Pipe section early warning system based on ultrasonic water meter

Abstract

The application discloses a pipe section early warning system based on an ultrasonic water meter, and belongs to the pipe section monitoring field.The pipe section early warning system comprises a data acquisition module, a data processing module and a pipe section early warning module.The data acquisition module is used for acquiring acoustic wave data, pressure wave data and pipe section parameters of each pipe section in a target pipe network.The data processing module comprises a pressure fluctuation submodule, a pipe section division submodule, a sliding window submodule, a pressure attenuation submodule and a leaking pipe section submodule.The pipe section early warning module is used for determining a to-be-repaired interval of a leaking sliding sub-pipe section according to the acoustic wave data, the pressure wave data and the pipe section parameters of the leaking sliding sub-pipe section, and marking the to-be-repaired interval from an image of the target pipe section for three-dimensional display.The application can improve the automation level of pipe network leakage early warning.

Description

Technical Field

[0001] This invention relates to the field of pipeline monitoring, and more particularly to a pipeline early warning system based on ultrasonic water meters. Background Technology

[0002] In the operation and management of urban water supply networks, pipeline leaks not only waste water resources but can also trigger secondary disasters such as road collapses and water pollution. Therefore, efficient leak early warning and location technologies are urgently needed. Traditional leak detection methods mainly rely on manual inspections, regional metering, or passive listening, which suffer from low efficiency, high cost, and difficulty in detecting early, minute leaks. In recent years, with the development of sensor technology and signal processing methods, online monitoring technologies based on pressure waves and sound waves have been gradually applied to pipeline leak identification.

[0003] In existing technologies, pressure sensors are typically used to collect pressure changes in pipelines, and pressure transient inversion technology is used to identify leaks; alternatively, ground-based listening poles, intelligent leak detectors, and other devices are used to collect acoustic signals generated by pipeline leaks for location. However, methods relying solely on pressure data are susceptible to interference from flow fluctuations and valve operations, resulting in a high false alarm rate and insensitivity to minor leaks; while relying solely on acoustic detection requires densely deployed sensors, which is costly, and suffers from low signal-to-noise ratios in noisy environments, making large-scale continuous monitoring difficult. Therefore, there is an urgent need to propose a pipeline segment early warning system to address these issues. Summary of the Invention

[0004] This invention provides a pipe section early warning system based on ultrasonic water meters, which solves the technical problem of insufficient intelligence in the early warning of pipe network leaks in the prior art, and achieves the technical effect of improving the automation level of pipe network leak early warning.

[0005] In a first aspect, the present invention provides a pipeline section early warning system based on an ultrasonic water meter, comprising:

[0006] The data acquisition module is used to acquire acoustic wave data, pressure wave data, and pipe segment parameters of each pipe segment in the target pipeline network;

[0007] The data processing module includes a pressure fluctuation submodule, a pipe segment division submodule, a sliding window submodule, a pressure attenuation quantum module, and a leaking pipe segment submodule. Specifically, the pressure fluctuation submodule determines whether pressure fluctuations exist in the target pipe segment based on its pressure wave data; the pipe segment division submodule divides the target pipe segment into several sub-segments based on its pressure wave data when pressure fluctuations are present; the sliding window submodule combines these sub-segments to obtain several sliding sub-segments; the pressure attenuation quantum module determines the normal pressure attenuation of each sliding sub-segment and, based on the normal and actual pressure attenuation, filters and eliminates sub-segments to obtain several sliding sub-segments to be checked; and the leaking pipe segment submodule identifies leaking sliding sub-segments from these sub-segments based on their acoustic wave data.

[0008] The pipe section early warning module is used to determine the repair section of the leaking sliding sub-pipe section based on the acoustic wave data, pressure wave data, and pipe section parameters, and to mark the repair section from the image of the target pipe section for three-dimensional display.

[0009] Furthermore, based on the acoustic wave data, pressure wave data, and pipe parameters of the leaking sliding sub-section, the repairable section of the leaking sliding sub-section is determined, including:

[0010]

[0011]

[0012]

[0013] in, The location of the area to be repaired. To predict leak points using sound waves, To predict leak points using pressure waves, To estimate the variance of sound waves, To estimate the variance of the pressure wave, To disclose the length of the sliding sub-pipe section, The speed of sound in the pipe. The average inner diameter, The average wall thickness is... The elastic modulus of the pipe. Bulk modulus For constraint coefficients, The time difference between the arrival of the sound wave at both ends of the leaking sliding sub-pipe section. The propagation speed of the pressure wave in the pipeline, The time difference between the arrival of the pressure wave at both ends of the leaking sliding sub-pipe section. This is the preset length.

