CN106644375B - Multi-source Information Fusion method and application based on Wavelet Multiresolution Decomposition bilayer complex network - Google Patents
Multi-source Information Fusion method and application based on Wavelet Multiresolution Decomposition bilayer complex network Download PDFInfo
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Abstract
A kind of Multi-source Information Fusion method and application based on Wavelet Multiresolution Decomposition bilayer complex network: wavelet coefficient complex network is constructed respectively to the multichannel time series obtained by the cycle motivation dual-modality sensor being made of cycle motivation multi-electrode conductivity sensor and cycle motivation multielectrode capacitance sensor；Wavelet Multiresolution Decomposition converging network is constructed, and drawing respectively includes the figure energy of flow pattern information and the relational graph for intersecting convergence factor entropy and resolution ratio；Small echo bilayer complex network is constructed, and draws the mean value of node rendezvous coefficient, the mean value of the mean value of node degree and node diagnostic vector center and the relational graph of resolution ratio in the every layer network for including flow pattern information respectively.Vertical oil-water two-phase flow experiment is carried out using the cycle motivation dual-modality sensor being made of cycle motivation multi-electrode conductivity sensor and cycle motivation multielectrode capacitance sensor；The proportion of fixing oil phase and water phase changes oil and is mutually tested with the flow of water phase.The present invention realizes the identification to complex flow structure.
Description
Technical field
The present invention relates to a kind of two phase flow bimodal multi-sources Information Fusion Methods.Water-oil phase is directed to more particularly to one kind
Flow the Multi-source Information Fusion based on Wavelet Multiresolution Decomposition bilayer complex network of cycle motivation dual-modality sensor multi channel signals
Method and application.
Background technique
Oil-water two-phase flow is widely present in oil exploitation and transportation industry.In water-oil phase streaming system, the distribution of each phase
As time and space are constantly changing, different nowed formings, referred to as flow pattern are formd.The flow pattern of two phase flow is complicated more
Become, local flow information is difficult to accurately capture, so that there are many difficult points for the measurement of the two-phase flow parameters such as phase content.This is right
In oil exploitation and technological transformation at many influences.Currently, the research for flow pattern mainly uses observation method, wavelet character
Analysis and fuzzy C-means clustering, fuzzy logic and genetic algorithm, Digital Image Processing algorithm etc..The measurement of phase content is mostly used
Conductance method, capacitance method, optical method and ray method etc..The annular conductivity sensor and double helix capacitance sensing that traditional measurement uses
Device etc. is single channel sensor, is easily lost microcosmic local flow information.And distributing triggers reorganization and excitation cycle swash
Multi channel signals can then be acquired simultaneously by encouraging sensor etc., capture richer micro flow information, be drilled for flow pattern and flow pattern
Flow mechanism research in change provides important technology support.
Nowadays Complex Networks Theory has been flourished multi-field since foundation, be research complication system
One important tool, especially it has made major contribution in time series analysis in recent years.Practice have shown that complex network pair
It include that there is apparent advantage, effect with the important information in Kind of Nonlinear Dynamical System in Nonlinear Time Series in excavating
Significantly.Its while succeeding in single channel time series is applied, but also scientific research personnel begin to focus on how by its
It is applied in the convergence analysis of multi-channel data.In addition, the past is concentrated mainly on single layer network for the research of complex network
In analysis, and the appearance of double-layer network then compensates for unicity of the single layer network in terms of fuse information, can more preferably merge not
With the information of attribute, to excavate the intrinsic characteristic of complication system more abundant.
Summary of the invention
The technical problem to be solved by the invention is to provide a kind of identifications that can be realized to complex flow structure, portray
Flow pattern develop in internal motivation mechanism Multi-source Information Fusion method based on Wavelet Multiresolution Decomposition bilayer complex network and answer
With.
