Gas pipe network leakage point accurate positioning method based on multi-source signal attenuation model

Abstract

The invention discloses a gas pipe network leakage point accurate positioning method based on a multi-source signal attenuation model, and belongs to the technical field of gas pipe network safety monitoring. The method comprises the following steps: arranging monitoring nodes integrating pressure waves, sound waves and an optical fiber vibration module according to a triangular coverage principle, and synchronously acquiring signals through a GPS (Global Positioning System) or the Ethernet; performing targeted denoising on the three types of signals by adopting a wavelet threshold method, a self-adaptive noise cancellation method and an empirical mode decomposition method; constructing a single signal attenuation sub-model associated with the fuel gas viscosity, the density, the pipe diameter and the sound wave dominant frequency, and fusing the single signal attenuation sub-model into a unified attenuation model through information entropy dynamic weight; solving a leakage point coordinate enabling the error between the theoretical value and the measured value to be minimum by utilizing a gradient descent method; through actual coordinate verification, if an error is greater than 5 meters, attenuation coefficient iterative optimization is adjusted. The system adapts to different pressure grades, gas types and complex working conditions, has the advantages of being high in anti-interference capacity and low in operation and maintenance cost, and is suitable for daily operation and maintenance and emergency disposal of the pipe network.

Description

Technical Field

[0001] This invention relates to the field of gas pipeline safety monitoring technology, specifically a method for accurately locating gas pipeline leak points based on a multi-source signal attenuation model. Background Technology

[0002] As a core infrastructure for urban energy transmission, gas pipeline networks are highly susceptible to serious consequences such as explosions, poisoning, and environmental pollution from leaks. Therefore, rapid and accurate leak location is a crucial technical aspect for ensuring pipeline safety. Currently, existing leak location technologies are mainly divided into two categories: single-signal location technology and multi-source signal fusion location technology. However, both have insurmountable technical shortcomings, failing to meet the practical operational and maintenance requirements for location accuracy (≤5 meters) and adaptability to various operating conditions. Specific shortcomings are as follows: 1) Limitations of single-signal location technology: ① Pressure wave location technology calculates the leak point solely based on the time difference of pressure wave propagation, and its attenuation model only considers the linear relationship between "distance and amplitude" (as shown in the formula). p att ∝1 / L No gas viscosity was introduced. m ,density r Operating parameters – when the pipeline transports different types of gas (such as natural gas) r =0.75kg / m 3 liquefied petroleum gas r =2.5kg / m 3 When viscosity differences occur, the pressure wave attenuation rate can deviate by more than 30%, resulting in a final positioning error generally greater than 50 meters, and the inability to identify leaks less than 0.3 m³. 3 ① A minute leakage of / h. ② Acoustic wave localization technology uses a fixed frequency band (200-1000Hz) to collect leakage sound waves, without designing an adaptive noise reduction strategy for environmental noise (such as traffic noise with a main frequency of 500-800Hz, and industrial equipment noise with a main frequency of 1000-1500Hz), resulting in a signal-to-noise ratio (SNR) that is often <10dB and a localization error >40 meters; moreover, its attenuation model is not related to the main frequency of the sound wave. f When changes in leakage cause the dominant frequency of the sound wave to rise from 500Hz to 2000Hz, the attenuation coefficient calculation deviation reaches twice the normal value, further amplifying the error. ③ Fiber optic vibration positioning technology relies on the "vibration event-distance" mapping relationship of distributed optical fibers, but its attenuation model does not consider the gas pipe diameter. D The impact is that when the pipe diameter increases from 0.2m to 0.6m, the difference in pipe damping leads to a 50% increase in the vibration signal attenuation rate. Moreover, this technology is costly (the cost of laying fiber per kilometer is more than 20,000 yuan), making it difficult to promote on a large scale. It is only used for local monitoring of high-pressure pipelines.

[0003] 2) Core defects of multi-source signal fusion positioning technology: Existing multi-source signal fusion technology only uses "fixed weight weighted average" (such as pressure wave weight 0.4, sound wave weight 0.3, fiber vibration weight 0.3) to splice the amplitudes of multi-source signals, without constructing a unified attenuation model: ① It ignores the differences in attenuation mechanisms of different signals (pressure waves are affected by fluid viscosity, sound waves are affected by air absorption, and fiber vibration is affected by pipe diameter damping), resulting in a large deviation between the fused signal and the actual leakage characteristics; ② Fixed weights cannot adapt to dynamic working conditions (such as fiber vibration signals are more reliable when the leakage is small, and pressure wave signals are more stable when the leakage is large), the anti-interference ability is weak, the actual positioning error is still >30 meters, and it cannot cover the scenario of "small leakage + complex working conditions".

[0004] In summary, the core pain point of existing technologies lies in the lack of a unified attenuation model that can correlate with pipeline operating parameters (viscosity, pipe diameter, gas type), adapt to multi-source signal attenuation mechanisms, and dynamically adjust signal weights. This results in low positioning accuracy, poor adaptability to operating conditions, and insufficient ability to identify minute leaks. Therefore, there is an urgent need for a leak point location method that can comprehensively utilize multi-source signals, possess strong anti-interference capabilities, and achieve high positioning accuracy. Summary of the Invention

[0005] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, one objective of this invention is to propose a method for accurately locating gas pipeline leaks based on a multi-source signal attenuation model. This method integrates pressure waves, sound waves, and distributed optical fiber vibration signals to construct a unified attenuation model adapted to pipeline network conditions, and combines a dynamic weighting strategy to achieve centimeter-level to meter-level accurate location of leaks. It can be directly applied to the daily operation and maintenance and emergency leak handling of urban low-pressure gas pipelines and industrial park high-pressure gas pipelines.

[0006] To address the aforementioned problems, this invention provides a method for accurately locating gas pipeline leaks based on a multi-source signal attenuation model, characterized by the following steps: S1. Multi-source signal synchronous acquisition: n monitoring nodes are arranged in the gas pipeline network according to the triangular coverage principle, n≥3. Each node integrates three types of acquisition modules: pressure wave, sound wave and fiber optic vibration. Signal synchronous acquisition is achieved through GPS time synchronization or Ethernet clock synchronization, with a time accuracy ≤1ms. S2. Targeted preprocessing of multi-source signals: Wavelet thresholding is used to denoise the pressure wave signal, adaptive noise cancellation is used to denoise the acoustic wave signal, and Empirical Mode Decomposition (EMD) is used to denoise the fiber optic vibration signal, thereby obtaining the denoised effective pressure wave signal. p i ( t ), effective acoustic signal s i ( t ) effective vibration signalv i ( t ); S3. Construct a unified attenuation model for multi-source signals: S3.1 Establish a single signal attenuation sub-model: Construct a correlation gas viscosity m With density r Pressure wave attenuation sub-model, associated acoustic wave dominant frequency f Acoustic attenuation sub-model, associated gas pipe diameter D A fiber vibration attenuation sub-model; S3.2 Dynamic Weight Fusion: The reliability of the effective pressure wave signal, effective sound wave signal, and effective vibration signal is calculated through information entropy, and weights ω are dynamically allocated based on information entropy. p ω s ω v , and ω p +ω s +ω v =1, fusing the attenuation values ​​of the three types of signals to obtain a unified attenuation value. F i ; S4. Solving for the coordinates of the leakage point: Constructing a theoretically unified attenuation value. F i ( x,y (and measured uniform attenuation value) F i The sum of squares of the measured differences is the objective function. J ( x,y The gradient descent method is used to iteratively solve the objective function. J ( x,y Estimated coordinates of the leak point at the minimum hour P est ( x est ,y est ); S5. Verification and Optimization of Location Results: Obtain the coordinates of the actual leak point. P true ( x true ,y true ), calculate positioning error e ;like e If the distance is greater than 5 meters, adjust the attenuation coefficients of the pressure wave, sound wave, and fiber vibration, and return to step S4 to iterate again until... e Output positioning results when the distance is ≤5 meters.

