CN106644375A - Multi-source information fusion method based on wavelet multi-resolution double-layer complex network and application thereof - Google Patents

Abstract

The invention relates to a multi-source information fusion method based on a wavelet multi-resolution double-layer complex network and an application thereof. The multi-source information fusion method comprises the steps of respectively building wavelet coefficient complex networks for multichannel time sequences acquired through a cyclic excitation dual-mode sensor formed by a cyclic excitation multi-electrode conductive sensor and a cyclic excitation multi-electrode capacitive sensor; building a wavelet multi-resolution aggregation network, and respectively drawing a relation diagram containing flow pattern information between diagram energy and the resolution and a relation diagram containing flow pattern information between an interactive clustering coefficient entropy and the resolution; and building a wavelet double-layer complex network, and respectively drawing a relation diagram containing flow pattern information between the mean of node clustering coefficients in each layer of the network and the resolution, the mean of node degrees in each layer of the network and the resolution and the mean of node feature vector centralities in each layer of the network and the resolution. According to the invention, a vertical oil-water two-phase flow experiment conducted by adopting the cyclic excitation dual-mode sensor formed by the cyclic excitation multi-electrode conductive sensor and the cyclic excitation multi-electrode capacitive sensor; and the experiment is conducted by fixing the proportion of an oil phase and a water phase and changing the flow of the oil phase and the water phase. The multi-source information fusion method realizes recognition for a complex flow structure.

Description

Multi-source Information Fusion method and application based on Wavelet Multiresolution Decomposition bilayer complex network

Technical field

The present invention relates to a kind of two phase flow bimodal multi-sources Information Fusion Method.It is more particularly to a kind of to be directed to water-oil phase The Multi-source Information Fusion based on Wavelet Multiresolution Decomposition bilayer complex network of stream cycle motivation dual-modality sensor multi channel signals Method and application.

Background technology

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 It is being continually changing with space over time, is defining different nowed formings, referred to as flow pattern.The flow pattern of two phase flow is complicated more Become, local flow information is difficult to accurately seizure so that the measurement of the two-phase flow parameter such as phase content has many difficult points.This is right In oil exploitation with technological transformation into many impacts.At present, for the research of flow pattern mainly adopts observation method, wavelet character Analysis and fuzzy C-means clustering, fuzzy logic and genetic algorithm, Digital Image Processing algorithm etc..For phase content measurement more adopt Conductance method, capacitance method, optical method and ray method etc..Annular conductivity sensor and double helix capacitance sensing that traditional measurement is adopted Device etc., is single channel sensor, is easily lost the local flow information of microcosmic.And distributing triggers reorganization and excitation cycle swash Encouraging sensor etc. then can capture more rich micro flow information while gather multi channel signals, be that flow pattern and flow pattern are drilled Flow mechanism research in change provides important technology support.

Nowadays Complex Networks Theory has been flourished multi-field since the foundation, is research complication system One important tool, especially in recent years it has made major contribution in time series analysis.Practice have shown that, complex network pair There is obvious advantage, effect with the important information in Kind of Nonlinear Dynamical System in excavating to be included in Nonlinear Time Series Significantly.While its succeeding in single channel time series is applied, scientific research personnel is also caused how to begin to focus on it In being applied to the convergence analysis of multi-channel data.Additionally, 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 compensate for unicity of the single layer network in terms of fuse information, can more preferably merge not With the information of attribute, so as to excavate the intrinsic characteristic of more abundant complication system.

The content of the invention

The technical problem to be solved be to provide it is a kind of can realize the identification to complex flow structure, portray Flow pattern develop in internal motivation mechanism based on Wavelet Multiresolution Decomposition bilayer complex network Multi-source Information Fusion method and answer With.