The technical scheme adopted by the invention is that: a kind of two phase flow multi-source information based on Wavelet Multiresolution Decomposition double-layer network
Fusion method includes the following steps:
1) it is followed to by what is be made of cycle motivation multi-electrode conductivity sensor and cycle motivation multielectrode capacitance sensor
The multichannel time series that ring excitation dual-modality sensor obtains constructs wavelet coefficient complex network respectively；
2) Wavelet Multiresolution Decomposition converging network is constructed, and drafting includes figure energy and the intersection aggregation of flow pattern information respectively
The relational graph of coefficient entropy and resolution ratio；
3) small echo bilayer complex network is constructed, and draws the node rendezvous in the every layer network for including flow pattern information respectively
The relational graph of the mean value of coefficient, the mean value of the mean value of node degree and node diagnostic vector center and resolution ratio.
Step 1) includes:
(1) obtain by cycle motivation dual-modality sensor obtain include two phase flow fluid local flow information S
Group length is the multichannel time series of L
(2) for the time series in each channel, the wavelet transformation of 6 resolution ratio is carried out, each resolution ratio obtains 1
Low frequency coefficient subband, that is, approximation coefficient and 1 high frequency coefficient subband, that is, detail coefficients, are obtained 6 low frequency coefficient subbands and 6
High frequency coefficient subband seeks the maximum value x of each low-frequency band He each high frequency band1, minimum value x2, average value x3, standard deviation
x4, steepness function x5With kurtosis function x6；In this way for each resolution ratio, 12 characteristic values of low-frequency band and high frequency band
Form a feature vector
(3) for multichannel time seriesIn each resolution ratio, when calculating every two channel
Between sequence feature vector Euclidean distance d=| | X (m)-X (n) | | m=1,2 ..., S n=1,2 ..., S, wherein | | |
| it indicates to calculate Euclidean distance, X (m) indicates the feature vector of channel m, the feature vector of the channel X (n) n；By the letter in each channel
Node number as complex network determines the company side of two nodes in complex network with the Euclidean distance of two interchannels；It calculates all
The mean value M of Euclidean distancenAnd standard deviation, then a threshold value R=M can be obtainedn+ q σ, wherein q=0.15；If two channels
Euclidean distance is greater than the threshold value, then does not connect side between two nodes, conversely, then there is even side between two nodes；As a result, in difference point
Wavelet coefficient complex network is constructed under resolution respectively；In this way, 6 wavelet systems can be obtained altogether for a multichannel time series
Number complex network.
Step 2) includes:
(1) one is respectively obtained by cycle motivation multi-electrode conductivity sensor and cycle motivation multielectrode capacitance sensor
Multichannel time series obtains a wavelet coefficient complex web in each resolution ratio respectively for each multichannel time series
Network；
(2) consider cycle motivation multi-electrode conductivity sensor and cycle motivation multielectrode capacitance sensor in each resolution ratio
It is lower to construct a wavelet coefficient complex network respectively, so the two wavelet coefficient complex networks obtained under each resolution ratio, it will
The shared company side of the two wavelet coefficient complex networks retains, and interstitial content is constant, to constitute one under each resolution ratio
Wavelet Multiresolution Decomposition converging network adjacency matrix A corresponding with it；
(3) it to the Wavelet Multiresolution Decomposition converging network, sets figure energy indexes E (A):Its
In, λiIndicate the characteristic value of wavelet coefficient complex network adjacency matrix A new under each resolution ratio, what n was indicated is characteristic value
Number；Set convergence factor entropy index EC:
Wherein, TvThe number of the closing triangle of to be in a new wavelet coefficient complex network the include node v indicated
Mesh, kvWhat is indicated is the degree of new wavelet coefficient Node Contraction in Complex Networks v, and what C (v) was indicated is the convergence factor of node v；
(4) drawing respectively includes the figure energy of flow pattern information and the relational graph for intersecting convergence factor entropy and resolution ratio, is led to
The transformation for studying the two indexs in flow pattern evolutionary process is crossed, to recognize complex flow structure, indicates flow pattern evolutionary process
In internal motivation mechanism.
