A natural gas pipeline micro-leakage detection method, device and readable storage medium

Abstract

The present application relates to a kind of natural gas pipeline micro-leakage detection method, device and readable storage medium.The method comprises: based on Duffing equation and multiphase Duffing chaotic oscillator array constructs micro-leakage detection system, wherein Duffing equation is the double-coupled Duffing equation of scale transformation after changing the number of non-linear restoring force on the basis of conventional Duffing equation and coupling to damping term, and multiphase Duffing chaotic oscillator array includes at least 3 chaotic oscillators;Acquire the signal to be detected;The first input signal and the first frequency range of the signal to be detected are obtained by pre-processing the signal to be detected;Second input signal is obtained by reconstructing first input signal;Second input signal is input to the micro-leakage detection system, to analyze second input signal based on micro-leakage detection system, obtain corresponding analytical result, and whether natural gas pipeline micro-leakage occurs is judged according to analytical result.The present application improves the detection range and stability of natural gas pipeline micro-leakage detection system.

Description

Technical Field

[0001] This invention relates to the field of weak signal detection technology, and in particular to a method, apparatus and readable storage medium for detecting micro-leakage in natural gas pipelines. Background Technology

[0002] During the transportation of oil and natural gas, leaks in oil and gas pipelines can occur due to human factors, quality issues, and natural disasters, causing significant impacts on national and societal development. Over the decades, numerous leak detection technologies have emerged, including methods based on principles such as negative pressure waves, acoustic waves, magnetic flux leakage, and optical fibers. Among these, negative pressure wave leak detection is currently the primary method used for oil and gas pipeline leak detection in my country. However, it frequently experiences false alarms and missed detections when detecting leaks less than 3% of the instantaneous flow rate. This is because the negative pressure wave signal generated by an oil and gas pipeline leak is accompanied by strong background noise. Therefore, a method capable of effectively detecting even weak leak signals in oil and gas pipelines is needed.

[0003] In recent years, with the development of chaos theory, the technology for detecting weak leak signals has advanced rapidly, providing new ideas for detecting micro-leaks in oil and gas pipelines. Wang Qiang et al. first proposed applying chaos theory to micro-leak detection. Since then, extensive simulations of chaotic oscillators have been conducted, yielding various characteristics and making related improvements. Currently, the Duffing chaotic oscillator detects weak leak signals of different frequencies in natural gas pipelines by scaling the Duffing nonlinear differential equation, enabling adaptive detection of weak leak signals of arbitrary frequencies. The initial phase angle of the periodic signal in the weak leak signal is determined by changing the phase angle of the periodic driving force in the Duffing nonlinear differential equation. Finally, the Runge-Kutta method is used to solve the Duffing nonlinear differential equation, and the changes in the image before and after signal input are compared to verify whether a micro-leak has occurred in the natural gas pipeline.

[0004] However, existing technologies using the Runge-Kutta algorithm rarely use integer step sizes, leading to deviations in the calculation of weak leakage signals. This means the Runge-Kutta method does not operate with a fixed step size, resulting in erroneous calculations and detection failures. Furthermore, existing Duffing chaotic oscillator detection systems have limited detection range for the phase angle of weak leakage periodic signals, failing to detect signals with phase angles outside this range. This results in false alarms and poor overall system stability, making them susceptible to interference. Summary of the Invention

[0005] In view of this, it is necessary to provide a method, device and readable storage medium for detecting micro-leakage in natural gas pipelines, so as to solve the technical problems of the detection system being prone to false alarms or missed alarms and poor stability.

[0006] To address the aforementioned issues, this invention provides a method for detecting micro-leaks in natural gas pipelines, comprising: constructing a micro-leak detection system based on the Duffing equation and a multi-phase Duffing chaotic oscillator array, wherein the Duffing equation is a dual-coupled Duffing equation that modifies the order of the nonlinear restoring force and couples the damping term before performing a scale transformation based on the conventional Duffing equation, and the multi-phase Duffing chaotic oscillator array includes at least three chaotic oscillators;

[0007] Collect the signals to be detected from the natural gas pipeline;

[0008] The signal to be detected is preprocessed to obtain a first input signal and a first frequency range of the signal to be detected;

[0009] The first input signal is reconstructed to obtain the second input signal;

[0010] The second input signal is input to the micro-leakage detection system, and the micro-leakage detection system analyzes the second input signal to obtain the corresponding analysis result. Based on the analysis result, it is determined whether a micro-leakage has occurred in the natural gas pipeline.