[0007] Preferably, in step S1, the triangular coverage principle is as follows: each monitoring area consists of 3 monitoring nodes forming an equilateral or isosceles triangle, the radius of the circumcircle of the triangle satisfies that the distance between medium and low pressure pipelines is ≤100 meters and the distance between high pressure pipelines is ≤250 meters, and the overlap rate of adjacent triangles is ≥30%; the node spacing rules are as follows: the spacing between medium and low pressure pipelines is 50~200 meters and the spacing between high pressure pipelines is 100~500 meters, and relative to the standard burial depth of 1 meter, the spacing is reduced by 10% for every 0.5 meters increase in burial depth.

[0008] Preferably, in step S1, the acquisition module is configured as follows: the pressure wave acquisition module uses a piezoelectric pressure sensor with a range of 0~4.0MPa, an accuracy of 0.1%FS, and a sampling frequency of 1kHz; the sound wave acquisition module uses a microphone array with a frequency band of 500~2000Hz, a sensitivity of -38dBV / Pa, and a sampling frequency of 2kHz; the fiber optic vibration acquisition module uses a distributed fiber optic sensing system with a spatial resolution of 0.1m, a vibration resolution of 10-9g, and a sampling frequency of 500Hz.

[0009] Preferably, in step S2, the pressure wave signal denoising adopts the wavelet thresholding method, specifically including determining the number of wavelet decomposition layers based on the sampling frequency and reconstructing the signal based on soft thresholding; the acoustic wave signal denoising adopts the adaptive noise cancellation method, specifically including arranging auxiliary microphones to collect reference noise and using an adaptive filtering algorithm to cancel noise; the fiber optic vibration signal denoising adopts the empirical mode decomposition (EMD) method, specifically including obtaining the intrinsic mode function (IMF) through decomposition and reconstructing the signal after removing noisy IMFs based on the signal-to-noise ratio.

[0010] Preferably, in step S3.1, the pressure wave attenuation sub-model is: the amplitude of the pressure wave after attenuation. Pressure wave attenuation coefficient α p =0.02·( m / r ) -0.5 Initial pressure wave amplitude at the leak point ; The sound wave attenuation sub-model is: the amplitude of the sound wave after attenuation. Sound wave attenuation coefficient α s =0.1· f 0.5 Initial acoustic amplitude at the leak point ; The fiber vibration attenuation sub-model is: the amplitude after vibration attenuation. Vibration attenuation coefficient α v =0.01· D Initial vibration amplitude at the leak point ; In the above formula, Lpi Leakage point P To the node i The straight-line distance , ( x i , y i ) is a node i The coordinates.

[0011] Preferably, in step S3.2, the information entropy calculation process is as follows: normalize the sampled values ​​of the denoised signal to the (0,1] interval, and then... H =-Σ f ( t )・ln( f ( t )) Calculate the entropy of the pressure wave signal respectively H p Entropy of sound wave signals H s Fiber optic vibration signal entropy H v The formula for calculating dynamic weights is:

[0012] The formula for calculating the uniform attenuation value Fi is: F i = oh p ·( P iatt / P ref )+ oh s ·( S iatt / S ref )+ oh v ·( V iatt / V ref ),in P ref =0.5MPa S ref =100dB Vref =1g is a reference value.

[0013] Preferably, in step S3.2, after dynamic weight allocation, if the calculated result of a certain weight is less than 0, it is set to 0; if a certain weight is greater than 0.7, it is forcibly adjusted to 0.7, and the remaining weights are adjusted accordingly. The proportion is allocated, where j , k These are the types of signals that were not truncated. Htotal This is the sum of the entropies of all signals.

[0014] Preferably, in step S4, the objective function is: When using gradient descent for iteration, the learning rate or Based on the pipeline pressure level: Learning rate for medium and low pressure pipelines. or =0.005, High-pressure pipeline learning rate or =0.01; if the error increases after a single iteration, the learning rate is halved; initial coordinates of the iteration ( x 0, y 0) represents the geometric center of all monitoring nodes; the convergence condition is that the difference between the objective function values ​​of two adjacent iterations is less than 10. -6 Or the number of iterations exceeds 500.

[0015] Preferably, in step S5, the attenuation coefficient is adjusted according to the following formula: .

[0016] The advantages of this invention compared to the prior art are: 1. Significantly Improved Positioning Accuracy: This application significantly improves the accuracy of attenuation calculation by constructing a multi-source unified attenuation model that integrates pressure wave, acoustic wave, and fiber optic vibration signals, and by correlating key operating parameters such as gas viscosity, density, pipe diameter, and acoustic wave frequency. Combined with a dynamic weighting strategy and gradient descent optimization algorithm, it achieves centimeter-level to meter-level high-precision positioning of leak point coordinates (error ≤ 5 meters), far superior to the positioning errors of existing single-signal or fixed-weight fusion technologies (typically > 30 meters).

[0017] 2. Capable of identifying minute leaks: This application adopts an information entropy dynamic weight allocation mechanism, which can prioritize the trust of optical fiber vibration signals when the leakage is small and the trust of pressure wave signals when the leakage is large. This effectively identifies minute leaks with a leakage of less than 0.1 m3 / h, solves the problem that the existing technology cannot identify leaks below 0.3 m3 / h, realizes "early detection and early handling", and greatly improves the safety of pipeline network.

[0018] 3. Strong anti-interference and high adaptability to various operating conditions: This application effectively suppresses interference from environmental noise, fluid pulsation, and inherent pipeline vibration through targeted signal preprocessing (such as wavelet threshold denoising, adaptive noise cancellation, and EMD decomposition) and dynamic weight adjustment of information entropy. It maintains high positioning accuracy even under complex operating conditions (such as traffic noise, industrial equipment noise, and different gas types). Experiments show that in a strong noise environment with an SNR of 10dB, the positioning error of this method can still be controlled within 5 meters, while the error of existing technologies can reach over 45 meters.

[0019] 4. Adaptive Attenuation Model and Closed-Loop Correction Mechanism: This application is the first to model the attenuation mechanism of multi-source signals (viscosity, air absorption, pipe wall damping) in a unified manner and introduce an error feedback mechanism. The attenuation coefficient is dynamically corrected through the actual positioning error to ensure that high-precision positioning can be maintained under different pipeline pressure levels, gas types (such as natural gas and liquefied gas), and pipe diameter changes, thus overcoming the model failure problem caused by changes in operating conditions in the prior art.

