CN105692726A - Device for treating low-concentration and difficult-to-degrade organic industrial wastewater - Google Patents

Abstract

The invention discloses a device for treating low-concentration and difficult-to-degrade organic industrial wastewater. The device for treating the low-concentration and difficult-to-degrade organic industrial wastewater is provided with a first valve with a chuck connector, a first chuck and a third valve with a chuck connector, wherein a filtering device is arranged between the first valve with the chuck connector and the first chuck; the filtering device comprises a water inlet, a water storage basin outer wall, a rolling wheel, a transverse beam, a filtering screen, a speed reducer, a supporting stand column and a water outlet; the third valve with the chuck connector is provided with an Internet of Things water quality detection module; the water quality detection modules of the devices for treating the low-concentration and difficult-to-degrade organic industrial wastewater, which are mounted at different positions, are connected through an Internet of Things router. On the basis of taking the advantages of a filtering method and an ozone catalytic oxidation method, the disadvantages of the filtering method and the ozone catalytic oxidation method are overcome, so that deep treatment of the low-concentration and difficult-to-degrade organic industrial wastewater becomes possible.

Description

A kind of process low concentration refractory organic industrial sewage device

Technical field

The invention belongs to field of waste water treatment, particularly relate to a kind of process low concentration refractory organic industrial sewage device。

Background technology

For low concentration refractory organic industrial sewage (concentration of COD is lower than 150mg/L), in such waste water, organic concentration is too low on the one hand is substantially free of recovery value；On the other hand namely such waste water do not reach the wastewater discharge standard of corresponding industry and more can not meet the respective standard of waste water recycling。Advanced treating is carried out so having to。

At present the processing method of such waste water mainly has following three kinds, it may be assumed that one, resin adsorption method, and two, Filtration, three, catalytic ozonation method。Wherein, for first method: resin adsorption method, due to shortcomings such as resin price is high and adsorption capacity is limited, regeneration difficulty, the life-span are short, operating cost is high, greatly limit its use；For second method: Filtration, owing to the compound majority molecular weight in low concentration refractory organic industrial sewage is relatively small, its molecular diameter is also less, it is necessary to just can filter with the filter membrane of smaller aperture due, and use the filter membrane of smaller aperture due, then can be substantially reduced the filter capacity of unit plane integrated membrane, cause ton water to process cost of investment to be greatly increased, and filter membrane very easily blocks, regeneration difficulty after blocking, so that operating cost is high, greatly limit the use of filter membrane；And for the third method, catalytic ozonation method, the factor of 3 aspects affects efficiency and the effect of catalytic ozonation method below: 1) low concentration refractory organic industrial sewage along with concentration reduce can be oxidized in unit volume in waste water anakmetomeres quantity be gradually lowered, thus also reducing the chance of the oxidized decomposition of Organic substance；2) ozone is at ambient pressure, and the dissolubility in waste water is limited, and the ozone concentration namely dissolved in unit volume sewage is too low, have impact on organic speed and effect in ozone oxidation sewage；3) the aeration aperture of ozonation aerated head can not be accomplished sufficiently small, cause aeration bubbles diameter excessive, can not be fully contacted with the Organic substance in sewage, and then (at present, the most I in the aperture of the titanium alloy aeration head of resistance to ozone oxidation is accomplished: 450nm) with organic reaction to affect it。These factors not only leverage efficiency and the effect of catalytic ozonation method, and cause the hardly degraded organic substance in the organic industrial sewage of part low concentration at all cannot by catalytic ozonation method oxidation Decomposition。

Due to the various shortcoming that three of the above method itself exists, significantly restricting and limit their popularization and application, therefore how low cost, efficiently process low concentration refractory organic industrial sewage had become the difficult problem that sewage treatment industry is urgently to be resolved hurrily already。

Summary of the invention

It is an object of the invention to provide a kind of process low concentration refractory organic industrial sewage device, it is intended to solve the problem that processing method operating cost is high, treatment effeciency is low of current low concentration refractory organic industrial sewage。

The present invention is realized in, a kind of process low concentration refractory organic industrial sewage device, it is characterized in that, described process low concentration refractory organic industrial sewage device is provided with the first valve of chuck joint, the first chuck and the 3rd valve with chuck joint, it is provided with defecator between described the first valve and the first chuck with chuck joint

Described defecator includes: water inlet, cistern outer wall, roller, crossbeam, drainage screen, reductor, support post, outlet；

Described cistern outer wall left end upper position is provided with water inlet, the two ends of crossbeam are connected to roller, the upper end of cistern outer wall is provided with guide rail, roller rolls on guide rail, reductor is arranged on the speed reducer mounting base of the centre of crossbeam, and the positive lower end of reductor is support post, and reductor and support post link together, the lower end of crossbeam is provided with removable filter net, and the right-hand member upper position of cistern outer wall is provided with outlet；The outer section of described water inlet and outlet is welded with mounting flange, described crossbeam generally cross row, centre is provided with speed reducer mounting base, the both sides of crossbeam are provided with railing, described support post is arranged on the middle of cistern, the upper end of support post is provided with the axis hole coordinated with speed reducer output shaft, and described filter material is the wire side that stainless steel silk is welded, and the upper end of wire side is wrapped in organic wastewater absorption degradation agent；

Being provided with Internet of Things water quality detection module at described the 3rd valve place with chuck joint, the water quality detection module processed on low concentration refractory organic industrial sewage device being arranged on different location is connected by router of internet of things；

Described water quality detection module includes:

The measurement pipe being distributed up and down during use, this measurement infratubal port be provided with illuminator and in use this lower port enter the water surface, measure pipe upper port place set camera head, this camera head is suitable to shoot the water surface image in described measurement pipe；

Video acquisition module, is connected with described camera head, is suitable to the image gathered is transformed to digital picture；

With the image processing module that described video acquisition module is connected, this image processing module storage has the first sample data, and described first sample data is suitable to record the gray value of various water quality；

Be connected with described image processing module for receiving remote control signal and export the wireless communication module of water quality situation；Wireless communication module is provided with trust value computing module；

Described image processing module is suitable to described digital picture is carried out gray proces, compares and draws water quality situation obtaining the gray value of described water surface image, this gray value and the first sample data, and image processing module includes high spectrum image color visualization model。

Further, the method that realizes of described trust value computing module includes:

Step one, the interaction times of different time sheet between acquisition node, according to the data setup time sequence obtained, predict the interaction times of next timeslice between node by third index flatness, using the relative error of interaction times predictive value and the actual value direct trust value as node；The concrete calculation procedure of direct trust value is:

The interaction times of n timeslice between collection network observations node i and node j:

Choosing certain time interval t as an observation time sheet, using observer nodes i and tested node j, the interaction times in 1 timeslice is as observation index, and true interaction times is denoted as y_t, record the y of n timeslice successively_n, and save it in the communications records table of node i；

The interaction times of (n+1)th timeslice of prediction:

Interaction times according to n the timeslice collected sets up time series, adopts the interaction times between the next timeslice n+1 interior nodes i and j of third index flatness prediction, it was predicted that interaction times, is denoted asComputing formula is as follows:

${\hat{y}}_{n+1}=a_n+b_n+c_n$

Predictive coefficient a_n、b_n、c_nValue can by equation below calculate obtain:

$a_n=3{\hat{y}}_{n+1}^{\left(1\right)}-3{\hat{y}}_{n+1}^{\left(2\right)}+{\hat{y}}_{n+1}^{\left(3\right)}$

$b_n=\frac{\mathrm{\α}}{2{(1-\mathrm{\α})}^2}\[(6-5\mathrm{\α}){\hat{y}}_{n+1}^{\left(1\right)}-2(5-4\mathrm{\α}){\hat{y}}_{n+1}^{\left(2\right)}+(4-3\mathrm{\α}){\hat{y}}_{n+1}^{\left(3\right)}\]$

$c_n=\frac{{\mathrm{\α}}^2}{2{(1-\mathrm{\α})}^2}\[{\hat{y}}_{n+1}^{\left(1\right)}-2{\hat{y}}_{n+1}^{\left(2\right)}+{\hat{y}}_{n+1}^{\left(3\right)}\]$

