CN113188055A - Pipeline leakage self-adaptive dynamic compensation positioning method based on negative pressure wave attenuation driving - Google Patents

Pipeline leakage self-adaptive dynamic compensation positioning method based on negative pressure wave attenuation driving Download PDF

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CN113188055A
CN113188055A CN202110600157.8A CN202110600157A CN113188055A CN 113188055 A CN113188055 A CN 113188055A CN 202110600157 A CN202110600157 A CN 202110600157A CN 113188055 A CN113188055 A CN 113188055A
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pressure
point
negative pressure
leakage
attenuation
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胡旭光
马大中
宋秦风
孟冠军
张化光
刘金海
王伟亮
柳辉
汪刚
冯健
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Northeastern University China
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
    • F17D—PIPE-LINE SYSTEMS; PIPE-LINES
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Abstract

The invention provides a negative pressure wave attenuation drive-based pipeline leakage self-adaptive dynamic compensation positioning method, and relates to the technical field of pipeline detection. The invention can effectively analyze a large amount of pressure attenuation data acquired on site in a reasonable time, and combines experiments and fitting data to form a dynamic pressure attenuation model. Establishing a corresponding objective function around two indexes of the signal edge condition and the predicted leakage rate of the attenuation model to form a multi-objective optimization mathematical model, solving the model by adopting a multi-objective optimization method, and finding out an optimal characteristic point in a characteristic interval, wherein the method is visual and simple, and has high sensitivity and low rate of missing report; and the wavelet change of the pressure data with different characteristics at two ends is realized by adopting different scale factors, so that the stability of the result is improved. Therefore, the invention adopts a multi-objective optimization strategy and a self-adaptive dynamic compensation method to solve the leakage detection and positioning problems of the state of excessive attenuation of the negative pressure wave in the pipeline, and can simultaneously achieve the aims of high precision and high accuracy.

Description

Pipeline leakage self-adaptive dynamic compensation positioning method based on negative pressure wave attenuation driving
Technical Field
The invention relates to the technical field of pipeline detection, in particular to a pipeline leakage self-adaptive dynamic compensation positioning method based on negative pressure wave attenuation driving.
Background
With the rapid development of economy and society, petroleum pipeline transportation is more and more widely applied to the life service industry and industrial production of people. In view of the internationally specified inherent life of pipelines, oil pipeline leakage events due to equipment aging and damage from natural and man-made factors occur occasionally; the social impact and economic loss caused by the leakage of the petroleum pipeline are huge, and even further casualties can be caused. Therefore, the fluid conveying pipeline is monitored in time, corresponding emergency measures are taken, the leakage accidents are prevented from being further expanded, and the method has important economic significance and social benefits.
The detection method of the fluid conveying pipeline is various, and the most suitable detection method for the petroleum pipeline network in China at present is a negative pressure wave detection method from the aspects of economy, safety, detection effect and the like, and meanwhile, the negative pressure wave detection method is the most applied method in China at present. At present, other information detection methods such as flow detection and acoustic wave detection are used as auxiliary means for negative pressure wave detection. Therefore, more researchers are focusing on how to continuously improve the system performance of negative pressure wave detection.
From the application of the current negative pressure wave detection method, for a short pipeline, the pressure of monitoring stations at two ends of the pipeline can generate obvious sudden drop when the pipeline leaks, and the detection and positioning effects are good. However, for a long-distance pipeline, the attenuation generated in the transmission process of the negative pressure wave is large, the pressure drop at one end which is far away from the leakage point or opposite to the positive transmission direction of the negative pressure wave may be in a slow descending trend, and an abrupt characteristic point is difficult to find in a slow descending curve, so that the positioning effect is poor, and the phenomenon of inaccurate positioning often exists. Besides the long distance of the pipeline, the phenomenon of large attenuation in the transmission process of the negative pressure wave can be caused by large running flow of the pipeline, so that the positioning precision of the leakage detection is greatly reduced.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a pipeline leakage self-adaptive dynamic compensation positioning method based on negative pressure wave attenuation driving aiming at the defects of the prior art, and the positioning precision and the detection effect can be greatly improved.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a self-adaptive dynamic compensation positioning method for pipeline leakage based on negative pressure wave attenuation driving comprises the following steps:
step 1: establishing a dynamic negative pressure wave attenuation model;
analyzing the dynamic process of the fluid in the pipeline, solving a pipe flow equation by using a characteristic line solution method, and establishing a dynamic negative pressure wave attenuation model by combining a negative pressure wave attenuation rule observed in an experiment; performing data fitting on the high point and the pressure difference by using field experimental data to form a fitting formula, and fusing the fitting formula into the established dynamic negative pressure wave attenuation model; the pipe flow equations include continuous equations and equations of motion of the fluid;
step 2: extracting pressure characteristics at two ends of a pipeline, and setting a characteristic interval of a signal;
firstly, extracting pressure characteristic data at two ends of a leakage time period; secondly, selecting a proper wavelet scale, performing denoising and filtering processing on the pressure data by using a wavelet analysis method, searching an extreme point of wavelet transformation, and determining the extreme point as an edge point of a deterministic signal; finally, setting a characteristic interval around the extreme point; the deterministic signal is a deterministic signal of pressure data, and the pressure data consists of the deterministic signal and a noise signal;
and step 3: compensating the attenuation of the negative pressure wave, and searching an optimal characteristic point in the characteristic interval;
firstly, determining a characteristic reference point and a characteristic interval, judging whether a pressure signal in the interval meets a compensation condition, if not, indicating that the characteristic of the signal in the interval obviously does not need to be compensated, directly returning the reference point as a leakage characteristic point, and if so, compensating the attenuation of the negative pressure wave by taking the characteristic point as a center in the characteristic interval; secondly, establishing a corresponding objective function around two indexes of signal edge conditions and predicted leakage rates to form a multi-objective optimization mathematical model, solving the multi-objective optimization mathematical model by adopting a multi-objective particle swarm optimization method, and finding out an optimal characteristic point in a characteristic interval; judging whether the optimal point meets set index conditions or not, if not, substituting the optimal point as a new reference point for calculation of the multi-objective optimization model again, and if so, returning the optimal point as a leakage characteristic point;
and 4, step 4: and performing effect evaluation based on the obtained positioning result, wherein the evaluated effect comprises a negative pressure wave compensation and restoration effect and a positioning precision effect.
