A method and system for detecting leaks in a gathering pipeline

Abstract

The application discloses a kind of method and system for detecting gathering pipeline leakage, comprising: according to the real-time vibration signal of different collection positions of the gathering pipeline to be detected, the power spectral density of different monitoring time is obtained;According to the power spectral density of different monitoring time and reference power spectral density, the frequency characteristic value of different collection positions is calculated;According to the frequency characteristic value of different positions, determine whether the abnormal event of the gathering pipeline to be detected occurs;When the abnormal event of the gathering pipeline to be detected occurs, according to the real-time vibration signal of the collection position of abnormal event, whether the abnormal event is a leakage event by analyzing target leakage source position to diagnose. The application can effectively realize the discrimination of gathering pipeline abnormal event by the change of power spectral density of vibration signal, and the real-time vibration signal of multiple positions can realize abnormal event type determination and accurate positioning of leakage source.

Description

Technical Field

[0001] This invention relates to the field of safety monitoring technology for oil and gas field engineering, and in particular to a method and system for detecting leaks in gathering and transportation pipelines. Background Technology

[0002] Pipeline transportation is the most common mode of transporting oil and gas over long distances. With the development of the oil and gas industry, pipeline leaks caused by factors such as pipeline aging, corrosion perforation, geological disasters, and third-party sabotage occur frequently, resulting in serious loss of life and property and significant social impact. Therefore, it is necessary to study accurate methods for pipeline leak detection and location in order to control the scale of leaks in a timely manner and reduce their impact.

[0003] Because pipelines are laid underground and transport a wide variety of media with complex compositions and diverse leakage modes, the technologies and detection methods used in pipeline leak research vary greatly. Common oil and gas gathering and transportation pipeline detection technologies include negative pressure wave detection, fiber optic detection, and infrasound detection. When a pipeline leaks, the medium inside the pipe is ejected under strong internal pressure. The friction between the medium and the pipe wall generates vibration waves within the pipeline. Vibration sensors pre-deployed on the pipeline can collect these leakage signals in real time, and the leak information can be obtained through analysis and processing. The vibration waves caused by leaks decay slowly and travel long distances, making pipeline vibration wave localization of leak points a promising approach.

[0004] An existing patent document (publication number CN108050396A) discloses a fluid pipeline leak source monitoring and location system and method. This scheme determines whether a leak has occurred in the pipeline by acquiring time-frequency domain images and standard pipeline time-frequency domain images, and locates the leak source location based on the average time difference. This scheme processes the signals acquired by the sensors. Since the signals acquired by the sensors in actual monitoring records are the combined result of factors such as the leak source, propagation medium, and coupling medium, the continuous vibration signal wave field is complex and often contains interference noise in the recording. Therefore, the initial arrival of the vibration signal is difficult to pick up and there are errors, which leads to inaccurate location results.

[0005] A method for accurately locating two-point leaks in pressure pipelines is disclosed in an existing patent document (publication number CN107435817A). This method uses correlator detection data to calculate the propagation speed of the leaking sound wave in the pipeline, and finally calculates the location of the leak source based on a cross-correlation localization algorithm. Unlike sudden vibration signals, the continuous vibration signals generated by leaks are inseparable in the time domain, which makes conventional vibration source localization methods unsuitable for calculating the location of the leak source.

[0006] A method for detecting and accurately locating small leaks in pressure pipelines is proposed in an existing patent document (publication number CN106290578A). This method establishes a small leak detection and location model for pressure pipelines and introduces independent component analysis (ICA) technology to achieve signal separation for small leak location. This method determines the location of the small leak by separating its location data. However, unlike sudden vibration signals, the continuous vibration signals generated by a leak are not separable in the time domain.

[0007] Therefore, existing technologies utilize the propagation speed of the leak source signal within the pipeline and the arrival time difference of signals between sensors to locate the leak source. However, since the signals collected by sensors in actual monitoring records are a comprehensive result of factors such as the leak source, the propagation medium, and the coupling medium, and the wavelength of continuous vibration signals is complex and often contains interference noise, it is difficult and error-prone to pick up the initial arrival of the vibration signal, leading to inaccurate location results. Unlike sudden vibration signals, the continuous vibration signals generated by leaks are inseparable in the time domain, making conventional vibration source location methods unsuitable for leak source location calculations.

[0008] In summary, there is a need in the existing technology to propose an accurate and effective scheme for leak event detection and location based on pipeline vibration signals. Summary of the Invention

[0009] The purpose of this invention is to provide an accurate and effective solution for detecting leak events and locating leak sources based on pipeline vibration signals, thereby solving the problems of inaccurate leak source location and inaccurate leak event detection in the prior art.

[0010] To address the aforementioned technical problems, embodiments of the present invention provide a method for detecting leaks in gathering and transportation pipelines, comprising: obtaining power spectral density at different monitoring times based on real-time vibration signals at different sampling locations of the gathering and transportation pipeline to be detected; calculating frequency characteristic values ​​at different sampling locations based on the power spectral density at different monitoring times and a reference power spectral density; determining whether an abnormal event has occurred in the gathering and transportation pipeline to be detected based on the frequency characteristic values ​​at the different locations; and diagnosing whether the abnormal event is a leak event by analyzing the target leak source location based on the real-time vibration signal at the sampling location of the abnormal event when the abnormal event occurs in the gathering and transportation pipeline to be detected.

[0011] Preferably, the reference power spectral density is determined based on historical vibration signals, including: acquiring historical vibration signals within multiple preset time periods, wherein the preset time periods are the time periods corresponding to the stable operation of the gathering and transportation pipeline to be detected; obtaining the power spectral density corresponding to different preset time periods based on the historical vibration signals; obtaining the average power spectral density based on the power spectral density corresponding to all preset time periods, and using the average power spectral density as the reference power spectral density.

[0012] Preferably, the average power spectral density is calculated using the following expression:

[0013]

[0014] in, The average power spectral density is represented by M, which represents the number of preset time periods, and PSD is the value of PSD. m (f) represents the power spectral density of the historical vibration signal within the m-th preset time period, and f represents the frequency sequence.

[0015] Preferably, the step of calculating the frequency characteristic values ​​of different acquisition locations based on the power spectral density at different monitoring times and the reference power spectral density includes: obtaining the power spectral density difference at different locations based on the power spectral density at different monitoring times and the reference power spectral density; extracting the abnormal deviation sequence generated at different acquisition locations based on the variance of the power spectral density difference at different monitoring times and the reference power spectral density; and calculating the frequency characteristic value at the corresponding acquisition location based on the abnormal deviation sequence.