Wherein:Be respectively once, secondary, Three-exponential Smoothing number, by equation below calculate obtain:

${\hat{y}}_{n+1}^{\left(1\right)}=\mathrm{\α}\×y_n+(1-\mathrm{\α})\×{\hat{y}}_n^{\left(1\right)}$

${\hat{y}}_{n+1}^{\left(2\right)}=\mathrm{\α}\×{\hat{y}}_{n+1}^{\left(1\right)}+(1-\mathrm{\α})\×{\hat{y}}_n^{\left(2\right)}$

${\hat{y}}_{n+1}^{\left(3\right)}=\mathrm{\α}\×{\hat{y}}_{n+1}^{\left(2\right)}+(1-\mathrm{\α})\×{\hat{y}}_n^{\left(3\right)}$

Being the initial value of third index flatness, its value is

${\hat{y}}_0^{\left(1\right)}={\hat{y}}_0^{\left(2\right)}={\hat{y}}_0^{\left(3\right)}=\frac{y_1+y_2+y_3}{3}$

α is smoothing factor 0 < α < 1, embodies the time attenuation characteristic trusted, namely from predictive value more close to the y of timeslice_tWeight is more big, from predictive value more away from the y of timeslice_tWeight is more little；Usually, if data fluctuations is relatively big, and long-term trend amplitude of variation is relatively big, presents α when substantially rising or falling trend rapidly and should take higher value 0.6～0.8, it is possible to increases the Recent data impact on predicting the outcome；When data have a fluctuation, but when long-term trend change is little, α can between 0.1～0.4 value；If data fluctuations is steady, α should take smaller value 0.05～0.20；

Calculate direct trust value:

The direct trust value TD of node j_ijFor prediction interaction timesWith true interaction times y_n+1Relative error, ${\mathrm{TD}}_{ij}=\frac{\left|{\hat{y}}_{n+1}-y_{n+1}\right|}{{\hat{y}}_{n+1}};$

Step 2, adopts multipath trust recommendation mode and the calculating formula that obtains calculates indirect trust values；Adopt multipath trust recommendation mode and concrete calculation procedure that the calculating formula that obtains calculates indirect trust values is:

Collect the trusted node direct trust value to node j:

Node i meets TD to all_ikThe credible associated nodes of≤φ inquires its direct trust value to node j, and wherein φ is the believability threshold of recommended node, the precision prescribed according to credibility, and the span of φ is 0～0.4；

Calculate indirect trust values:

Trust value collected by COMPREHENSIVE CALCULATING, obtains the indirect trust values TR of node j_ij,Wherein, Set (i) for there being mutual and its direct trust value to meet TD with j node in the associated nodes of observer nodes i_ikThe node set of≤φ；

Step 3, is drawn comprehensive trust value by direct trust value and indirect trust values conformity calculation, direct trust value and indirect trust values conformity calculation show that the concrete calculation procedure of comprehensive trust value is:

Comprehensive trust value T_ijComputing formula as follows: T_ij=β TD_ij+(1-β)TR_ij, wherein β represents the weight of direct trust value, 0≤β≤1；When β=0, node i and node j do not have direct interaction relation, and the calculating of comprehensive trust value arises directly from indirect trust values, it is judged that more objective；When β=1, node i to the comprehensive trust value of node j all from direct trust value, in this case, it is judged that comparatively subjective, Practical Calculation can determine the value of β as required。

Further, the method that realizes of described high spectrum image color visualization model comprises the following steps:

Step one, for each pixel of hyperspectral image data, is calculated spoke brightness value by the gray value of each spectral coverage, and is normalized one curve of spectrum of composition；Adopt each pixel to calculate spoke brightness value to constitute the curve of spectrum at the gray value of each spectral coverage, specifically include following steps:

The first step, spectral imaging apparatus is calibrated, choose 5～10 calibration gray value D and measure corresponding calibration spoke brightness value F, adopt least square fitting to go out following formula and map the parameter alpha of expression formula, β, ε, thus each pixel to tested region, the gray value of each spectral coverage is substituted into following formula and calculates spoke brightness value；

D=α F^β+ ε；

Second step, with maximum gradation value D_maxCorresponding spoke brightness value F_maxFor benchmark, each pixel is normalized at the spoke brightness value of each spectral coverage, constitutes a curve of spectrum；

Step 2, for the curve of spectrum that each pixel obtains in step one, adopts Savitzky-Golay wave filter to be smoothed, eliminates spectral noise retaining on the basis of more curvilinear characteristic, obtain the curve of spectrum after each pixel smooths(λ)；

Step 3, is obtained the curve of spectrum after each pixel smooths by step 2(λ) in conjunction with the color matching function of CIE1931 standard colorimetric systemAdopting following formula to calculate the CIEXYZ tristimulus values (X, Y, Z) under CIE1931 standard colorimetric system, wherein Δ λ is the spectrum sample interval of imaging spectral instrument；

Step 4, the tristimulus values (X according to standard illuminants D65_D65, Y_D65, Z_D65), the CIEXYZ tristimulus values that step 3 is obtained each pixel by following formula is converted to homogeneous color aware space CIEL^*C^*h^*, it is thus achieved that three Color perception parameters, i.e. lightness, chromaAnd tone h₁；

$\left\{\begin{array}{c}{L}_1^{*}=116f(Y/Y_{D65})-16\\ a^{*}=500\&lsqb;f(X/X_{D65})-f(Y/Y_{D65})\&rsqb;\\ b^{*}=200\&lsqb;f(Y/Y_{D65})-f(Z/Z_{D65})\&rsqb;\end{array}\right.;$

$\left\{\begin{array}{cc}f(X/X_{D65})=\left\{\begin{array}{c}{(X/X_{D65})}^{1/3}\\ 7.787(X/X_{D65})+16/116\end{array}\right.& \begin{array}{c}X/X_{D65}>0.008856\\ X/X_{D65}\&le;0.008856\end{array}\\ f(Y/Y_{D65})=\left\{\begin{array}{c}{(Y/Y_{D65})}^{1/3}\\ 7.787(Y/Y_{D65})+16/116\end{array}\right.& \begin{array}{c}Y/Y_{D65}>0.008856\\ Y/Y_{D65}\&le;0.008856\end{array}\\ f(Z/Z_{D65})=\left\{\begin{array}{c}{(Z/Z_{D65})}^{1/3}\\ 7.787(Z/Z_{D65})+16/116\end{array}\right.& \begin{array}{c}Z/Z_{D65}>0.008856\\ Z/Z_{D65}\&le;0.008856\end{array}\end{array}\right.;$

$\left\{\begin{array}{c}{C}_1^{*}={\&lsqb;{\left(a^{*}\right)}^2+{\left(b^{*}\right)}^2\&rsqb;}^{1/2}\\ h_1=\arctan (b^{*}/a^{*})\end{array}\right.;$

Wherein,

X_D65=95.047, Y_D65=100, Z_D65=108.883；

Step 5, arranges brightness coefficient k_L, chroma coefficient k_CWith tone coefficient k_hValue, obtained the lightness of each pixel by following formula modulation step four, chromaAnd tone h₁, Color perception parameter after being modulated, i.e. lightness, chromaAnd tone h₂, make effect of visualization meet fidelity reproduction demand, then k_L=k_C=1, k_h=0, change k_LRealize regulating the demand of image light and shade, change k_CRealize regulating the demand of the bright-coloured degree of image, change k_hRealize regulating the demand of image white balance；

$\left\{\begin{array}{c}{L}_2^{*}=k_L{C}_1^{*}\\ C_2^{*}=k_C{C}_1^{*}\\ h_2=h_1+k_h\end{array}\right.;$

Step 6, the white point tristimulus values (X according to display device_W, Y_W, Z_W), by following formula, step 5 is obtained the lightness of each pixel, chromaAnd tone h₂It is converted to CIEXYZ value (X ', Y ', Z ') to be shown on the display device；

$\left\{\begin{array}{c}{L}^{*}=L_2^{*}\\ a^{*}=C_2^{*}\&CenterDot;cos({\mathrm{\&pi;h}}_2/180)\\ b^{*}=C_2^{*}\&CenterDot;sin({\mathrm{\&pi;h}}_2/180)\end{array}\right.;$