Further, the step 1 comprises:
step 1.1, analyzing the dynamic process of fluid in a pipeline and establishing a pipe flow equation; the pipe flow equation comprises two partial differential equations of a continuous equation and a motion equation of fluid to form a group of hyperbolic partial differential equations;
step 1.2, solving the hyperbolic partial differential equation set by using a characteristic line solution method and a finite difference method, and combining the boundary condition of the leakage point and the definition of the leakage rate to obtain a negative pressure wave expression at the leakage point as follows:
Figure BDA0003092450110000021
wherein, Δ plFor pressure changes at the leak, n is the leak rate, vpThe wave velocity of the negative pressure wave, v is the average flow velocity of the section, and rho is the average density of the fluid;
step 1.3, through multiple leakage experiment observation and analysis, the assigned attenuation generated at the leakage point is similar to exponential attenuation, the attenuation exponent and the length of the pipeline are in a direct proportion relation, a dynamic coefficient is fused into a traditional negative pressure wave attenuation model to form a dynamic attenuation factor, namely, the expression of the negative pressure wave pressure at the two ends of the pipeline is as follows:
Figure BDA0003092450110000022
Figure BDA0003092450110000023
the expression is the established dynamic negative pressure wave attenuation model, wherein psIs the pressure value of negative pressure wave at the inlet of the pipeline, peThe pressure value of the negative pressure wave at the outlet of the pipeline is adopted, x is the distance between a leakage point and the inlet of the pipeline, l is the length of the pipeline, beta is an attenuation factor of the negative pressure wave, k is a dynamic pipeline proportionality coefficient, and the expression of k is as follows:
Figure BDA0003092450110000031
wherein lthIs a pipeline length threshold value, and the attenuation factor exceeding the threshold value is a dynamic attenuation variable;
step 1.4, performing oil drainage tests with the same leakage flow for multiple times by adjusting different running flows to obtain changed pressure difference data; and fitting the obtained pressure difference data to obtain an attenuation formula of pressure attenuation caused by a high point of the pipeline, wherein the attenuation formula is as follows:
Δp＝f(x,v,h,xh)
wherein, Δ p is the pressure value of the negative pressure wave, h is the highest altitude of the pipe section, and xhIs a one-dimensional vector of the altitude of the pipeline;
and (3) integrating an attenuation formula into the established dynamic negative pressure wave attenuation model:
Figure BDA0003092450110000032
Figure BDA0003092450110000033
further, the step 2 comprises:
step 2.1, discrete pressure queues at two ends of the pipeline meeting the leakage condition are extracted, wherein the pressure queue of the monitoring station with the starting end close to the leakage point is x1(T) the pressure queue of the monitoring station at the terminating end, i.e. farther from the leaking end, is x2(T); using three-spline interpolation method to make signal hold and generate continuous pressure signal x1(t) and x2(t); wherein T is a pressure signal sampling period; the leakage condition comprises two types of conditions, namely a pressure integral reduction condition and a local sudden reduction condition; the starting end needs to satisfy two types of conditions simultaneously, and the terminating end only needs to satisfy the overall pressure reduction condition, so that the pressure signals at the two ends can be considered to satisfy the leakage condition;
step 2.2, denoising the pressure data by using a wavelet transformation method, which comprises the following specific steps:
step 2.2.1, the pressure signal is formed by overlapping a deterministic signal and noise, and data after wavelet change is the sum of wavelet transformation of two parts of signals; under large scale, the edge of a deterministic signal, namely the inflection point of a negative pressure wave can be accurately extracted; let Ψ (t) be a gaussian low-pass function,
Figure BDA0003092450110000034
the extreme point solution of wavelet transform is:
Figure BDA0003092450110000035
wherein, a is a scale factor, b is a displacement parameter,
Figure BDA0003092450110000036
is the scale-wise scaling of the basic wavelet Ψ (t); wavelet transform scaling by Ψ (t) in scaleAnd a shift in the time domain to analyze the signal;
step 2.2.2, according to the leakage condition, under the condition that the pressure signal of the starting end meets the overall descending condition and the local sudden descending condition and the pressure overall descending condition is only met by the terminating end; selecting wavelet change scale factors of signals at two ends differently, wherein the selection of the scale factors is smaller than that of a terminating end in order to ensure the effectiveness of local sudden drop signals at the starting end;
step 2.3, solving to obtain an extreme point x (t)i) Setting a leakage interval
Figure BDA0003092450110000037
Wherein l is the length of the pipeline, vpIs the wave velocity, t, of the negative pressure waveiThe time corresponding to the extreme point.
Further, the step 3 comprises:
step 3.1, setting a negative pressure wave compensation condition, and judging whether the pressure signal meets the compensation condition; if not, skipping the step 3 and the step 4; if yes, maintaining the initial end pressure signal, and comparing the final end pressure signal x2(t) performing negative pressure wave attenuation compensation; the negative pressure wave compensation condition is that the pressure signal at the termination end only meets the integral pressure drop condition and does not meet the local sudden drop condition;
step 3.2, setting initial set characteristic datum point as step 2 to solve x2(t) derived extreme point x (t)i) Characteristic interval
Figure BDA0003092450110000041
Step 3.3, setting the time length of the pressure signal processing queue to be
Figure BDA0003092450110000042
Taking the characteristic reference point t as a midpoint and extracting the time length of
Figure BDA0003092450110000043
Discrete pressure queue of
Figure BDA0003092450110000044
Figure BDA0003092450110000045
Wherein l2vp ═ nT;
generation of pressure differential queue [ delta p ] using extracted discrete pressure queue1、……Δpm-2、Δpm-1}，m∈[2,n]The queue length is m-1 and the total pressure difference is
Figure BDA0003092450110000046
Wherein any Δ piAre all more than 0, i is 1, 2, …, m-2, m-1; proportional weighting of differential pressure queue and total pressure difference
Figure BDA0003092450110000047
Generating a queue of weight coefficients { λ }1、……λm-2、λm-1}；
And 3.4, substituting the characteristic point of the starting end and the characteristic point of the ending end into a positioning distance formula to obtain the distance d (t) between the leakage point and the starting end, and substituting into a negative pressure wave attenuation formula:
ΔP＝ΔPde-β(l-d(t))
wherein, Δ PdIs the pressure difference at the leak point; the attenuation pressure of the negative pressure wave is delta Ps＝ΔPd-ΔP＝(eβ(l-d(t))-1)ΔP；
Attenuation compensation is carried out on the pressure difference queue according to the weight coefficient and the negative pressure wave attenuation pressure, and delta pi′＝Δpi+λiΔPsForming a new pressure difference queue [ delta p'1、Δp′2……、Δp′m-1}; the compensated pressure difference queue is combined with the original discrete pressure queue,
Figure BDA0003092450110000048
forming a new discrete pressure queue { X '(T-l 4vp, X' T-l4vp + T, … … X 'T-l 4vp + n-2T, X' T + l4vp };
performing signal holding processing on the compensated pressure queue by using a three-spline interpolation method to form a compensated continuous pressure signal xs(t)；
3.5, establishing a mathematical model of multi-objective constraint optimization; setting a corresponding objective function around two indexes of the edge condition and the predicted leakage rate as follows:
Figure BDA0003092450110000049
wherein the content of the first and second substances,
Figure BDA00030924501100000410