[0016] Preferably, the step of extracting the abnormal deviation sequence generated at different locations based on the power spectral density difference at different monitoring time periods and the standard deviation of the reference power spectral density includes: calculating the ratio of the power spectral density difference at different monitoring times at a certain acquisition location to the variance of the reference power spectral density at different frequencies to obtain the normalized value of the power spectral deviation at different monitoring times; and extracting the abnormal deviation sequence by identifying abnormal deviation values ​​based on the normalized value of the power spectral deviation at different monitoring times, wherein...

[0017] The normalized value of the power spectrum deviation is calculated using the following expression:

[0018]

[0019] in, Misfit represents the normalized power spectral deviation at monitoring time t at the l-th acquisition location. l (f) represents the power spectral density difference at different monitoring periods at the l-th acquisition location, std(f) represents the variance of the reference power spectral density at different frequencies, and f represents the frequency sequence;

[0020] The abnormal deviation sequence is extracted using the following expression:

[0021]

[0022] in, This represents the abnormal deviation value at the t-th monitoring time in the abnormal deviation sequence at the l-th acquisition location.

[0023] Preferably, the abnormal deviation sequence is converted into frequency feature values ​​at corresponding positions using the following expression:

[0024]

[0025] Where, Φ l (t) represents the frequency characteristic value at the l-th sampling location, N f The frequency sequence number represents the number of points in the frequency sequence, and f represents the frequency sequence. The abnormal deviation value at the t-th monitoring time in the abnormal deviation sequence at the l-th acquisition location.

[0026] Preferably, the step of determining whether an abnormal event has occurred in the collection and transportation pipeline to be detected based on the frequency characteristic values ​​of the different acquisition locations includes: determining that there is an abnormal signal at the current acquisition location when the frequency characteristic value at a certain acquisition location is greater than a preset detection threshold; and determining that an abnormal event has occurred in the collection and transportation pipeline to be detected when there are abnormal signals at at least two acquisition locations.

[0027] Preferably, the step of diagnosing whether an abnormal event is a leakage event by analyzing the location of a target leakage source based on the real-time vibration signal at the abnormal event acquisition location includes: obtaining normalized correlation function values ​​and observation time differences between different abnormal event acquisition locations based on the real-time vibration signals at at least two abnormal event acquisition locations; determining the search range of the target leakage source location based on the at least two abnormal event acquisition locations, and obtaining multiple candidate locations based on the leakage source search range and a preset leakage source search step size; determining the velocity search range based on the propagation velocity of the vibration signal at the at least two abnormal event acquisition locations, and determining multiple candidate velocities based on this and a preset velocity search step size; obtaining at least one location target function value using a preset target function, based on the multiple candidate locations and multiple candidate velocities, combined with the normalized correlation function value and the observation time difference; obtaining the target leakage source location based on the location corresponding to the smallest location target function value among all location target function values; and diagnosing whether the abnormal event is a leakage event based on the target leakage source location.

[0028] Preferably, the cross-correlation function is calculated using the following expression:

[0029]

[0030] Among them, R k1k2 (τ) represents the cross-correlation function value between the sampling locations of the k1th and k2th anomalous events, N represents the number of vibration signal sampling points, and x k1 (n) represents the real-time vibration signal at the location of the k1th abnormal event, xk2 (n) represents the real-time vibration signal at the location of the k2th abnormal event, and τ is the time lag.

[0031] Preferably, the target function value for positioning is calculated using the following expression:

[0032]

[0033] Where g represents the objective function value of the positioning parameters, K represents the number of abnormal event collection locations, and R k1k2 Δt represents the normalized cross-correlation function value between the sampling locations of the k1th and k2th anomaly events. k1k2 Δat represents the theoretical time difference. k1k2 tt represents the observation time difference. k1 and tt k2 This represents the arrival time (in seconds) of the vibration signals collected by the k1th and k2th sensors, determined at a specified alternative location and speed. i Let a represent the value of the i-th position among all candidate positions. k1 and a k2 Indicates the positions of the k1th and k2th sensors, v j At represents the j-th speed value among all candidate speeds. k1 The arrival time of the vibration signal at the target risk location is indicated by at. k2 This indicates the arrival time of the vibration signal at the location where the k2th abnormal event was collected.

[0034] Preferably, the step of diagnosing whether the abnormal event is a leakage event based on the target leakage source location includes: determining at least two abnormal event acquisition locations related to the current target leakage source location, and determining the duration of the abnormal event occurring at the at least two abnormal event acquisition locations; locating the dynamic leakage source location within the duration based on the at least two abnormal event acquisition locations, thereby obtaining the distance deviation standard deviation based on the dynamic leakage source location and the current target leakage source location; and determining the abnormal event as a leakage event when the duration is greater than a preset time threshold and the distance deviation standard deviation is less than a preset deviation threshold.

[0035] On the other hand, embodiments of the present invention also provide a system for detecting leaks in gathering and transportation pipelines. The system is used to implement the method described above, wherein the system includes: a power spectral density calculation module configured to obtain power spectral densities at different monitoring times based on real-time vibration signals at different sampling locations of the gathering and transportation pipeline to be detected; a sampling location feature calculation module configured to calculate frequency characteristic values ​​at different sampling locations based on the power spectral densities at different monitoring times and a reference power spectral density; an abnormal event discrimination module configured to determine whether an abnormal event has occurred in the gathering and transportation pipeline to be detected based on the frequency characteristic values ​​at the different locations; and a leakage event discrimination module configured to diagnose whether the abnormal event is a leakage event by analyzing the target leakage source location based on the real-time vibration signals at the abnormal event sampling location when an abnormal event occurs in the gathering and transportation pipeline to be detected.

[0036] Compared with the prior art, one or more embodiments of the above solutions may have the following advantages or beneficial effects:

[0037] This invention proposes a method and system for detecting leaks in gathering and transportation pipelines. This method and system effectively identify abnormal events in gathering and transportation pipelines by utilizing changes in the power spectral density of vibration signals. Furthermore, by using real-time vibration signals from multiple locations, the type of abnormal event can be determined and the leak source accurately located, enabling accurate and effective detection of leak events and accurate and effective location of leak sources.

[0038] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the description, claims, and drawings. Attached Figure Description

[0039] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0040] Figure 1 This is a schematic diagram illustrating the steps of a method for detecting leaks in a gathering and transportation pipeline according to an embodiment of this application.