[0011] Furthermore, the signal to be detected is preprocessed to obtain the first input signal, including: performing VMD decomposition on the signal to be detected to obtain the first input signal.

[0012] Furthermore, reconstructing the first input signal to obtain the second input signal includes: substituting the first input signal into a cubic spline interpolation algorithm for reconstruction to obtain the second input signal.

[0013] Furthermore, the Duffing equation is:

[0014]

[0015] in, x Let be the first independent variable in the double-coupled Duffing equation. y 1 is the first dependent variable; u The second independent variable in the dual-coupled Duffing equation is... y 2 is the second dependent variable; As a driving force of the cycle, For the periodic driving force amplitude, The periodic angular frequency, This is the initial phase; The signal to be measured is denoted as .

[0016] Furthermore, before reconstructing the first input signal to obtain the second input signal, the process also includes:

[0017] Set the system frequency of the micro-leakage detection system;

[0018] The system frequency is compared with the maximum value of the first frequency range. If the system frequency is less than or equal to the maximum value of the first frequency range, the process proceeds to the step of reconstructing the first input signal to obtain the second input signal. If the system frequency is greater than the maximum value of the first frequency range, it is determined that no micro-leakage has occurred in the natural gas pipeline.

[0019] Furthermore, before inputting the second input signal to the micro-leakage detection system, the following steps are also included:

[0020] Adjust the periodic driving amplitude of each chaotic oscillator in the Duffing chaotic oscillator array to bring the micro-leakage detection system into a critical chaotic state.

[0021] Furthermore, the analytical result is the phase trajectory of each of the chaotic oscillators after the input of the second input signal.

[0022] Furthermore, based on the analysis results, determining whether a micro-leak has occurred in the natural gas pipeline includes:

[0023] If at least one chaotic oscillator in the chaotic oscillator array has a phase trajectory that is in a large-scale periodic state, then it is determined that a micro-leak has occurred in the natural gas pipeline.

[0024] If the phase trajectories of the chaotic oscillators in the chaotic oscillator array are all in a chaotic state, then the system frequency is increased by a preset threshold frequency and the process returns to the step of setting the system frequency of the micro-leakage detection system.

[0025] The present invention also provides a micro-leakage detection device for natural gas pipelines, comprising:

[0026] Signal acquisition module, signal processing module, frequency setting module, signal reconstruction module, micro-leakage detection module;

[0027] The signal acquisition module is used to acquire the signal to be detected;

[0028] The signal processing module is used to preprocess the signal to be detected to obtain the first input signal;

[0029] The frequency setting module is used to set the system frequency;

[0030] The signal reconstruction module is used to reconstruct the first input signal to obtain the second input signal;

[0031] The micro-leakage detection module is used to analyze the second input signal and determine whether a micro-leakage has occurred in the natural gas pipeline.

[0032] The present invention also provides a readable storage medium, the readable storage medium including a stored computer program, which, when executed, controls the device where the readable storage medium is located to execute the natural gas pipeline micro-leakage detection method according to any one of the method embodiments of the present invention.

[0033] The beneficial effects of using the above embodiments are:

[0034] This invention provides a method, device, and readable storage medium for detecting micro-leakage in natural gas pipelines. It employs a dual-coupled multi-phase Duffing chaotic oscillator array to detect weak leak signals in pipelines, eliminating the influence of irrelevant factors on the detection results and enabling the detection of weak leak signals with arbitrary frequencies and initial phases. By preprocessing and reconstructing the signal to be detected, the sensitivity of the detection system to micro-leakage signals in the signal to be detected is enhanced, further reducing the possibility of missed or false detection of weak leak signals and improving the stability and noise immunity of the Duffing system. Attached Figure Description

[0035] Figure 1 This is a flowchart illustrating an embodiment of the natural gas pipeline micro-leakage detection method of the present invention.