is a pressure signal xs(t) obtaining a derivative function after wavelet transformation, wherein the absolute value of the derivative represents the rapid degree of the independent variable changing along with the dependent variable at the current point, and the smaller the absolute value is, the more gradual the change of the function value is; n is1(t) is the initial end pressure difference Δ PsSubstituting the leakage rate n obtained by the dynamic negative pressure wave attenuation model2(t) the leakage rate is obtained by substituting the pressure difference delta P of the terminating end into the dynamic negative pressure wave attenuation model, wherein the pressure difference of the starting end is a constant, and the pressure difference of the terminating end is a dependent variable of t; if the leakage rate obtained by substituting the pressure difference between the two ends into the dynamic negative pressure wave attenuation model is gradually similar, the characteristic points gradually approach to the real characteristic points; n is1(t) and n2The formula for calculation of (t) is:
Figure BDA0003092450110000051
step 3.6, setting constraint conditions of multi-objective constraint optimization according to the pipeline operation parameter standard, as follows:
Figure BDA0003092450110000052
wherein the first constraint is a differential pressure constraint, Δ PCThe interference compensation of the pressure difference at the termination end is related to the adjustment value of the working condition equipment; the second constraint is the moveout constraint, where tsSetting the inflection point time of the initial end according to the compensation condition, setting the end characteristic when the leakage point is located in the interval from the initial section to the middle point of the pipelineThe point time variable should be greater than the inflection point time of the starting end plus one quarter of the pipeline time; forming a complete multi-target constraint optimization mathematical model as shown in the following formula;
Figure BDA0003092450110000053
3.7, selecting a multi-target particle swarm algorithm to solve the multi-target constraint optimization mathematical model; setting an initial parameter, iteration times and a termination condition for optimizing, and solving an optimal solution set;
step 3.7.1, considering the characteristics of multi-objective solution, the solution set is an optimal non-dominant front surface, namely a pareto solution set; the multi-target constraint optimization mathematical model has two targets, and the fitness value of the particles in the pareto solution set is determined by two corresponding target values and the maximum and minimum target values; because the target function and the compensation condition have partial coupling, the weight coefficient of the target function for fitness calculation is set according to the coupling degree;
3.7.2, according to the size of the fitness value in the pareto solution set, setting the maximum dynamic fitness value as the current global optimal solution; dynamically changing the weight coefficient and guiding the iterative process of the particles by taking the global optimal solution as a guide, circularly executing the step 3.7.1 process, and continuously updating the pareto solution set until the highest iterative times are reached; after the iteration is finished, the current global optimal solution is the optimal solution obtained by solving in the particle swarm optimization.
Further, the step 4 performs effect evaluation according to the final recognition result, which performs effect evaluation according to the following evaluation criteria:
evaluation criterion 1: the positioning precision is as follows:
Figure BDA0003092450110000061
wherein, Δ d is the difference between the positioning distance and the actual distance, that is, after the multi-objective function finds the optimal feature point, the distance d (t) between the leakage point corresponding to the feature point and the starting end is the difference between the actual leakage point and the starting end;
evaluation criterion 2: whether the pressure signal meets the local sudden drop condition after the negative pressure wave attenuation compensation.
Adopt the produced beneficial effect of above-mentioned technical scheme to lie in: compared with the prior art, the pipeline leakage self-adaptive dynamic compensation positioning method based on the negative pressure wave attenuation driving provided by the invention has the following advantages and effects:
(1) different from the relation research of a general negative pressure wave attenuation model and the leakage rate, the invention firstly provides the idea of compensating the negative pressure wave attenuation to highlight the edge characteristic of a pressure signal so as to improve the positioning accuracy.
(2) The actual engineering problem is converted into an optimized mathematical problem around two important indexes of the edge condition and the leakage rate of the pressure signal, an objective function is established around the two indexes, a mathematical model of multi-objective constraint optimization is constructed, and the positioning accuracy of the traditional method can be improved through model solution.
(3) The method is different from the traditional negative pressure wave attenuation model, the influence of the elevation on the negative pressure wave attenuation is considered for the first time, experimental data is used for data fitting, the elevation influence is integrated into the traditional attenuation model, and the anti-interference capability of the model on the elevation factor is improved.
(4) The method has practical application value by considering the practical application condition of the current negative pressure wave leakage detection, and a certain application result is obtained at present.
Drawings
Fig. 1 is a flowchart of a pipeline leakage adaptive dynamic compensation positioning method based on negative pressure wave attenuation driving according to an embodiment of the present invention;
FIG. 2 is a front view of a small unit of a pipeline provided in an embodiment of the present invention;
fig. 3 is a head-to-end station pressure curve corresponding to a larger negative pressure wave attenuation provided by an embodiment of the present invention; wherein (a) is an upstream station and (b) is a downstream station;
FIG. 4 is a high-point diagram of a pipe section at a location according to an embodiment of the present invention;
FIG. 5 is a flowchart of a multi-objective particle swarm optimization method provided by an embodiment of the present invention;
FIG. 6 is a distribution diagram of pareto solution sets provided by an embodiment of the present invention;
fig. 7 is a comparison diagram before and after compensation of attenuation of negative pressure wave according to the embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
Aiming at the defects in the prior art, the method is different from the relation research of a general negative pressure wave attenuation model and the leakage rate, and firstly proposes the idea of performing compensation on the negative pressure wave attenuation to highlight the edge characteristic of the pressure signal so as to improve the positioning accuracy. Firstly, establishing an attenuation model of pipeline negative pressure waves by using a fluid equation, determining an attenuation coefficient of the attenuation model by an experimental method, and integrating high-point information on negative pressure wave attenuation into a traditional negative pressure wave attenuation model by fitting experimental data; secondly, performing wavelet transformation processing on pressure signals at two ends of the pipeline which meet the leakage condition, extracting extreme points which meet the edge condition of the signals, and establishing a characteristic interval; and finally, establishing a corresponding objective function around two important indexes, namely the edge condition and the predicted leakage rate to form a multi-objective optimization mathematical model, solving the model by adopting a multi-objective particle swarm optimization method, finding out the optimal characteristic point in a characteristic interval, and achieving the effect of improving the positioning precision.