[0041] Figure 2 This is a schematic flowchart illustrating the method for detecting leaks in gathering and transportation pipelines according to an embodiment of this application.

[0042] Figure 3 This is a schematic diagram of the sensor deployment scheme in a natural gas pipeline leakage simulation experiment under the first scenario of the method for detecting leakage in gathering and transmission pipelines according to an embodiment of this application.

[0043] Figure 4 This is a schematic diagram of real-time vibration signals collected at different locations during a natural gas pipeline leakage simulation experiment in the first case of the method for detecting leakage in a gathering and transmission pipeline according to an embodiment of this application.

[0044] Figure 5 This is a schematic diagram showing the frequency characteristic values ​​of the real-time vibration signal in a natural gas pipeline leakage simulation experiment under the first scenario of the method for detecting leakage in a gathering and transmission pipeline according to an embodiment of this application.

[0045] Figure 6 This is a schematic diagram of the location of the leak source in a natural gas pipeline leak simulation experiment under the first scenario of the method for detecting leaks in gathering and transmission pipelines according to embodiments of this application.

[0046] Figure 7 This is a schematic diagram showing the result of the dynamic leak source location corresponding to the real-time vibration signal in the first case of the method for detecting leaks in gathering and transmission pipelines according to an embodiment of this application.

[0047] Figure 8 This is a schematic diagram of the location of the leak source in a natural gas pipeline leak simulation experiment under the second scenario of the method for detecting leaks in gathering and transmission pipelines according to embodiments of this application.

[0048] Figure 9 This is a schematic diagram showing the result of the dynamic leak source location corresponding to the real-time vibration signal in the natural gas pipeline leak simulation experiment under the second case of the method for detecting leaks in gathering and transmission pipelines according to embodiments of this application.

[0049] Figure 10 This is a schematic diagram of the location of the leak source in a natural gas pipeline leak simulation experiment under the third scenario of the method for detecting leaks in gathering and transmission pipelines according to embodiments of this application.

[0050] Figure 11 This is a schematic diagram showing the location of the leakage source corresponding to the real-time vibration signal in a natural gas pipeline leakage simulation experiment under the third scenario of the method for detecting leakage in gathering and transmission pipelines according to embodiments of this application.

[0051] Figure 12 This is a schematic diagram of real-time vibration signals collected at different times during a natural gas pipeline leakage simulation experiment in the fourth case of the method for detecting leakage in gathering and transmission pipelines according to embodiments of this application.

[0052] Figure 13 This is a schematic diagram showing the frequency characteristic values ​​of the real-time vibration signal in a natural gas pipeline leakage simulation experiment under the fourth scenario of the method for detecting leakage in gathering and transmission pipelines according to embodiments of this application.

[0053] Figure 14This is a schematic diagram showing the result of the dynamic leak source location corresponding to the real-time vibration signal in the fourth case of the method for detecting leaks in gathering and transmission pipelines according to embodiments of this application.

[0054] Figure 15 This is a schematic diagram of a system for detecting leaks in gathering and transportation pipelines according to an embodiment of this application. Detailed Implementation

[0055] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings and examples, so that the process of how the present invention uses technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly. It should be noted that, as long as there is no conflict, the various embodiments and features in the various embodiments of the present invention can be combined with each other, and the resulting technical solutions are all within the protection scope of the present invention.

[0056] Furthermore, the steps illustrated in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although a logical order is shown in the flowcharts, in some cases the steps shown or described may be performed in a different order than that presented here.

[0057] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments. Unless the context clearly indicates otherwise, the singular forms “a” and “an” as used herein are also intended to include the plural. It should also be understood that the terms “comprising” and / or “including” as used herein specify the presence of the stated features, integers, steps, operations, units, and / or components, without excluding the presence or addition of one or more other features, integers, steps, operations, units, components, and / or combinations thereof.

[0058] An existing patent document (publication number CN108050396A) discloses a fluid pipeline leak source monitoring and location system and method. This scheme determines whether a leak has occurred in the pipeline by acquiring time-frequency domain images and standard pipeline time-frequency domain images, and locates the leak source location based on the average time difference. This scheme processes the signals acquired by the sensors. Since the signals acquired by the sensors in actual monitoring records are the combined result of factors such as the leak source, propagation medium, and coupling medium, the continuous vibration signal wave field is complex and often contains interference noise in the recording. Therefore, the initial arrival of the vibration signal is difficult to pick up and there are errors, which leads to inaccurate location results.

[0059] A method for accurately locating two-point leaks in pressure pipelines is disclosed in an existing patent document (publication number CN107435817A). This method uses correlator detection data to calculate the propagation speed of the leaking sound wave in the pipeline, and finally calculates the location of the leak source based on a cross-correlation localization algorithm. Unlike sudden vibration signals, the continuous vibration signals generated by leaks are inseparable in the time domain, which makes conventional vibration source localization methods unsuitable for calculating the location of the leak source.

[0060] A method for detecting and accurately locating small leaks in pressure pipelines is proposed in an existing patent document (publication number CN106290578A). This method establishes a small leak detection and location model for pressure pipelines and introduces independent component analysis (ICA) technology to achieve signal separation for small leak location. This method determines the location of the small leak by separating its location data. However, unlike sudden vibration signals, the continuous vibration signals generated by a leak are not separable in the time domain.

[0061] Therefore, existing technologies utilize the propagation speed of the leak source signal within the pipeline and the arrival time difference of signals between sensors to locate the leak source. However, since the signals collected by sensors in actual monitoring records are a comprehensive result of factors such as the leak source, the propagation medium, and the coupling medium, and the wavelength of continuous vibration signals is complex and often contains interference noise, it is difficult and error-prone to pick up the initial arrival of the vibration signal, leading to inaccurate location results. Unlike sudden vibration signals, the continuous vibration signals generated by leaks are inseparable in the time domain, making conventional vibration source location methods unsuitable for leak source location calculations.

[0062] In summary, there is a need in the existing technology to propose an accurate and effective scheme for leak event detection and location based on pipeline vibration signals.

[0063] Example 1

[0064] Figure 1 This is a schematic diagram illustrating the steps of a method for detecting leaks in a gathering and transportation pipeline according to an embodiment of this application. Figure 2 This is a schematic flowchart illustrating a method for detecting leaks in gathering and transportation pipelines according to an embodiment of this application. The following is in conjunction with... Figure 1 and Figure 2 The specific process of the method for detecting leakage in gathering and transportation pipelines (hereinafter referred to as the "pipeline leakage detection method") described in the embodiments of the present invention will be explained.