[0036] Figure 2 This is a threshold diagram showing the corresponding initial phase angles for different embodiments of the natural gas pipeline micro-leakage detection method of the present invention.

[0037] Figure 3 The waveforms of the original signal and the signal after VMD decomposition are provided in an embodiment of the natural gas pipeline micro-leakage detection method of the present invention.

[0038] Figure 4 This is a timing diagram of the original signal provided in an embodiment of the natural gas pipeline micro-leakage detection method of the present invention;

[0039] Figure 5 This is a signal spectrum diagram after VMD decomposition provided in an embodiment of the natural gas pipeline micro-leakage detection method of the present invention;

[0040] Figure 6 The periodic driving force amplitude provided in an embodiment of the natural gas pipeline micro-leakage detection method of the present invention f The spectrum of the Lyapunov exponent with the largest variation;

[0041] Figure 7 The phase trajectory diagram of a chaotic oscillator in a critical chaotic state is provided in an embodiment of the natural gas pipeline micro-leakage detection method of the present invention.

[0042] Figure 8 A phase trajectory diagram of a chaotic oscillator in a large-scale periodic state, provided as an embodiment of the natural gas pipeline micro-leakage detection method of the present invention;

[0043] Figure 9 This is a phase trajectory diagram of each chaotic oscillator in a chaotic oscillator array when no signal is applied, provided in an embodiment of the natural gas pipeline micro-leakage detection method of the present invention.

[0044] Figure 10 This is a phase trajectory diagram of each chaotic oscillator in the chaotic oscillator array after the addition of a signal, provided in an embodiment of the natural gas pipeline micro-leakage detection method of the present invention.

[0045] Figure 11 This is a schematic diagram of an embodiment of the natural gas pipeline micro-leakage detection device of the present invention. Detailed Implementation

[0046] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0047] Example 1:

[0048] like Figure 1 As shown, Embodiment 1 of the present invention discloses a method for detecting micro-leakage in natural gas pipelines, comprising:

[0049] Step S101: Construct a micro-leakage detection system based on the Duffing equation and a multi-phase Duffing chaotic oscillator array. The Duffing equation is a dual-coupled Duffing equation that modifies the order of the nonlinear restoring force and couples the damping term before scaling, based on the conventional Duffing equation. The multi-phase Duffing chaotic oscillator array includes at least three chaotic oscillators.

[0050] As one example, the Duffing equation is:

[0051] (1)

[0052] in, x Let be the first independent variable in the double-coupled Duffing equation. y 1 is the first dependent variable; u The second independent variable in the dual-coupled Duffing equation is... y 2 is the second dependent variable; As a driving force of the cycle, For the periodic driving force amplitude, The periodic angular frequency, This is the initial phase; The signal to be measured is denoted as .

[0053] Specifically, the conventional Duffing nonlinear differential equation is shown below:

[0054] (2)

[0055] In equation (2), The damping coefficient; It is a nonlinear restoring force.

[0056] make Equation (2) is transformed into a state equation:

[0057] (3)

[0058] This invention modifies the nonlinear restoring force order of the conventional Duffing nonlinear differential equation and couples it with its damping term to establish the following Duffing equation:

[0059] (4)

[0060] in, The larger the value, the higher the coupling degree between the oscillators and the stronger the synchronization. When the coupling between the two oscillators is zero, their characteristics are consistent with those of a single-oscillator system. At this time, there is a coupling effect between the two oscillators, and they gradually tend to synchronize over time. In this embodiment, simulation experiments show that when... The time-coupled system achieves optimal performance.