Step 3) includes:
(1) two wavelet coefficient complex networks of each resolution ratio built for step 1), it is multiple respectively as small echo bilayer
One layer of miscellaneous network, to obtain small echo bilayer complex network, the adjacency matrix of the small echo bilayer complex network is expressed asWherein, α indicates the number of plies of the double-deck wavelet coefficient complex network, if node i and j have company at α layers
It connects, then corresponding element in adjacency matrixOtherwiseInterstitial content in two-tier network is all S；
(2) convergence factor of any layer α interior joint i of small echo bilayer complex network
Wherein, α ' indicates to be different from α layers of another layer in small echo bilayer complex network,WithRespectively indicate α
Element value in adjacency matrix of layer interior joint i and node j, the element value of α ' layer interior joint j and m in adjacency matrix, in α layers
The element value of node m and i in adjacency matrix；The convergence factor of each node in each layer is obtained, every layer of interior joint aggregation is calculated
The mean value C of coefficientα:
(3) degree of the arbitrary node i in any α layers in small echo bilayer complex network is calculated Its
In,Indicate the element value of α layers of interior joint i and j in adjacency matrix；The degree of each node in each layer is obtained, is calculated every
The mean value k of layer interior joint degreeα:
(4) eigenvector centrality of any α layers of interior joint i of small echo bilayer complex networkIndicate α layers of adjoining square
Battle array A[α]The corresponding feature vector of dominant eigenvalue i-th of element value；It obtains in each layer in the feature vector of each node
Disposition value calculates the mean value E of every layer of interior joint eigenvector centralityα:
(5) the mean value C of the node rendezvous coefficient in the every layer network for including flow pattern information is drawn respectivelyα, node degree
Mean value kαWith the mean value E of node diagnostic vector centerαWith the relational graph of resolution ratio；Cycle motivation multi-electrode conductivity sensor pair
The low oil content in part measures sensitive height, and cycle motivation multielectrode capacitance sensor is high to the high oil content measurement sensitivity in part, electricity
The effective integration that conduction holds multi-source metrical information realizes complementary two phase flow spatial flow infomation detection, includes stream by research
The mean value C of node rendezvous coefficient in every layer network of the every layer network of type informationα, node degree mean value kαWith node diagnostic vector
Central mean value EαWith the relational graph of resolution ratio, the mean value C of the node rendezvous coefficient in every layer of every layer network is studiedα, node
The mean value k of degreeαWith the mean value E of node diagnostic vector centerαTransformation of three indexs in flow pattern evolutionary process is realized to double
The fusion of modal information indicates the internal motivation mechanism in flow pattern evolutionary process.
A kind of application of the two phase flow Multi-source Information Fusion method based on Wavelet Multiresolution Decomposition double-layer network swashs using by recycling
The cycle motivation dual-modality sensor for encouraging multi-electrode conductivity sensor and cycle motivation multielectrode capacitance sensor composition hangs down
Straight oil-water two-phase flow experiment；The proportion of fixing oil phase and water phase changes oil and is mutually tested with the flow of water phase；Experimentation packet
Include following steps:
1) proportion of fixed water phase and oily phase, a certain amount of water is passed through into vertical ascent pipeline, then gradually to pipeline
In be passed through oily phase, after water-oil phase sufficiently merges and is gradually stable, using cycle motivation conductivity sensor and cycle motivation electricity
Hold the signal that sensor measures multichannel respectively, and records flow pattern with high-speed camera instrument；
2) after one acquisition, change oil mutually with the flow of water phase, continue to acquire by step 1) process, until in fixation
Proportion under designed operating condition all complete；
3) proportion for changing oily phase and water phase again, the process for repeating step 1) to step 2) complete the measurement of this wheel, directly
All operating conditions to design are all measured；
4) how electric for cycle motivation multi-electrode conductivity sensor and cycle motivation based on obtained multi-channel measurement signal
Electrode capacitance sensor establishes the wavelet coefficient complex network under different resolution respectively, and it is more to obtain a small echo for two-tier network
Resolution ratio converging network；
5) figure energy and convergence factor entropy are calculated, draw respectively include flow pattern information different operating condition following figure energy,
Convergence factor entropy portrays the internal motivation mechanism in the evolutionary process of different flow patterns with the relational graph of change resolution；
6) for two-tier network construct small echo bilayer complex network, calculate each layer network node rendezvous Coefficient Mean,
Node degree mean value, node diagnostic vector center mean value, drafting includes the node rendezvous Coefficient Mean of flow pattern information, section respectively
The relational graph of point degree mean value, node diagnostic vector center mean value 3 indexs and change resolution develops to study in flow pattern
In the process, the variation of complex flow structure and internal motivation mechanism.