[0065] Step S110: Based on the real-time vibration signals at different collection locations of the pipeline to be tested, obtain the power spectral density at different monitoring times.

[0066] refer to Figure 2First, real-time vibration signals at different locations are collected by an accelerometer that is attached to the pipeline to be tested. The power spectral density at different monitoring times is calculated, and then the power spectral density at different times is used to determine whether an abnormal event has occurred.

[0067] Specifically, when the power spectral density is obtained at different monitoring times, the Welch method is used to calculate the power spectral density of the real-time vibration signal within a certain time window along a sliding time window, thus obtaining the power spectral density PSD at different monitoring times. t (f), where t is the monitoring time.

[0068] Step S120: Calculate the frequency characteristic values ​​of different acquisition locations based on the power spectral density and reference power spectral density at different monitoring times.

[0069] refer to Figure 2 After obtaining the power spectral density at different monitoring times, the power spectral density of all monitoring times corresponding to the acquisition location is compared with the reference power spectral density. The frequency domain characteristic values ​​of different sampling locations are calculated, and the relationship between the frequency characteristic values ​​and the preset detection threshold is used to determine whether an abnormal event has occurred at the acquisition location.

[0070] In one embodiment, the reference power spectral density is determined based on historical vibration signals. Specifically, determining the reference power spectral density includes the following steps A1-A3:

[0071] Step A1: Obtain historical vibration signals within multiple preset time periods.

[0072] Step A2: Based on historical vibration signals, obtain the power spectral density corresponding to different preset time periods.

[0073] Step A3: Based on the power spectral density corresponding to all preset time periods, obtain the average power spectral density, and use the average power spectral density as the reference power spectral density.

[0074] Specifically, in determining the reference power spectral density, the first step is to select historical vibration signals from M time periods corresponding to the stable operation of the pipeline to be tested. The Welch method is then used to calculate the power spectral density of the historical vibration signals in different preset time periods. Finally, the average power spectral density is obtained based on the power spectral density corresponding to all preset time periods, and the average power spectral density is used as the reference power spectral density.

[0075] In step A1, the preset time period is the time period corresponding to the stable operation of the gathering and transportation pipeline to be tested.

[0076] In step A2, the method for obtaining the power spectral density corresponding to different preset time periods is not limited and can be reasonably selected according to actual application requirements. For example, the Welch method can be used.

[0077] In sub-step A3, the average power spectral density is calculated using the following expression:

[0078]

[0079] in, The average power spectral density is represented by M, which represents the number of preset time periods, and PSD is the value of PSD. m (f) represents the power spectral density of the historical vibration signal within the m-th preset time period, and f represents the frequency sequence.

[0080] In one embodiment, when calculating the frequency characteristic values ​​at different acquisition locations, the power spectral density difference at different locations is first obtained. Then, the power spectral density difference at different monitoring periods is compared with the standard deviation to extract the abnormal deviation sequence generated at different acquisition locations. The frequency domain characteristic values ​​are then calculated based on the abnormal deviation sequence. Specifically, step S120 includes the following sub-steps B1-B3:

[0081] Sub-step B1: Based on the power spectral density at different monitoring times and the reference power spectral density, obtain the power spectral density difference at different locations.

[0082] Sub-step B2: Based on the power spectral density difference and the variance of the reference power spectral density during different monitoring periods, extract the abnormal deviation sequence generated at different acquisition locations.

[0083] Sub-step B3: Calculate the frequency characteristic value at the corresponding acquisition location based on the abnormal deviation sequence.

[0084] In sub-step B1, the power spectral density difference is calculated using the following expression:

[0085]

[0086] Among them, Misfit l (f) represents the power spectral density difference at the l-th acquisition location during different monitoring periods. This represents the power spectral density at the l-th acquisition location at the t-th monitoring time. denoted as the reference power spectral density, and f represents the frequency sequence.

[0087] In sub-step B2, during the extraction of the abnormal deviation sequence, the normalized values ​​of the power spectrum deviation at different monitoring times are first obtained. Then, based on these normalized values, the abnormal deviation sequence is extracted by identifying outliers. Specifically, sub-step B2 includes the following steps B21-B22:

[0088] Step B21: Calculate the ratio of the power spectral density difference at different monitoring times at a certain acquisition location to the variance of the reference power spectral density at different frequencies, and obtain the normalized value of the power spectral deviation at different monitoring times.

[0089] Step B22: Based on the normalized values ​​of power spectrum deviation at different monitoring times, extract the abnormal deviation sequence by identifying outlier values.

[0090] In step B21, the normalized value of the power spectrum deviation is calculated using the following expression:

[0091]

[0092] in, Misfit represents the normalized power spectral deviation at monitoring time t at the l-th acquisition location. l (f) represents the power spectral density difference at different monitoring periods at the l-th acquisition location, std(f) represents the variance of the reference power spectral density at different frequencies, and f represents the frequency sequence.

[0093] In step B22, the abnormal deviation sequence is extracted using the following expression:

[0094]

[0095] in, This represents the abnormal deviation value at the t-th monitoring time in the abnormal deviation sequence at the l-th acquisition location.

[0096] In sub-step B3, the abnormal deviation sequence is converted into frequency feature values ​​at the corresponding positions using the following expression:

[0097]

[0098] Where, Φ l (t) represents the frequency characteristic value at the l-th sampling location, N f The frequency sequence number represents the number of points in the frequency sequence, and f represents the frequency sequence. The abnormal deviation value at the t-th monitoring time in the abnormal deviation sequence at the l-th acquisition location.

[0099] Specifically, when calculating the frequency domain feature values ​​at different locations, firstly, the power spectral density difference at different locations is calculated using expression (2), then the power spectral density difference is divided by the standard deviation of the reference power spectral density using expression (3) to obtain the power spectral deviation normalization value at different monitoring times, then the power spectral deviation normalization value is compared with 1 using expression (4) to extract the abnormal deviation sequence, and finally the abnormal deviation sequence is converted into the frequency domain feature value at the corresponding location using expression (5).

[0100] Step S130: Determine whether an abnormal event has occurred in the gathering and transportation pipeline to be detected based on the frequency characteristic values ​​at different locations.

[0101] Continue to refer to Figure 2 After obtaining the frequency domain feature values ​​at different locations, the relationship between the frequency domain feature values ​​at the corresponding locations and the preset detection threshold is compared to determine whether an abnormal event has occurred in the collection and transportation pipeline to be detected.