The technical solution adopted in the present invention is：A kind of two phase flow multi-source information based on Wavelet Multiresolution Decomposition double-layer network Fusion method, comprises the steps：

1) to by following for being made up of cycle motivation multi-electrode conductivity sensor and cycle motivation multielectrode capacitance sensor The multichannel time series that ring excitation dual-modality sensor is obtained builds respectively wavelet coefficient complex network；

2) Wavelet Multiresolution Decomposition converging network is built, and draws the figure energy for including flow pattern information respectively and intersect aggregation The graph of a relation of coefficient entropy and resolution ratio；

3) small echo bilayer complex network is built, and draws the node rendezvous in the every layer network for including flow pattern information respectively The graph of a relation of the average of coefficient, the average of the average of node degree and node diagnostic vector center and resolution ratio.

Step 1) include：

(1) S for including two phase flow fluid local flow information obtained by cycle motivation dual-modality sensor is obtained Group length is the multichannel time series of L

(2) for the time series of each passage, the wavelet transformation of 6 resolution ratio is carried out, each resolution ratio obtains 1 Low frequency coefficient subband is approximation coefficient and 1 high frequency coefficient subband i.e. detail coefficients, and 6 low frequency coefficient subbands and 6 are obtained High frequency coefficient subband, seeks maximum x of each low-frequency band and each high frequency band₁, minimum of a value x₂, mean value x₃, standard deviation x₄, steepness function x₅With kurtosis function x₆；So for each resolution ratio, 12 characteristic values of low-frequency band and high frequency band One characteristic vector of composition

(3) for multichannel time seriesIn each resolution ratio, when calculating every two passage Between sequence characteristic vector Euclidean distance d=| | X (m)-X (n) | | m=1,2 ..., S n=1,2 ..., S, wherein, | | | | represent and calculate Euclidean distance, X (m) represents the characteristic vector of passage m, the characteristic vector of X (n) passage n；By the letter of each passage Number as complex network node, with the company side that two interchannel Euclidean distances determine two nodes in complex networks；Calculate all Average M of Euclidean distance_nAnd standard deviation, then threshold value R=M is obtained_n+ q σ, wherein q=0.15；If two passages Euclidean distance is more than the threshold value, then do not connect side between two nodes, conversely, then there is even side between two nodes；Thus, at different points Build wavelet coefficient complex network under resolution respectively；So, for a multichannel time series, 6 wavelet systems are obtained altogether Number complex network.

Step 2) include：

(1) one is respectively obtained by cycle motivation multi-electrode conductivity sensor and cycle motivation multielectrode capacitance sensor Multichannel time series, for each multichannel time series obtains a wavelet coefficient complex web in each resolution ratio respectively Network；

(2) consider cycle motivation multi-electrode conductivity sensor and cycle motivation multielectrode capacitance sensor in each resolution ratio It is lower to build a wavelet coefficient complex network respectively, so the two wavelet coefficient complex networks obtained under each resolution ratio, will The total company side of the two wavelet coefficient complex networks retains, and interstitial content is constant, so as to constitute one under each resolution ratio Wavelet Multiresolution Decomposition converging network and its corresponding adjacency matrix A；

(3) to described Wavelet Multiresolution Decomposition converging network, figure energy indexes E (A) is set：Wherein, λ_iThe characteristic value of wavelet coefficient complex network adjacency matrix A new under each resolution ratio is represented, what n was represented is the number of characteristic value Mesh；Setting convergence factor entropy index E_C：

Wherein, T_vWhat is represented is the number of the closing triangle for including node v in a new wavelet coefficient complex network Mesh, k_vWhat is represented is the degree of new wavelet coefficient Node Contraction in Complex Networks v, and what C (v) was represented is the convergence factor of node v；

(4) the figure energy for including flow pattern information and the graph of a relation for intersecting convergence factor entropy and resolution ratio are drawn respectively, are led to The transformation for studying the two indexs in flow pattern evolutionary process is crossed, to recognize complex flow structure, flow pattern evolutionary process is indicated In internal motivation mechanism.