Based on the above principle, the present embodiment provides a pipeline leakage adaptive dynamic compensation positioning method based on negative pressure wave attenuation driving, as shown in fig. 1, and the specific method is as follows.
Step 1, establishing a negative pressure wave attenuation model.
Analyzing the dynamic process of the fluid in the pipeline, solving a pipe flow equation by using a characteristic line solution method, and establishing a dynamic negative pressure wave attenuation model by combining the negative pressure wave attenuation rule observed in an experiment. And performing data fitting on the high point and the pressure difference by using field experimental data to form a fitting formula, and fusing the fitting formula into the established dynamic negative pressure wave attenuation model. The pipe flow equations include continuous equations and equations of motion of the fluid. The method specifically comprises the following steps:
step 1.1, analyzing the dynamic process of fluid in a pipeline and establishing a pipe flow equation; the pipe flow equation comprises two partial differential equations of a continuous equation and a motion equation of fluid to form a group of hyperbolic partial differential equations.
As shown in fig. 2, is a diagram of a minute unit separated from a pipe. The length of the micro unit is x, the cross section area is A, the average pressure of the cross section is p, v is the average flow velocity of the cross section, rho is the average density of the fluid, D is the inner diameter of the pipeline, the included angle formed by the central line of the pipeline and the horizontal line is alpha, the flowing direction of the liquid is the positive direction, when the medium moves to the ground, the included angle is a negative value, otherwise, the included angle is a positive value, and g is the gravity acceleration.
Applying Newton's law to the control body of the isolation unit, i.e. taking pA as the pressure of the upstream liquid to the control body and pA as the pressure of the downstream liquid to the control body
Figure BDA0003092450110000071
The component force of the gravity of the comprehensive infinitesimal control body in the motion direction, the pressure acting on the annular side surface and the frictional resistance caused by the inner wall of the pipeline are tau pi Dx, and the resultant force borne by the control body is tau pi Dx
Figure BDA0003092450110000072
Wherein, F ═ ma ═ ρ Adx · dV/dt, τ ═ ρ kv | v |/8, a ═ pi D2And k is a hydraulic friction coefficient, and is substituted into a pressure formula of downstream liquid to the control body to obtain a motion equation
Figure BDA0003092450110000081
In a stable state of the fluid, the mass of the medium entering the inlet of the control body in unit time t is pAvdt, and the mass of the medium flowing out of the outlet of the control body is pAvdt
Figure BDA0003092450110000082
Variation of control body in unit timeA rate of
Figure BDA0003092450110000083
And applying the mass conservation law and carrying out sorting calculation to obtain a continuous equation of the fluid.
And step 1.2, solving the group of hyperbolic partial differential equations by using a characteristic line solution method and a finite difference method.
The characteristic line and the characteristic equation are as follows:
Figure BDA0003092450110000084
Figure BDA0003092450110000085
the inverse characteristic line and its characteristic equation are:
Figure BDA0003092450110000086
Figure BDA0003092450110000087
solving a characteristic equation by using a finite difference method, wherein the finite difference equation of the node i at the time j along the characteristic line is as follows under the condition of neglecting elevation change:
Figure BDA0003092450110000088
Figure BDA0003092450110000089
wherein, the pipeline is divided into N sections which are respectively marked as 1, 2, 1.,. i-1, i +1, N; each section of pipe is delta l in length and is dispersed into a plurality of time sections 0, delta t, · (j-1) delta t and j delta t along a time axis. p is a radical ofi,jIndicating the pressure at node i at time j, vi,jRepresenting velocity, v, of the inode at time jpThe wave velocity of the negative pressure wave.
In combination with the boundary conditions of the leak point, the following can be obtained:
Δpi,j＝pi,j-pi,j-1＝-ρvpΔvi
wherein, Δ viThe speed variation before and after leakage of the leakage point.
In combination with the definition of the leak rate, a negative pressure wave expression at the leak point can be obtained:
Figure BDA00030924501100000810
wherein Δ plFor the pressure change at the leak, n is the leak rate, which is defined as (v)i-,j-vi+,j)/vi-,j，vpThe wave velocity of the negative pressure wave, v is the average flow velocity of the cross section, and ρ is the average density of the fluid.
Step 1.3, through multiple leakage experiment observation and analysis, the assigned attenuation generated at the leakage point is similar to exponential attenuation, the attenuation exponent and the length of the pipeline are in a direct proportion relation, a dynamic coefficient is fused into a traditional negative pressure wave attenuation model to form a dynamic attenuation factor, namely, the expression of the negative pressure wave pressure at the two ends of the pipeline is as follows:
Figure BDA0003092450110000091
the expression is the established dynamic negative pressure wave attenuation model, wherein psIs the pressure value of negative pressure wave at the inlet of the pipeline, peThe pressure value of the negative pressure wave at the outlet of the pipeline is adopted, x is the distance between a leakage point and the inlet of the pipeline, l is the length of the pipeline, beta is an attenuation factor of the negative pressure wave, k is a dynamic pipeline proportionality coefficient, and the expression of k is as follows:
Figure BDA0003092450110000092
wherein lthIs a pipeline length threshold, and the attenuation factor exceeding the threshold is the dynamic attenuation variable.
In a specific implementation, the attenuation coefficient is determined experimentally. As shown in fig. 3, the pressure curve of the two ends of the pipeline after oil leakage and oil discharge is simulated by the in-station regulating download valve in the south part of a long distance pipeline. It can be seen from the curve that the station negative pressure wave far from the simulated leakage point has larger pressure attenuation.
Step 1.4, performing oil drainage tests with the same leakage flow for multiple times by adjusting different running flows to obtain changed pressure difference data; and fitting the obtained pressure difference data to obtain an attenuation formula of pressure attenuation caused by a high point of the pipeline, wherein the attenuation formula is as follows:
Δp＝f(x,v,h,xh)
wherein, Δ p is the pressure value of the negative pressure wave, h is the highest altitude of the pipe section, and xhIs a one-dimensional vector of the altitude of the pipeline.