$\left\{\begin{array}{cc}\begin{array}{c}{Y}^{\&prime;}=\left\{\begin{array}{c}{Y}_W\&CenterDot;{\&lsqb;(L^{*}+16)/116\&rsqb;}^{1/3}\\ Y_W\&CenterDot;L^{*}/903.3\end{array}\right.\\ f_Y=\left\{\begin{array}{c}{(Y^{\&prime;}/Y_W)}^{1/3}\\ 7.787(Y^{\&prime;}/Y_W)+16/116\end{array}\right.\\ X^{\&prime;}=\left\{\begin{array}{c}{X}_W\&CenterDot;{(a^{*}/500+f_Y)}^3\\ X_W\&CenterDot;(a^{*}/500+f_Y-16/116)/7.787\end{array}\right.\\ Z^{\&prime;}=\left\{\begin{array}{c}{Z}_W\&CenterDot;{(f_Y-b^{*}/200)}^3\\ Z_W\&CenterDot;(f_Y-b^{*}/200-16/116)/7.787\end{array}\right.\end{array}& \begin{array}{c}{\&lsqb;(L^{*}+16)/116\&rsqb;}^{1/3}>0.008856\\ {\&lsqb;(L^{*}+16)/116\&rsqb;}^{1/3}\&le;0.008856\\ Y^{\&prime;}/Y_W>0.008856\\ Y^{\&prime;}/X_W\&le;0.008856\\ {(a^{*}/500+f_Y)}^3>0.008856\\ {(a^{*}/500+f_Y)}^3\&le;0.008856\\ {(f_Y-b^{*}/200)}^3>0.008856\\ {(f_Y-b^{*}/200)}^3\&le;0.008856\end{array}\end{array}\right.;$

Step 7, according to the three-channel primary colors tristimulus values (X of display device red, green, blue_Rmax, Y_Rmax, Z_Rmax)、(X_Gmax, Y_Gmax, Z_Gmax、(X_Bmax, Y_Bmax, Z_Bmax) in conjunction with three-channel gamma factor γ_R、γ_G、γ_B, it is established that such as the characterization model of following formula, by characterization model, step 6 is obtained the CIEXYZ value (X ', Y ', Z ') of each pixel and is calculated to corresponding digital drive values (d_R, d_G, d_B), namely complete the color visualization of high spectrum image, wherein N is the single pass storage bit number of display device；

$\left[\begin{array}{c}{T}_R\\ T_G\\ T_B\end{array}\right]={\left[\begin{array}{ccc}1& X_{G\max }/Y_{G\max }& X_{B\max }/Z_{B\max }\\ Y_{R\max }/X_{R\max }& 1& Y_{B\max }/Z_{B\max }\\ Z_{R\max }/X_{R\max }& Z_{G\max }/Y_{G\max }& 1\end{array}\right]}^{-1}\left[\begin{array}{c}{X}^{\&prime;}\\ Y^{\&prime;}\\ Z^{\&prime;}\end{array}\right];$

$\left[\begin{array}{c}{d}_R\\ d_G\\ d_B\end{array}\right]=\left[\begin{array}{c}(2^N-1)\&CenterDot;{\left(T_R\right)}^{1/{\mathrm{\&gamma;}}_R}\\ (2^N-1)\&CenterDot;{\left(T_G\right)}^{1/{\mathrm{\&gamma;}}_G}\\ (2^N-1)\&CenterDot;{\left(T_B\right)}^{1/{\mathrm{\&gamma;}}_B}\end{array}\right].$

The present invention, inheriting on the basis of advantage of Filtration and catalytic ozonation method, overcomes Filtration and the shortcoming of catalytic ozonation method so that the advanced treating of low concentration refractory organic industrial sewage is possibly realized。The present invention includes: direct trust value calculates, indirect trust values calculates and comprehensive trust value computing three phases, direct trust value calculates the first interaction times of different time sheet between acquisition node, according to the data setup time sequence obtained, then pass through third index flatness to predict the interaction times of next timeslice between node, using the relative error of interaction times predictive value and the actual value direct trust value as node, the calculating formula of indirect trust values adopts multipath trust recommendation mode to obtain, comprehensive trust value is to be drawn by direct trust value and indirect trust values conformity calculation；The present invention is that node trust value computing provides a method that, concrete condition according to network, optional adapt smoothing factor α, believability threshold φ, direct trust value weight beta value, guarantee time attenuation characteristic and the objectivity of trust value, objectively and accurately describe the credibility of node, computation complexity is low and communication cost is little, it is applicable to wireless network, there is stronger popularization and using value。The present invention can effectively introduce the impact of color parameters aspect between different display device, makes distinct device show identical Color perception parameter with different digital motivation value, efficiently solves the problem that color effect of visualization is different because of equipment；Further it is proposed that with lightness factor k_L, chroma coefficient k_CWith tone coefficient k_hThe method regulating Color perception parameter, it is possible to by formulating the modulation requirement to parameters such as lightness, chroma, tones, meets different types of color reproduction demand。The present invention is directed to high spectrum image and carry out color visualization, color reproduction result is good with human eye visual perception concordance, and method is implemented simple, and practical, the suitability is strong。The present invention can process low concentration refractory organic industrial sewage, simple in construction to greatest extent, and processing speed is fast, can also be regenerated the low-concentration organic in recycle-water to greatest extent by the adsorption of organic wastewater absorption degradation agent。During solution process low concentration refractory organic industrial sewage now, cost is high, difficulty is big, the problem that the Organic substance in waste water cannot be recycled。

Accompanying drawing explanation

Fig. 1 is the structural representation processing low concentration refractory organic industrial sewage device that the embodiment of the present invention provides。

Fig. 2 is the 5th chuck that provides of the embodiment of the present invention and the structural representation of the 6th chuck。

Fig. 3 is the filter apparatus configuration schematic diagram that the embodiment of the present invention provides。

In figure: 1, the first valve；2, the first chuck；3, the first sealing gasket；4, the second chuck；5, the first chuck joint；6, sewage water inlet pipe；7, the first cavity；8, intake elbow；9, the 3rd chuck；10, the second sealing gasket；11, the 4th chuck；12, the second valve with chuck joint；13, the 5th chuck；14, the 3rd sealing gasket；15, the 6th chuck；16, the 7th chuck；17, the 4th sealing gasket；18, the 8th chuck；19, overcoat；20, inner sleeve；21, outer filter element；22, the second cavity；23, inner filter core；24, filter cylinder；25, the second chuck joint；26, the 9th chuck；27, the 5th sealing gasket；28, the tenth chuck；29, the 3rd valve with chuck joint；30, shrouding；31, exhaustor；32, endoporus；33, bonding wire；34, water hole；35, labelling point；36, defecator；36-1, water inlet；36-2, cistern outer wall；36-3, roller；36-4, crossbeam；36-5, drainage screen；36-6, reductor；36-7, support post；36-8, outlet。

Detailed description of the invention

For the summary of the invention of the present invention, feature and effect can be further appreciated that, hereby enumerate following example, and it is as follows to coordinate accompanying drawing to describe in detail。

Consult Fig. 1 and Fig. 2:

The present invention is realized in, a kind of low concentration refractory organic industrial sewage device that processes includes: with the first valve 1 of chuck joint, first chuck 2, first sealing gasket 3, second chuck 4, first chuck joint 5, sewage water inlet pipe 6, first cavity 7, intake elbow 8, 3rd chuck 9, second sealing gasket 10, 4th chuck 11, the second valve 12 with chuck joint, 5th chuck 13, 3rd sealing gasket 14, 6th chuck 15, 7th chuck 16, 4th sealing gasket 17, 8th chuck 18, overcoat 19, inner sleeve 20, outer filter element 21, second cavity 22, inner filter core 23, filter cylinder 24, second chuck joint 25, 9th chuck 26, 5th sealing gasket 27, tenth chuck 28, the 3rd valve 29 with chuck joint, shrouding 30, exhaustor 31, endoporus 32, bonding wire 33, water hole 34, labelling point 35, defecator 36。