[0014] Furthermore, the pipe segmentation submodule is used to divide the target pipe segment into several sub-segments based on the pressure wave data of the target pipe segment when pressure fluctuations occur, including:

[0015] Based on the pressure wave data of the target pipe section, determine the pressure change amplitude at each point in the target pipe section:

[0016]

[0017] in, For the target pipe section The magnitude of pressure changes at each point For the target pipe section Pressure wave values ​​at each point For the target pipe section Pressure wave values ​​at each point Greater than or equal to 1;

[0018] When the pressure change at a point exceeds the preset change range, sub-pipe sections are defined.

[0019] Furthermore, the pressure decay quantum module is used to determine the normal pressure decay amount for each sliding sub-segment, including:

[0020]

[0021] in, For the first The typical pressure drop of each sliding sub-pipe section, For the first The initial pressure wave value of each sliding sub-pipe segment, The angular frequency of the pressure wave. For the kinematic viscosity of the fluid in the pipe, For fluid dynamic viscosity, For fluid density, The average inner diameter of the sliding sub-tube section. For the first The length of each sliding sub-pipe segment For the thermal diffusivity of the fluid, The geometric diffusion coefficient is... It is a natural constant.

[0022] Furthermore, the pressure decay quantum module is also used to screen and eliminate sliding sub-segments based on the conventional pressure decay and actual pressure decay, resulting in several sliding sub-segments to be verified, including:

[0023] Based on the normal pressure decay, actual pressure decay, and length of the sliding sub-section, determine the excess pressure decay of the sliding sub-section:

[0024]

[0025] in, For the first The excess pressure attenuation of each sliding sub-pipe section To preset the standard pipe section length, For the first The length of each sliding sub-pipe segment For the first The actual pressure attenuation of each sliding sub-pipe section;

[0026] If the excess pressure attenuation of the sliding sub-pipe segment is less than the preset attenuation threshold, the sliding sub-pipe segment is removed; otherwise, the sliding sub-pipe segment is retained and designated as a sliding sub-pipe segment to be checked.

[0027] Furthermore, the leaking pipe segment submodule is used to determine the leaking sliding pipe segment from the sliding pipe segment to be checked based on the acoustic wave data of the sliding pipe segment to be checked, including:

[0028] Short-time Fourier transform and continuous wavelet transform are performed on the acoustic data of the sliding sub-segment to be checked.

[0029] Based on the acoustic wave data of the sliding sub-segment to be checked, determine the spectral distribution of the sliding sub-segment to be checked;

[0030] The acoustic data of the sliding sub-segment to be checked is decomposed into several IMF components;

[0031] Based on the results of the short-time Fourier transform and continuous wavelet transform of the sliding sub-segment to be checked, the spectral distribution, and several IMF components, it is determined whether the sliding sub-segment to be checked is a leaking sliding sub-segment.

[0032] Furthermore, based on the results of the short-time Fourier transform and continuous wavelet transform of the sliding sub-segment to be checked, its spectral distribution, and several IMF components, it is determined whether the sliding sub-segment to be checked is a leaking sliding sub-segment, including:

[0033] If the PSD energy of the sliding sub-segment to be checked is greater than the energy threshold in the preset frequency band, the duration is greater than the preset duration, and the IMF component shows an upward trend in the time sequence, then the sliding sub-segment to be checked is a leaking sliding sub-segment.

[0034] Furthermore, the sliding window submodule is used to combine several sub-segments according to the sliding window to obtain several sliding sub-segments, including:

[0035] Determine the size of the sliding window;

[0036] Based on the size of the sliding window, several sub-segments are combined sequentially according to their arrangement order to obtain several sliding sub-segments.

[0037] Furthermore, the pressure fluctuation submodule is used to determine whether there is pressure fluctuation in the target pipe segment based on the pressure wave data of the target pipe segment, including:

[0038] If the pressure difference between the inlet end and the outlet end of the target pipe section is less than the preset pressure difference, then there is no pressure fluctuation in the target pipe section.

[0039] Otherwise, there will be pressure fluctuations in the target pipe section.

[0040] Furthermore, it also includes:

[0041] If no pressure fluctuation is observed, the acoustic and pressure wave data for the target pipe section should be reacquired.

[0042] One or more technical solutions provided in this invention have at least the following technical effects or advantages:

[0043] This solution achieves efficient and accurate identification of pipeline network leak early warning and repair zones through a multi-level collaborative mechanism of pressure screening, acoustic wave precision judgment, and fusion positioning. First, pressure wave data is dynamically used to divide sub-pipe segments and an analysis unit is generated using a sliding window. Normal pipe segments are quickly eliminated by comparing the normal and actual pressure attenuation, significantly narrowing the key monitoring range. Next, suspicious sliding sub-pipe segments to be checked are detected using high-sensitivity acoustic waves. Leak confirmation is achieved by combining time-frequency analysis, spectral characteristics, and IMF components. Finally, the dual-source positioning results of acoustic and pressure waves are fused, and the repair zone is accurately delineated based on variance-weighted optimization and considering engineering margins. This invention balances efficiency and accuracy, effectively reducing false alarm rates and maintenance costs, and improving the automation, intelligence, and practicality of pipeline network leakage management.