[0061] Because the frequency of the periodic driving force in the equation will affect the threshold The changes cause repetitive calculations when calculating periodic signals of different frequencies. Therefore, it is necessary to perform a time-scale transformation on equation (4) to make... We can obtain:

[0062] (5)

[0063] The Duffing equation can be rewritten using a scaling transformation as follows:

[0064] (6)

[0065] In this embodiment, the Duffing equation is scaled, thus eliminating the influence of the time factor on the equation. Therefore, the threshold of equation (6) is... It will not be affected by the periodic driving frequency (which is inversely proportional to time), thus enabling the Duffing chaotic oscillator to adaptively detect signals of any frequency in weak pipeline leakage signals.

[0066] Meanwhile, since the mathematical properties of a single Duffing oscillator are identical to those of the coupled Duffing oscillator, it is sufficient to analyze one equation in equation (6) to study the influence of the phase angle on the system detection. The total periodic driving force of the Duffing chaotic oscillator is:

[0067] (7)

[0068] In equation (7) , .

[0069] Calculations yield the following results. , causing the cyclical driving force amplitude The system is tuned to a critical chaotic state. When the initial phase angle of the weak leakage periodic signal satisfies the following equation, the system will transition from a chaotic state to a large-scale periodic state, thus detecting a periodic signal of the same frequency:

[0070] (8)

[0071] The initial phase angle of the periodic signal contained in the weak leakage signal of the pipeline is often difficult to detect. If the initial phase angle in the weak leakage periodic signal satisfies equation (8), the total periodic driving force amplitude will be greater than the system threshold, and the weak leakage periodic signal can be detected; if equation (8) is not satisfied, the weak leakage periodic signal cannot be detected.

[0072] Changes in the initial phase angle will affect the threshold of the Duffing system. The detection threshold and signal-to-noise ratio are affected. First, the impact of the phase angle on the Duffing system threshold is analyzed. By considering the influence of changing the initial phase angle and using the Wolf algorithm to calculate the Lyapunov exponent spectrum of the system at each initial phase, the system threshold at different initial phases can be obtained. Size, such as Figure 2 As shown in the image, the threshold range for different initial phases is between 0.7333 and 0.7335. Therefore, a threshold can be set. Total cycle driving force amplitude This allows us to obtain the maximum detection range of a single Duffing system. When the amplitude of the weak leakage periodic signal... angular frequency Substituting into equation (8), we can obtain the initial phase angle. Only when equation (9) is satisfied can the driving phase of different periods be achieved. Under the condition of detecting a periodic signal with the same frequency:

[0073] (9)

[0074] Clearly, a single Duffing chaotic oscillator cannot detect weak leakage periodic signals with arbitrary initial phases. Therefore, the impact of the phase angle on the detection amplitude threshold and signal-to-noise ratio of the Duffing system is analyzed. The initial phase angle is varied to create a phase difference with the periodic driving force signal. Under different phase differences, the amplitude of the weak leakage periodic signal is continuously reduced. The corresponding system detection amplitude threshold is determined and the detection signal-to-noise ratio is calculated. Table 1 shows the system detection amplitude threshold and detection signal-to-noise ratio corresponding to different phase differences.

[0075] Table 1. Signal-to-noise ratio for detection at different phase differences

[0076]

[0077] As shown in Table 1, the initial phase difference between the phase of the initial phase and the phase of the periodic driving force signal is less than... When the phase difference is small, the detection threshold and signal-to-noise ratio of the chaotic oscillator are relatively close, while when the phase difference is large... At that time, the detection threshold and signal-to-noise ratio will decrease significantly, making it difficult to detect weak leakage signals. Therefore, this embodiment will focus on the area... Divided into three regions, namely the periodic driving phases of three Duffing chaotic oscillators. They are 0 respectively. , Each phase Substituting into equation (9), we can obtain the ranges of the three Duffing chaotic oscillators as follows: , , Thus, the total range of the chaotic oscillator array is obtained. Greater than the total phase region It can detect weak leakage signals with arbitrary initial phase.

[0078] Step S102: Collect the signal to be detected from the natural gas pipeline.

[0079] Step S103: Preprocess the signal to be detected to obtain the first input signal and the first frequency range of the signal to be detected;

[0080] As one embodiment, preprocessing the signal to be detected to obtain a first input signal includes: performing VMD decomposition on the signal to be detected to obtain the first input signal.