Step 3) include：

(1) for step 1) two wavelet coefficient complex networks of each resolution ratio for building, it is double-deck multiple respectively as small echo One layer of miscellaneous network, so as to obtain small echo bilayer complex network, the adjacency matrix of the small echo bilayer complex network is expressed asWherein, α represents the number of plies of double-deck wavelet coefficient complex network, if node i and j have connection in α layers, 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 ' represents another layer that α layers are different from small echo bilayer complex network,Withα is represented respectively The element value of layer interior joint i and node j in adjacency matrix, 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 Average C of coefficient_α：

(3) degree of the arbitrary node i in the arbitrary α layers in small echo bilayer complex network is calculated Its In,Represent the element value of α layer interior joint i and j in adjacency matrix；The degree of each node in each layer is obtained, calculates every Average k of layer interior joint degree_α：

(4) eigenvector centrality of arbitrary α layers interior joint i of small echo bilayer complex networkRepresent the adjacent square of α layers Battle array A^[α]The corresponding characteristic vector of dominant eigenvalue i-th element value；Obtain in each layer in the characteristic vector of each node Disposition value, calculates average E of every layer of interior joint eigenvector centrality_α：

(5) average C of the node rendezvous coefficient in the every layer network for including flow pattern information is drawn respectively_α, node degree Average k_αWith average E of node diagnostic vector center_αWith the graph of a relation of resolution ratio；Cycle motivation multi-electrode conductivity sensor pair Locally low oil content measures sensitive height, and cycle motivation multielectrode capacitance sensor is high to the high oil content measurement sensitivity in local, electricity Conduction holds the effective integration of multi-source metrical information and realizes complementary two phase flow spatial flow infomation detection, and by research stream is included Average C of the node rendezvous coefficient in every layer network of the type information per layer network_α, node degree average k_αWith node diagnostic vector Central average E_αWith the graph of a relation of resolution ratio, average C of the node rendezvous coefficient in per layer of every layer network is studied_α, node Average k of degree_αWith average E of node diagnostic vector center_αThe transformation of three indexs in flow pattern evolutionary process, realizes 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, using sharp by circulation The cycle motivation dual-modality sensor for encouraging multi-electrode conductivity sensor and cycle motivation multielectrode capacitance sensor composition is hung down Straight oil-water two-phase flow experiment；The proportioning of fixed oil phase and water phase, the flow for changing oil phase and water phase is tested；Experimentation bag Include following steps：

1) proportioning of fixed water phase and oil phase, is passed through a certain amount of water, then gradually to pipeline toward vertical ascent pipeline In be passed through oil phase, when water-oil phase fully merge and it is gradually stable after, using cycle motivation conductivity sensor and cycle motivation electricity Hold sensor and measure multichannel signal respectively, and flow pattern is recorded with high-speed camera instrument；

2) after once collection terminates, the flow of oil phase and water phase is changed, by step 1) process continues collection, until fixing Proportioning under designed operating mode all complete；

3) change the proportioning of oil phase and water phase, repeat step 1 again) to step 2) process complete the measurement of this wheel, directly All operating modes to design are all measured；

4) it is how electric for cycle motivation multi-electrode conductivity sensor and cycle motivation based on the multi-channel measurement signal for obtaining The wavelet coefficient complex network that electrode capacitance sensor is set up respectively under different resolution, for two-tier network, to obtain a small echo more Resolution ratio converging network；

5) figure energy and convergence factor entropy are calculated, draw respectively include flow pattern information different operating mode figure below energy, Convergence factor entropy portrays the internal motivation mechanism in the evolutionary process of different flow patterns with the graph of a relation of change resolution；

6) for two-tier network structure small echo bilayer complex network, the node rendezvous Coefficient Mean of each layer network of calculating, Node degree average, node diagnostic vector center average, draw respectively node rendezvous Coefficient Mean, the section for including flow pattern information The graph of a relation of point degree average, 3 indexs of node diagnostic vector center average and change resolution, develops to study in flow pattern During, the change of complex flow structure and internal motivation mechanism.