The first valve 1 with chuck joint is tightly connected by clip and the first chuck joint 5, wherein equipped with politef first sealing gasket 3 of ozone-resistant oxidation between the first chuck 2 and second chuck 4 of the first chuck joint of the first valve with chuck joint, the other end of the first chuck joint 5 is welded on sewage water inlet pipe 6；Defecator 36 is arranged on between the first valve 1 and first chuck 2 of chuck joint。

One of intake elbow 8 is welded with the 3rd chuck 9, 3rd chuck 9 is tightly connected with the second valve 12 with chuck joint by clip, wherein the 3rd chuck 9 and politef the second sealing gasket 10 with aoxidizing equipped with ozone-resistant between the 4th chuck 11 of the second valve of chuck joint, the other end of intake elbow 8 is welded with the periphery of the endoporus 32 of the 5th chuck 13, again the bonding wire 33 of the upper end of sewage water inlet pipe 6 with the 5th chuck 13 is welded, sewage water inlet pipe 6 welds with the cross section of intake elbow 7, the first cavity 7 is formed between inner tubal wall and the outer tube wall of intake elbow 8 of sewage water inlet pipe 6；

After the periphery of inner sleeve 20 with the endoporus 32 of the 6th chuck 15 is welded, the bonding wire 33 of overcoat 19 with the 6th chuck 15 is welded, again the 7th chuck 16 being welded on overcoat 19 and form inner-outer sleeve assembly, inner filter core 23 and outer filter element 21 are set to successively and are formed on filter core assembly by inner sleeve 20 and overcoat 19；

One of 8th chuck 18 and filter cylinder 24 is welded, and shrouding 30 welds with the other end of filter cylinder 24, then on the centre of shrouding 30 is welded exhaustor 31, the side, top of filter cylinder forms filter cylinder assembly after welding upper second chuck joint 25；

7th chuck 16 of filter core assembly is tightly connected by the 8th chuck 18 of clip with filter cylinder assembly, wherein equipped with politef the 4th sealing gasket 17 of ozone-resistant oxidation between the 7th chuck 16 and the 8th chuck 18；Again sewage water inlet pipe and intake elbow are tightly connected with clip by the 5th chuck 13 and the 6th chuck 15, wherein equipped with politef the 3rd sealing gasket 14 of ozone-resistant oxidation between the 5th chuck 13 and the 6th chuck 15；Finally the tenth chuck 28 of the 3rd valve 29 with chuck joint is tightly connected by clip and the 9th chuck 26, wherein equipped with politef the 5th sealing gasket 27 of ozone-resistant oxidation between the 9th chuck 26 and the tenth chuck 28；

Being provided with Internet of Things water quality detection module at described the 3rd valve 29 place with chuck joint, the water quality detection module processed on low concentration refractory organic industrial sewage device being arranged on different location is connected by router of internet of things。

Further, inner-outer sleeve assembly is put into and is taken out and take advantage of cold inner filter core 23 and outer filter element 21 being set to successively immediately in low temperature liquid nitrogen after 1 minute and formed on filter core assembly by inner sleeve 20 and overcoat 19。

Further, the size design of the cylindrical of the cylindrical of inner sleeve 20 and the internal orifice of inner filter core 23 and overcoat 19 and the internal orifice of outer filter element 21 adds man-hour by Interference Fit Design manufacture, and the filter sizes of inner filter core is 20nm, and the filter sizes of outer filter element is 10nm。In addition for preventing inside and outside filter element by ozone oxidation, inside and outside filter element all adopts high-purity Al₂O₃Or zirconia ceramics material makes。

Further, when the 5th chuck 13 and the 6th chuck 15 assemble, it is ensured that the labelling point 35 of the 5th chuck overlaps with the labelling point 35 of the 6th chuck to make sewage can enter the second cavity 22 from the first cavity 7 by the water hole 34 of the 5th chuck and the water hole 34 of the 6th chuck。

Further, except sealing gasket and filter element, remaining part all adopts the rustless steel of 316L to make, and the weld seam between parts and parts all does preservative treatment。

Further, the measurement pipe being distributed up and down during use, this measurement infratubal port be provided with illuminator and in use this lower port enter the water surface, measure pipe upper port place set camera head, this camera head is suitable to shoot the water surface image in described measurement pipe；

Video acquisition module, is connected with described camera head, is suitable to the image gathered is transformed to digital picture；

With the image processing module that described video acquisition module is connected, this image processing module storage has the first sample data, and described first sample data is suitable to record the gray value of various water quality；

Be connected with described image processing module for receiving remote control signal and export the wireless communication module of water quality situation；Wireless communication module is provided with trust value computing module；

Described image processing module is suitable to described digital picture is carried out gray proces, compares and draws water quality situation obtaining the gray value of described water surface image, this gray value and the first sample data, and image processing module includes high spectrum image color visualization model。

As it is shown on figure 3, defecator 36 includes: water inlet 36-1, cistern outer wall 36-2, roller 36-3, crossbeam 36-4, drainage screen 36-5, reductor 36-6, support post 36-7, outlet 36-8；

Cistern outer wall 36-2 left end upper position is provided with water inlet 36-1, the two ends of crossbeam 36-4 are connected to roller 36-3, the upper end of cistern outer wall 36-2 is provided with guide rail, roller 36-3 rolls on guide rail, reductor 36-6 is arranged on the speed reducer mounting base of the centre of crossbeam 36-4, the positive lower end of reductor 36-6 is support post 36-7, reductor 36-6 and support post 36-7 links together, the lower end of crossbeam 36-4 is provided with removable filter net 36-5, and the right-hand member upper position of cistern outer wall 36-2 is provided with outlet 36-8。

Described water inlet 36-1 and the outer section of outlet 36-8 are welded with mounting flange。

Described crossbeam 36-4 generally cross row, centre is provided with speed reducer mounting base, and the both sides of crossbeam 36-4 are provided with railing。

Described support post 36-7 is arranged on the middle of cistern, and the upper end of support post 36-7 is provided with the axis hole coordinated with reductor 36-6 output shaft。

Described drainage screen 36-5 material is the wire side that stainless steel silk is welded, and the upper end of wire side is wrapped in organic wastewater absorption degradation agent。

Another object of the present invention is to provide the trust value computing method of the wireless network node of a kind of described process low concentration refractory organic industrial sewage device, the trust value computing method of this wireless network node comprises the following steps:

Step one, the interaction times of different time sheet between acquisition node, according to the data setup time sequence obtained, predict the interaction times of next timeslice between node by third index flatness, using the relative error of interaction times predictive value and the actual value direct trust value as node；The concrete calculation procedure of direct trust value is:

The interaction times of n timeslice between collection network observations node i and node j:

Choosing certain time interval t as an observation time sheet, using observer nodes i and tested node j, the interaction times in 1 timeslice is as observation index, and true interaction times is denoted as y_t, record the y of n timeslice successively_n, and save it in the communications records table of node i；

The interaction times of (n+1)th timeslice of prediction:

Interaction times according to n the timeslice collected sets up time series, adopts the interaction times between the next timeslice n+1 interior nodes i and j of third index flatness prediction, it was predicted that interaction times, is denoted as, computing formula is as follows:

${\hat{y}}_{n+1}=a_n+b_n+c_n$

Predictive coefficient a_n、b_n、c_nValue can by equation below calculate obtain:

$a_n=3{\hat{y}}_{n+1}^{\left(1\right)}-3{\hat{y}}_{n+1}^{\left(2\right)}+{\hat{y}}_{n+1}^{\left(3\right)}$

$b_n=\frac{\mathrm{\&alpha;}}{2{(1-\mathrm{\&alpha;})}^2}\&lsqb;(6-5\mathrm{\&alpha;}){\hat{y}}_{n+1}^{\left(1\right)}-2(5-4\mathrm{\&alpha;}){\hat{y}}_{n+1}^{\left(2\right)}+(4-3\mathrm{\&alpha;}){\hat{y}}_{n+1}^{\left(3\right)}\&rsqb;$

$c_n=\frac{{\mathrm{\&alpha;}}^2}{2{(1-\mathrm{\&alpha;})}^2}\&lsqb;{\hat{y}}_{n+1}^{\left(1\right)}-2{\hat{y}}_{n+1}^{\left(2\right)}+{\hat{y}}_{n+1}^{\left(3\right)}\&rsqb;$