[0102] In one embodiment, the step of determining whether an abnormal event has occurred in the gathering and transportation pipeline to be detected includes the following sub-steps C1-C2:

[0103] Sub-step C1: When the frequency characteristic value at a certain sampling location is greater than the preset detection threshold, it is determined that there is an abnormal signal at the current sampling location.

[0104] Sub-step C2: If abnormal signals are present at at least two acquisition locations, determine that an abnormal event has occurred in the collection and transportation pipeline to be detected.

[0105] Specifically, a detection threshold is set. If the frequency domain characteristic value at a certain acquisition location is greater than the preset detection threshold, then an abnormal signal is considered to exist at that monitoring moment. That is, when Φ l When (t) is greater than the preset detection threshold, it is considered that there is an abnormal signal at time t of the l-th acquisition position. When the frequency domain characteristic value of two or more acquisition positions is greater than the preset detection threshold, it is considered that an abnormal event has occurred in the collection and transportation pipeline to be detected.

[0106] In sub-step C1, the specific value of the preset detection threshold is not limited and can be reasonably selected according to the actual application requirements.

[0107] Step S140: When an abnormal event occurs in the gathering and transportation pipeline to be detected, the abnormal event is diagnosed as a leakage event by analyzing the location of the target leakage source based on the real-time vibration signal at the location of the abnormal event.

[0108] Continue to refer to Figure 2When an abnormal event occurs in the gathering and transportation pipeline to be detected, the location of the target leakage source is determined based on the real-time vibration signal at the location of the abnormal event. Then, the location of the target leakage source is analyzed to diagnose whether the abnormal event is a leakage event. If it is a leakage event, the time and location of the leakage are determined.

[0109] In one embodiment, when analyzing the location of a target leak source to diagnose whether an abnormal event is a leak event, firstly, based on real-time vibration signals from at least two abnormal event acquisition locations, the similarity coefficient of the vibration signals and the observation time difference between different abnormal event acquisition locations are obtained. Then, multiple candidate locations and multiple candidate velocities are determined, so as to diagnose whether an abnormal event is a leak event based on the signal similarity coefficient, observation time difference, candidate locations, and candidate velocities. Specifically, step S140 includes the following sub-steps D1-D6:

[0110] Sub-step D1: Based on the real-time vibration signals at at least two abnormal event acquisition locations, obtain the normalized correlation function value and observation time difference between different abnormal event acquisition locations.

[0111] Sub-step D2: Based on the locations of at least two abnormal events, determine the search range of the target leak source location, and based on the leak source search range and the preset leak source search step size, obtain multiple candidate locations.

[0112] Sub-step D3: Determine the velocity search range based on the propagation speed of the vibration signals at at least two abnormal event acquisition locations, and determine multiple candidate velocities based on the velocity search range and a preset velocity search step size.

[0113] Sub-step D4: Based on multiple candidate locations and multiple candidate velocities, and combining the normalized correlation function value and the observation time difference, at least one positioning objective function value is obtained using a preset objective function.

[0114] Sub-step D5: Based on the location corresponding to the smallest location target function value among all location target function values, obtain the location of the target leakage source.

[0115] Sub-step D6: Based on the location of the target leak source, diagnose whether the abnormal event is a leak event.

[0116] Specifically, the process of obtaining the location of the target leak source includes: assuming that sensors at K acquisition locations detect an abnormal event, the kth acquisition location is denoted as a. k The normalized cross-correlation function R is obtained for the vibration records of each pair of sensors. k1k2 The maximum value Rm corresponding to (t) k1k2 The similarity coefficient of vibration signals between two different abnormal event acquisition locations is given by time Δat. k1k2 For observation time difference.

[0117] The location of the target leakage source is achieved by superimposing multi-channel cross-correlation functions. The leakage source search range and preset leakage source search step size are set to [s1,s2,Δs], and the velocity search range and preset velocity search step size are set to [v1,v2,Δv]. The arrival time tt of the k-th sensor corresponding to each possible event source and velocity value is calculated respectively. k Then, through the obtained theory, the time difference Δtt ij and observation time difference Δat ij The target function is constructed, and finally, the location of the target leakage source is obtained based on the location corresponding to the smallest target function value among all the target function values.

[0118] In sub-step D1, the cross-correlation function of the normalized correlation function value is calculated using the following expression:

[0119]

[0120] Among them, R k1k2 (τ) represents the cross-correlation function value between the sampling locations of the k1th and k2th anomalous events, N represents the number of vibration signal sampling points, and x k1 (n) represents the real-time vibration signal at the location of the k1th abnormal event, x k2 (n) represents the real-time vibration signal at the location of the k2th abnormal event, and τ is the time lag.

[0121] Among them, R k1k2 (τ) represents the cross-correlation function value between the k1th and k2th abnormal event acquisition locations, which is the correlation between the two signals after a time lag τ.

[0122] In sub-step D2, the specific value of the preset leak source search step size is not limited and can be reasonably selected according to the actual application requirements.

[0123] In sub-step D3, the specific value of the preset speed search step size is not limited and can be reasonably selected according to the actual application requirements.

[0124] In sub-step D4, the target function value is calculated using the following expression:

[0125]

[0126] Where g represents the objective function value of the positioning parameters, K represents the number of abnormal event collection locations, and R k1k2 Δt represents the normalized cross-correlation function value between the sampling locations of the k1th and k2th anomaly events. k1k2 Δat represents the theoretical time difference.k1k2 tt represents the observation time difference. k1 and tt k2 This represents the arrival time (in seconds) of the vibration signals collected by the k1th and k2th sensors, determined at a specified alternative location and speed. i Let a represent the value of the i-th position among all candidate positions. k1 and a k2 Indicates the positions of the k1th and k2th sensors, v j At represents the j-th speed value among all candidate speeds. k1 The arrival time of the vibration signal at the target risk location is indicated by at. k2 This indicates the arrival time of the vibration signal at the location where the k2th abnormal event was collected.

[0127] Furthermore, in sub-step D6, when diagnosing whether an abnormal event is a leakage event based on the target leakage source location, real-time vibration signals are continuously collected at at least two abnormal event acquisition locations related to the current target leakage source location, and the dynamic leakage source location is determined. Therefore, the relationship between the dynamic leakage source location and the target leakage source location is used to diagnose whether the abnormal event is a leakage event. Specifically, sub-step D6 includes the following steps D61-D63:

[0128] Step D61: Determine at least two abnormal event collection locations related to the current target leak source location, and determine the duration of the abnormal events occurring at at least two abnormal event collection locations.