The Multi-source Information Fusion method based on Wavelet Multiresolution Decomposition bilayer complex network of the present invention and application, by by following Ring encourages multi-electrode conductivity sensor and the cycle motivation dual-modality sensor of cycle motivation multielectrode capacitance sensor composition to obtain To multi-channel data using the thought of wavelet transformation set up Wavelet Multiresolution Decomposition bilayer complex network method carry out multi-source letter Breath fusion, extraction can indicate that the double-deck complex network index that flow pattern is generated and developed, and realize the identification to complex flow structure, Portray the internal motivation mechanism during flow pattern develops.Have the advantages that：

(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) the method can portray the internal motivation mechanism in oil-water two-phase flow flow pattern evolutionary process.

Description of the drawings

Fig. 1 is analysis schematic diagram of the present invention based on the Multi-source Information Fusion method of Wavelet Multiresolution Decomposition bilayer complex network.

Specific embodiment

With reference to the multi-source information based on Wavelet Multiresolution Decomposition bilayer complex network of embodiment and accompanying drawing to the present invention Fusion method and application are described in detail.

The two phase flow Multi-source Information Fusion method based on Wavelet Multiresolution Decomposition double-layer network of the present invention, proposes that a kind of small echo is more Resolution ratio bilayer complex network networking method, by adopting small echo to the multi-channel data that cycle motivation dual-modality sensor is obtained The thought of conversion sets up the method for double-deck complex network and carries out Multi-source Information Fusion, extracts double-deck complex network index and portrays in stream In type evolutionary process, internal motivation mechanism.

The two phase flow Multi-source Information Fusion method based on Wavelet Multiresolution Decomposition double-layer network of the present invention, comprises the steps：

1) to by following for being made up of cycle motivation multi-electrode conductivity sensor and cycle motivation multielectrode capacitance sensor The multichannel time series that ring excitation dual-modality sensor is obtained builds respectively wavelet coefficient complex network；Including：

(1) S for including two phase flow fluid local flow information obtained by cycle motivation dual-modality sensor is obtained Group length is the multichannel time series of L

(2) for the time series of each passage, the wavelet transformation of 6 resolution ratio is carried out, it is little using Daubechies2 ranks Ripple (db2) decomposes to it, and Decomposition order is the resolution ratio of 6 layers, i.e., 6.Each resolution ratio obtains 1 low frequency coefficient subband I.e. approximation coefficient and 1 high frequency coefficient subband are detail coefficients, be obtained after the Time Series 6 low frequency coefficient subbands and 6 high frequency coefficient subbands, seek maximum x of each low-frequency band and each high frequency band₁, minimum of a value x₂, mean value x₃, standard Deviation x₄, steepness function x₅With kurtosis function x₆；So for each resolution ratio, 12 spies of low-frequency band and high frequency band Value indicative constitutes a characteristic vector

(3) for multichannel time seriesIn each resolution ratio, when calculating every two passage Between sequence characteristic vector Euclidean distance d=| | X (m)-X (n) | | m=1,2 ..., S n=1,2 ..., S, wherein, | | | | represent and calculate Euclidean distance, X (m) represents the characteristic vector of passage m, the characteristic vector of X (n) passage n；By the letter of each passage Number as complex network node, with the company side that two interchannel Euclidean distances determine two nodes in complex networks；Calculate all Average M of Euclidean distance_nAnd standard deviation, make coefficient q=0.15, then threshold value R=M is obtained_n+ q σ, wherein q= 0.15；If the Euclidean distance of two passages is more than the threshold value, side is not connected between two nodes, conversely, then having between two nodes Lian Bian；Thus, wavelet coefficient complex network is built respectively under different resolution；So, for a multichannel time series, 6 wavelet coefficient complex networks are obtained altogether.

