Pipeline leakage positioning method and system based on negative pressure waves

Abstract

The invention relates to a pipeline leakage positioning method and system based on negative pressure waves, and belongs to the technical field of pipeline leakage monitoring. At least two detection terminals are arranged along the pipeline, and each terminal obtains and maintains a unified standard clock through a built-in Beidou satellite time service module, synchronously collects pipeline negative pressure wave data and records an accurate timestamp of the point; carrying out data preprocessing, carrying out mutation feature enhancement and accurate identification, and uploading obtained mutation point timestamps of the upstream and downstream detection terminals to a cloud processing platform; and the cloud processing platform calculates the difference T between the two timestamps, and calculates the accurate position of the leakage point by combining the propagation velocity V of the negative pressure wave in the pipeline medium and the pipeline length L between the upstream terminal and the downstream terminal and utilizing a positioning formula. According to the method, a set of innovative signal processing link and time synchronization mechanism is constructed, so that quick, reliable and meter-scale accurate positioning of a pipeline leakage point is realized.

Description

Technical Field

[0001] This invention relates to a pipeline leak location method and system based on negative pressure waves, belonging to the field of pipeline leak monitoring technology. Background Technology

[0002] Pipeline transportation is a lifeline of the national economy, and its safe and stable operation is of paramount importance. However, pipeline leaks caused by corrosion, aging, and other factors occur frequently, resulting in enormous resource waste, economic losses, and environmental pollution. Therefore, developing rapid and accurate pipeline leak detection and location technologies is of great significance.

[0003] Existing pipeline leak detection technologies can be mainly categorized as follows: Flow balance-based methods, which determine leaks by calculating the flow difference between the pipeline inlet and outlet. However, this method only provides coarse-level location at the regional level and cannot pinpoint the exact leak location. Sound / vibration-based methods utilize the sound signals generated by the leak for location, but these signals attenuate rapidly during propagation and are easily affected by environmental noise, leading to a high false alarm rate.

[0004] Pressure fluctuations caused by water hammer and pump / valve start-up / shutdown during pipeline operation can drown out weak negative pressure wave signals. Traditional threshold or slope methods for detecting abrupt changes lack sufficient sensitivity and reliability in strong noise environments, making it difficult to accurately identify the start time of the negative pressure wave. Secondly, existing technologies mostly employ single filtering methods, making it difficult to achieve a balance between effective noise reduction and preservation of signal abrupt change edge characteristics. Furthermore, the detection terminals distributed along the pipeline rely on their own independent clocks, and long-term operation can cause clock drift, resulting in asynchronous timestamps among the terminals.

[0005] In summary, existing leak detection schemes for pipeline networks still suffer from poor positioning accuracy and numerous interferences, leaving room for improvement. There is an urgent need for a pipeline leak location technology that can effectively suppress noise, accurately identify abrupt changes, and possess high-precision time synchronization capabilities to overcome the shortcomings of existing methods, such as low positioning accuracy and weak anti-interference ability. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a pipeline leak location method and system based on negative pressure waves. This method achieves rapid, reliable and meter-level accurate location of pipeline leak points by constructing an innovative signal processing link and time synchronization mechanism.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A pipeline leak location method based on negative pressure waves includes the following steps: S1. Data acquisition and time synchronization: At least two detection terminals are deployed along the pipeline. Each terminal acquires and maintains a unified standard clock through the built-in Beidou satellite timing module, synchronously acquires pipeline negative pressure wave data and records the precise timestamp of that point. S2. Data preprocessing: Perform Fourier transform on the acquired raw negative pressure wave signal to convert it to the frequency domain. Remove high-frequency noise through frequency domain filtering, and then perform inverse Fourier transform to obtain the denoised time-domain negative pressure wave signal. S3. Enhancement and accurate identification of mutation features: Wavelet transform is performed on the denoised signal obtained in step S2 to extract its low-frequency approximation coefficients at a specific scale and remove other small fluctuations to enhance mutation features. Then, the Mann-Kendall trend test algorithm is applied to analyze the low-frequency approximation coefficients. The accurate mutation point of the negative pressure wave is identified by statistical significance test, and the accurate timestamp of the point is recorded according to the Beidou clock. S4. Leakage point location calculation: Upload the timestamps of the abrupt change points of the upstream and downstream detection terminals obtained in step S3 to the cloud processing platform; The cloud processing platform calculates the difference ΔT between the two timestamps, and combines the propagation speed V of the negative pressure wave in the pipeline medium and the pipeline length L between the upstream and downstream terminals to calculate the precise location of the leak point using the location formula.