As shown in FIG. 4, a pipeline high point diagram of a long-distance pipeline in the south is shown, wherein a high point is marked on the diagram, and the distance one-dimensional vector of the high point is a one-dimensional vector x of the altitude of the pipelinehSet to { xh1,xh2,......,xhn}。
And (3) integrating an attenuation formula into the established dynamic negative pressure wave attenuation model:
Figure BDA0003092450110000093
Figure BDA0003092450110000094
step 2: and extracting pressure characteristics of two ends of the pipeline, and setting a characteristic interval of the signal. As shown in fig. 1, pressure characteristic data at both ends of a leakage period is extracted first; secondly, selecting a proper wavelet scale, performing denoising and filtering processing on the pressure data by using a wavelet analysis method, searching an extreme point of wavelet transformation, and determining the extreme point as an edge point of a deterministic signal; finally, setting a characteristic interval around the extreme point; the deterministic signal is a deterministic signal of pressure data, which consists of a deterministic signal and a noise signal. The method specifically comprises the following steps:
step 2.1, discrete pressure queues at two ends of the pipeline meeting the leakage condition are extracted, wherein the pressure queue of the monitoring station with the starting end close to the leakage point is x1(T) the pressure queue of the monitoring station at the terminating end, i.e. farther from the leaking end, is x2(T). Using three-spline interpolation method to make signal hold and generate continuous pressure signal x1(t) and x2(t); where T is the pressure signal sampling period. The leakage condition comprises two types of conditions, namely a pressure integral reduction condition and a local sudden reduction condition; the starting end needs to satisfy two types of conditions simultaneously, and the terminating end only needs to satisfy the overall pressure reduction condition, so that the pressure signals at the two ends can be considered to satisfy the leakage condition.
Step 2.2, denoising the pressure data by using a wavelet transformation method, which comprises the following specific steps:
step 2.2.1, the pressure signal is formed by overlapping a deterministic signal and noise, and data after wavelet change is the sum of wavelet transformation of two parts of signals; wherein the extreme value of the wavelet transform corresponding to the deterministic signal edge increases or slowly decays with increasing scale; the extreme values of the wavelet transform of the noise signal decay rapidly with increasing scale. Under large scale, the extreme point of wavelet transform of the pressure signal mainly belongs to the edge of the deterministic signal, so that the edge of the deterministic signal, namely the inflection point of the negative pressure wave, can be accurately extracted; let Ψ (t) be a gaussian low-pass function,
Figure BDA0003092450110000101
the extreme point solution of wavelet transform is:
Figure BDA0003092450110000102
wherein, a is a scale factor, b is a displacement parameter,
Figure BDA0003092450110000103
is the scale-wise scaling of the basic wavelet Ψ (t); the wavelet transform analyzes the signal by scale-wise scaling and time-domain shifting of Ψ (t).
Step 2.2.2, according to the leakage condition, under the condition that the pressure signal of the starting end meets the overall descending condition and the local sudden descending condition and the pressure overall descending condition is only met by the terminating end; the wavelet change scale factors of signals at two ends are selected differently, in order to guarantee the effectiveness of local sudden drop signals at the starting end, the scale factors are selected to be smaller than those at the terminating end, and particularly, the size of the small scale factor is determined according to the specific conditions of a pipe section all the time.
Step 2.3, solving to obtain an extreme point x (t)i) Setting a leakage interval
Figure BDA0003092450110000104
Wherein l is the length of the pipeline, vpIs the wave velocity, t, of the negative pressure waveiThe time corresponding to the extreme point.
And step 3: and compensating the attenuation of the negative pressure wave, and searching for an optimal characteristic point in the characteristic interval. As shown in fig. 1, firstly, determining a characteristic reference point and a characteristic interval, and determining whether the pressure signal in the interval meets a compensation condition, if not, it indicates that the signal characteristic of the segment obviously does not need compensation, and directly returning the reference point as a leakage characteristic point, if so, compensating the attenuation of the negative pressure wave by taking the characteristic reference point as a center in the characteristic interval; secondly, establishing a corresponding objective function around two indexes of signal edge conditions and predicted leakage rates to form a multi-objective optimization mathematical model, solving the multi-objective optimization mathematical model by adopting a multi-objective particle swarm optimization method, and finding out an optimal characteristic point in a characteristic interval; and judging whether the optimal point meets set index conditions or not, if not, substituting the optimal point as a new reference point for calculation of the multi-objective optimization model, and if so, returning the optimal point as a leakage characteristic point. The method specifically comprises the following steps:
step 3.1, setting a negative pressure wave compensation condition, and judging whether the pressure signal meets the compensation condition; if not, skipping the step 3 and the step 4; if yes, maintaining the pressure signal of the starting end and the pressure of the ending endForce signal x2And (t) performing negative pressure wave attenuation compensation. The negative pressure wave compensation condition is that the pressure signal at the termination end only meets the overall pressure drop condition and does not meet the local sudden drop condition.
Step 3.2, setting initial set characteristic datum point as step 2 to solve x2(t) derived extreme point x (t)i) Characteristic interval
Figure BDA0003092450110000111
Step 3.3, setting the time length of the pressure signal processing queue to be
Figure BDA0003092450110000112
Taking the characteristic reference point t as a midpoint and extracting the time length of
Figure BDA0003092450110000113
Discrete pressure queue of
Figure BDA0003092450110000114
Figure BDA0003092450110000115
Where l2vp ═ nT.
Generation of pressure differential queue [ delta p ] using extracted discrete pressure queue1、……Δpm-2、Δpm-1}，m∈[2,n]The queue length is m-1 and the total pressure difference is
Figure BDA0003092450110000116
Wherein any Δ piAre all more than 0, i is 1, 2, …, m-2 and m-1. Proportional weighting of differential pressure queue and total pressure difference
Figure BDA0003092450110000117
Generating a queue of weight coefficients { λ }1、……λm-2、λm-1}。
And 3.4, substituting the characteristic point of the starting end and the characteristic point of the ending end into a positioning distance formula to obtain the distance d (t) between the leakage point and the starting end, and substituting into a negative pressure wave attenuation formula:
ΔP＝ΔPde-β(l-d(t))
wherein, Δ PdIs the pressure difference at the leak point; the attenuation pressure of the negative pressure wave is delta Ps＝ΔPd-ΔP＝(eβ(l-d(t))-1)ΔP。
Performing attenuation compensation on the pressure difference queue according to the weight coefficient and the negative pressure wave attenuation pressure, delta p'i＝Δpi+λiΔPsForming a new pressure difference queue [ delta p'1、Δp′2……、Δp′m-1}. The compensated pressure difference queue is combined with the original discrete pressure queue,
Figure BDA0003092450110000118
a new discrete pressure queue { X '(T-l 4vp, X' T-l4vp + T, … … X 'T-l 4vp + n-2T, X' T + l4vp } is formed.