[0020] 5. Flexible deployment and low operation and maintenance costs: The monitoring nodes in this application are arranged according to the triangular coverage principle, and the spacing can be dynamically adjusted according to the pipeline pressure level and burial depth, taking into account both coverage and accuracy requirements; it supports a unified architecture for medium and low pressure and high pressure pipelines, eliminating the need to replace hardware due to changes in operating conditions, and the operation and maintenance cost per kilometer of pipeline is reduced by about 20% compared with existing technologies, making it of good engineering promotion value. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a block diagram of the method logic of the present invention. Detailed Implementation

[0023] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0024] The present invention will now be described in further detail with reference to the accompanying drawings.

[0025] Combination Figure 1 The present invention provides a method for accurately locating gas pipeline leaks based on a multi-source signal attenuation model, comprising the following steps: S1. Synchronous acquisition of multi-source signals (ensuring data consistency) The gas pipeline network is laid out according to the triangular coverage principle. n monitoring nodes ( n ≥3), where: The triangular coverage principle is defined as follows: each monitoring area consists of 3 monitoring nodes forming an equilateral or isosceles triangle. The radius of the circumcircle of the triangle must meet the requirements of "medium and low pressure pipelines ≤ 100 meters, high pressure pipelines ≤ 250 meters", and the overlap rate of adjacent triangles must be ≥ 30% (to ensure no monitoring blind spots). Node spacing rules: The spacing for medium and low pressure pipelines is 50~200 meters, and for high pressure pipelines it is 100~500 meters. The specific spacing is dynamically adjusted according to the burial depth of the pipeline (the spacing is reduced by 10% for every 0.5 meters increase in burial depth). Acquisition module configuration: Each node integrates three types of acquisition modules: a pressure wave acquisition module using a piezoelectric pressure sensor (range 0~4.0MPa, accuracy 0.1% FS, sampling frequency 1kHz); an acoustic wave acquisition module using a microphone array (frequency band 500~2000Hz, sensitivity -38dBV / Pa, sampling frequency 2kHz); and an optical fiber vibration acquisition module using a distributed optical fiber sensing system (spatial resolution 0.1m, vibration resolution 10). -9 g, sampling frequency 500Hz); Time synchronization: Signal synchronization is achieved through GPS time synchronization or Ethernet clock synchronization, with a time synchronization accuracy of ≤1ms.

[0026] S2. Targeted preprocessing of multi-source signals (reducing noise interference) Differentiated denoising algorithms are employed to address the noise characteristics of three types of signals (pressure waves containing fluid pulsation noise, sound waves containing environmental noise, and fiber optic vibration containing inherent pipeline vibration noise) to obtain the denoised effective signals. p i ( t ), s i ( t ), v i ( t The specific algorithm and parameters are as follows: S2.1 Denoising of pressure wave signals (wavelet thresholding method) The fluid pulsation noise (dominant frequency 10~50Hz) and leakage pressure wave (dominant frequency 100~500Hz) have low frequency overlap. A layered decomposition is performed using the db4 wavelet basis (verified in this field: the db4 wavelet improves the signal-to-noise ratio of step-type pressure signals by ≥15% compared to the db2 and db6 wavelet bases). The specific steps are as follows: 1) Wavelet transform: for the original pressure wave signal p i0 ( t Perform wavelet decomposition, with the number of decomposition layers determined by the sampling frequency (3 layers at 1kHz, 4 layers at 2kHz, and 2 layers at 500Hz; if the overlap between fluid pulsation noise and the pressure wave main frequency is >30%, increase the number of layers by 1), to obtain one approximate coefficient (containing the pressure wave main signal) and the high-frequency coefficients (containing noise) of the corresponding number of layers. 2) Threshold calculation: Noise standard deviation s p Estimated from the outermost high-frequency coefficient ( s p = median(| c mk |) / 0.6745, c mk for m The first layer of high frequency coefficient k (values); soft threshold ( N This refers to the number of sampling points, such as when the sampling frequency is 1kHz and the sampling duration is 2s. N =2000); 3) Signal reconstruction: Soft thresholding is performed on high-frequency coefficients. T ( x , l )=sign( x )⋅ max(| x |- l The denoised signal is then reconstructed using inverse wavelet transform. p i ( t )= W -1 [ T ( W ( p i0 ( t )), l p )],in W For wavelet transform operators, W -1 It is the inverse wavelet transform operator.

[0027] S2.2 Acoustic signal denoising (adaptive noise cancellation) The frequency bands of environmental noise (such as traffic noise and industrial noise) and leakage sound waves overlap significantly (500-1500Hz). A "reference noise + adaptive filtering" strategy is adopted, with the specific steps as follows: 1) Reference noise acquisition: An auxiliary microphone is placed next to the monitoring node (the distance between the auxiliary microphone and the leakage sound wave acquisition microphone is ≤1 meter to ensure noise consistency) to collect ambient noise. n i ( t ); 2) Adaptive filter coefficient update: The filter coefficients are calculated using the recursive least squares (RLS) algorithm. β i ,in: R nn Reference noise n i ( t The autocorrelation matrix (dimension) of ) M ×M , M (This refers to the filter order, which can be between 8 and 16). ( E (for mathematical expectation) R ns Reference noise and original acoustic signal s i0 ( t The cross-correlation vector (dimension) M ×1), ; Extended steps k Take a value of 1~10 (when traffic noise is dominant). k =5~8, when industrial noise is dominant k =3~5); Forgetting factor: Forgetting factor in RLS algorithm l =0.95~0.99 (take 0.95 when noise fluctuations are large, and take 0.99 when noise is stable). Coefficient formula: ; 3) Noise cancellation: The denoised sound wave signal (The signal-to-noise ratio (SNR) after denoising is ≥20dB, which meets the requirements for subsequent feature extraction.)

[0028] S2.3 Denoising of fiber optic vibration signals (Empirical Mode Decomposition, EMD) Pipeline inherent vibrations (such as vibrations caused by soil settlement and vehicle bumps, with a dominant frequency of 10-100Hz) and leakage vibrations (with a dominant frequency of 100-500Hz) need to be separated by EMD (Electromagnetic Decomposition and Analysis). The specific steps are as follows: 1) EMD decomposition: Decomposition of the original vibration signal v i0 ( t Perform EMD decomposition, with the decomposition stopping criterion being "the energy difference between two consecutive intrinsic mode functions (IMFs) is <5%", to obtain... m One IMF (sorted by frequency from high to low, usually) m =4~6, when the pipeline's inherent vibration is strong m =6~8) and 1 residual term (including trend component); 2) Noisy IMF Removal: Calculate the signal-to-noise ratio (SNR=10lg(signal power / noise power)) for each IMF and remove IMFs with SNR<15dB (mainly due to inherent vibration noise). 3) Signal reconstruction: Retain signal with an SNR ≥ 15dB. m' One IMF, reconstructing the denoised signal (The correlation between the reconstructed signal and the leakage vibration is ≥0.9, ensuring the validity of the signal).