[0008] In the above method, in step S2, the frequency domain filtering is a low-pass filter, and the cutoff frequency is set according to the characteristic spectrum of the pressure fluctuation during normal operation of the pipeline, so as to retain the key frequency band of the negative pressure wave.

[0009] In step S3, the wavelet transform is decomposed using Haar wavelet basis functions, and the low-frequency approximation coefficients of the last decomposition layer are selected for subsequent analysis.

[0010] In step S3, the Mann-Kendall algorithm sets a significance level threshold. When the statistic exceeds the threshold, the point is determined to be a valid mutation point.

[0011] In step S4, the formula for calculating the leak point is: .

[0012] Another object of the present invention is to provide a pipeline leak location system based on negative pressure waves, comprising at least two negative pressure wave detection terminals and a cloud processing platform, wherein the negative pressure wave detection terminals include: The data acquisition module is used to monitor negative pressure wave data in the pipeline in real time; The signal preprocessing module is used to filter and denoise the negative pressure wave data. The BeiDou satellite timing module is used to receive BeiDou satellite signals and provide a high-precision, unified standard clock signal for the negative pressure wave detection terminal. The local data processing unit, connected to the data acquisition module and the BeiDou satellite timing module, is used to execute the negative pressure wave mutation point detection algorithm and record the mutation point timestamp according to the standard clock signal; The communication module is connected to the local data processing unit and is used to upload the mutation point timestamp to the cloud processing platform; The cloud processing platform calculates the precise location of the leak point by measuring the difference ΔT between two timestamps, combining this with the propagation speed V of the negative pressure wave in the pipeline medium and the pipeline length L between the upstream and downstream terminals. The calculation formula is as follows: .

[0013] The BeiDou satellite timing module continuously outputs a unified standard clock to the local data processing unit through the 1PPS clock synchronization function. The local data processing unit performs clock calibration once per second, and its clock synchronization accuracy is better than 1 millisecond.

[0014] The beneficial effects of this invention are: (1) This invention deeply integrates the BeiDou satellite timing system with a multi-level signal processing link at the hardware and algorithm levels. Each detection terminal maintains a unified time reference through the BeiDou module, eliminating clock drift and providing a reliable basis for subsequent time difference positioning; at the same time, this synchronization mechanism, together with Fourier transform, wavelet transform, and Mann-Kendall test, forms a collaborative workflow to ensure the consistency and reliability of timestamps and signal characteristics in the time-frequency-statistical domain; (2) A three-level processing link of “frequency domain coarse screening - time domain fine extraction - statistical decision” was constructed. Fourier transform provides global noise suppression, wavelet transform accurately extracts abrupt change features, and Mann-Kendall algorithm provides objective statistical decision. The three work together to effectively solve the problem of detecting weak negative pressure wave signals in strong noise environment. (3) By introducing BeiDou satellite time synchronization, a unified and stable microsecond-level time reference is provided for the distributed detection terminal, which fundamentally eliminates the positioning error caused by clock asynchrony and lays the foundation for high-precision positioning; the Mann-Kendall algorithm improves the anti-interference capability, and the high-precision timestamp and reliable abrupt change point identification enable the present invention to accurately calculate the time difference of negative pressure wave propagation. This allows for meter-level precise location of the leak point; (4) The present invention completes high computational load signal processing and timestamp extraction locally on the terminal, and only uploads lightweight timestamp data to the cloud platform, which greatly reduces communication bandwidth requirements and cloud computing pressure, and improves system response speed and scalability; (5) The entire process of this invention does not require manual intervention or setting of experience thresholds. The algorithm automatically completes signal processing and decision-making. The system has strong adaptability and high reliability, and is suitable for long-term online monitoring of long-distance pipelines. Attached Figure Description

[0015] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a system framework diagram of the present invention; Figure 3 This is a schematic diagram illustrating the principle of leak location positioning in this invention. Detailed Implementation

[0016] The present invention will be further described below with reference to specific embodiments.