Performing signal holding processing on the compensated pressure queue by using a three-spline interpolation method to form a compensated continuous pressure signal xs(t)。
3.5, establishing a mathematical model of multi-objective constraint optimization; setting a corresponding objective function around two indexes of the edge condition and the predicted leakage rate as follows:
Figure BDA0003092450110000119
wherein the content of the first and second substances,
Figure BDA00030924501100001110
is a pressure signal xs(t) obtaining a derivative function after wavelet transformation, wherein the absolute value of the derivative represents the rapid degree of the change of the independent variable along with the change of the dependent variable at the current point, and the smaller the absolute value is, the more gradual the change of the function value is, and the possibility that the function value is to reach a certain extreme point is high; n is1(t) is the initial end pressure difference Δ PsSubstituting the leakage rate n obtained by the dynamic negative pressure wave attenuation model2(t) the leakage rate is obtained by substituting the pressure difference delta P of the terminating end into the dynamic negative pressure wave attenuation model, wherein the pressure difference of the starting end is a constant, and the pressure difference of the terminating end is a dependent variable of t;if the leakage rate obtained by substituting the pressure difference between the two ends into the dynamic negative pressure wave attenuation model is gradually similar, the characteristic point gradually approaches to the real characteristic point. n is1(t) and n2The formula for calculation of (t) is:
Figure BDA0003092450110000121
step 3.6, setting constraint conditions of multi-objective constraint optimization according to the pipeline operation parameter standard, as follows:
Figure BDA0003092450110000122
wherein the first constraint is a differential pressure constraint, Δ PCFor disturbance compensation of the end differential pressure, it is related to the adjustment value of the working condition equipment. In theory, the pressure difference at the starting end should be larger than that at the ending end, but the pressure difference may be changed due to the adjustment of working conditions such as pump frequency modulation and cleaner operation, and in practice, the pressure difference at the starting end should be larger than the sum of the pressure difference at the ending end and the interference compensation. The second constraint is the moveout constraint, where tsSetting the inflection point time of the starting end according to the compensation condition, wherein the leakage point is positioned between the starting section and the middle point of the pipeline, and setting the characteristic point time variable of the terminating end to be greater than the inflection point time of the starting end plus one fourth of the pipeline time. Forming a complete multi-target constraint optimization mathematical model as shown in the following formula;
Figure BDA0003092450110000123
3.7, selecting a multi-target particle swarm optimization method to solve the multi-target constraint optimization mathematical model; setting the initial parameter, iteration times and termination conditions of optimization, and solving an optimal solution set.
As shown in fig. 5, first, the particle population position, velocity, inertial weight, learning factor and iteration number are initialized; calculating to obtain an Archive set according to a pareto domination principle; calculating the crowdedness of the Archive set, selecting a gbest in the Archive set, updating the speed, the position and the adaptive value of the particle, and updating the Archive set; and judging whether the current Archive set is a symbol iteration ending condition or not, if not, updating the particle speed, position and adaptive value calculation again, and if so, returning to the current Archive set.
In the particle swarm optimization, the position of each particle represents the solution of the problem to be optimized, the degree of the performance of each particle depends on the fitness value determined by the objective function of the problem to be optimized, and the particles continuously modify the advancing direction and the speed of the particles through iteration to finally find the globally optimal solution. In the iteration process, the iteration formula of the (p + 1) th time is as follows:
Figure BDA0003092450110000124
where v is the particle velocity, t is the particle position, pbestThe individual optimal solution, g, found for the particlebestFor the current global optimal solution of the whole population, omega is the inertia weight, c1,c2Is a learning factor.
And 3.7.1, selecting non-inferior solutions to form a pareto solution set, and calculating the fitness value of the particles in the pareto solution set by using a niche technology, wherein the smaller the fitness value of the particles is, the higher the aggregation degree of the particles is. The ith fitness calculation formula is as follows:
Figure BDA0003092450110000131
wherein s (d (i, j)) is a fitness sharing function of the ith particle and the jth particle, and n is the number of pareto solutions.
Figure BDA0003092450110000132
Wherein, deltashIs a niche radius.
And 3.7.2, according to the size of the fitness value in the pareto solution set, setting the dynamic fitness value with the maximum value as the current global optimal solution. Dynamically changing the weight coefficients and guiding the iterative process of the particles by taking the global optimal solution as a guide, circularly executing the variable updating and corresponding compensation processes of the steps 3.3 and 3.4, carrying out target solving on a corresponding function model by utilizing the step 3.7.1, and continuously updating the pareto solution set through the obtained target value until the highest iterative times are reached; after the iteration is finished, the current global optimal solution is the optimal solution obtained by solving in the particle swarm optimization. As shown in fig. 6, for the optimal solution set generated after 50 iterations, the ordinate represents the predicted leakage rate similarity index, the abscissa represents the signal edge condition index, the two indexes have respective weight components according to different problems, and the optimal solution set in the solution set can be solved after weighting.
And 4, step 4: and performing effect evaluation based on the obtained positioning result, wherein the evaluated effect comprises a negative pressure wave compensation and restoration effect and a positioning precision effect. The evaluation of the effect was carried out according to the following evaluation criteria:
evaluation criterion 1: comparing the positioning accuracy; the positioning precision is as follows:
Figure BDA0003092450110000133
wherein, Δ d is the difference between the positioning distance and the actual distance, that is, after the multi-objective function finds the optimal feature point, the distance d (t) between the leakage point corresponding to the feature point and the starting end is different from the distance between the actual leakage point and the starting end.
Evaluation criterion 2: whether the pressure signal after the negative pressure wave attenuation compensation meets the local sudden drop condition or not can be identified by naked eyes. Fig. 7 shows the pressure compensation curve after determining the optimal solution set, and it can be seen that the pressure compensation curve has a significant sudden drop characteristic compared with the original curve.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions and scope of the present invention as defined in the appended claims.