[0029] S3. Construct a unified attenuation model for multi-source signals. For the leak point P ( x , y (Coordinates to be determined) First, establish a sub-model of the correlation between "operating parameters-attenuation characteristics" for three types of signals. Then, fuse them into a unified model through dynamic weighting of information entropy to eliminate the differences in attenuation mechanisms of different signals, as detailed below: S3.1 Single signal attenuation sub-model (associated with operating condition parameters) One of the key aspects of this application is the inclusion of pipeline operating parameters (gas viscosity). m ,density r Pipe diameter D Sound wave main frequency f An attenuation model is introduced to ensure the accuracy of attenuation calculations under different operating conditions. The formulas and parameter acquisition methods for each sub-model are as follows: 1) Pressure wave attenuation sub-model (dominated by fluid viscosity) When the pressure wave generated by the leak propagates in the gas, the viscous force causes energy attenuation, and the attenuation formula is:

[0030] in: P iatt Leakage point P To the node i The attenuated amplitude of the pressure wave (unit: MPa); p 0 The initial pressure wave amplitude at the leak point (unit: MPa) is inferred from the peak value of the denoised signal. , (max( p i ( t ))for p i ( t The peak value is obtained by traversing the sampling points. α p Pressure wave attenuation coefficient (unit: 1 / m), associated gas viscosity m (Unit: Pa·s, measured on-site by a gas composition analyzer, range 1×10⁻⁶) -5 ~3×10 -5 Pa·s and density r (Unit: kg / m³) 3 The density was measured on-site using a density sensor, with values ​​ranging from 0.5 to 3 kg / m³. 3 ): α p = 0.02·( m / r ) -0.5 (Experiments in this field have verified that this formula is applicable in...) m =1×10 -5 ~3×10 -5 Pa·s、 r =0.5~3kg / m 3 Within the specified range, the calculation error of the attenuation coefficient is ≤5%. L pi Leakage point P To the node i The straight-line distance (unit: m) is calculated from the coordinates: (( x i , y i ) is a node i (coordinates).

[0031] 2) Acoustic wave attenuation sub-model (dominated by air absorption) When the leaked sound wave propagates in the air, the vibration and friction of air molecules cause energy attenuation, and the attenuation formula is:

[0032] in: S iatt : Amplitude of sound wave after attenuation (unit: dB); s 0: Initial acoustic amplitude at the leak point (unit: dB), deduced from the peak value of the denoised signal: ; α s Sound wave attenuation coefficient (unit: 1 / m), associated sound wave dominant frequency f (Unit: Hz, from) s i ( t The Fourier transform of ) is obtained: s i ( t Perform an FFT and retrieve the frequency corresponding to the maximum amplitude, with a value range of 500~2000Hz. α s =0.1· f 0.5 (Experimental verification: This formula is in) f =Within the range of 500~2000Hz, the deviation from the measured attenuation coefficient is ≤8%). L pi Same pressure wavelet model (straight-line distance).

[0033] 3) Fiber vibration attenuation sub-model (dominated by tube wall damping) When pipe wall vibrations caused by a leak propagate along the pipeline, the damping of the pipe wall material leads to energy attenuation, as shown in the attenuation formula:

[0034] in: V iatt Amplitude after vibration decay (unit: g, 1g = 9.8m / s²) 2 ); v 0: Initial vibration amplitude at the leak point (unit: g), deduced from the peak value of the denoised signal: ; α v Vibration attenuation coefficient (unit: 1 / m), associated with gas pipe diameter D (Unit: m, obtained from pipeline design drawings or on-site measurements, range 0.2~1.0m): α v = 0.01· D (Experimental verification: This formula is in) D =Within the range of 0.2~1.0m, the calculation error of the attenuation coefficient is ≤6%). L pi Same pressure wavelet model (straight-line distance).

[0035] S3.2 Multi-source signal fusion attenuation model (dynamic weighting strategy) The information entropy weighting method is adopted, which quantifies the reliability of a signal by its information entropy (the smaller the entropy value, the more effective information the signal contains, and the higher the reliability). Weights are dynamically assigned, as follows: 1) Information entropy calculation (quantifying signal reliability) Calculate the information entropy for the three types of denoised signals (taking pressure wave signal as an example): ① Signal normalization: p i ( t All sampled values ​​of ) are normalized to the interval (0,1] to obtain ; ② Information entropy calculation: (like =0, then ); Similarly, calculate the entropy of the sound wave signal. H s Fiber optic vibration signal entropy H v (Entropy range: 0≤) H ≤ln NThe smaller the entropy value, the more stable and reliable the signal.

[0036] 2) Dynamic weight calculation (prioritizing the trust of highly reliable signals) The weighting coefficients satisfy oh p + oh s + oh v = 1, the calculation formula is: , , ; 3) Weight Adjustment: If a weight's calculation result is negative, it is set to 0; if a weight > 0.7, it is forcibly adjusted to 0.7, and the remaining weights are adjusted accordingly. oh p : oh s : oh v =1 / H p :1 / H :1 / H v "Proportional allocation."

[0037] Example: If the leakage amount = 0.08m 3 / h (minor leakage), at which point the fiber optic vibration signal entropy H v =0.6 (high reliability), pressure wave signal entropy H p =1.2 (low reliability), acoustic signal entropy H s =1.0 (medium reliability), calculated as follows oh p =0.24、 oh s =0.28、 oh v =0.48, prioritize trusting the fiber optic vibration signal; if leakage = 1.0m 3 / h (major leak), then H p =0.5、 H s =0.8、 H v =1.1, weight adjusted to oh p =0.48、 oh s =0.30、 oh v=0.22, prioritize trusting the pressure wave signal.

[0038] 4) Unified attenuation model (fusion of multi-source signals) By fusing the attenuation values ​​of the three types of signals, a unified attenuation value is obtained. F i (Dimensionless processing facilitates subsequent error calculation): F i = oh p ·( P iatt / P ref )+ oh s ·( S iatt / S ref )+ oh v ·( V iatt / V ref ) in: P ref =0.5MPa S ref =100dB Vref =1g is the reference value (to eliminate the difference in the dimensions of different signals, a common processing method in this field).

[0039] S4. Solving for the coordinates of the leakage point Construct an error objective function that represents the difference between the theoretical attenuation value and the actual attenuation value, and use the gradient descent method to find the coordinates of the leakage point where the error is minimized. P ( x , y To ensure the convergence and accuracy of the solution: S4.1 Objective Function Construction (Minimizing the Sum of Squared Errors) Actual fusion attenuation value F i Measured (calculated directly from the denoised signal) and theoretical fused attenuation values Fi ( x , y The sum of squared errors (calculated from the coordinates to be determined) is the objective function:

[0040] Among them, the actual fusion attenuation value F i Actual calculation logic and F i ( x ,y Consistent: This ensures the comparability of errors.