[0017] Example 1: A pipeline leak location method based on negative pressure waves, including steps (such as...) Figure 1 )as follows: S1. Data acquisition and time synchronization: At least two detection terminals are deployed along the pipeline. Each terminal acquires and maintains a unified standard clock through the built-in Beidou satellite timing module, synchronously acquires pipeline negative pressure wave data and records the precise timestamp of that point. Taking a section of water pipeline as an example, a negative pressure wave detection terminal is installed at upstream point A and downstream point B, which are 5000 meters apart. The terminal continuously collects negative pressure wave data. When a leak occurs, the pressure data drops. The sampling frequency is 1kHz.

[0018] S2. Data preprocessing: Perform Fourier transform on the acquired raw negative pressure wave signal to convert it to the frequency domain. Remove high-frequency noise through frequency domain filtering, and then perform inverse Fourier transform to obtain the denoised time-domain negative pressure wave signal. The frequency domain filtering is a low-pass filter, and the cutoff frequency is set according to the characteristic spectrum of pressure fluctuations during normal pipeline operation in order to retain the key frequency band of negative pressure waves.

[0019] Perform a Fast Fourier Transform (FFT) on a data segment (e.g., 500ms), then pass it through a low-pass filter, and finally perform an inverse FFT to obtain a denoised and smoothed pressure signal.

[0020] S3. Enhancement and accurate identification of mutation features: Wavelet transform is performed on the denoised signal obtained in step S2 to extract its low-frequency approximation coefficients at a specific scale and remove other small fluctuations to enhance mutation features. Then, the Mann-Kendall trend test algorithm is applied to analyze the low-frequency approximation coefficients. The accurate mutation point of the negative pressure wave is identified by statistical significance test, and the accurate timestamp of the point is recorded according to the Beidou clock. Wavelet transform uses Haar wavelet basis functions for decomposition, and selects the low-frequency approximation coefficients of the last decomposition layer for subsequent analysis. The Mann-Kendall method algorithm sets a significance level threshold; when the statistic exceeds this threshold, the point is considered a valid abrupt change.

[0021] Wavelet transform: The signal is decomposed into 5 levels using Haar wavelets, and the fifth-level approximation coefficients A1 are extracted. The A1 component clearly describes the contours of the signal's abrupt changes.

[0022] Mann-Kendall method detection: The Mann-Kendall method algorithm is applied to the low-frequency approximation coefficient A1. The algorithm calculates a statistical sequence. When the sequence exceeds the preset significance level threshold of 0.05, it is considered that a statistically significant mutation has occurred. The intersection of its forward and reverse sequences is the arrival time stamp T of the negative pressure wave. Terminal A measures T1 and terminal B measures T2.

[0023] S4. Leakage point location calculation: Upload the timestamps of the abrupt change points of the upstream and downstream detection terminals obtained in step S3 to the cloud processing platform; The cloud processing platform calculates the difference ΔT between the two timestamps, and combines the propagation speed V of the negative pressure wave in the pipeline medium and the pipeline length L between the upstream and downstream terminals to calculate the precise location of the leak point using the location formula.

[0024] The formula for calculating the leak point is: The principle is as follows Figure 3 .

[0025] Terminals A and B report T1 and T2 to the cloud processing platform via the 4G network. The cloud processing platform calculates the time difference. Assuming Milliseconds; the propagation speed V of a negative pressure wave in water is approximately 1200 m / s. Substituting this into the formula: .

[0026] Therefore, the leak point was determined to be 2560 meters downstream of upstream sensor A. The cloud processing platform immediately generated an alarm message with precise location coordinates and pushed it to the management personnel.

[0027] Example 2: A pipeline leak location system based on negative pressure waves, comprising at least two negative pressure wave detection terminals and a cloud processing platform, wherein the negative pressure wave detection terminals include: The data acquisition module is used to monitor negative pressure wave data in the pipeline in real time; The signal preprocessing module is used to filter and denoise the negative pressure wave data. The BeiDou satellite timing module is used to receive BeiDou satellite signals and provide a high-precision, unified standard clock signal for the negative pressure wave detection terminal. The BeiDou satellite timing module continuously outputs a unified standard clock to the local data processing unit through the 1PPS clock synchronization function. The local data processing unit performs clock calibration once per second, and its clock synchronization accuracy is better than 1 millisecond. The local data processing unit, connected to the data acquisition module and the BeiDou satellite timing module, is used to execute the negative pressure wave mutation point detection algorithm and record the mutation point timestamp according to the standard clock signal; The communication module is connected to the local data processing unit and is used to upload the mutation point timestamp to the cloud processing platform; The cloud processing platform calculates the precise location of the leak point by measuring the difference ΔT between two timestamps, combining this with the propagation speed V of the negative pressure wave in the pipeline medium and the pipeline length L between the upstream and downstream terminals. The calculation formula is as follows: .