Claims (5)

1. A pipeline leakage self-adaptive dynamic compensation positioning method based on negative pressure wave attenuation driving is characterized by comprising the following steps: the method comprises the following steps:
step 1: establishing a dynamic negative pressure wave attenuation model;
analyzing the dynamic process of the fluid in the pipeline, solving a pipe flow equation by using a characteristic line solution method, and establishing a dynamic negative pressure wave attenuation model by combining a negative pressure wave attenuation rule observed in an experiment; performing data fitting on the high point and the pressure difference by using field experimental data to form a fitting formula, and fusing the fitting formula into the established dynamic negative pressure wave attenuation model; the pipe flow equations include continuous equations and equations of motion of the fluid;
step 2: extracting pressure characteristics at two ends of a pipeline, and setting a characteristic interval of a signal;
firstly, extracting pressure characteristic data at two ends of a leakage time period; secondly, selecting a proper wavelet scale, performing denoising and filtering processing on the pressure data by using a wavelet analysis method, searching an extreme point of wavelet transformation, and determining the extreme point as an edge point of a deterministic signal; finally, setting a characteristic interval around the obtained extreme point; the deterministic signal is a deterministic signal of pressure data, and the pressure data consists of the deterministic signal and a noise signal;
and step 3: compensating the attenuation of the negative pressure wave, and searching an optimal characteristic point in the characteristic interval;
firstly, determining a characteristic reference point and a characteristic interval, judging whether a pressure signal in the interval meets a compensation condition, if not, indicating that the characteristic of the signal in the interval obviously does not need to be compensated, directly returning the reference point as a leakage characteristic point, and if so, compensating the attenuation of the negative pressure wave by taking the characteristic point as a center in the characteristic interval; secondly, establishing a corresponding objective function around two indexes of signal edge conditions and predicted leakage rates to form a multi-objective optimization mathematical model, solving the multi-objective optimization mathematical model by adopting a multi-objective particle swarm optimization method, and finding out an optimal characteristic point in a characteristic interval; judging whether the optimal point meets set index conditions or not, if not, substituting the optimal point as a new reference point for calculation of the multi-objective optimization model again, and if so, returning the optimal point as a leakage characteristic point;
and 4, step 4: and performing effect evaluation based on the obtained positioning result, wherein the evaluated effect comprises a negative pressure wave compensation and restoration effect and a positioning precision effect.
2. The adaptive dynamic compensation positioning method for the pipeline leakage based on the negative pressure wave attenuation driving as claimed in claim 1, wherein the adaptive dynamic compensation positioning method comprises the following steps: the step 1 comprises the following steps:
step 1.1, analyzing the dynamic process of fluid in a pipeline and establishing a pipe flow equation; the pipe flow equation comprises two partial differential equations of a continuous equation and a motion equation of fluid to form a group of hyperbolic partial differential equations;
step 1.2, solving the hyperbolic partial differential equation set by using a characteristic line solution method and a finite difference method, and combining the boundary condition of the leakage point and the definition of the leakage rate to obtain a negative pressure wave expression at the leakage point as follows:
Figure FDA0003092450100000011
wherein, Δ plFor pressure changes at the leak, n is the leak rate, vpThe wave velocity of the negative pressure wave, v is the average flow velocity of the section, and rho is the average density of the fluid;
step 1.3, through multiple leakage experiment observation and analysis, the assigned attenuation generated at the leakage point is similar to exponential attenuation, the attenuation exponent and the length of the pipeline are in a direct proportion relation, a dynamic coefficient is fused into a traditional negative pressure wave attenuation model to form a dynamic attenuation factor, namely, the expression of the negative pressure wave pressure at the two ends of the pipeline is as follows:
Figure FDA0003092450100000021
Figure FDA0003092450100000022
the expression is the established dynamic negative pressure wave attenuation model, wherein psIs the pressure value of negative pressure wave at the inlet of the pipeline, peThe pressure value of the negative pressure wave at the outlet of the pipeline is adopted, x is the distance between a leakage point and the inlet of the pipeline, l is the length of the pipeline, beta is an attenuation factor of the negative pressure wave, k is a dynamic pipeline proportionality coefficient, and the expression of k is as follows:
Figure FDA0003092450100000023
wherein lthIs a pipeline length threshold value, and the attenuation factor exceeding the threshold value is a dynamic attenuation variable;
step 1.4, performing oil drainage tests with the same leakage flow for multiple times by adjusting different running flows to obtain changed pressure difference data; and fitting the obtained pressure difference data to obtain an attenuation formula of pressure attenuation caused by a high point of the pipeline, wherein the attenuation formula is as follows:
Δp＝f(x,v,h,xh)
wherein, Δ p is the pressure value of the negative pressure wave, h is the highest altitude of the pipe section, and xhIs a one-dimensional vector of the altitude of the pipeline;
and (3) integrating an attenuation formula into the established dynamic negative pressure wave attenuation model:
Figure FDA0003092450100000024
Figure FDA0003092450100000025
3. the adaptive dynamic compensation positioning method for the pipeline leakage based on the negative pressure wave attenuation driving as claimed in claim 2, wherein the adaptive dynamic compensation positioning method comprises the following steps: the step 2 comprises the following steps:
step 2.1, discrete pressure queues at two ends of the pipeline meeting the leakage condition are extracted, wherein the pressure queue of the monitoring station with the starting end close to the leakage point is x1(T) the pressure queue of the monitoring station at the terminating end, i.e. farther from the leaking end, is x2(T); using three-spline interpolation method to make signal hold and generate continuous pressure signal x1(t) and x2(t); wherein T is a pressure signal sampling period; the leakage condition comprises two types of conditions, namely a pressure integral reduction condition and a local sudden reduction condition; the starting end needs to satisfy two types of conditions simultaneously, and the terminating end only needs to satisfy the overall pressure reduction condition, so that the pressure signals at the two ends can be considered to satisfy the leakage condition;
step 2.2, denoising the pressure data by using a wavelet transformation method, which comprises the following specific steps:
step 2.2.1, the pressure signal is formed by overlapping a deterministic signal and noise, and data after wavelet change is the sum of wavelet transformation of two parts of signals; under large scale, the edge of a deterministic signal, namely the inflection point of a negative pressure wave can be accurately extracted; let Ψ (t) be a gaussian low-pass function,
Figure FDA0003092450100000026
the extreme point solution of wavelet transform is:
Figure FDA0003092450100000031
wherein, a is a scale factor, b is a displacement parameter,
Figure FDA0003092450100000032
is the scale-wise scaling of the basic wavelet Ψ (t); the wavelet transform analyzes the signal by scale-wise scaling and time-domain shifting of Ψ (t);
step 2.2.2, according to the leakage condition, under the condition that the pressure signal of the starting end meets the overall descending condition and the local sudden descending condition and the pressure overall descending condition is only met by the terminating end; selecting wavelet change scale factors of signals at two ends differently, wherein the selection of the scale factors is smaller than that of a terminating end in order to ensure the effectiveness of local sudden drop signals at the starting end;
step 2.3, solving to obtain an extreme point x (t)i) Setting a leakage characteristic interval
Figure FDA0003092450100000033
Wherein l is the length of the pipeline, vpIs the wave velocity, t, of the negative pressure waveiThe time corresponding to the extreme point.