Multi-source Information Fusion method and application based on Wavelet Multiresolution Decomposition bilayer complex network of the invention, by by following
The cycle motivation dual-modality sensor of ring excitation multi-electrode conductivity sensor and cycle motivation multielectrode capacitance sensor composition obtains
To multi-channel data using wavelet transformation thought establish Wavelet Multiresolution Decomposition bilayer complex network method carry out multi-source letter
Breath fusion, extracts the double-deck complex network index that can indicate that flow pattern generates and develops, and realizes the identification to complex flow structure,
Portray the internal motivation mechanism in flow pattern evolution.It has the following beneficial effects:
(1) a kind of oil-water two-phase flow Multi-source Information Fusion side based on Wavelet Multiresolution Decomposition bilayer complex network is proposed
Method；
(2) this method can portray the internal motivation mechanism in oil-water two-phase flow flow pattern evolutionary process.
Detailed description of the invention
Fig. 1 is that the present invention is based on the analysis schematic diagrames of the Multi-source Information Fusion method of Wavelet Multiresolution Decomposition bilayer complex network.
Specific embodiment
Below with reference to embodiment and attached drawing to the multi-source information of the invention based on Wavelet Multiresolution Decomposition bilayer complex network
Fusion method and application are described in detail.
Two phase flow Multi-source Information Fusion method based on Wavelet Multiresolution Decomposition double-layer network of the invention, proposes that a kind of small echo is more
Resolution ratio bilayer complex network networking method uses small echo by the multi-channel data obtained to cycle motivation dual-modality sensor
The method that the thought of transformation establishes the double-deck complex network carries out Multi-source Information Fusion, extracts the double-deck complex network index and portrays and is flowing
In type evolutionary process, internal motivation mechanism.
Two phase flow Multi-source Information Fusion method based on Wavelet Multiresolution Decomposition double-layer network of the invention, includes the following steps:
1) it is followed to by what is be made of cycle motivation multi-electrode conductivity sensor and cycle motivation multielectrode capacitance sensor
The multichannel time series that ring excitation dual-modality sensor obtains constructs wavelet coefficient complex network respectively；Include:
(1) obtain by cycle motivation dual-modality sensor obtain include two phase flow fluid local flow information S
Group length is the multichannel time series of L
(2) for the time series in each channel, the wavelet transformation of 6 resolution ratio is carried out, it is small using Daubechies2 rank
Wave (db2) decomposes it, and Decomposition order is 6 layers, i.e. 6 resolution ratio.Each resolution ratio obtains 1 low frequency coefficient subband
That is approximation coefficient and 1 high frequency coefficient subband, that is, detail coefficients, be obtained after the Time Series 6 low frequency coefficient subbands and
6 high frequency coefficient subbands seek the maximum value x of each low-frequency band He each high frequency band1, minimum value x2, average value x3, standard
Deviation x4, steepness function x5With kurtosis function x6；In this way for each resolution ratio, 12 spies of low-frequency band and high frequency band
Value indicative forms a feature vector
(3) for multichannel time seriesIn each resolution ratio, when calculating every two channel
Between sequence feature vector Euclidean distance d=| | X (m)-X (n) | | m=1,2 ..., S n=1,2 ..., S, wherein | | |
| it indicates to calculate Euclidean distance, X (m) indicates the feature vector of channel m, the feature vector of the channel X (n) n；By the letter in each channel
Node number as complex network determines the company side of two nodes in complex network with the Euclidean distance of two interchannels；It calculates all
The mean value M of Euclidean distancenAnd standard deviation, coefficient q=0.15 is enabled, then a threshold value R=M can be obtainedn+ q σ, wherein q=
0.15；If the Euclidean distance in two channels is greater than the threshold value, do not connect side between two nodes, conversely, then having between two nodes
Lian Bian；Construct wavelet coefficient complex network respectively under different resolution as a result,；In this way, for a multichannel time series,
6 wavelet coefficient complex networks can be obtained altogether.