[0081] Specifically, the VMD decomposition method has excellent noise resistance and is exceptionally sensitive to the local characteristics of signals, showing good decomposition performance for weak pipeline leakage signals containing high noise and indistinct local characteristics. The algorithm can be divided into two processes: constructing and solving the variational problem. The VMD variational model is shown in equation (9):

[0082] (10)

[0083] in, These are the Intrinsic Mode Functions (IMFs) after decomposition. The center frequencies of each IMF component after decomposition. , Let be the impulse function. yes The Hilbert transform.

[0084] To solve the constrained optimization problem of equation (10), it is necessary to transform the constrained variational problem into an unconstrained variational problem and utilize the quadratic penalty term. and Lagrange multipliers Construct the augmented Lagrangian function, which is shown in equation (11):

[0085] (11)

[0086] By combining the alternating direction multiplier algorithm with Fourier isometric transform to update the center frequency and bandwidth of each IMF component, the optimized results are obtained as follows: (12), (13), (14):

[0087] (12)

[0088] (13)

[0089] (14)

[0090] In the formula For noise margin, For the number of iterations, , , as well as They are respectively , , and The Fourier isometry transform quantity.

[0091] Continuously improve the functional Perform updates and iterations until the iterative constraints shown in equation (15) are satisfied:

[0092] (15)

[0093] In this embodiment, the signal to be decomposed is set as follows:

[0094] (16)

[0095] in, It is a Gaussian white noise signal with a standard deviation of 0.1.

[0096] Original signal And the images of each IMF component after VMD decomposition, such as Figure 3 As shown, it can be seen that no mode aliasing occurs among the various IMF components decomposed by VMD, and most of the noise has been filtered out.

[0097] The weak leakage signal from the original pipeline (hereinafter referred to as the original signal) was decomposed using VMD. The timing diagram of the original signal is shown below. Figure 4 As shown. The spectrum of the signal after decomposition and denoising is obtained, as shown. Figure 5 As shown. From Figure 5 The frequency of the periodic signal in the weak pipeline leakage signal is estimated to be 16~20Hz.

[0098] As one embodiment, before reconstructing the first input signal to obtain the second input signal, the method further includes:

[0099] Step S104: Set the system frequency of the micro-leakage detection system.

[0100] Step S105: Compare the system frequency with the maximum value of the first frequency range.

[0101] Step S106: If the system frequency is greater than the maximum value of the first frequency range, it is determined that no micro-leakage has occurred in the natural gas pipeline.

[0102] Specifically, since the phase trajectory will only enter a large-scale periodic state and detect the weak leakage periodic signal when there is a weak leakage periodic signal with the same frequency as the periodic driving force and the phase is within a certain range in the signal to be tested, in this embodiment, the operating frequency of the detection system needs to be set to 16~20Hz. In order to achieve full coverage detection of the weak detection signal, the initial operating frequency of the detection system needs to be set to the minimum value of the first frequency range, i.e., 16Hz. At the same time, in order to improve the accuracy of the detection system, this embodiment sets the preset threshold frequency to 0.1Hz.

[0103] Step S107: If the system frequency is less than or equal to the maximum value of the first frequency range, reconstruct the first input signal to obtain the second input signal.

[0104] As one embodiment, reconstructing the first input signal to obtain the second input signal includes: substituting the first input signal into a cubic spline interpolation algorithm for reconstruction to obtain the second input signal.

[0105] Specifically, the periodic signal in the weak pipeline leakage signal is set as The amplitude is angular frequency is Phase is .make weak leakage periodic signal After scaling, we can obtain The actual data sampling rate was 100%, and a total of 1800 data points were collected. The calculated sampling step size is... However, the Runge-Kutta method, by using non-integer step sizes to solve the Duffing nonlinear differential equation, leads to numerical instability in the solution, resulting in signal instability. The deviation in the selected values ​​ultimately prevents the Runge-Kutta method from strictly adhering to the step size. Performing the solution will result in errors in the calculated value, causing the system to miss or miscalculate.