[0129] Step D62: Based on the locations of at least two abnormal events, locate the location of the dynamic leakage source within the duration, and then obtain the standard deviation of the distance deviation based on the location of the dynamic leakage source and the current target leakage source location.

[0130] Step D63: When the duration is greater than a preset time threshold and the standard deviation of the distance deviation is less than a preset deviation threshold, the abnormal event is determined to be a leakage event.

[0131] Specifically, when diagnosing whether an abnormal event is a leakage event, a preset time threshold is first set. When the duration of the abnormal event exceeds the preset time threshold and the location of the dynamic leakage source is less than 10% of the maximum monitoring distance, the abnormal event is determined to be a leakage event. This eliminates the possibility that abnormal vibrations in the pipeline caused by processes such as air filling in the pipeline during maintenance and resumption of production, thus avoiding the interference of these situations being mistaken for pipeline leaks.

[0132] In step D63, the value of the preset time threshold is not limited and can be reasonably selected according to the actual application requirements.

[0133] In step D63, the value of the preset distance deviation threshold is not limited and can be reasonably selected according to actual application requirements. For example, it can be 10% of the maximum monitoring distance.

[0134] Example 2

[0135] Based on the above embodiment one, the following describes the specific process of applying the method for detecting leakage in gathering and transmission pipelines described in the embodiment of the present invention to a natural gas pipeline leakage simulation experiment in the first case for leakage detection.

[0136] The first scenario involves simulating a natural gas pipeline leak by using a small rupture disc to burst under pressure, thus mimicking a pipeline perforation or crack.

[0137] Figure 3 This is a schematic diagram of the sensor deployment scheme in the natural gas pipeline leakage simulation experiment under the first scenario. (Refer to...) Figure 3 In the diagram, the star represents the installation location of the rupture disc (its actual relative position is 46.6m), and the triangles represent seven sampling locations (the numbers are the different sampling location numbers, with relative positions from left to right: 2.93m, 13.48m, 24.11m, 34.66m, 40.53m, 46.6m, and 53.51m). Each sampling location uses an accelerometer fixed with adhesive to continuously collect real-time vibration signals before and after the rupture disc ruptures, from the time the nitrogen container is filled into the natural gas pipeline. The sampling results are shown below. Figure 4 As shown.

[0138] Figure 4 (a) is a record of continuously acquired real-time vibration signals, with a sampling frequency of 12800Hz. Figure 4 (b) and Figure 4 (c) Records from 2000 acquisition locations at 11s and 25s respectively. The record at 11s contains the blasting signal. As the monitoring distance increases, the amplitude of the real-time vibration signal decreases. The continuous leakage signal after the blasting (taking 25s as an example) is a broadband signal with no obvious initial arrival amplitude.

[0139] The reference power spectral density is calculated from the real-time vibration signal during a period of stable operation. Then, using a sliding time window, the power spectral density of the real-time vibration signal within a certain time window (e.g., 0.5 s) is calculated to obtain the frequency characteristic values. The results are as follows: Figure 5 As shown, the preset detection threshold is set to 10 ( Figure 5 As shown by the dashed line, it can be seen that abnormal signals were continuously detected at all 7 acquisition locations after 10 seconds in the monitoring record. Even real-time vibration signals without obvious strong high-frequency energy amplitudes were identified some time after the explosion.

[0140] During the leak source localization process, the leak source search range was set to 0–70 m, the preset search step size was 0.1 m, the preset signal velocity search range was 500–6000 m / s, and the preset velocity search step size was 10 m / s. Then, the target function value was calculated using a formula to determine the leak source location.

[0141] Figure 6 (a) and Figure 6 (b) is Figure 4 The diagram shows the distribution of the target function value at the 11th and 25th seconds. The location with the minimum target function value is the location of the target leakage source. Compared with the actual location, the errors of the location results recorded at these two times are 0m and 1.3m, respectively.

[0142] Figure 7 In the distribution of dynamic leak source location results, the dashed line area in the figure represents the confidence interval considering the maximum monitoring distance. The vast majority of event locations fall within this confidence interval, indicating a concentration of abnormal events in spatial locations. Through anomaly detection in real-time vibration signals and real-time location of dynamic leak sources, it can be determined that... Figure 4 (a) If abnormal events continue to exist in the monitoring record after 10 seconds, it can be determined as a leak source event and the start time and location of the leak can be obtained. Statistical analysis shows that the location of the leak source is 45.3, and the location error is 2.8% of the maximum observation distance.

[0143] Example 3

[0144] Based on the above embodiments one and two, the following describes the specific process of applying the method for detecting leaks in gathering and transmission pipelines described in the embodiments of the present invention to a natural gas pipeline leak simulation experiment for leak detection in the second case.

[0145] In the second scenario, the natural gas pipeline leakage simulation experiment reduces the number of sampling points compared to Example 2, in order to analyze the effectiveness of leak source location and leakage event detection with fewer sampling points. Utilizing... Figure 3 The real-time vibration signals from four acquisition locations (numbered 1, 3, 5, and 7, with relative positions from left to right of 2.93m, 24.11m, 40.53m, and 53.51m respectively) were used to detect leakage events. The leakage source search range, leakage source search step size, velocity search range, and preset velocity search step size were all the same as in Example 2.

[0146] Figure 8 (a) and Figure 8(b) shows the distribution of the location target function value recorded at 11s and 25s. The location corresponding to the minimum value of the location target function is the location of the leak source obtained by inversion. Compared with the true location, the errors of the location results recorded at these two times are 0.4m and -0.6m, respectively. Figure 9 The distribution of dynamic leak source locations is shown. The dashed line range represents the confidence interval considering the maximum monitoring distance. The vast majority of event locations are within the confidence interval, indicating that the spatial locations of abnormal events are concentrated. Statistical analysis revealed that the target leak source location is 45.7, with a location error of 2.1% of the maximum observation distance.

[0147] Example 4

[0148] Based on the above embodiments one and two, the following describes the specific process of applying the method for detecting leaks in gathering and transmission pipelines described in the embodiments of the present invention to a natural gas pipeline leak simulation experiment for leak detection in a third case.

[0149] In the third scenario, the natural gas pipeline leakage simulation experiment was based on Example 2, but with a reduction in the number of sampling locations and a lower sampling frequency, in order to analyze the effectiveness of leakage event detection under conditions of fewer measurements and lower sampling frequency.