2) it is Wavelet Multiresolution Decomposition converging network to build Wavelet Multiresolution Decomposition aggregation network, and is painted respectively System includes the figure energy of flow pattern information and intersects the graph of a relation of convergence factor entropy and resolution ratio；Including：

(1) one is respectively obtained by cycle motivation multi-electrode conductivity sensor and cycle motivation multielectrode capacitance sensor Multichannel time series, for each multichannel time series obtains a wavelet coefficient complex web in each resolution ratio respectively Network；

(2) consider cycle motivation multi-electrode conductivity sensor and cycle motivation multielectrode capacitance sensor in each resolution ratio It is lower to build a wavelet coefficient complex network respectively, so the two wavelet coefficient complex networks obtained under each resolution ratio, will The total company side of the two wavelet coefficient complex networks retains, and interstitial content is constant, so as to constitute one under each resolution ratio Wavelet Multiresolution Decomposition converging network and its corresponding adjacency matrix A；

(3) to described Wavelet Multiresolution Decomposition converging network, figure energy indexes E (A) is set：Its In, λ_iThe characteristic value of wavelet coefficient complex network adjacency matrix A new under each resolution ratio is represented, what n was represented is characteristic value Number；Setting convergence factor entropy index E_C：

Wherein, T_vWhat is represented is the number of the closing triangle for including node v in a new wavelet coefficient complex network Mesh, k_vWhat is represented is the degree of new wavelet coefficient Node Contraction in Complex Networks v, and what C (v) was represented is the convergence factor of node v；

(4) the figure energy for including flow pattern information and the graph of a relation for intersecting convergence factor entropy and resolution ratio are drawn respectively, are led to The transformation for studying the two indexs in flow pattern evolutionary process is crossed, to recognize complex flow structure, flow pattern evolutionary process is indicated In internal motivation mechanism.

3) small echo bilayer complex network is built, and draws the node rendezvous in the every layer network for including flow pattern information respectively The graph of a relation of the average of coefficient, the average of the average of node degree and node diagnostic vector center and resolution ratio, including：

(1) for step 1) two wavelet coefficient complex networks of each resolution ratio for building are double-deck complicated respectively as small echo One layer of network, so as to obtain small echo bilayer complex network, the adjacency matrix of the small echo bilayer complex network is expressed asWherein, α represents the number of plies of small echo bilayer complex network, if node i and j have connection in 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 ' represents another layer that α layers are different from small echo bilayer complex network,Withα is represented respectively The element value of layer interior joint i and node j in adjacency matrix, 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 Average C of coefficient_α：

(3) degree of the arbitrary node i in the arbitrary α layers in small echo bilayer complex network is calculated Its In,Represent the element value of α layer interior joint i and j in adjacency matrix；The degree of each node in each layer is obtained, calculates every Average k of layer interior joint degree_α：

(4) eigenvector centrality of arbitrary α layers interior joint i of small echo bilayer complex networkRepresent the adjacent square of α layers Battle array A^[α]The corresponding characteristic vector of dominant eigenvalue i-th element value；Obtain in each layer in the characteristic vector of each node Disposition value, calculates average E of every layer of interior joint eigenvector centrality_α：

(5) average C of the node rendezvous coefficient in the every layer network for including flow pattern information is drawn respectively_α, node degree Average k_αWith average E of node diagnostic vector center_αWith the graph of a relation of resolution ratio；Cycle motivation multi-electrode conductivity sensor pair Locally low oil content measures sensitive height, and cycle motivation multielectrode capacitance sensor is high to the high oil content measurement sensitivity in local, electricity Conduction holds the effective integration of multi-source metrical information and realizes complementary two phase flow spatial flow infomation detection, and by research stream is included Average C of the node rendezvous coefficient in every layer network of the type information per layer network_α, node degree average k_αWith node diagnostic vector Central average E_αWith the graph of a relation of resolution ratio, average C of the node rendezvous coefficient in per layer of every layer network is studied_α, node Average k of degree_αWith average E of node diagnostic vector center_αThe transformation of three indexs in flow pattern evolutionary process, realizes to double The fusion of modal information, indicates the internal motivation mechanism in flow pattern evolutionary process.