Wherein:Be respectively once, secondary, Three-exponential Smoothing number, by equation below calculate obtain:

${\hat{y}}_{n+1}^{\left(1\right)}=\mathrm{\&alpha;}\&times;y_n+(1-\mathrm{\&alpha;})\&times;{\hat{y}}_n^{\left(1\right)}$

${\hat{y}}_{n+1}^{\left(2\right)}=\mathrm{\&alpha;}\&times;{\hat{y}}_{n+1}^{\left(1\right)}+(1-\mathrm{\&alpha;})\&times;{\hat{y}}_n^{\left(2\right)}$

${\hat{y}}_{n+1}^{\left(3\right)}=\mathrm{\&alpha;}\&times;{\hat{y}}_{n+1}^{\left(2\right)}+(1-\mathrm{\&alpha;})\&times;{\hat{y}}_n^{\left(3\right)}$

Being the initial value of third index flatness, its value is

${\hat{y}}_0^{\left(1\right)}={\hat{y}}_0^{\left(2\right)}={\hat{y}}_0^{\left(3\right)}=\frac{y_1+y_2+y_3}{3}$

α is smoothing factor (0 < α < 1), embodies the time attenuation characteristic trusted, namely from predictive value more close to the y of timeslice_tWeight is more big, from predictive value more away from the y of timeslice_tWeight is more little；Usually, if data fluctuations is relatively big, and long-term trend amplitude of variation is relatively big, presents α when substantially rising or falling trend rapidly and should take higher value (0.6～0.8), it is possible to increases the Recent data impact on predicting the outcome；When data have a fluctuation, but when long-term trend change is little, α can between 0.1～0.4 value；If data fluctuations is steady, α should take smaller value (0.05～0.20)；

Calculate direct trust value:

The direct trust value TD of node j_ijFor prediction interaction timesWith true interaction times y_n+1Relative error, ${\mathrm{TD}}_{ij}=\frac{\left|{\hat{y}}_{n+1}-y_{n+1}\right|}{{\hat{y}}_{n+1}};$

Step 2, adopts multipath trust recommendation mode and the calculating formula that obtains calculates indirect trust values；Adopt multipath trust recommendation mode and concrete calculation procedure that the calculating formula that obtains calculates indirect trust values is:

Collect the trusted node direct trust value to node j:

Node i meets TD to all_ikThe credible associated nodes of≤φ inquires its direct trust value to node j, and wherein φ is the believability threshold of recommended node, the precision prescribed according to credibility, and the span of φ is 0～0.4；

Calculate indirect trust values:

Trust value collected by COMPREHENSIVE CALCULATING, obtains the indirect trust values TR of node j_ij,Wherein, Set (i) for there being mutual and its direct trust value to meet TD with j node in the associated nodes of observer nodes i_ikThe node set of≤φ；

Step 3, is drawn comprehensive trust value by direct trust value and indirect trust values conformity calculation, direct trust value and indirect trust values conformity calculation show that the concrete calculation procedure of comprehensive trust value is:

Comprehensive trust value (T_ij) computing formula as follows: T_ij=β TD_ij+(1-β)TR_ij, wherein β (0≤β≤1) represents the weight of direct trust value, and when β=0, node i and node j do not have direct interaction relation, and the calculating of comprehensive trust value arises directly from indirect trust values, it is judged that more objective；When β=1, node i to the comprehensive trust value of node j all from direct trust value, in this case, it is judged that comparatively subjective, Practical Calculation can determine the value of β as required。

Another object of the present invention is to provide the high spectrum image color method for visualizing of the image processing module of a kind of described process low concentration refractory organic industrial sewage device, described high spectrum image color method for visualizing specifically includes following steps:

Step one, for each pixel of hyperspectral image data, is calculated spoke brightness value by the gray value of each spectral coverage, and is normalized one curve of spectrum of composition；Adopt each pixel to calculate spoke brightness value to constitute the curve of spectrum at the gray value of each spectral coverage, specifically include following steps:

The first step, spectral imaging apparatus is calibrated, choose 5～10 calibration gray value D and measure corresponding calibration spoke brightness value F, adopt least square fitting to go out following formula and map the parameter alpha of expression formula, β, ε, thus each pixel to tested region, the gray value of each spectral coverage is substituted into following formula and calculates spoke brightness value；

D=α F^β+ ε；

Second step, with maximum gradation value D_maxCorresponding spoke brightness value F_maxFor benchmark, each pixel is normalized at the spoke brightness value of each spectral coverage, constitutes a curve of spectrum；

Step 2, for the curve of spectrum that each pixel obtains in step one, adopts Savitzky-Golay wave filter to be smoothed, eliminates spectral noise retaining on the basis of more curvilinear characteristic, obtain the curve of spectrum after each pixel smooths(λ)；

Step 3, is obtained the curve of spectrum after each pixel smooths by step 2(λ) in conjunction with the color matching function of CIE1931 standard colorimetric systemAdopting following formula to calculate the CIEXYZ tristimulus values (X, Y, Z) under CIE1931 standard colorimetric system, wherein Δ λ is the spectrum sample interval of imaging spectral instrument；

Step 4, the tristimulus values (X according to standard illuminants D65_D65, Y_D65, Z_D65), the CIEXYZ tristimulus values that step 3 is obtained each pixel by following formula is converted to homogeneous color aware space CIEL^*C^*h^*, it is thus achieved that three Color perception parameters, i.e. lightness, chromaAnd tone h₁；

$\left\{\begin{array}{c}{L}_1^{*}=116f(Y/Y_{D65})-16\\ a^{*}=500\&lsqb;f(X/X_{D65})-f(Y/Y_{D65})\&rsqb;\\ b^{*}=200\&lsqb;f(Y/Y_{D65})-f(Z/Z_{D65})\&rsqb;\end{array}\right.;$

$\left\{\begin{array}{cc}f(X/X_{D65})=\left\{\begin{array}{c}{(X/X_{D65})}^{1/3}\\ 7.787(X/X_{D65})+16/116\end{array}\right.& \begin{array}{c}X/X_{D65}>0.008856\\ X/X_{D65}\&le;0.008856\end{array}\\ f(Y/Y_{D65})=\left\{\begin{array}{c}{(Y/Y_{D65})}^{1/3}\\ 7.787(Y/Y_{D65})+16/116\end{array}\right.& \begin{array}{c}Y/Y_{D65}>0.008856\\ Y/Y_{D65}\&le;0.008856\end{array}\\ f(Z/Z_{D65})=\left\{\begin{array}{c}{(Z/Z_{D65})}^{1/3}\\ 7.787(Z/Z_{D65})+16/116\end{array}\right.& \begin{array}{c}Z/Z_{D65}>0.008856\\ Z/Z_{D65}\&le;0.008856\end{array}\end{array}\right.;$

$\left\{\begin{array}{c}{C}_1^{*}={\&lsqb;{\left(a^{*}\right)}^2+{\left(b^{*}\right)}^2\&rsqb;}^{1/2}\\ h_1=\arctan (b^{*}/a^{*})\end{array}\right.;$

Wherein,

X_D65=95.047, Y_D65=100, Z_D65=108.883；

Step 5, arranges brightness coefficient k_L, chroma coefficient k_CWith tone coefficient k_hValue, obtained the lightness of each pixel by following formula modulation step four, chromaAnd tone h₁, Color perception parameter after being modulated, i.e. lightness, chromaAnd tone h₂, make effect of visualization meet fidelity reproduction demand, then k_L=k_C=1, k_h=0, change k_LRealize regulating the demand of image light and shade, change k_CRealize regulating the demand of the bright-coloured degree of image, change k_hRealize regulating the demand of image white balance；

$\left\{\begin{array}{c}{L}_2^{*}=k_L{C}_1^{*}\\ C_2^{*}=k_C{C}_1^{*}\\ h_2=h_1+k_h\end{array}\right.;$