4. The adaptive dynamic compensation positioning method for the pipeline leakage based on the negative pressure wave attenuation driving as claimed in claim 3, wherein the adaptive dynamic compensation positioning method comprises the following steps: the step 3 comprises the following steps:
step 3.1, setting a negative pressure wave compensation condition, and judging whether the pressure signal meets the compensation condition; if not, skipping the step 3 and the step 4; if yes, maintaining the initial end pressure signal, and comparing the final end pressure signal x2(t) performing negative pressure wave attenuation compensation; the negative pressure wave compensation condition is that the pressure signal at the termination end only meets the integral pressure drop condition and does not meet the local sudden drop condition;
step 3.2, setting initial set characteristic datum point as step 2 to solve x2(t) derived extreme point x (t)i) Characteristic interval
Figure FDA0003092450100000034
Step 3.3, setting the time length of the pressure signal processing queue to be
Figure FDA0003092450100000035
Taking the characteristic reference point t as a midpoint and extracting the time length of
Figure FDA0003092450100000036
Discrete pressure queue of
Figure FDA0003092450100000037
Figure FDA0003092450100000038
Wherein l2vp ═ nT;
generation of pressure differential queue [ delta p ] using extracted discrete pressure queue1、……Δpm-2、Δpm-1}，m∈[2,n]The queue length is m-1 and the total pressure difference is
Figure FDA0003092450100000039
Wherein any Δ piAre all more than 0, i is 1, 2, …, m-2, m-1; proportional weighting of differential pressure queue and total pressure difference
Figure FDA00030924501000000310
Generating a queue of weight coefficients { λ }1、……λm-2、λm-1}；
And 3.4, substituting the characteristic point of the starting end and the characteristic point of the ending end into a positioning distance formula to obtain the distance d (t) between the leakage point and the starting end, and substituting into a negative pressure wave attenuation formula:
ΔP＝ΔPde-β(l-d(t))
wherein, Δ PdIs the pressure difference at the leak point; the attenuation pressure of the negative pressure wave is delta Ps＝ΔPd-ΔP＝(eβ(l-d(t))-1)ΔP；
Performing attenuation compensation on the pressure difference queue according to the weight coefficient and the negative pressure wave attenuation pressure, delta p'i＝Δpi+λiΔPsForming a new pressure difference queue [ delta p'1、Δp′2……、Δp′m-1}; the compensated pressure difference queue is combined with the original discrete pressure queue,
Figure FDA0003092450100000041
forming new discrete pressure queues
Figure FDA0003092450100000042
Figure FDA0003092450100000043
Performing signal holding processing on the compensated pressure queue by using a three-spline interpolation method to form a compensated continuous pressure signal xs(t)；
3.5, establishing a mathematical model of multi-objective constraint optimization; setting a corresponding objective function around two indexes of the edge condition and the predicted leakage rate as follows:
Figure FDA0003092450100000044
wherein the content of the first and second substances,
Figure FDA0003092450100000045
is a pressure signal xs(t) obtaining a derivative function after wavelet transformation, wherein the absolute value of the derivative represents the rapid degree of the independent variable changing along with the dependent variable at the current point, and the smaller the absolute value is, the more gradual the change of the function value is; n is1(t) is the initial end pressure difference Δ PsSubstituting the leakage rate n obtained by the dynamic negative pressure wave attenuation model2(t) the leakage rate is obtained by substituting the pressure difference delta P of the terminating end into the dynamic negative pressure wave attenuation model, wherein the pressure difference of the starting end is a constant, and the pressure difference of the terminating end is a dependent variable of t; if the leakage rate obtained by substituting the pressure difference between the two ends into the dynamic negative pressure wave attenuation model is gradually similar, the characteristic points gradually approach to the real characteristic points; n is1(t) and n2The formula for calculation of (t) is:
Figure FDA0003092450100000046
step 3.6, setting constraint conditions of multi-objective constraint optimization according to the pipeline operation parameter standard, as follows:
Figure FDA0003092450100000047
wherein the first constraint is a differential pressure constraint, Δ PCThe interference compensation of the pressure difference at the termination end is related to the adjustment value of the working condition equipment; the second constraint is the moveout constraint, where tsSetting the inflection point time of the starting end according to the compensation condition, wherein the leakage point is positioned in the interval from the starting section to the middle point of the pipeline, and setting the characteristic point time variable of the terminating end to be greater than the inflection point time of the starting end plus one fourth of the pipeline time; forming a complete multi-target constraint optimization mathematical model as shown in the following formula;
Figure FDA0003092450100000051
3.7, selecting a multi-target particle swarm algorithm to solve the multi-target constraint optimization mathematical model; setting an initial parameter, iteration times and a termination condition for optimizing, and solving an optimal solution set;
step 3.7.1, considering the characteristics of multi-objective solution, the solution set is an optimal non-dominant front surface, namely a pareto solution set; the multi-target constraint optimization mathematical model has two targets, and the fitness value of the particles in the pareto solution set is determined by two corresponding target values and the maximum and minimum target values; because the target function and the compensation condition have partial coupling, the weight coefficient of the target function for fitness calculation is set according to the coupling degree;
3.7.2, according to the size of the fitness value in the pareto solution set, setting the maximum dynamic fitness value as the current global optimal solution; dynamically changing the weight coefficients and guiding the iterative process of the particles by taking the global optimal solution as a guide, circularly executing the variable updating and corresponding compensation processes of the steps 3.3 and 3.4, carrying out target solving on a corresponding function model by utilizing the step 3.7.1, and continuously updating the pareto solution set through the obtained target value until the highest iterative times are reached; after the iteration is finished, the current global optimal solution is the optimal solution obtained by solving in the particle swarm optimization.
5. The adaptive dynamic compensation positioning method for the pipeline leakage based on the negative pressure wave attenuation driving as claimed in claim 4, wherein the adaptive dynamic compensation positioning method comprises the following steps: and 4, performing effect evaluation according to the final recognition result, wherein the effect evaluation is performed according to the following evaluation criteria:
evaluation criterion 1: the positioning precision is as follows:
Figure FDA0003092450100000052
wherein, Δ d is the difference between the positioning distance and the actual distance, that is, after the multi-objective function finds the optimal feature point, the distance d (t) between the leakage point corresponding to the feature point and the starting end is the difference between the actual leakage point and the starting end;
evaluation criterion 2: whether the pressure signal meets the local sudden drop condition after the negative pressure wave attenuation compensation.
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