2) Wavelet Multiresolution Decomposition aggregation network, that is, Wavelet Multiresolution Decomposition converging network is constructed, and is drawn respectively
System includes the figure energy of flow pattern information and the relational graph for intersecting convergence factor entropy and resolution ratio；Include:
(1) one is respectively obtained by cycle motivation multi-electrode conductivity sensor and cycle motivation multielectrode capacitance sensor
Multichannel time series obtains a wavelet coefficient complex web in each resolution ratio respectively for each multichannel time series
Network；
(2) consider cycle motivation multi-electrode conductivity sensor and cycle motivation multielectrode capacitance sensor in each resolution ratio
It is lower to construct a wavelet coefficient complex network respectively, so the two wavelet coefficient complex networks obtained under each resolution ratio, it will
The shared company side of the two wavelet coefficient complex networks retains, and interstitial content is constant, to constitute one under each resolution ratio
Wavelet Multiresolution Decomposition converging network adjacency matrix A corresponding with it；
(3) it to the Wavelet Multiresolution Decomposition converging network, sets figure energy indexes E (A):Its
In, λiIndicate the characteristic value of wavelet coefficient complex network adjacency matrix A new under each resolution ratio, what n was indicated is characteristic value
Number；Set convergence factor entropy index EC:
Wherein, TvThe number of the closing triangle of to be in a new wavelet coefficient complex network the include node v indicated
Mesh, kvWhat is indicated is the degree of new wavelet coefficient Node Contraction in Complex Networks v, and what C (v) was indicated is the convergence factor of node v；
(4) drawing respectively includes the figure energy of flow pattern information and the relational graph for intersecting convergence factor entropy and resolution ratio, is led to
The transformation for studying the two indexs in flow pattern evolutionary process is crossed, to recognize complex flow structure, indicates flow pattern evolutionary process
In internal motivation mechanism.
3) small echo bilayer complex network is constructed, and draws the node rendezvous in the every layer network for including flow pattern information respectively
The relational graph of the mean value of coefficient, the mean value of the mean value of node degree and node diagnostic vector center and resolution ratio, comprising:
(1) two wavelet coefficient complex networks of each resolution ratio built for step 1) are double-deck complicated respectively as small echo
One layer of network, to obtain small echo bilayer complex network, the adjacency matrix of the small echo bilayer complex network is expressed asWherein, α indicates the number of plies of small echo bilayer complex network, if node i and j have connection at α layers,
Corresponding element in adjacency matrixOtherwiseInterstitial content in two-tier network is all S；
(2) convergence factor of any layer α interior joint i of small echo bilayer complex network
Wherein, α ' indicates to be different from α layers of another layer in small echo bilayer complex network,WithRespectively indicate α
Element value in adjacency matrix of layer interior joint i and node j, the element value of α ' layer interior joint j and m in adjacency matrix, in α layers
The element value of node m and i in adjacency matrix；The convergence factor of each node in each layer is obtained, every layer of interior joint aggregation is calculated
The mean value C of coefficientα:
(3) degree of the arbitrary node i in any α layers in small echo bilayer complex network is calculated Its
In,Indicate the element value of α layers of interior joint i and j in adjacency matrix；The degree of each node in each layer is obtained, is calculated every
The mean value k of layer interior joint degreeα:
(4) eigenvector centrality of any α layers of interior joint i of small echo bilayer complex networkIndicate α layers of adjoining square
Battle array A[α]The corresponding feature vector of dominant eigenvalue i-th of element value；It obtains in each layer in the feature vector of each node
Disposition value calculates the mean value E of every layer of interior joint eigenvector centralityα:
(5) the mean value C of the node rendezvous coefficient in the every layer network for including flow pattern information is drawn respectivelyα, node degree
Mean value kαWith the mean value E of node diagnostic vector centerαWith the relational graph of resolution ratio；Cycle motivation multi-electrode conductivity sensor pair
The low oil content in part measures sensitive height, and cycle motivation multielectrode capacitance sensor is high to the high oil content measurement sensitivity in part, electricity
The effective integration that conduction holds multi-source metrical information realizes complementary two phase flow spatial flow infomation detection, includes stream by research
The mean value C of node rendezvous coefficient in every layer network of the every layer network of type informationα, node degree mean value kαWith node diagnostic vector
Central mean value EαWith the relational graph of resolution ratio, the mean value C of the node rendezvous coefficient in every layer of every layer network is studiedα, node
The mean value k of degreeαWith the mean value E of node diagnostic vector centerαTransformation of three indexs in flow pattern evolutionary process is realized to double
The fusion of modal information indicates the internal motivation mechanism in flow pattern evolutionary process.