Step 6, the white point tristimulus values (X according to display device_W, Y_W, Z_W), by following formula, step 5 is obtained the lightness of each pixel, chromaAnd tone h₂It is converted to CIEXYZ value (X ', Y ', Z ') to be shown on the display device；

$\left\{\begin{array}{c}{L}^{*}=L_2^{*}\\ a^{*}=C_2^{*}\&CenterDot;cos({\mathrm{\&pi;h}}_2/180)\\ b^{*}=C_2^{*}\&CenterDot;sin({\mathrm{\&pi;h}}_2/180)\end{array}\right.;$

$\left\{\begin{array}{cc}\begin{array}{c}{Y}^{\&prime;}=\left\{\begin{array}{c}{Y}_W\&CenterDot;{\&lsqb;(L^{*}+16)/116\&rsqb;}^{1/3}\\ Y_W\&CenterDot;L^{*}/903.3\end{array}\right.\\ f_Y=\left\{\begin{array}{c}{(Y^{\&prime;}/Y_W)}^{1/3}\\ 7.787(Y^{\&prime;}/Y_W)+16/116\end{array}\right.\\ X^{\&prime;}=\left\{\begin{array}{c}{X}_W\&CenterDot;{(a^{*}/500+f_Y)}^3\\ X_W\&CenterDot;(a^{*}/500+f_Y-16/116)/7.787\end{array}\right.\\ Z^{\&prime;}=\left\{\begin{array}{c}{Z}_W\&CenterDot;{(f_Y-b^{*}/200)}^3\\ Z_W\&CenterDot;(f_Y-b^{*}/200-16/116)/7.787\end{array}\right.\end{array}& \begin{array}{c}{\&lsqb;(L^{*}+16)/116\&rsqb;}^{1/3}>0.008856\\ {\&lsqb;(L^{*}+16)/116\&rsqb;}^{1/3}\&le;0.008856\\ Y^{\&prime;}/Y_W>0.008856\\ Y^{\&prime;}/X_W\&le;0.008856\\ {(a^{*}/500+f_Y)}^3>0.008856\\ {(a^{*}/500+f_Y)}^3\&le;0.008856\\ {(f_Y-b^{*}/200)}^3>0.008856\\ {(f_Y-b^{*}/200)}^3\&le;0.008856\end{array}\end{array}\right.;$

Step 7, according to the three-channel primary colors tristimulus values (X of display device red, green, blue_Rmax, Y_Rmax, Z_Rmax)、(X_Gmax, Y_Gmax, Z_Gmax、(X_Bmax, Y_Bmax, Z_Bmax) in conjunction with three-channel gamma factor γ_R、γ_G、γ_B, it is established that such as the characterization model of following formula, by characterization model, step 6 is obtained the CIEXYZ value (X ', Y ', Z ') of each pixel and is calculated to corresponding digital drive values (d_R, d_G, d_B), namely complete the color visualization of high spectrum image, wherein N is the single pass storage bit number of display device；

$\left[\begin{array}{c}{T}_R\\ T_G\\ T_B\end{array}\right]={\left[\begin{array}{ccc}1& X_{G\max }/Y_{G\max }& X_{B\max }/Z_{B\max }\\ Y_{R\max }/X_{R\max }& 1& Y_{B\max }/Z_{B\max }\\ Z_{R\max }/X_{R\max }& Z_{G\max }/Y_{G\max }& 1\end{array}\right]}^{-1}\left[\begin{array}{c}{X}^{\&prime;}\\ Y^{\&prime;}\\ Z^{\&prime;}\end{array}\right];$

$\left[\begin{array}{c}{d}_R\\ d_G\\ d_B\end{array}\right]=\left[\begin{array}{c}(2^N-1)\&CenterDot;{\left(T_R\right)}^{1/{\mathrm{\&gamma;}}_R}\\ (2^N-1)\&CenterDot;{\left(T_G\right)}^{1/{\mathrm{\&gamma;}}_G}\\ (2^N-1)\&CenterDot;{\left(T_B\right)}^{1/{\mathrm{\&gamma;}}_B}\end{array}\right].$

Because the aperture of the membranous wall of inner filter core is 20nm, more much smaller than the titanium alloy aeration head minimum-value aperture 450nm of current resistance to ozone oxidation, therefore the bubble diameter split by inner filter core 23 than catalytic ozonation time aeration produce bubble much smaller, after this ozone bubbles enters the second cavity 22 by the membranous wall of inner filter core 23, part ozone and the larger molecular organics being concentrated in the second cavity 22 and become the small organic molecule that polymolecular state of aggregation exists to react the little molecular inorganics such as the generation water of environmental sound, carbon dioxide。

Why this ozone bubbles can be difficult to, catalytic ozonation method, the larger molecular organics that decomposes after entering the second cavity 22 and is oxidized to the little molecular inorganics such as the water of environmental sound, carbon dioxide with the small organic molecule becoming polymolecular state of aggregation to exist, it is primarily due to this device and compares the oxidability that all improve ozone in following three compared with catalytic ozonation: one, because the larger molecular organics in sewage is concentrated with the small organic molecule becoming polymolecular state of aggregation to exist in the second cavity 22, thus improve the concentration of oxidized material in sewage；Two, enter the sewage of the second cavity and ozone bubbles due to the existence of the pump pressure of sewage and the air pressure of ozonator, add the dissolubility of ozone in the second cavity 22；Three, isolated as less bubble (aeration bubbles during with catalytic ozonation is compared) after entering the second cavity 22 due to ozone by the membranous wall of interior filter membrane。Additionally, the single small organic molecule in another part ozone and hydrone and sewage may proceed to the membranous wall by outer filter element 21 under the effect of the pressure。When by the membranous wall of outer filter element 21, ozone is divided into the higher less bubble of oxidability (ozone bubbles relative in the second cavity 22), this little ozone bubbles together with small organic molecule by outer filter element time collide and the small organic molecule such as water generation reaction, carbon dioxide。

Why do not have the oxidized small organic molecule can its reason oxidized has two: one, ozone bubbles is isolated into less bubble (because the aperture 20nm of inner filter core is bigger than the aperture 10nm of outer filter element 1 times) further when by outer filter element 21, adds the oxidability of ozone self when by outer filter element 21 in the second cavity 22；Two, with the ceramic material in cellular space ozone oxidation had catalytic action, and the membranous wall of outer filter element 21 also has a cellular filter opening as a kind of special ceramic element, therefore outer filter element 21 has both the effect of solid catalyst of ozone oxidation at the same time。Additionally, when ozone bubbles passes through the membranous wall of outer filter element 21 together with containing the sewage of small organic molecule, owing to ozone is decomposed into the little molecular inorganics such as water, carbon dioxide oxidation operation in time, thus preventing the adhesion on the membranous wall of the small organic molecule filter element 21 outside with certain viscosity to accumulate, and then greatly reduce the membranous wall of the filter element possibility by the bigger Organic Material Blocking of viscosity, increase substantially the life-span of filter element, stabilize the flux of film, reduce the use cost of filter element；

Step 6, sewage and ozone bubbles are by after outer filter element 21, remaining ozone and sewage constantly can rise in filter cylinder 24 along with ozone and constantly entering of sewage in filter cylinder 24, sewage and ozone bubbles continue the Organic substance remaining with sewage in filter cylinder 24 and react, and the size for this filter cylinder should ensure that the sewage water conservancy time of staying in filter cylinder 24 is at more than 30min。When sewage rises to the position of the second chuck joint 25, process qualified sewage and flow into clear water reserviors by the second chuck joint 25 and the 3rd valve 29 with chuck joint, when last remaining ozone rises to the top of filter cylinder 24, enter ozone breaker by exhaustor 31, after being broken to oxygen, enter air。

Its water quality of sewage through above step process is higher than the requirement of the technique in " urban sewage reutilization industry water standard " (GB/T19923-2005) and product water, the present invention is inheriting on the basis of advantage of Filtration and catalytic ozonation method, overcome Filtration and the shortcoming of catalytic ozonation method so that the advanced treating of low concentration refractory organic industrial sewage is possibly realized。

The above is only to presently preferred embodiments of the present invention, not the present invention is done any pro forma restriction, every technical spirit according to the present invention, to any simple modification made for any of the above embodiments, equivalent variations and modification, belongs in the scope of technical solution of the present invention。