[0150] use Figure 3 Four data acquisition locations, numbered 1, 3, 5, and 7 (relative positions from left to right: 2.93m, 24.11m, 40.53m, and 53.51m respectively), were used, with the data sampling rate reduced to 2000Hz. The leak source search range, leak source search step size, velocity search range, and preset velocity search step size were all the same as in Example 2.

[0151] Figure 10 (a) and Figure 10 (b) shows the distribution of the target function values ​​recorded at 11s and 25s. The location corresponding to the minimum value of the target function is the location of the leakage source. Compared with the actual location, the errors of the location results recorded at these two times are 0.7m and -1.9m, respectively.

[0152] Figure 11 The distribution of dynamic leak source locations is shown. The dashed line represents the confidence interval considering the maximum monitoring distance. All dynamic leak source locations fall within this confidence interval, indicating a concentration of abnormal events in spatial locations. Statistical analysis yielded a leak source location of 45.3, with a location error of 2.77% of the maximum observation distance.

[0153] Example 5

[0154] Based on the above embodiments one and two, the following describes the specific process of applying the method for detecting leaks in gathering and transmission pipelines described in the embodiments of the present invention to a natural gas pipeline leak simulation experiment in the fourth case for leak detection.

[0155] In the fourth scenario, the natural gas pipeline leakage simulation experiment involves simulating pipeline vibration signals by striking the natural gas pipeline with a hammer. The sensor deployment scheme in this embodiment is the same as in embodiment two, with the hammer striking point at 40.53m.

[0156] Figure 12 This data represents four consecutive hammer impact events. A reference power spectral density was calculated from the pipeline monitoring vibration data during a period of stable operation. Then, using a sliding time window, the power spectral density of the real-time vibration signal within a certain time window (0.5 s) was calculated to obtain the frequency characteristic values ​​of the real-time vibration signal, such as... Figure 13 As shown. The preset detection threshold is set to 10 ( Figure 13 As shown by the dashed line, it can be seen that abnormal signals were detected around 10s, 20s, 30s and 40s in the monitoring record.

[0157] During the leak source localization process, the leak source search range was set to 0–70 m, with a preset search step size of 0.1 m. The preset signal velocity search range was 500–6000 m / s, with a preset velocity search step size of 10 m / s. The target function value was then calculated using a formula to determine the leak source location. The time window for the cross-correlation calculation was 1 second.

[0158] Figure 14 This is a schematic diagram of the results of the dynamic leakage source location. Since the abnormal event only appears for a short time (duration < 5s) and disappears quickly, it is determined that this type of abnormal event is a non-leakage signal event.

[0159] Example 6

[0160] Based on the method for detecting leaks in gathering and transportation pipelines provided in Embodiments 5 above, this invention also provides a system for detecting leaks in gathering and transportation pipelines. This system for detecting leaks in gathering and transportation pipelines is used to implement the method for detecting leaks in gathering and transportation pipelines as described above.

[0161] Figure 15 This is a schematic diagram of a system for detecting leaks in gathering and transportation pipelines, according to an embodiment of this application. Figure 15 As shown in the embodiment of the present invention, the system for detecting leakage in gathering and transportation pipelines includes: a power spectral density calculation module 1501, a data acquisition location feature calculation module 1502, an abnormal event discrimination module 1503, and a leakage event discrimination module 1504.

[0162] Specifically, the power spectral density calculation module 1501 is implemented according to the method described in step S110 above, and is configured to obtain the power spectral density at different monitoring times based on the real-time vibration signals at different collection locations of the pipeline to be tested; the collection location feature calculation module 1502 is implemented according to the method described in step S120 above, and is configured to calculate the frequency characteristic values ​​at different collection locations based on the power spectral density at different monitoring times and the reference power spectral density; the abnormal event discrimination module 1503 is implemented according to the method described in step S130 above, and is configured to determine whether an abnormal event has occurred in the pipeline to be tested based on the frequency characteristic values ​​at different locations; the leakage event discrimination module 1504 is implemented according to the method described in step S140 above, and is configured to diagnose whether the abnormal event is a leakage event by analyzing the target leakage source location based on the real-time vibration signal at the abnormal event collection location when an abnormal event occurs in the pipeline to be tested.

[0163] This invention proposes a method and system for detecting leaks in gathering and transportation pipelines. This method and system effectively identify abnormal events in gathering and transportation pipelines by utilizing changes in the power spectral density of vibration signals. Furthermore, by using real-time vibration signals from multiple locations, the type of abnormal event can be determined and the leak source accurately located, enabling accurate and effective detection of leak events and accurate and effective location of leak sources.

[0164] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0165] In the description of this invention, unless otherwise stated, "a plurality of" means two or more; the terms "upper," "lower," "left," "right," "inner," "outer," "front end," "rear end," "head," "tail," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0166] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "connected" and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0167] It should be understood that the embodiments disclosed herein are not limited to the specific structures, processing steps, or materials disclosed herein, but should be extended to equivalent substitutions of these features as understood by those skilled in the art. It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting.

[0168] The phrase "an embodiment" or "an embodiment" used in this specification means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Therefore, the phrase "an embodiment" or "an embodiment" appearing in various places throughout the specification does not necessarily refer to the same embodiment.

[0169] While the embodiments disclosed in this invention are as described above, the content is merely for the purpose of facilitating understanding of the invention and is not intended to limit the invention. Any person skilled in the art to which this invention pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope disclosed herein; however, the scope of patent protection of this invention shall still be determined by the scope defined in the appended claims.

Claims

1. A method for detecting leaks in gathering and transportation pipelines, characterized in that, include: Based on the real-time vibration signals at different collection locations of the gathering and transportation pipeline under test, the power spectral density at different monitoring times is obtained; Based on the power spectral density and reference power spectral density at different monitoring times, calculate the frequency characteristic values ​​at different acquisition locations; Based on the frequency characteristic values ​​at different locations, determine whether an abnormal event has occurred in the collection and transportation pipeline to be detected; When an abnormal event occurs in the pipeline to be detected, the abnormal event is diagnosed as a leakage event by analyzing the location of the target leakage source based on the real-time vibration signal at the location of the abnormal event.

2. The method according to claim 1, characterized in that, The reference power spectral density is determined based on historical vibration signals, including: Historical vibration signals within multiple preset time periods are acquired, wherein the preset time periods are the time periods corresponding to when the gathering and transportation pipeline to be detected is in a stable operating condition; Based on the historical vibration signals, the power spectral density corresponding to different preset time periods is obtained; The average power spectral density is obtained based on the power spectral density corresponding to all preset time periods, and the average power spectral density is used as the reference power spectral density.