The application of two phase flow Multi-source Information Fusion method based on Wavelet Multiresolution Decomposition double-layer network of the invention, using by following
Ring motivate multi-electrode conductivity sensor and cycle motivation multielectrode capacitance sensor composition cycle motivation dual-modality sensor into
The vertical oil-water two-phase flow experiment of row；The proportion of fixing oil phase and water phase changes oil and is mutually tested with the flow of water phase；Each biography
Sensor is all made of 16 electrodes, and every time in measurement, one of electrode is grounded as excitation end, an electrode, and remaining 14
Electrode receives, and one cycle can measure and obtain the signal in the channel 16 × 14=224.Since electrode cycle excitation speed is relative to stream
It is very fast for the speed of body flowing, it is possible to which that the signal for being equivalent to this 224 channel measures simultaneously, thus can effectively catch
Catch local flow information abundant.Experimentation includes the following steps:
1) proportion of fixed water phase and oily phase, a certain amount of water is passed through into vertical ascent pipeline, then gradually to pipeline
In be passed through oily phase, after water-oil phase sufficiently merges and is gradually stable, using cycle motivation conductivity sensor and cycle motivation electricity
Hold the signal that sensor measures multichannel respectively, and records flow pattern with high-speed camera instrument；
2) after one acquisition, change oil mutually with the flow of water phase, continue to acquire by step 1) process, until in fixation
Proportion under designed operating condition all complete；
3) proportion for changing oily phase and water phase again, the process for repeating step 1) to step 2) complete the measurement of this wheel, directly
All operating conditions to design are all measured；
4) how electric for cycle motivation multi-electrode conductivity sensor and cycle motivation based on obtained multi-channel measurement signal
Electrode capacitance sensor establishes the wavelet coefficient complex network under different resolution respectively, and it is more to obtain a small echo for two-tier network
Resolution ratio converging network；
5) figure energy and convergence factor entropy are calculated, draw respectively include flow pattern information different operating condition following figure energy,
Convergence factor entropy portrays the internal motivation mechanism in the evolutionary process of different flow patterns with the relational graph of change resolution；
6) for two-tier network construct small echo bilayer complex network, calculate each layer network node rendezvous Coefficient Mean,
Node degree mean value, node diagnostic vector center mean value, drafting includes the node rendezvous Coefficient Mean of flow pattern information, section respectively
The relational graph of point degree mean value, node diagnostic vector center mean value 3 indexs and change resolution develops to study in flow pattern
In the process, the variation of complex flow structure and internal motivation mechanism.
The present invention is divided by the multi-channel data obtained to cycle motivation dual-modality sensor using the method for wavelet transformation
Not Gou Jian Wavelet Multiresolution Decomposition converging network and small echo bilayer complex network under different resolution, respectively under different resolution
Complex network indices are calculated, drafting includes the different complex network indexs of flow pattern information with the pass of change resolution
System's figure, to study the internal motivation mechanism in flow pattern evolutionary process.
Above to the description of the present invention and embodiment, it is not limited to which this, the description in embodiment is only reality of the invention
One of mode is applied, it is without departing from the spirit of the invention, any not inventively to design and the technical solution
Similar structure or embodiment, category protection scope of the present invention.