[0106] The principle of cubic spline interpolation is to reconstruct discrete data by constructing a set of cubic polynomials to approximate the data points in a weak pipeline leakage signal. This generates smooth curves between the discrete data points while preserving their shape and trend, thus addressing the problem of numerical instability in the Runge-Kutta method due to its non-integer step size. In this embodiment, the decomposed first input signal is substituted into the cubic spline interpolation algorithm to generate smooth curves between discrete data points while preserving their shape and trend, resulting in the second input signal.

[0107] As one embodiment, before inputting the second input signal to the micro-leakage detection system, the system further includes:

[0108] Step S108: Adjust the periodic driving force amplitude of each chaotic oscillator in the Duffing chaotic oscillator array to bring the micro-leakage detection system into a critical chaotic state.

[0109] Specifically, in the conventional Duffing nonlinear differential equation, the amplitude of the periodic driving force is changed. This can change its phase trajectory. When When the phase is small, the phase trajectory exhibits periodic oscillations of the phase point near the focal point; as the phase becomes smaller... As the magnitude increases, the phase trajectory will successively exhibit homoclinic orbits, period-doubling bifurcation, and chaotic phenomena; when... Increase to near the threshold At this point, the system will be in a critical chaotic state, transitioning from a chaotic state to a large-scale periodic state; when Increase until it is just greater than the threshold At this point, the system will immediately enter a large-scale periodic state. Based on this Duffing chaotic oscillator detection principle, the phase trajectory of the Duffing equation is first adjusted to a critical chaotic state, i.e. Then the signal to be tested When a weak leakage periodic signal with the same frequency as the periodic driving force exists in the measured signal, i.e., when the measured signal is included in the equation, When this happens, the phase trajectory quickly enters a large-scale periodic state, thus detecting weak leakage periodic signals. Therefore, by determining whether the system has entered a large-scale periodic state, it is possible to determine whether the signal under test contains weak leakage periodic signals.

[0110] For the threshold in Duffing's nonlinear differential equation The Lyapunov exponent method is used to solve this problem. The principle of the Lyapunov exponent method is that for a Duffing chaotic oscillator, the existence of at least one Lyapunov exponent greater than zero indicates that the system is in a chaotic state; if all Lyapunov exponents are less than zero, the system is in an ordered state. Therefore, the threshold... That is, the periodic driving force amplitude corresponding to the instant when the maximum Lyapunov exponent changes from positive to negative. .

[0111] Let the parameter in equation (2) be , , , , , The Lyapunov exponent is calculated by first using the fourth-order Runge-Kutta algorithm to calculate equation (2), where the fixed step size is set to 0.001 and the total simulation time is 100s. Then, the Wolf algorithm is used to calculate the time series obtained in the first step, where the periodic driving force amplitude is set to... With a step size of 0.0001, the amplitude of the periodic driving force is obtained. The spectrum of the largest Lyapunov exponent changes, such as Figure 6 As shown in the figure. The threshold of the Duffing system can be obtained from the figure. =0.7333. Substituting this value into equation (2), we get... At this point, the system is in a critical chaotic state, and its phase trajectory is as follows: Figure 7 As shown; then the periodic driving force amplitude Increase the step size by one, so that At this point, the system is in a large-scale periodic state, and its phase trajectory is as follows: Figure 8 As shown.

[0112] Step S109: Input the second input signal to the micro-leakage detection system, and analyze the second input signal based on the micro-leakage detection system to obtain the corresponding analysis result;

[0113] Step S110: Determine whether a micro-leak has occurred in the natural gas pipeline based on the analysis results.

[0114] As one embodiment, the analysis result is the phase trajectory of each chaotic oscillator after the input of the second input signal.

[0115] Specifically, in this embodiment, the Runge-Kutta method is used to solve the Duffing equation to achieve the analysis of the second input signal, and finally the phase trajectory diagrams of the three Duffing chaotic oscillators are obtained.

