obtain by two neighboring variance in the noise variance vector of last layer
Estimation, evaluation method is similar with above-mentioned noise variance vector evaluation method, but since signal variance concentrates on part wavelet systems
In number, there is larger difference on each wavelet decomposition layer, need to carry out layering estimation, in all directions coefficient matrix of the layer calculated
It is slid laterally respectively in the row vector of center, using all wavelet coefficients respectively decomposed in this layer of window on direction, estimates this layer
Signal variance vector, is set dynamically according to the change rate of signal variance vector, resets serial ports width, setting signal window tune
Whole threshold value, every layer of signal variance vector is estimated using new window again, then acquires the threshold vector of every layer of matrix of wavelet coefficients,
For every a line of high frequency transverse direction coefficient matrix, longitudinal high frequency coefficient matrix, diagonal high frequency coefficient matrix, threshold vector is used
Contraction noise reduction is carried out, spectrum matrix is changed into the wavelet coefficient contravariant after noise reduction, takes the center row vector of spectrum matrix as drop
Spectral signal after making an uproar；By treated, spectrum matrix signal is compared with given threshold, and to the signal value more than threshold value
Early warning is carried out, the threshold value is the COD preset values of the waste obtained by statistical.
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Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105300909A (en) * | 2015-12-02 | 2016-02-03 | 重庆大学 | Direct power-spectral method-based all-weather long drainage basin water quality monitoring and early-warning system |
-
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Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105300909A (en) * | 2015-12-02 | 2016-02-03 | 重庆大学 | Direct power-spectral method-based all-weather long drainage basin water quality monitoring and early-warning system |
Non-Patent Citations (2)
| Title |
|---|
| 吴德操: "面向地表水环境的分布式紫外-可见光谱水质在线检测监测系统关键技术研究", 《中国博士学位论文全文数据库 工程科技Ⅰ辑》 * |
| 尚廷义 等: "《基于光谱技术的寒地水稻稻瘟病害分析及机理研究》", 30 June 2016, 哈尔滨工程大学出版社 * |
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