Claims (3)
1. a kind of two phase flow Multi-source Information Fusion method based on Wavelet Multiresolution Decomposition double-layer network, which is characterized in that including as follows
Step:
1) circulation by being made of cycle motivation multi-electrode conductivity sensor and cycle motivation multielectrode capacitance sensor is swashed
It encourages the multichannel time series that dual-modality sensor obtains and constructs wavelet coefficient complex network respectively；Include:
(1) obtain by cycle motivation dual-modality sensor obtain include two phase flow fluid local flow information S group it is long
Degree is the multichannel time series of L
(2) for the time series in each channel, the wavelet transformation of 6 resolution ratio is carried out, each resolution ratio obtains 1 low frequency
Coefficient subband, that is, approximation coefficient and 1 high frequency coefficient subband, that is, detail coefficients, are obtained 6 low frequency coefficient subbands and 6 high frequencies
Coefficient subband seeks the maximum value x of each low-frequency band He each high frequency band1, minimum value x2, average value x3, standard deviation x4、
Steepness function x5With kurtosis function x6；In this way for each resolution ratio, 12 eigenvalue clusters of low-frequency band and high frequency band at
One feature vector
(3) for multichannel time seriesIn each resolution ratio, every two channel times sequence is calculated
The Euclidean distance d=of the feature vector of column | | X (m)-X (n) | | m=1,2 ..., S n=1,2 ..., S, wherein | | | | it indicates
Euclidean distance is calculated, X (m) indicates the feature vector of channel m, the feature vector of the channel X (n) n；Using the signal in each channel as
The node of complex network determines the company side of two nodes in complex network with the Euclidean distance of two interchannels；Calculate it is all it is European away from
From mean value MnAnd standard deviation, then a threshold value R=M can be obtainedn+ q σ, wherein q=0.15；If the Euclidean in two channels away from
From the threshold value is greater than, then do not connect side between two nodes, conversely, then there is even side between two nodes；As a result, under different resolution
Wavelet coefficient complex network is constructed respectively；In this way, 6 wavelet coefficient complexity can be obtained altogether for a multichannel time series
Network；
2) Wavelet Multiresolution Decomposition converging network is constructed, and drafting includes the figure energy and intersection convergence factor of flow pattern information respectively
The relational graph of entropy and resolution ratio；
3) small echo bilayer complex network is constructed, and draws the node rendezvous coefficient in the every layer network for including flow pattern information respectively
Mean value, the mean value of the mean value of node degree and node diagnostic vector center and the relational graph of resolution ratio, comprising:
(1) two wavelet coefficient complex networks of each resolution ratio built for step 1), respectively as small echo bilayer complex web
One layer of network, to obtain small echo bilayer complex network, the adjacency matrix of the small echo bilayer complex network is expressed asWherein, α indicates the number of plies of the double-deck wavelet coefficient complex network, if node i and j have company at α layers
It connects, then corresponding element in adjacency matrixOtherwiseInterstitial content in two-tier network is all S；
(2) the convergence factor C of any layer α interior joint i of small echo bilayer complex networki [α]:
Wherein, α ' indicates to be different from α layers of another layer in small echo bilayer complex network,WithIt respectively indicates in α layers
The element value of node i and node j in adjacency matrix, the element value of α ' layer interior joint j and m in adjacency matrix, α layers of interior joint
Element value of the m and i in adjacency matrix；The convergence factor of each node in each layer is obtained, every layer of interior joint convergence factor is calculated
Mean value Cα:
(3) degree of the arbitrary node i in any α layers in small echo bilayer complex network is calculated Wherein,Indicate the element value of α layers of interior joint i and j in adjacency matrix；The degree of each node in each layer is obtained, calculates every layer
The mean value k of interior joint degreeα:
(4) eigenvector centrality of any α layers of interior joint i of small echo bilayer complex networkIndicate α layers of adjacency matrix A[α]The corresponding feature vector of dominant eigenvalue i-th of element value；Obtain the eigenvector centrality of each node in each layer
Property value, calculate every layer of interior joint eigenvector centrality mean value Eα:
(5) the mean value C of the node rendezvous coefficient in the every layer network for including flow pattern information is drawn respectivelyα, node degree mean value kα
With the mean value E of node diagnostic vector centerαWith the relational graph of resolution ratio；Cycle motivation multi-electrode conductivity sensor is low to part
Oil content measures sensitive height, and cycle motivation multielectrode capacitance sensor is high to the high oil content measurement sensitivity in part, conduction capacity
The effective integration of multi-source metrical information realizes complementary two phase flow spatial flow infomation detection, includes flow pattern information by research
The mean value C of node rendezvous coefficient in every layer network of every layer networkα, node degree mean value kαWith node diagnostic vector center
Mean value EαWith the relational graph of resolution ratio, the mean value C of the node rendezvous coefficient in every layer of every layer network is studiedα, node degree it is equal
Value kαWith the mean value E of node diagnostic vector centerαTransformation of three indexs in flow pattern evolutionary process is realized and is believed bimodal
The fusion of breath indicates the internal motivation mechanism in flow pattern evolutionary process.