[0028] The above is a further description of the present invention in conjunction with specific embodiments, and the scope of protection of the present invention is not limited thereto.

Claims

1. A pipeline leak location method based on negative pressure waves, characterized in that, The steps include the following: S1. Data acquisition and time synchronization: At least two detection terminals are deployed along the pipeline. Each terminal acquires and maintains a unified standard clock through the built-in Beidou satellite timing module, synchronously acquires pipeline negative pressure wave data and records the precise timestamp of that point. S2. Data preprocessing: Perform Fourier transform on the acquired raw negative pressure wave signal to convert it to the frequency domain. Remove high-frequency noise through frequency domain filtering, and then perform inverse Fourier transform to obtain the denoised time-domain negative pressure wave signal. S3. Enhancement and accurate identification of mutation features: Wavelet transform is performed on the denoised signal obtained in step S2 to extract its low-frequency approximation coefficients at a specific scale and remove other small fluctuations to enhance mutation features. Then, the Mann-Kendall trend test algorithm is applied to analyze the low-frequency approximation coefficients. The accurate mutation point of the negative pressure wave is identified by statistical significance test, and the accurate timestamp of the point is recorded according to the Beidou clock. S4. Leakage point location calculation: Upload the timestamps of the mutation points of the upstream and downstream detection terminals obtained in step S3 to the cloud processing platform; The cloud processing platform calculates the difference ΔT between the two timestamps, and combines the propagation speed V of the negative pressure wave in the pipeline medium with the pipeline length L between the upstream and downstream terminals to calculate the precise location of the leak point using the positioning formula.

2. The pipeline leak location method based on negative pressure waves according to claim 1, characterized in that, In step S2, the frequency domain filtering is a low-pass filter, and the cutoff frequency is set according to the characteristic spectrum of pressure fluctuation during normal operation of the pipeline.

3. The pipeline leak location method based on negative pressure waves according to claim 1, characterized in that, In step S3, the wavelet transform is decomposed using Haar wavelet basis functions, and the low-frequency approximation coefficients of the last decomposition layer are selected for subsequent analysis.

4. The pipeline leak location method based on negative pressure waves according to claim 1, characterized in that, In step S3, the Mann-Kendall algorithm sets a significance level threshold. When the statistic exceeds the threshold, the point is determined to be a valid mutation point.

5. The pipeline leak location method based on negative pressure waves according to claim 1, characterized in that, In step S4, the formula for calculating the leak point is: 。 6. A pipeline leak location system based on negative pressure waves, characterized in that, It includes at least two negative pressure wave detection terminals and a cloud processing platform, wherein the negative pressure wave detection terminals include: The data acquisition module is used to monitor negative pressure wave data in the pipeline in real time; The signal preprocessing module is used to filter and denoise the negative pressure wave data. The BeiDou satellite timing module is used to receive BeiDou satellite signals and provide a high-precision, unified standard clock signal for the negative pressure wave detection terminal. The local data processing unit, connected to the data acquisition module and the BeiDou satellite timing module, is used to execute the negative pressure wave mutation point detection algorithm and record the mutation point timestamp according to the standard clock signal; The communication module is connected to the local data processing unit and is used to upload the mutation point timestamp to the cloud processing platform; The cloud processing platform calculates the precise location of the leak point by measuring the difference ΔT between two timestamps, combining this with the propagation speed V of the negative pressure wave in the pipeline medium and the pipeline length L between the upstream and downstream terminals. The calculation formula is as follows: 。 7. The pipeline leak location system based on negative pressure wave according to claim 6, characterized in that, The BeiDou satellite timing module continuously outputs a unified standard clock to the local data processing unit through the 1PPS clock synchronization function. The local data processing unit performs clock calibration once per second, and its clock synchronization accuracy is better than 1 millisecond.

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