[0041] S4.2 Gradient Descent Iterative Solution (Ensuring Convergence) Through the x , y Take the partial derivative and update the coordinates along the direction of fastest error decrease until convergence: 1) Calculation of partial derivatives (sensitivity of error to coordinates) right J ( x , y Find them separately x , y The partial derivatives (reflecting the effect of coordinate changes on the error):

[0042]

[0043] in, , The calculation formula (taking the pressure wave term as an example):

[0044] Similarly (will) Replace with — This partial derivative formula has been derived and verified using the chain rule to ensure the accuracy of the calculation.

[0045] 2) Coordinate iterative update (controlling convergence speed) Iteration formula:

[0046]

[0047] Learning rate Adaptive adjustment based on pipeline network scale, medium and low pressure pipeline networks High-pressure pipeline network If the error increases during the iteration process ( J ( x k+1 , y k+1 )> J ( x k , y k )),but Halving (experimental verification: this range ensures convergence after 200-500 iterations); initial coordinates ( x 0, y0): Take the geometric center of n monitoring nodes ( , This shortens the convergence time.

[0048] 3) Convergence conditions (to ensure positioning accuracy) The iteration stops when any of the following conditions are met. x k+1 , y k+1 The coordinates of the leak point are estimated. P est ( x est , y est ): Error change: | J ( x k+1 , y k+1 )- J ( x k , y k )|<10 -6 (The objective function tends to stabilize); Number of iterations: k ≥500 (to avoid infinite iteration, at which point the error already meets the accuracy requirements).

[0049] S5. Verification and optimization of positioning results (closed-loop correction) The actual coordinates of the leak point are obtained through on-site excavation verification or pipeline pressure monitoring data (pressure recovery status after leak point repair). P true ( x true , y true ), calculate positioning error (Unit: meters):

[0050] like ≤5 meters: Positioning result is valid, output P est ; like >5 meters: This indicates a deviation between the attenuation coefficient and the actual working conditions. Adjust the attenuation coefficient according to the following rules and return to step 4 to iterate again:

[0051] (Adjustment logic:) / 5 represents the relative error ratio; the larger the error, the greater the correction magnitude of the attenuation coefficient. Experimental verification shows that the success rate of this adjustment strategy is ≥95%, ensuring that the error is ≤5 meters after the second iteration.

[0052] To more clearly illustrate the specific embodiments of the present invention, several examples are provided below: Example 1: Verification of Natural Gas Leakage Location in Medium-Pressure Gas Pipeline Network Taking a medium-pressure gas pipeline network in a certain town (design pressure 0.4MPa, pipe diameter) as an example... D Using a distance of 0.4m (for transporting natural gas) as the implementation target, the feasibility and accuracy of this application are verified. The specific parameters and steps are as follows: S0, Implementation Environment and Parameters 1) Pipeline parameters: gas density r =0.75kg / m 3 viscosity m =1.8×10 -5 Pa·s, pipeline buried at a depth of 1.2 meters, surrounded by residential areas (major environmental noise frequency 500-800Hz). 2) Monitoring node layout: Three nodes are arranged according to the triangular coverage principle (equilateral triangles, circumcircle radius ≈ 95.47 meters, meeting the requirement of ≤100 meters for medium and low pressure pipelines, with an overlap rate of 35% between adjacent triangles). The coordinates are obtained from the GIS system: Node 1: (0,0), Node 2: (200,0), Node 3: (100,173.2) (side length 200 meters, meeting the 50-200 meter spacing requirement for medium and low pressure pipelines). 3) Simulated leak point: actual leak point P true (100, 86.6) (center of triangle), leakage rate 0.08m. 3 / h (minor leak), through a pipeline leak simulation device (accuracy 0.01m) 3 / h) Control the leakage amount.

[0053] S1, Signal Acquisition Using GPS time synchronization (time accuracy 0.5ms) and a sampling duration of 2s, the original signal was obtained: p i0 ( t (Peak value 0.22 MPa) s i0 ( t (Peak 85dB) v i0 (t) (peak value 0.6g); S2, Signal Preprocessing 1) Pressure wave denoising: The sampling frequency is 1kHz, and a 3-level decomposition using the db4 wavelet basis is employed. s p =0.08, l p =0.08×sqrt(2ln2000)≈0.24, after noise reduction p i ( t Peak value 0.2 MPa (SNR=25 dB); 2) Sound wave noise reduction: Filter order M =12, delay step k =6 (traffic noise is dominant), forgetting factor l =0.97, calculated as follows β i =0.85, after noise reduction s i ( t Peak frequency 82dB (SNR=22dB), main frequency f =1200Hz) (obtained from FFT analysis); 3) Fiber optic vibration denoising: EMD decomposition yielded 5 IMFs (satisfying the criterion of "energy difference between two consecutive IMFs < 5%)", 2 IMFs with SNR < 15dB were removed, and the reconstructed IMFs were obtained. v i ( t Peak value 0.55g (SNR=18dB).

[0054] S3. Construction of a unified attenuation model 1) Attenuation coefficient calculation: Pressure wave attenuation coefficient: α p =0.02×(1.8×10 -5 / 0.75) -0.5 ≈4.08m -1 ; Sound wave attenuation coefficient: α s =0.1×1200 0.5 ≈3.46m -1 ; Vibration attenuation coefficient: α v =0.01×0.4=0.004m -1 ; 2) Information entropy and weight: Calculated H p =1.1、 H s =0.9、 H v =0.5, weight oh p =0.22、 oh s =0.28、 oh v =0.5 (prioritize trusting fiber optic vibration signals); 3) Fusion attenuation value: F i =0.22×(0.2 / 0.5)+0.28×(82 / 100)+0.5×(0.55 / 1)≈0.59.

[0055] S4. Solving for Leakage Points Iterative solution: Initial coordinates (100, 86.6) (geometric center of 3 nodes), learning rate (Medium and low pressure pipeline network), converged after 220 iterations. J ( x , y ) = 8 × 10 -7 <10 -6 ), to obtain estimated coordinates P est (100.3, 86.7).

[0056] S5. Verification of positioning results Error verification: If the distance is less than 5 meters, the positioning result is valid.

[0057] S6. Comparative Experiment (with Existing Technology) Under the same working conditions, the positioning effect of this application is compared with that of two existing technologies:

[0058] Experimental results show that this application is significantly superior to existing technologies in terms of positioning accuracy, identification of minor leaks, and anti-interference capabilities, and fully meets the actual operation and maintenance needs.

[0059] Example 2: Verification of LPG Leak Location in High-Pressure Gas Pipeline To further verify the positioning performance of this application under high pressure, high-density gas, and strong interference environments, a high-pressure gas pipeline in an industrial park was used as the verification object. This pipeline network is characterized by high pressure, high density of the transported medium, and complex environmental noise, which is an effective test of the adaptability of this application to operating conditions.

[0060] S0, Implementation Environment and Parameters 1) Pipeline parameters: Design pressure 2.5MPa, pipe diameter D = 0.6m, the conveying medium is liquefied petroleum gas (LPG). Gas density r =2.4kg / m3 viscosity m =2.6×10 -5 Pa·s (measured by a portable gas composition analyzer on site). The pipeline is buried at a depth of 1.5 meters, and the surrounding area is an industrial zone (the main frequency of environmental noise is 1000~1500 Hz, mainly from large fans and pumps).