The application of the two phase flow Multi-source Information Fusion method based on Wavelet Multiresolution Decomposition double-layer network of the present invention, using by following Ring encourages multi-electrode conductivity sensor and the cycle motivation dual-modality sensor of cycle motivation multielectrode capacitance sensor composition to enter The vertical oil-water two-phase flow experiment of row；The proportioning of fixed oil phase and water phase, the flow for changing oil phase and water phase is tested；Each biography Sensor is all made up of 16 electrodes, and every time in measurement, one of electrode is grounded as excitation end, an electrode, remaining 14 Electrode is received, the measurable signal for obtaining 16 × 14=224 passages of one cycle.Because electrode cycle encourages speed 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 passage is measurement simultaneously, thus can effectively be caught Catch abundant local flow information.Experimentation comprises the steps：

1) proportioning of fixed water phase and oil phase, is passed through a certain amount of water, then gradually to pipeline toward vertical ascent pipeline In be passed through oil phase, when water-oil phase fully merge and it is gradually stable after, using cycle motivation conductivity sensor and cycle motivation electricity Hold sensor and measure multichannel signal respectively, and flow pattern is recorded with high-speed camera instrument；

2) after once collection terminates, the flow of oil phase and water phase is changed, by step 1) process continues collection, until fixing Proportioning under designed operating mode all complete；

3) change the proportioning of oil phase and water phase, repeat step 1 again) to step 2) process complete the measurement of this wheel, directly All operating modes to design are all measured；

4) it is how electric for cycle motivation multi-electrode conductivity sensor and cycle motivation based on the multi-channel measurement signal for obtaining The wavelet coefficient complex network that electrode capacitance sensor is set up respectively under different resolution, for two-tier network, to obtain a small echo more Resolution ratio converging network；

5) figure energy and convergence factor entropy are calculated, draw respectively include flow pattern information different operating mode figure below energy, Convergence factor entropy portrays the internal motivation mechanism in the evolutionary process of different flow patterns with the graph of a relation of change resolution；

6) for two-tier network structure small echo bilayer complex network, the node rendezvous Coefficient Mean of each layer network of calculating, Node degree average, node diagnostic vector center average, draw respectively node rendezvous Coefficient Mean, the section for including flow pattern information The graph of a relation of point degree average, 3 indexs of node diagnostic vector center average and change resolution, develops to study in flow pattern During, the change 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 Wavelet Multiresolution Decomposition converging network that Gou Jian be under different resolution and small echo bilayer complex network, respectively under different resolution Complex network indices are calculated, drafting includes the different complex network index 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 the reality of the present invention One of mode is applied, it is any to design and the technical scheme without creative in the case of without departing from the invention objective Similar structure or embodiment, belong to protection scope of the present invention.

Claims (5)

1. a kind of two phase flow Multi-source Information Fusion method based on Wavelet Multiresolution Decomposition double-layer network, it is characterised in that including as follows Step：

1) circulation by being made up of cycle motivation multi-electrode conductivity sensor and cycle motivation multielectrode capacitance sensor is swashed Encourage the multichannel time series that dual-modality sensor obtains and build wavelet coefficient complex network respectively；

2) Wavelet Multiresolution Decomposition converging network is built, and draws the figure energy for including flow pattern information respectively and intersect convergence factor The graph of a relation of entropy and resolution ratio；

3) small echo bilayer complex network is built, and draws the node rendezvous coefficient in the every layer network for including flow pattern information respectively Average, the average of the average of node degree and node diagnostic vector center and resolution ratio graph of a relation.

2. the two phase flow Multi-source Information Fusion method based on Wavelet Multiresolution Decomposition double-layer network according to claim 1, it is special Levy and be, step 1) include：

(1) the S groups for including two phase flow fluid local flow information obtained by cycle motivation dual-modality sensor are obtained long Degree is the multichannel time series of L

(2) for the time series of each passage, the wavelet transformation of 6 resolution ratio is carried out, each resolution ratio obtains 1 low frequency Coefficient subband is approximation coefficient and 1 high frequency coefficient subband i.e. detail coefficients, and 6 low frequency coefficient subbands and 6 high frequencies are obtained Coefficient subband, seeks maximum x of each low-frequency band and each high frequency band₁, minimum of a value x₂, mean value x₃, standard deviation x₄、 Steepness function x₅With kurtosis function x₆；So for each resolution ratio, 12 eigenvalue clusters of low-frequency band and high frequency band into One characteristic vector