2. the two phase flow Multi-source Information Fusion method according to claim 1 based on Wavelet Multiresolution Decomposition double-layer network, special
Sign is that step 2) includes:
(1) multi-pass is respectively obtained by cycle motivation multi-electrode conductivity sensor and cycle motivation multielectrode capacitance sensor
Road time series obtains a wavelet coefficient complex network in each resolution ratio respectively for each multichannel time series；
(2) consider that cycle motivation multi-electrode conductivity sensor and cycle motivation multielectrode capacitance sensor divide under each resolution ratio
Not Gou Jian a wavelet coefficient complex network, so the two wavelet coefficient complex networks obtained under each resolution ratio, by this two
The shared company side of a wavelet coefficient complex network retains, and interstitial content is constant, to constitute a small echo under each resolution ratio
Multiresolution converging network adjacency matrix A corresponding with it；
(3) it to the Wavelet Multiresolution Decomposition converging network, sets figure energy indexes E (A):Wherein, λiTable
Show the characteristic value of wavelet coefficient complex network adjacency matrix A new under each resolution ratio, what n was indicated is the number of characteristic value；
Set convergence factor entropy index EC:
Wherein, TvThe number of the closing triangle of to be in a new wavelet coefficient complex network the include node v indicated, kv
What is indicated is the degree of new wavelet coefficient Node Contraction in Complex Networks v, and what C (v) was indicated is the convergence factor of node v；
(4) drawing respectively includes the figure energy of flow pattern information and the relational graph for intersecting convergence factor entropy and resolution ratio, by grinding
Study carefully transformation of the two indexs in flow pattern evolutionary process, to recognize complex flow structure, indicates in flow pattern evolutionary process
Internal motivation mechanism.
3. a kind of two phase flow Multi-source Information Fusion method described in claim 1 based on Wavelet Multiresolution Decomposition double-layer network is answered
With, which is characterized in that using what is be made of cycle motivation multi-electrode conductivity sensor and cycle motivation multielectrode capacitance sensor
Cycle motivation dual-modality sensor carries out vertical oil-water two-phase flow experiment；The proportion of fixing oil phase and water phase changes oil phase and water
The flow of phase is tested；Experimentation includes the following steps:
1) proportion of fixed water phase and oily phase, a certain amount of water is passed through into vertical ascent pipeline, is then gradually led into pipeline
Enter oily phase, after water-oil phase sufficiently merges and is gradually stable, is passed using cycle motivation conductivity sensor and cycle motivation capacitor
Sensor measures the signal of multichannel respectively, and records flow pattern with high-speed camera instrument；
2) after one acquisition, change oil mutually with the flow of water phase, continue to acquire by step 1) process, until matching in fixed
It is all completed than lower designed operating condition；
3) proportion for changing oily phase and water phase again, the process for repeating step 1) to step 2) completes the measurement of this wheel, until setting
All operating conditions of meter are all measured；
4) based on obtained multi-channel measurement signal, for cycle motivation multi-electrode conductivity sensor and cycle motivation multi-electrode electricity
Hold sensor and establish the wavelet coefficient complex network under different resolution respectively, a wavelet multiresolution is obtained for two-tier network
Rate converging network；
5) figure energy and convergence factor entropy are calculated, drafting includes the different operating condition following figure energy of flow pattern information, aggregation respectively
Coefficient entropy portrays the internal motivation mechanism in the evolutionary process of different flow patterns with the relational graph of change resolution；
6) small echo bilayer complex network is constructed for two-tier network, calculates node rendezvous Coefficient Mean, the node of each layer network
Mean value, node diagnostic vector center mean value are spent, drafting includes node rendezvous Coefficient Mean, the node degree of flow pattern information respectively
The relational graph of mean value, node diagnostic vector center mean value 3 indexs and change resolution, to study in flow pattern evolutionary process
In, the variation of complex flow structure and internal motivation mechanism.
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