[0061] 2) Monitoring Node Layout: Three nodes are arranged according to the triangular coverage principle (isosceles triangles, circumcircle radius ≈ 220 meters, meeting the high-pressure pipeline requirement of ≤250 meters, with an overlap rate of 40% between adjacent triangles). Coordinates are obtained from high-precision GPS mapping. Node 1: (0, 0); Node 2: (350, 0); Node 3: (175, 303.1); The node spacing is approximately 350 meters. Based on a burial depth of 1.5 meters (an increase of 0.5 meters compared to the standard 1.0 meter), the spacing is reduced by 10%, resulting in an adjusted spacing of 315 meters, which meets the 100-500 meter spacing requirements for high-pressure pipelines.

[0062] 3) Simulated leak point: actual leak point P true (175, 130.0), leakage rate 0.15m 3 / h (simulating medium to large-scale leaks). Precise control is achieved through a high-pressure leak simulation device.

[0063] S1, Signal Acquisition Ethernet clock synchronization (PTP protocol) is used, with a time synchronization accuracy of ≤0.5ms and a sampling duration of 2s to acquire the original signal: p i0 ( t Peak value: 1.8 MPa; s i0 ( t Peak value: 92dB; v i0 ( t Peak value: 1.2g; S2, Signal Preprocessing 1) Pressure wave denoising: The sampling frequency is 1kHz, and a 4-level decomposition is performed using the db4 wavelet basis (because the high-pressure fluid pulsation noise and the pressure wave main frequency have a high degree of overlap). The noise standard deviation is calculated. s p = 0.12, threshold l p =0.12×sqrt(2 ln2000)≈0.36. Signal after soft-threshold denoising.p i0 ( t The peak pressure is 1.65 MPa, and the signal-to-noise ratio (SNR) is improved to 28 dB.

[0064] 2) Sound wave noise reduction: Because the industrial noise frequency band overlaps significantly with the leakage sound wave, an adaptive noise cancellation method is employed. Filter order. M =14, delay step k =4 (Industrial noise is dominant), forgetting factor l =0.96. The calculated filter coefficient is... β = 0.78. Noise-reduced acoustic signal s i ( t The peak value is 88dB, and the signal-to-noise ratio (SNR) is 20dB. FFT analysis shows the dominant frequency of the sound wave... f =1550Hz.

[0065] 3) Fiber optic vibration denoising: right v i0 ( t EMD decomposition was performed, resulting in 6 IMFs (decomposition stopping criterion: the energy difference between two consecutive IMFs is 4.3% < 5%). The signal-to-noise ratio (SNR) of each IMF was calculated, and 3 IMFs with an SNR < 15dB were removed (mainly corresponding to equipment foundation vibration noise). The reconstructed signal... v i ( t The peak value is 1.05g, and the signal-to-noise ratio (SNR) is 19dB.

[0066] S3. Construction of a unified attenuation model 1) Attenuation coefficient calculation (related operating parameters): Pressure wave attenuation coefficient: α p =0.02×(2.6×10 -5 / 2.4) -0.5 ≈6.08m -1 ; Sound wave attenuation coefficient: α s =0.1×1550 0.5 ≈3.93m -1 ; Vibration attenuation coefficient: α v =0.01×0.6=0.006m -1 ; 2) Information entropy and dynamic weight calculation: Calculate the entropy of each signal: H p = 0.7,H s = 1.3, H v = 1.0 Initial weight calculation: oh p =0.45、 oh s =0.24、 oh v =0.31 Weighting adjustment: All weights are within the range of (0, 0.7), so no adjustment is needed. The weighting allocation is consistent with the expectation that the pressure wave signal is most reliable during a large leak.

[0067] 3) Calculation of fusion attenuation value: F i =0.45×1.65 / 0.5+ 0.24×88 / 100+0.31×1.05 / 1≈2.02.

[0068] S4. Solving for Leakage Points Initial coordinates: take the geometric center of the node (175.0, 101.0).

[0069] Learning rate: High-pressure pipeline network setup .

[0070] Iteration process: After 185 iterations, it converged, satisfying the convergence condition. J ( x , y ) = 6.9 × 10 -7 <10 -6 ).

[0071] Estimated coordinates: P est (175.2, 130.5) S5. Verification and Optimization of Positioning Results Initial positioning error: m<5 meters.

[0072] Conclusion: The positioning error is far less than 5 meters, eliminating the need to activate the attenuation coefficient adjustment mechanism; the results are effective. This demonstrates the accuracy of this application under complex operating conditions involving high pressure, liquefied gas, and industrial noise.

[0073] S6. Comparative Experiment Under the same conditions, a comparison was made with traditional methods:

[0074] Conclusion: Example 2 fully demonstrates that by introducing an adaptive attenuation model of operating parameters and a dynamic weighting strategy, this application effectively overcomes the challenges posed by high pressure, high-density gas, and complex noise environments, achieving accurate positioning and further verifying its broad adaptability to operating conditions and technical advantages.

[0075] Example 3: Location Verification of Leakage in Urban Villages Using Aging Low-Pressure Natural Gas Pipelines S0. Implementation environment and parameters (highlighting adaptability to "complex terrain + minor leakage + multi-source interference") 1) Pipeline parameters: A low-pressure gas pipeline network in an urban village (design pressure 0.01MPa), over 20 years of service (local corrosion, bent routing), pipe diameter D=0.3m, transporting natural gas (density... r =0.72kg / m³, viscosity The burial depth is uneven (0.8~1.5m), the surrounding area is densely populated with residential buildings (dominant frequency of residential noise 300~600Hz), and there is sporadic renovation and construction work (dominant frequency of impact and vibration noise 50~150Hz).

[0076] 2) Monitoring node layout: Four nodes are arranged according to the triangular coverage principle (isosceles triangles, circumcircle radius ≈ 80m, meeting the low-pressure pipeline network requirement of ≤100m, adjacent triangles overlap rate of 45%, adaptable to curved pipelines). The coordinates are obtained by GIS + manual surveying: Node 1 (0,0), Node 2 (150,0), Node 3 (75,129.9), Node 4 (120,80). Due to the maximum burial depth of 1.5m (1.0m more than the standard 1m), the node spacing is adjusted to 135m according to the principle of "reducing by 10% for every 0.5m increase" to avoid monitoring blind spots.

[0077] 3) Simulated leak point: actual leak point P true (90, 70) (Pipeline bend, typical leak location in older pipelines), leak volume 0.05m 3 / h (<0.1m) 3 / h, ultra-micro leakage), controlled by a micro-leakage simulation device.

[0078] S1, Signal Acquisition The original signal was acquired using Ethernet clock synchronization (0.8ms accuracy) and a sampling duration of 3s. p i0 ( t (Peak value 0.08 MPa) s i0 ( t (Peak 75dB) v i0 ( t (Peak value 0.4g).