Claims (3)

1. one kind processes low concentration refractory organic industrial sewage device, it is characterized in that, described process low concentration refractory organic industrial sewage device is provided with the first valve of chuck joint, the first chuck and the 3rd valve with chuck joint, it is provided with defecator between described the first valve and the first chuck with chuck joint

Described defecator includes: water inlet, cistern outer wall, roller, crossbeam, drainage screen, reductor, support post, outlet；

Described cistern outer wall left end upper position is provided with water inlet, the two ends of crossbeam are connected to roller, the upper end of cistern outer wall is provided with guide rail, roller rolls on guide rail, reductor is arranged on the speed reducer mounting base of the centre of crossbeam, and the positive lower end of reductor is support post, and reductor and support post link together, the lower end of crossbeam is provided with removable filter net, and the right-hand member upper position of cistern outer wall is provided with outlet；The outer section of described water inlet and outlet is welded with mounting flange, described crossbeam generally cross row, centre is provided with speed reducer mounting base, the both sides of crossbeam are provided with railing, described support post is arranged on the middle of cistern, the upper end of support post is provided with the axis hole coordinated with speed reducer output shaft, and described filter material is the wire side that stainless steel silk is welded, and the upper end of wire side is wrapped in organic wastewater absorption degradation agent；

Being provided with Internet of Things water quality detection module at described the 3rd valve place with chuck joint, the water quality detection module processed on low concentration refractory organic industrial sewage device being arranged on different location is connected by router of internet of things；

Described water quality detection module includes:

The measurement pipe being distributed up and down during use, this measurement infratubal port be provided with illuminator and in use this lower port enter the water surface, measure pipe upper port place set camera head, this camera head is suitable to shoot the water surface image in described measurement pipe；

Video acquisition module, is connected with described camera head, is suitable to the image gathered is transformed to digital picture；

With the image processing module that described video acquisition module is connected, this image processing module storage has the first sample data, and described first sample data is suitable to record the gray value of various water quality；

Be connected with described image processing module for receiving remote control signal and export the wireless communication module of water quality situation；Wireless communication module is provided with trust value computing module；

Described image processing module is suitable to described digital picture is carried out gray proces, compares and draws water quality situation obtaining the gray value of described water surface image, this gray value and the first sample data, and image processing module includes high spectrum image color visualization model。

2. process low concentration refractory organic industrial sewage device as claimed in claim 1, it is characterised in that the method that realizes of described trust value computing module includes:

Step one, the interaction times of different time sheet between acquisition node, according to the data setup time sequence obtained, predict the interaction times of next timeslice between node by third index flatness, using the relative error of interaction times predictive value and the actual value direct trust value as node；The concrete calculation procedure of direct trust value is:

The interaction times of n timeslice between collection network observations node i and node j:

Choosing certain time interval t as an observation time sheet, using observer nodes i and tested node j, the interaction times in 1 timeslice is as observation index, and true interaction times is denoted as y_t, record the y of n timeslice successively_n, and save it in the communications records table of node i；

The interaction times of (n+1)th timeslice of prediction:

Interaction times according to n the timeslice collected sets up time series, adopts the interaction times between the next timeslice n+1 interior nodes i and j of third index flatness prediction, it was predicted that interaction times, is denoted asComputing formula is as follows:

${\hat{y}}_{n+1}=a_n+b_n+c_n$

Predictive coefficient a_n、b_n、c_nValue can by equation below calculate obtain:

$a_n=3{\hat{y}}_{n+1}^{\left(1\right)}-3{\hat{y}}_{n+1}^{\left(2\right)}+{\hat{y}}_{n+1}^{\left(3\right)}$

$b_n=\frac{\mathrm{\&alpha;}}{2{(1-\mathrm{\&alpha;})}^2}\&lsqb;(6-5\mathrm{\&alpha;}){\hat{y}}_{n+1}^{\left(1\right)}-2(5-4\mathrm{\&alpha;}){\hat{y}}_{n+1}^{\left(2\right)}+(4-3\mathrm{\&alpha;}){\hat{y}}_{n+1}^{\left(3\right)}\&rsqb;$

$c_n=\frac{{\mathrm{\&alpha;}}^2}{2{(1-\mathrm{\&alpha;})}^2}\&lsqb;{\hat{y}}_{n+1}^{\left(1\right)}-2{\hat{y}}_{n+1}^{\left(2\right)}+{\hat{y}}_{n+1}^{\left(3\right)}\&rsqb;$

Wherein:Be respectively once, secondary, Three-exponential Smoothing number, by equation below calculate obtain:

${\hat{y}}_{n+1}^{\left(1\right)}=\mathrm{\&alpha;}\&times;y_n+(1-\mathrm{\&alpha;})\&times;{\hat{y}}_n^{\left(1\right)}$

${\hat{y}}_{n+1}^{\left(2\right)}=\mathrm{\&alpha;}\&times;{\hat{y}}_{n+1}^{\left(1\right)}+(1-\mathrm{\&alpha;})\&times;{\hat{y}}_n^{\left(2\right)}$

${\hat{y}}_{n+1}^{\left(3\right)}=\mathrm{\&alpha;}\&times;{\hat{y}}_{n+1}^{\left(2\right)}+(1-\mathrm{\&alpha;})\&times;{\hat{y}}_n^{\left(3\right)}$

Being the initial value of third index flatness, its value is

${\hat{y}}_0^{\left(1\right)}={\hat{y}}_0^{\left(2\right)}={\hat{y}}_0^{\left(3\right)}=\frac{y_1+y_2+y_3}{3}$

α is smoothing factor 0 < α < 1, embodies the time attenuation characteristic trusted, namely from predictive value more close to the yt weight of timeslice more big, from predictive value more away from the yt weight of timeslice more little；Usually, if data fluctuations is relatively big, and long-term trend amplitude of variation is relatively big, presents α when substantially rising or falling trend rapidly and should take higher value 0.6～0.8, it is possible to increases the Recent data impact on predicting the outcome；When data have a fluctuation, but when long-term trend change is little, α can between 0.1～0.4 value；If data fluctuations is steady, α should take smaller value 0.05～0.20；

Calculate direct trust value:

The direct trust value TD of node j_ijFor prediction interaction timesWith true interaction times y_n+1Relative error, ${\mathrm{TD}}_{ij}=\frac{|{\hat{y}}_{n+1}-y_{n+1}|}{{\hat{y}}_{n+1}};$

Step 2, adopts multipath trust recommendation mode and the calculating formula that obtains calculates indirect trust values；Adopt multipath trust recommendation mode and concrete calculation procedure that the calculating formula that obtains calculates indirect trust values is:

Collect the trusted node direct trust value to node j:

Node i meets TD to all_ikThe credible associated nodes of≤φ inquires its direct trust value to node j, and wherein φ is the believability threshold of recommended node, the precision prescribed according to credibility, and the span of φ is 0～0.4；

Calculate indirect trust values:

Trust value collected by COMPREHENSIVE CALCULATING, obtains the indirect trust values TR of node j_ij,Wherein, Set (i) for there being mutual and its direct trust value to meet TD with j node in the associated nodes of observer nodes i_ikThe node set of≤φ；

Step 3, is drawn comprehensive trust value by direct trust value and indirect trust values conformity calculation, direct trust value and indirect trust values conformity calculation show that the concrete calculation procedure of comprehensive trust value is:

Comprehensive trust value T_ijComputing formula as follows: T_ij=β TD_ij+(1-β)TR_ij, wherein β represents the weight of direct trust value, 0≤β≤1；When β=0, node i and node j do not have direct interaction relation, and the calculating of comprehensive trust value arises directly from indirect trust values, it is judged that more objective；When β=1, node i to the comprehensive trust value of node j all from direct trust value, in this case, it is judged that comparatively subjective, Practical Calculation can determine the value of β as required。

3. process low concentration refractory organic industrial sewage device as claimed in claim 1, it is characterised in that the method that realizes of described high spectrum image color visualization model comprises the following steps:

Step one, for each pixel of hyperspectral image data, is calculated spoke brightness value by the gray value of each spectral coverage, and is normalized one curve of spectrum of composition；Adopt each pixel to calculate spoke brightness value to constitute the curve of spectrum at the gray value of each spectral coverage, specifically include following steps:

The first step, spectral imaging apparatus is calibrated, choose 5～10 calibration gray value D and measure corresponding calibration spoke brightness value F, adopt least square fitting to go out following formula and map the parameter alpha of expression formula, β, ε, thus each pixel to tested region, the gray value of each spectral coverage is substituted into following formula and calculates spoke brightness value；

D=α F^β+ ε；

Second step, with maximum gradation value D_maxCorresponding spoke brightness value F_maxFor benchmark, each pixel is normalized at the spoke brightness value of each spectral coverage, constitutes a curve of spectrum；

Step 2, for the curve of spectrum that each pixel obtains in step one, adopts Savitzky-Golay wave filter to be smoothed, eliminates spectral noise retaining on the basis of more curvilinear characteristic, obtain the curve of spectrum after each pixel smooths

Step 3, is obtained the curve of spectrum after each pixel smooths by step 2Color matching function in conjunction with CIE1931 standard colorimetric systemAdopting following formula to calculate the CIEXYZ tristimulus values (X, Y, Z) under CIE1931 standard colorimetric system, wherein Δ λ is the spectrum sample interval of imaging spectral instrument；

Step 4, the tristimulus values (X according to standard illuminants D65_D65, Y_D65, Z_D65), the CIEXYZ tristimulus values that step 3 is obtained each pixel by following formula is converted to homogeneous color aware space CIEL^*C^*h^*, it is thus achieved that three Color perception parameters, i.e. lightnessChromaAnd tone h₁；

$\left\{\begin{array}{c}{L}_1^{*}=116f(Y/Y_{D65})-16\\ a^{*}=500\&lsqb;f(X/X_{D65})-f(Y/Y_{D65})\&rsqb;\\ b^{*}=200\&lsqb;f(Y/Y_{D65})-f(Z/Z_{D65})\&rsqb;\end{array}\right.;$

$\left\{\begin{array}{c}f\left(X/X_{D65}\right)=\left\{\begin{array}{cc}{\left(X/X_{D65}\right)}^{1/3}& X/X_{D65}>0.008856\\ 7.787\left(X/X_{D65}\right)+16/116& X/X_{D65}\&le;0.008856\end{array}\right.\\ f\left(Y/Y_{D65}\right)=\left\{\begin{array}{cc}{\left(Y/Y_{D65}\right)}^{1/3}& Y/Y_{D65}>0.008856\\ 7.787\left(Y/Y_{D65}\right)+16/116& Y/Y_{D65}\&le;0.008856\end{array}\right.\\ f\left(Z/Z_{D65}\right)=\left\{\begin{array}{cc}{\left(Z/Z_{D65}\right)}^{1/3}& Z/Z_{D65}>0.008856\\ 7.787\left(Z/Z_{D65}\right)+16/116& Z/Z_{D65}\&le;0.008856\end{array}\right.\end{array}\right.;$

$\left\{\begin{array}{c}{C}_1^{*}={\&lsqb;{\left(a^{*}\right)}^2+{\left(b^{*}\right)}^2\&rsqb;}^{1/2}\\ h_1=\arctan (b^{*}/a^{*})\end{array}\right.;$

Wherein,

X_D65=95.047, Y_D65=100, Z_D65=108.883；

Step 5, arranges brightness coefficient k_L, chroma coefficient k_CWith tone coefficient k_hValue, obtained the lightness of each pixel by following formula modulation step fourChromaAnd tone h₁, Color perception parameter after being modulated, i.e. lightnessChromaAnd tone h₂, make effect of visualization meet fidelity reproduction demand, then k_L=k_C=1, k_h=0, change k_LRealize regulating the demand of image light and shade, change k_CRealize regulating the demand of the bright-coloured degree of image, change k_hRealize regulating the demand of image white balance；

$\left\{\begin{array}{c}{L}_2^{*}=k_L{C}_1^{*}\\ C_2^{*}=k_C{C}_1^{*}\\ h_2=h_1+k_h\end{array}\right.;$

Step 6, the white point tristimulus values (X according to display device_W, Y_W, Z_W), by following formula, step 5 is obtained the lightness of each pixelChromaAnd tone h₂It is converted to CIEXYZ value (X ', Y ', Z ') to be shown on the display device；

$\left\{\begin{array}{c}{L}^{*}=L_2^{*}\\ a^{*}=C_2^{*}\&CenterDot;cos({\mathrm{\&pi;h}}_2/180)\\ b^{*}=C_2^{*}\&CenterDot;sin({\mathrm{\&pi;h}}_2/180)\end{array}\right.;$

$\left\{\begin{array}{c}{Y}^{\&prime;}=\left\{\begin{array}{cc}{Y}_W\&CenterDot;{\&lsqb;\left(L^{*}+16\right)/116\&rsqb;}^{1/3}& {\&lsqb;\left(L^{*}+16\right)/116\&rsqb;}^{1/3}>0.008856\\ Y_W\&CenterDot;L^{*}/903.3& {\&lsqb;\left(L^{*}+16\right)/116\&rsqb;}^{1/3}\&le;0.008856\end{array}\right.\\ f_Y=\left\{\begin{array}{cc}{\left(Y^{\&prime;}/Y_W\right)}^{1/3}& Y^{\&prime;}/Y_W>0.008856\\ 7.787\left(Y^{\&prime;}/Y_W\right)+16/116& Y^{\&prime;}/X_W\&le;0.008856\end{array}\right.\\ X^{\&prime;}=\left\{\begin{array}{cc}{X}_W\&CenterDot;{\left(a^{*}/500+f_Y\right)}^3& {\left(a^{*}/500+f_Y\right)}^3>0.008856\\ X_W\&CenterDot;\left(a^{*}/500+f_Y-16/116\right)/7.787& {\left(a^{*}/500+f_Y\right)}^3\&le;0.008856\end{array}\right.\\ Z^{\&prime;}=\left\{\begin{array}{cc}{Z}_W\&CenterDot;{\left(f_Y-b^{*}/200\right)}^3& {\left(f_Y-b^{*}/200\right)}^3>0.008856\\ Z_W\&CenterDot;\left(f_Y-b^{*}/200-16/116\right)/7.787& {\left(f_Y-b^{*}/200\right)}^3\&le;0.008856\end{array}\right.\end{array}\right.;$

Step 7, according to the three-channel primary colors tristimulus values (X of display device red, green, blue_Rmax, Y_Rmax, Z_Rmax)、(X_Gmax, Y_Gmax, Z_Gmax、(X_Bmax, Y_Bmax, Z_Bmax) in conjunction with three-channel gamma factor γ_R、γ_G、γ_B, it is established that such as the characterization model of following formula, by characterization model, step 6 is obtained the CIEXYZ value (X ', Y ', Z ') of each pixel and is calculated to corresponding digital drive values (d_R, d_G, d_B), namely complete the color visualization of high spectrum image, wherein N is the single pass storage bit number of display device；

$\left[\begin{array}{c}{T}_R\\ T_G\\ T_B\end{array}\right]={\left[\begin{array}{ccc}1& X_{G\max }/Y_{G\max }& X_{B\max }/Z_{B\max }\\ Y_{R\max }/X_{R\max }& 1& Y_{B\max }/Z_{B\max }\\ Z_{R\max }/X_{R\max }& Z_{G\max }/Y_{G\max }& 1\end{array}\right]}^{-1}\left[\begin{array}{c}{X}^{\&prime;}\\ Y^{\&prime;}\\ Z^{\&prime;}\end{array}\right];$

$\left[\begin{array}{c}{d}_R\\ d_G\\ d_B\end{array}\right]=\left[\begin{array}{c}(2^N-1)\&CenterDot;{\left(T_R\right)}^{1/{\mathrm{\&gamma;}}_R}\\ (2^N-1)\&CenterDot;{\left(T_G\right)}^{1/{\mathrm{\&gamma;}}_G}\\ (2^N-1)\&CenterDot;{\left(T_B\right)}^{1/{\mathrm{\&gamma;}}_B}\end{array}\right].$