[0116] As one embodiment, determining whether a micro-leak has occurred in the natural gas pipeline based on the analysis results includes:

[0117] Step S111: If the phase trajectory of at least one chaotic oscillator in the chaotic oscillator array is in a large-scale periodic state, then it is determined that a micro-leak has occurred in the natural gas pipeline.

[0118] Step S112: If the phase trajectories of the chaotic oscillators in the chaotic oscillator array are all in a chaotic state, then increase the system frequency by a preset threshold frequency and return to step S104.

[0119] Specifically, the phase trajectories of the three Duffing chaotic oscillators of the system when no weak leakage signal is input are as follows: Figure 9 As shown, the phase trajectories of the three Duffing chaotic oscillators after inputting a weak leakage signal are as follows: Figure 10 As shown, from Figure 9 , Figure 10 As can be seen, although the phase trajectories of chaotic oscillators 1 and 2 have changed, they are still in a chaotic state. However, the phase trajectory of chaotic oscillator 3 changes from a chaotic state before the input signal to a large-scale periodic state after the input signal, indicating that there is a micro-leakage within the detection range of chaotic oscillator 3. If the phase trajectories of all three chaotic oscillators after the input signal are in a chaotic state, it does not necessarily mean that there is no micro-leakage in the natural gas pipeline. At this point, the system frequency needs to be adjusted to further detect the natural gas pipeline, i.e., the system frequency is increased by 0.1Hz. When the system frequency is increased to 20.1Hz, it indicates that the system has completed the detection of the same frequency periodic signal of 16~20Hz and has not detected any weak leakage signal. At this point, it can be preliminarily determined that there is no micro-leakage in the natural gas pipeline.

[0120] Compared with existing technologies, this embodiment provides a method for detecting micro-leaks in natural gas pipelines. It employs a dual-coupled multi-phase Duffing chaotic oscillator array to detect micro-leaks in natural gas pipelines. The dual-coupled Duffing equation exhibits significant anti-interference capabilities and is less susceptible to the influence of the periodic driving frequency, thus improving system stability. It enables the Duffing chaotic oscillator to adaptively detect signals of arbitrary frequencies within the weak leakage periodic signal of the pipeline, increasing the detection range of the system. Furthermore, the use of multiple chaotic oscillators allows the detection system to detect weak leakage periodic signals of arbitrary phases, further enhancing the detection range, stability, and reliability. Simultaneously, by decomposing and reconstructing the signal to be detected, interference factors are eliminated, resulting in higher sensitivity of the detection system to the weak leakage periodic signal, improving the reliability of the detection results, and reducing the possibility of missed or false alarms.

[0121] Example 2:

[0122] like Figure 11 As shown, Embodiment 2 of the present invention provides a micro-leakage detection device for natural gas pipelines, characterized in that it includes:

[0123] Signal acquisition module 1101, signal processing module 1102, frequency setting module 1103, signal reconstruction module 1104, micro-leakage detection module 1105;

[0124] The signal acquisition module 1101 is used to acquire the signal to be detected;

[0125] The signal processing module 1102 is used to preprocess the signal to be detected to obtain the first input signal;

[0126] The frequency setting module 1103 is used to set the system frequency;

[0127] The signal reconstruction module 1104 is used to reconstruct the first input signal to obtain the second input signal;

[0128] The micro-leakage detection module 1105 is used to analyze the second input signal and determine whether a micro-leakage has occurred in the natural gas pipeline.

[0129] For ease of description and brevity, the embodiments of the device of the present invention include all the embodiments of the above-described natural gas pipeline micro-leakage detection method, and will not be repeated here.

[0130] Example 3:

[0131] Embodiment 3 of the present invention provides a readable storage medium, which includes a stored computer program. When the computer program is executed, it controls the device where the readable storage medium is located to execute the natural gas pipeline micro-leakage detection method as described in any embodiment of the method of the present invention.