[0079] S2 signal preprocessing 1) Pressure wave denoising: Sampling frequency 1kHz. Due to the 35% overlap between fluid pulsation noise and pressure wave in the old pipeline network, a 4-level decomposition using a db4 wavelet basis is employed. s p =0.05, l p =0.05×sqrt(2ln3000)≈0.18, after noise reduction p i ( t Peak value 0.07 MPa (SNR=23 dB); 2) Sound Denoising: Combines residential and construction noise, adjusting the filter order. M =16, delay step k =5 (dominated by mixed noise), forgetting factor l =0.96, β i =0.82, after noise reduction s i ( t Peak frequency 72dB (SNR=19dB), main frequency f =800Hz; 3) Fiber optic vibration denoising: EMD decomposes the fiber into 6 IMFs, removes 3 construction vibration IMFs with SNR < 15dB, and then reconstructs the fiber. v i ( t Peak value 0.38g (SNR=17dB).

[0080] S3. Construction of a unified attenuation model 1) Attenuation coefficient: α p =0.02×(1.7×10 -5 / 0.72) -0.5 ≈3.91m -1 , α s =0.1×800 0.5 ≈2.83m -1 , α v =0.01×0.3=0.003m -1 ; 2) Information entropy and weight: H p =1.3、 H s =1.1、 H v =0.4 (highest fiber vibration reliability under ultra-small leakage), weight oh p=0.21、 oh s =0.25、 oh v =0.54; 3) Fusion attenuation value: F i =0.21×(0.07 / 0.5)+0.25×(72 / 100)+0.54×(0.38 / 1)≈0.39.

[0081] S4. Solving for Leakage Points The initial coordinates are the geometric centers of the four nodes (93.8, 67.5), and the learning rate is... (Low-pressure pipeline network), converged after 250 iterations. J ( x , y ) = 7.2 × 10 -7 ), estimate coordinates P est (90.2, 70.1).

[0082] S5. Verification of positioning results error The positioning is effective.

[0083] S6. Comparative Experiment Under the same conditions, a comparison was made with traditional methods:

[0084] Example 3 addresses the pain points of "complex terrain, extremely small leakage, and diverse noise" in old pipe networks in urban villages, verifying the adaptability of the present invention to low-pressure / old pipe networks, as well as its ability to identify ultra-small leaks of <0.05m³ / h, with a positioning error of only 0.22 meters, far exceeding the existing technology.

[0085] Example 4: Leakage location verification in a chemical industrial park using a medium- and high-pressure pipeline network for mixed-transmission gas. S0. Implementation Environment and Parameters (Highlighting the advantages of "gas mixed transmission + strong electromagnetic interference + closed-loop correction") 1) Pipeline parameters: A medium-high pressure gas pipeline network (design pressure 1.6MPa) in a chemical industrial park, transporting a mixture of natural gas and coalbed methane (the ratio changes dynamically, and the actual density is measured). r =0.78kg / m³, viscosity The pipe has a diameter of D=0.5m and a burial depth of 1.8m (0.8m more than the standard). The surrounding area is a chemical plant area (strong electromagnetic interference + fan noise with a main frequency of 1200~1800Hz).

[0086] 2) Monitoring node layout: Three nodes are arranged according to the triangular coverage principle (equilateral triangle, circumcircle radius 200m, meeting the medium and high pressure ≤250m requirement, overlap rate 38%); the original node spacing of 346m is adjusted to 346×(1-0.8 / 0.5×10%)=284m according to the burial depth, which meets the 100~500m requirement. Coordinates: Node 1 (0,0), Node 2 (284,0), Node 3 (142,245.0).

[0087] 3) Simulated leak point: actual leak point P true (142,122.5), leakage rate 0.09 m³ / h (minor leakage), fluctuations in the gas-mixing ratio cause dynamic changes in signal attenuation characteristics.

[0088] S1, Signal Acquisition GPS time synchronization (accuracy 0.6ms) is used, but the original signal contains noise due to electromagnetic interference. p i0 ( t (Peak value 1.2 MPa) s i0 ( t (Peak value 88dB) v i0 ( t (Peak value 0.9g).

[0089] S2, Signal Preprocessing 1) Pressure wave denoising: Sampling frequency 1kHz, 3-level decomposition using db4 wavelet basis. s p =0.10, l p =0.10×sqrt(2ln2000)≈0.30, after noise reduction p i ( t Peak value 1.0 MPa (SNR=26 dB); 2) Acoustic noise reduction: Electromagnetic interference plus fan noise, filter order. M =14, delay step k =4 (Industrial noise is dominant), forgetting factor l =0.95, β i =0.88, after noise reduction s i ( t Peak frequency 85dB (SNR=21dB), main frequency f =1500Hz; 3) Fiber vibration denoising: EMD decomposes the fiber into 7 IMFs, removes 4 low SNR IMFs affected by electromagnetic interference, and then reconstructs the fiber.v i ( t Peak value 0.82g (SNR=18dB).

[0090] S3. Construction of a unified attenuation model 1) Initial attenuation coefficient: α p =0.02×(2.1×10 -5 / 0.78) -0.5 ≈4.85m -1 , α s =0.1×1500 0.5 ≈3.87m -1 , α v =0.01×0.5=0.005m -1 ; 2) Information entropy and weight: H p =0.8、 H s =1.2、 H v =0.6, weight oh p =0.35、 oh s =0.21、 oh v =0.44; 3) Fusion attenuation value: F i =0.35×(1.0 / 0.5)+0.21×(85 / 100)+0.44×(0.82 / 1)≈1.23.

[0091] S4, Solving for the initial leak point Initial coordinates (142, 81.7), learning rate (Medium and high pressure pipeline network), converged after 190 iterations, estimated coordinates. P est (145.0, 124.0).

[0092] S5. Location Result Verification and Closed-Loop Optimization First error: e = sqrt[(142-145.0) 2 +(122.5-124.0) 2 ]≈3.35m<5m (no correction required), adjusted to the first time due to the estimation deviation of the air-fuel mixture parameters, error ε=6.2m>5m, correction initiated; Attenuation coefficient correction: α p,new =4.85×(1+0.1×6.2 / 5)=5.43m -1 , α s,new =3.87 × 1.124 = 4.35m -1 , α v,new =0.005 × 1.124 = 0.0056m -1 ; Second iteration: Return to S4, convergence after 150 iterations, estimate coordinates. P est (142.3, 122.7), error e =0.36 meters < 5 meters, the result is valid.

[0093] S6. Comparative Experiment Under the same conditions, a comparison was made with traditional methods:

[0094] Example 4 focuses on the extreme working condition of "mixed gas transmission (dynamic parameter changes) + strong electromagnetic interference" in a chemical industrial park. Through a closed-loop correction mechanism, the initial error of 6.2 meters was reduced to 0.36 meters, highlighting the model's adaptability to multiple types of gas and its robustness under strong interference environment.

[0095] Finally, any aspects of this invention not described herein are applicable to the prior art.

[0096] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the present invention, such designs should fall within the protection scope of the present invention.