3. The method according to claim 2, characterized in that, The average power spectral density is calculated using the following expression: in, The average power spectral density is represented by M, which represents the number of preset time periods, and PSD is the value of PSD. m (f) represents the power spectral density of the historical vibration signal within the m-th preset time period, and f represents the frequency sequence.

4. The method according to claim 2 or 3, characterized in that, The step of calculating the frequency characteristic values ​​of different acquisition locations based on the power spectral density and reference power spectral density at different monitoring times includes: Based on the power spectral density at different monitoring times and the reference power spectral density, the power spectral density difference at different locations is obtained; Based on the power spectral density difference during different monitoring periods and the variance of the reference power spectral density, the abnormal deviation sequence generated at different acquisition locations is extracted; Based on the abnormal deviation sequence, calculate the frequency characteristic value at the corresponding acquisition location.

5. The method according to claim 4, characterized in that, The step of extracting the abnormal deviation sequence generated at different locations based on the power spectral density difference during different monitoring periods and the standard deviation of the reference power spectral density includes: Calculate the ratio of the power spectral density difference at different monitoring times at a certain acquisition location to the variance of the reference power spectral density at different frequencies to obtain the normalized value of the power spectral deviation at different monitoring times. Based on the normalized values ​​of power spectrum deviation at different monitoring times, anomaly deviation sequences are extracted by identifying outliers. The normalized value of the power spectrum deviation is calculated using the following expression: in, Misfit represents the normalized power spectral deviation at monitoring time t at the l-th acquisition location. l (f) represents the power spectral density difference at different monitoring periods at the l-th acquisition location, std(f) represents the variance of the reference power spectral density at different frequencies, and f represents the frequency sequence; The abnormal deviation sequence is extracted using the following expression: in, This represents the abnormal deviation value at the t-th monitoring time in the abnormal deviation sequence at the l-th acquisition location.

6. The method according to claim 4 or 5, characterized in that, The abnormal deviation sequence is converted into frequency feature values ​​at the corresponding positions using the following expression: Where, Φ l (t) represents the frequency characteristic value at the l-th sampling location, N f The frequency sequence number represents the number of points in the frequency sequence, and f represents the frequency sequence. The abnormal deviation value at the t-th monitoring time in the abnormal deviation sequence at the l-th acquisition location.

7. The method according to any one of claims 4 to 6, characterized in that, The step of determining whether an abnormal event has occurred in the collection and transportation pipeline to be detected based on the frequency characteristic values ​​of the different collection locations includes: When the frequency characteristic value at a certain sampling location is greater than the preset detection threshold, it is determined that there is an abnormal signal at the current sampling location; If abnormal signals are detected at at least two sampling locations, it is determined that an abnormal event has occurred in the pipeline to be detected.

8. The method according to claim 7, characterized in that, The step of diagnosing whether an abnormal event is a leakage event by analyzing the target leakage source location based on the real-time vibration signal at the abnormal event acquisition location includes: Based on the real-time vibration signals at at least two abnormal event acquisition locations, the normalized correlation function value and observation time difference between different abnormal event acquisition locations are obtained; Based on at least two abnormal event collection locations, determine the search range of the target leak source location, and based on the leak source search range and the preset leak source search step size, obtain multiple candidate locations. Based on the propagation speed of vibration signals at at least two abnormal event acquisition locations, determine the speed search range, and based on this, combined with a preset speed search step size, determine multiple candidate speeds. Based on multiple candidate locations and multiple candidate velocities, combined with the normalized correlation function value and the observation time difference, at least one positioning target function value is obtained using a preset target function; The location of the target leakage source is obtained by identifying the location corresponding to the smallest target function value among all the target function values. Based on the location of the target leakage source, diagnose whether the abnormal event is a leakage event.

9. The method according to claim 8, characterized in that, The cross-correlation function is calculated using the following expression: Among them, R k1k2 (τ) represents the cross-correlation function value between the sampling locations of the k1th and k2th anomalous events, N represents the number of vibration signal sampling points, and x k1 (n) represents the real-time vibration signal at the location of the k1th abnormal event, x k2 (n) represents the real-time vibration signal at the location of the k2th abnormal event, and τ is the time lag.

10. The method according to any one of claims 7 to 9, characterized in that, The target function value for localization is calculated using the following expression: Where g represents the objective function value of the positioning parameters, K represents the number of abnormal event collection locations, and R k1k2 Δt represents the normalized cross-correlation function value between the sampling locations of the k1th and k2th anomaly events. k1k2 Δat represents the theoretical time difference. k1k2 tt represents the observation time difference. k1 and tt k2 This represents the arrival time (in seconds) of the vibration signals collected by the k1th and k2th sensors, determined at a specified alternative location and speed. i Let a represent the value of the i-th position among all candidate positions. k1 and a k2 Indicates the positions of the k1th and k2th sensors, v j At represents the j-th speed value among all candidate speeds. k1 The arrival time of the vibration signal at the target risk location is indicated by at. k2 This indicates the arrival time of the vibration signal at the location where the k2th abnormal event was collected.

11. The method according to any one of claims 7 to 10, characterized in that, The step of diagnosing whether the abnormal event is a leakage event based on the location of the target leakage source includes: Identify at least two abnormal event acquisition locations related to locating the current target leak source location, and determine the duration of the abnormal events occurring at the at least two abnormal event acquisition locations; Based on the locations of at least two abnormal events, the location of the dynamic leakage source within the said duration is determined, and the standard deviation of the distance deviation is obtained based on the location of the dynamic leakage source and the current target leakage source location. When the duration is greater than a preset time threshold and the standard deviation of the distance deviation is less than a preset deviation threshold, the abnormal event is determined to be a leakage event.

12. A system for detecting leaks in gathering and transportation pipelines, characterized in that, The system is used to implement the method as described in any one of claims 1 to 11, wherein the system comprises: The power spectral density calculation module is configured to obtain the power spectral density at different monitoring times based on the real-time vibration signals at different collection locations of the pipeline to be tested. The acquisition location feature calculation module is configured to calculate the frequency characteristic values ​​of different acquisition locations based on the power spectral density and reference power spectral density at different monitoring times. An abnormal event detection module is configured to determine whether an abnormal event has occurred in the collection and transportation pipeline to be detected based on the frequency characteristic values ​​at different locations. The leakage event detection module is configured to, when an abnormal event occurs in the gathering and transportation pipeline to be detected, diagnose whether the abnormal event is a leakage event by analyzing the target leakage source location based on the real-time vibration signal at the abnormal event acquisition location.

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