[0132] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0133] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for detecting micro-leakage in natural gas pipelines, characterized in that, include: A micro-leakage detection system is constructed based on the Duffing equation and a multi-phase Duffing chaotic oscillator array. The Duffing equation is a dual-coupled Duffing equation, which modifies the order of the nonlinear restoring force and couples the damping term before scaling, based on the conventional Duffing equation. The multi-phase Duffing chaotic oscillator array comprises at least three chaotic oscillators, and the Duffing equation is as follows: in, x Let be the first independent variable in the double-coupled Duffing equation. y 1 is the first dependent variable; u The second independent variable in the dual-coupled Duffing equation is... y 2 is the second dependent variable; As a driving force of the cycle, For the periodic driving force amplitude, The periodic angular frequency, This is the initial phase; The signal to be measured; Collect the signals to be detected from the natural gas pipeline; The signal to be detected is preprocessed to obtain a first input signal and a first frequency range of the signal to be detected; The first input signal is reconstructed to obtain the second input signal; The second input signal is input to the micro-leakage detection system, and the micro-leakage detection system analyzes the second input signal to obtain the corresponding analysis result. Based on the analysis result, it is determined whether a micro-leakage has occurred in the natural gas pipeline.

2. The method for detecting micro-leakage in natural gas pipelines according to claim 1, characterized in that, The signal to be detected is preprocessed to obtain the first input signal, including: The first input signal is obtained by performing VMD decomposition on the signal to be detected.

3. The method for detecting micro-leakage in natural gas pipelines according to claim 1, characterized in that, Reconstructing the first input signal to obtain the second input signal includes: The first input signal is substituted into a cubic spline interpolation algorithm for reconstruction to obtain the second input signal.

4. The method for detecting micro-leakage in natural gas pipelines according to claim 1, characterized in that, Before reconstructing the first input signal to obtain the second input signal, the process also includes: Set the system frequency of the micro-leakage detection system; The system frequency is compared with the maximum value of the first frequency range. If the system frequency is less than or equal to the maximum value of the first frequency range, the process proceeds to the step of reconstructing the first input signal to obtain the second input signal. If the system frequency is greater than the maximum value of the first frequency range, it is determined that no micro-leakage has occurred in the natural gas pipeline.

5. The method for detecting micro-leakage in natural gas pipelines according to claim 1, characterized in that, Before inputting the second input signal to the micro-leakage detection system, the system further includes: Adjust the periodic driving amplitude of each chaotic oscillator in the Duffing chaotic oscillator array to bring the micro-leakage detection system into a critical chaotic state.

6. The method for detecting micro-leakage in natural gas pipelines according to claim 4, characterized in that, The analytical result is the phase trajectory of each of the chaotic oscillators after the second input signal is input.

7. The method for detecting micro-leakage in natural gas pipelines according to claim 6, characterized in that, Determining whether a micro-leak has occurred in the natural gas pipeline based on the analysis results includes: If at least one chaotic oscillator in the chaotic oscillator array has a phase trajectory that is in a large-scale periodic state, then it is determined that a micro-leak has occurred in the natural gas pipeline. If the phase trajectories of the chaotic oscillators in the chaotic oscillator array are all in a chaotic state, then the system frequency is increased by a preset threshold frequency and the process returns to the step of setting the system frequency of the micro-leakage detection system.

8. A natural gas pipeline micro-leakage detection device, used to perform the natural gas pipeline micro-leakage detection method as described in any one of claims 1 to 7, characterized in that, include: Signal acquisition module, signal processing module, frequency setting module, signal reconstruction module, micro-leakage detection module; The signal acquisition module is used to acquire the signal to be detected; The signal processing module is used to preprocess the signal to be detected to obtain the first input signal; The frequency setting module is used to set the system frequency; The signal reconstruction module is used to reconstruct the first input signal to obtain the second input signal; The micro-leakage detection module is used to analyze the second input signal and determine whether a micro-leakage has occurred in the natural gas pipeline.

9. A readable storage medium, characterized in that, The readable storage medium includes a stored computer program, which, when executed, controls the device containing the readable storage medium to perform the natural gas pipeline micro-leakage detection method as described in any one of claims 1 to 7.

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