Claims

1. A method for accurately locating leak points in gas pipeline networks based on a multi-source signal attenuation model, characterized in that, Includes the following steps: S1. Multi-source signal synchronous acquisition: n monitoring nodes are arranged in the gas pipeline network according to the triangular coverage principle, n≥3. Each node integrates three types of acquisition modules: pressure wave, sound wave and fiber optic vibration. Signal synchronous acquisition is achieved through GPS time synchronization or Ethernet clock synchronization, with a time accuracy ≤1ms. S2. Targeted preprocessing of multi-source signals: Wavelet thresholding is used to denoise the pressure wave signal, adaptive noise cancellation is used to denoise the acoustic wave signal, and Empirical Mode Decomposition (EMD) is used to denoise the fiber optic vibration signal, thereby obtaining the denoised effective pressure wave signal. p i ( t ), effective acoustic signal s i ( t ) effective vibration signal v i ( t ); S3. Construct a unified attenuation model for multi-source signals: S3.1 Establish a single signal attenuation sub-model: Construct a correlation gas viscosity μ With density ρ Pressure wave attenuation sub-model, associated acoustic wave dominant frequency f Acoustic attenuation sub-model, associated gas pipe diameter D A fiber vibration attenuation sub-model; S3.2 Dynamic Weight Fusion: The reliability of the effective pressure wave signal, effective sound wave signal, and effective vibration signal is calculated using information entropy, and weights ω are dynamically allocated based on the information entropy. p ω s ω v , and ω p +ω s +ω v =1, fusing the attenuation values ​​of the three types of signals to obtain a unified attenuation value. F i ; S4. Solving for the coordinates of the leakage point: Constructing a theoretically unified attenuation value. F i ( x,y (and measured uniform attenuation value) F i The sum of squares of the measured differences is the objective function. J ( x,y The objective function is solved iteratively using the gradient descent method. J ( x,y Estimated coordinates of the leak point at the minimum hour P est ( x est ,y est ); S5. Verification and Optimization of Location Results: Obtain the coordinates of the actual leak point. P true ( x true ,y true ), calculate positioning error ε ; like ε If the distance is greater than 5 meters, adjust the attenuation coefficients of the pressure wave, sound wave, and fiber vibration, and return to step S4 to iterate again until... ε Output positioning results when the distance is ≤5 meters.

2. The method for accurately locating gas pipeline leak points based on a multi-source signal attenuation model according to claim 1, characterized in that: In step S1, the triangular coverage principle is as follows: each monitoring area consists of 3 monitoring nodes forming an equilateral or isosceles triangle, the radius of the circumcircle of the triangle satisfies that the distance between medium and low pressure pipelines is ≤100 meters and the distance between high pressure pipelines is ≤250 meters, and the overlap rate of adjacent triangles is ≥30%; the node spacing rules are as follows: the spacing between medium and low pressure pipelines is 50~200 meters and the spacing between high pressure pipelines is 100~500 meters, and relative to the standard burial depth of 1 meter, the spacing is reduced by 10% for every 0.5 meters increase in burial depth.

3. The method for accurately locating gas pipeline leak points based on a multi-source signal attenuation model according to claim 1, characterized in that: In step S1, the acquisition module is configured as follows: the pressure wave acquisition module uses a piezoelectric pressure sensor with a range of 0~4.0MPa, an accuracy of 0.1%FS, and a sampling frequency of 1kHz; the sound wave acquisition module uses a microphone array with a frequency band of 500~2000Hz, a sensitivity of -38dBV / Pa, and a sampling frequency of 2kHz; the fiber optic vibration acquisition module uses a distributed fiber optic sensing system with a spatial resolution of 0.1m, a vibration resolution of 10-9g, and a sampling frequency of 500Hz.

4. The method for accurately locating gas pipeline leak points based on a multi-source signal attenuation model according to claim 1, characterized in that: In step S2, the pressure wave signal denoising adopts the wavelet thresholding method, specifically including determining the number of wavelet decomposition layers based on the sampling frequency and reconstructing the signal based on soft thresholding; the acoustic wave signal denoising adopts the adaptive noise cancellation method, specifically including arranging auxiliary microphones to collect reference noise and using an adaptive filtering algorithm to cancel noise; the optical fiber vibration signal denoising adopts the empirical mode decomposition (EMD) method, specifically including obtaining the intrinsic mode functions (IMFs) through decomposition and reconstructing the signal after removing noisy IMFs based on the signal-to-noise ratio.

5. The method for accurately locating gas pipeline leak points based on a multi-source signal attenuation model according to claim 1, characterized in that: In step S3.1, the pressure wave attenuation sub-model is: the amplitude of the pressure wave after attenuation. Pressure wave attenuation coefficient α p =0.02·( μ / ρ ) -0.5 Initial pressure wave amplitude at the leak point ; The sound wave attenuation sub-model is: the amplitude of the sound wave after attenuation. Sound wave attenuation coefficient α s =0.1· f 0.5 Initial acoustic amplitude at the leak point ; The fiber vibration attenuation sub-model is: amplitude after vibration attenuation. Vibration attenuation coefficient α v =0.01· D Initial vibration amplitude at the leak point ; In the above formula, L pi Leakage point P To the node i The straight-line distance , ( x i , y i ) is a node i The coordinates.

6. A method for accurately locating gas pipeline leak points based on a multi-source signal attenuation model according to claim 1 or 5, characterized in that: In step S3.2, the information entropy calculation process is as follows: normalize the sampled values ​​of the denoised signal to the (0,1] interval, and then... H =-Σ φ ( t )・ln( φ ( t )) Calculate the entropy of the pressure wave signal respectively H p Entropy of sound wave signals H s Fiber optic vibration signal entropy H v The dynamic weight calculation formula is as follows: ； The formula for calculating the uniform attenuation value Fi is as follows: F i = ω p ·( P iatt / P ref )+ ω s ·( S iatt / S ref )+ ω v ·( V iatt / V ref ),in P ref =0.5MPa S ref =100dB Vref =1g is a reference value.

7. The method for accurately locating gas pipeline leak points based on a multi-source signal attenuation model according to claim 6, characterized in that: In step S3.2, after dynamic weight allocation, if the calculated result of a certain weight is less than 0, it is set to 0; if a certain weight is greater than 0.7, it is forcibly adjusted to 0.7, and the remaining weights are adjusted accordingly. The proportion is allocated, where j , k These are the types of signals that were not truncated. H total This is the sum of the entropies of all signals.

8. The method for accurately locating gas pipeline leak points based on a multi-source signal attenuation model according to claim 1, characterized in that: In step S4, the objective function is ; When using gradient descent for iteration, the learning rate η Based on the pipeline pressure level: Learning rate for medium and low pressure pipelines. η =0.005, High-pressure pipeline learning rate η =0.01; if the error increases after a single iteration, the learning rate is halved; initial coordinates of the iteration ( x 0, y 0) represents the geometric center of all monitoring nodes; the convergence condition is that the difference between the objective function values ​​of two adjacent iterations is less than 10. -6 Or the number of iterations exceeds 500.

9. The method for accurately locating gas pipeline leak points based on a multi-source signal attenuation model according to claim 1, characterized in that: In step S5, the adjustment of the attenuation coefficient is performed according to the following formula: 。

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