(3) for multichannel time seriesIn each resolution ratio, every two channel times sequence is calculated Euclidean distance d=of the characteristic vector of row | | X (m)-X (n) | | m=1,2 ..., S n=1,2 ..., S, wherein, | | | | represent Euclidean distance is calculated, X (m) represents the characteristic vector of passage m, the characteristic vector of X (n) passage n；Using the signal of each passage as The node of complex network, with the company side that two interchannel Euclidean distances determine two nodes in complex network；Calculate it is all it is European away from From average M_nAnd standard deviation, then threshold value R=M is obtained_n+ q σ, wherein q=0.15；If the Euclidean of two passages away from From more than the threshold value, then side is not connected between two nodes, conversely, then there is even side between two nodes；Thus, under different resolution Wavelet coefficient complex network is built respectively；So, for a multichannel time series, 6 wavelet coefficient complexity are obtained altogether Network.

3. the two phase flow Multi-source Information Fusion method based on Wavelet Multiresolution Decomposition double-layer network according to claim 1, it is special Levy and be, step 2) include：

(1) respectively obtained by cycle motivation multi-electrode conductivity sensor and cycle motivation multielectrode capacitance sensor and led to more one Road time series, for each multichannel time series obtains a wavelet coefficient complex network in each resolution ratio respectively；

(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 total company side of individual wavelet coefficient complex network retains, and interstitial content is constant, so as to constitute a small echo under each resolution ratio Multiresolution converging network and its corresponding adjacency matrix A；

(3) to described Wavelet Multiresolution Decomposition converging network, figure energy indexes E (A) is set：Wherein, λ_iTable Show the characteristic value of wavelet coefficient complex network adjacency matrix A new under each resolution ratio, what n was represented is the number of characteristic value； Setting convergence factor entropy index E_C：

$C\left(v\right)=\frac{2T_v}{k_v(k_v-1)}$

$E_C=-\underset{v=1}{\overset{N}{\Σ}}\[C\left(v\right)/\underset{v=1}{\overset{N}{\Σ}}C\left(v\right)\]log\[C\left(v\right)/\underset{v=1}{\overset{N}{\Σ}}C\left(v\right)\]$

Wherein, T_vRepresent be include in a new wavelet coefficient complex network node v closing triangle number, k_v What is represented is the degree of new wavelet coefficient Node Contraction in Complex Networks v, and what C (v) was represented is the convergence factor of node v；

(4) the figure energy for including flow pattern information and the graph of a relation for intersecting convergence factor entropy and resolution ratio are drawn respectively, by grinding Transformation of the two indexs in flow pattern evolutionary process is studied carefully, to recognize complex flow structure, in indicating flow pattern evolutionary process Internal motivation mechanism.

4. the two phase flow Multi-source Information Fusion method based on Wavelet Multiresolution Decomposition double-layer network according to claim 1, it is special Levy and be, step 3) include：

(1) for step 1) two wavelet coefficient complex networks of each resolution ratio for building, respectively as the one of small echo bilayer complex network Layer, so as to obtain small echo bilayer complex network, the adjacency matrix of the small echo bilayer complex network is expressed as Wherein, α represents the number of plies of double-deck wavelet coefficient complex network, corresponding in adjacency matrix if node i and j have connection in α layers ElementOtherwiseInterstitial 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 network_i ^[α]：

$C_i^{\[\mathrm{\α}\]}=\frac{{\Σ}_{\mathrm{\α}}{\Σ}_{{\mathrm{\α}}^{\′}\≠\mathrm{\α}}{\Σ}_{j\≠i,m\≠i}\left(a_{ij}^{\[\mathrm{\α}\]}{a}_{jm}^{\[{\mathrm{\α}}^{\′}\]}{a}_{mi}^{\[\mathrm{\α}\]}\right)}{{\Σ}_{\mathrm{\α}}{\Σ}_{j\≠i,m\≠i}\left(a_{ij}^{\[\mathrm{\α}\]}{a}_{mi}^{\[\mathrm{\α}\]}\right)}$

Wherein, α ' represents another layer that α layers are different from small echo bilayer complex network,WithRepresent respectively 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 layer interior joints The element value of 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 Average C_α：

(3) degree of the arbitrary node i in the arbitrary α layers in small echo bilayer complex network is calculated Wherein,Represent the element value of α layer interior joint i and j in adjacency matrix；The degree of each node in each layer is obtained, per layer is calculated Average k of interior joint degree_α：

(4) eigenvector centrality of arbitrary α layers interior joint i of small echo bilayer complex networkRepresent the adjacency matrix A of α layers^[α]The corresponding characteristic vector of dominant eigenvalue i-th element value；Obtain the eigenvector centrality of each node in each layer Property value, calculate every layer of interior joint eigenvector centrality average E_α：

(5) average C of the node rendezvous coefficient in the every layer network for including flow pattern information is drawn respectively_α, node degree average k_α With average E of node diagnostic vector center_αWith the graph of a relation of resolution ratio；Cycle motivation multi-electrode conductivity sensor is low to local Oil content measures sensitive height, and cycle motivation multielectrode capacitance sensor is high to the high oil content measurement sensitivity in local, conduction capacity The effective integration of multi-source metrical information realizes complementary two phase flow spatial flow infomation detection, and by research flow pattern information is included Average C of the node rendezvous coefficient in every layer network per layer network_α, node degree average k_αWith node diagnostic vector center Average E_αWith the graph of a relation of resolution ratio, average C of the node rendezvous coefficient in per layer of every layer network is studied_α, node degree it is equal Value k_αWith average E of node diagnostic vector center_αThe transformation of three indexs in flow pattern evolutionary process, realizes believing bimodal The fusion of breath, indicates the internal motivation mechanism in flow pattern evolutionary process.

5. the two phase flow Multi-source Information Fusion method based on Wavelet Multiresolution Decomposition double-layer network described in a kind of claim 1 should With, it is characterised in that using what is be made up 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 proportioning of fixed oil phase and water phase, changes oil phase and water The flow of phase is tested；Experimentation comprises the steps：

1) proportioning of fixed water phase and oil phase, toward vertical ascent pipeline a certain amount of water is passed through, then gradually logical in pipeline Enter oil phase, after water-oil phase fully merges and gradually stablizes, passed using cycle motivation conductivity sensor and cycle motivation electric capacity Sensor measures respectively multichannel signal, and records flow pattern with high-speed camera instrument；

2) after once collection terminates, the flow of oil phase and water phase is changed, by step 1) process continues collection, until matching somebody with somebody in fixed All complete than lower designed operating mode；

3) change the proportioning of oil phase and water phase, repeat step 1 again) to step 2) process complete the measurement of this wheel, until setting All operating modes of meter are all measured；

4) based on the multi-channel measurement signal for obtaining, for cycle motivation multi-electrode conductivity sensor and cycle motivation multi-electrode electricity Hold the wavelet coefficient complex network that sensor is set up respectively under different resolution, for two-tier network obtains a wavelet multiresolution Rate converging network；

5) figure energy and convergence factor entropy are calculated, different operating mode figure below energy, the aggregation for including flow pattern information is drawn respectively Coefficient entropy portrays the internal motivation mechanism in the evolutionary process of different flow patterns with the graph of a relation of change resolution；

6) for two-tier network builds small echo bilayer complex network, node rendezvous Coefficient Mean, the node of each layer network are calculated Degree average, node diagnostic vector center average, draw respectively node rendezvous Coefficient Mean, the node degree for including flow pattern information The graph of a relation of average, 3 indexs of node diagnostic vector center average and change resolution, to study in flow pattern evolutionary process In, the change of complex flow structure and internal motivation mechanism.