[0044] This invention combines basic sub-segments using a sliding window approach to form multiple overlapping sliding sub-segments, enabling continuous and comprehensive scanning of the target segment. Because the boundaries of sub-segments are determined by pressure abrupt change points during the initial segmentation, there is a risk of missing some cross-boundary anomalies or failing to determine the optimal analysis scale. By using a sliding window, all possible local combinations can be covered in a progressive, segment-by-segment manner, avoiding the omission of potential anomaly regions due to fixed segmentation, thus improving the comprehensiveness and sensitivity of the detection.

[0045] Furthermore, the construction of sliding sub-segments makes subsequent pressure decay modeling and anomaly screening more flexible and robust. Each sliding sub-segment can independently calculate its normal and actual pressure decay characteristics, and suspicious areas can be identified by comparing the excess decay amount. The moving analysis unit approach not only enhances the ability to identify localized minor leaks, but also provides a unified structure for multi-source fusion analysis of acoustic and pressure wave data, ultimately accurately locating the pipe segments that need to be checked, significantly improving the automation level and location accuracy of pipeline network leak early warning.

[0046] This invention calculates the leak location using both acoustic correlation and pressure wave reflection methods, and dynamically estimates the location variance based on the signal quality of each method. Subsequently, a variance fusion strategy is employed, giving greater weight to the more accurate results in the final location calculation, effectively suppressing deviations caused by noise or operating conditions affecting a single method. Finally, by combining pipe section parameters to accurately model the sound velocity and wave propagation characteristics, and introducing engineering margins, a scientifically reasonable repair zone is formed. This invention not only improves location accuracy but also enhances the system's adaptability to complex environments, achieving a closed loop from coarse alarm to precise repair. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a schematic diagram of the pipe section early warning system based on an ultrasonic water meter provided by the present invention.

[0049] Figure 2 A schematic diagram of the pressure fluctuation submodule provided by the present invention. Detailed Implementation

[0050] This invention provides a pipe section early warning system based on an ultrasonic water meter, which solves the technical problem of insufficient intelligence in the early warning of pipe network leakage in the prior art.

[0051] The technical solution of this invention is to solve the above-mentioned technical problems, and the overall idea is as follows:

[0052] The ultrasonic water meter-based pipe segment early warning system includes: a data acquisition module for acquiring acoustic wave data, pressure wave data, and pipe segment parameters of each pipe segment in the target pipe network; and a data processing module including a pressure fluctuation submodule, a pipe segment division submodule, a sliding window submodule, a pressure attenuation quantum module, and a leakage pipe segment submodule. The pressure fluctuation submodule determines whether pressure fluctuations exist in the target pipe segment based on its pressure wave data; the pipe segment division submodule divides the target pipe segment into several sub-segments based on its pressure wave data when pressure fluctuations are present; and the sliding window submodule divides the sub-segments into sub-segments according to the sliding window. The components are combined to obtain several sliding sub-segments. The pressure attenuation quantum module is used to determine the normal pressure attenuation of each sliding sub-segment, and based on the normal pressure attenuation and actual pressure attenuation of the sliding sub-segments, the sliding sub-segments are screened and eliminated to obtain several sliding sub-segments to be checked. The leakage pipe segment sub-module is used to identify the leakage sliding sub-segments from the sliding sub-segments to be checked based on the acoustic wave data of the sliding sub-segments to be checked. The pipe segment early warning module is used to determine the repair section of the leakage sliding sub-segment based on the acoustic wave data, pressure wave data and pipe segment parameters of the leakage sliding sub-segment, and mark the repair section from the image of the target pipe segment for three-dimensional display.

[0053] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0054] The pipeline network referred to in this invention can include water transmission pipelines, gas transmission pipelines, etc., and is not limited here. Ultrasonic water meters can provide flow rate data and pressure wave data for early-stage pressure fluctuation monitoring and pipeline segment selection.

[0055] This invention provides, for example Figure 1 The pipeline section early warning system based on ultrasonic water meters shown includes a data acquisition module 10, a data processing module 11, and a pipeline section early warning module 12. The following is a detailed description of each module:

[0056] The data acquisition module 10 is used to acquire acoustic wave data, pressure wave data and pipe segment parameters of each pipe segment in the target pipeline network.

[0057] Pressure wave data of the pipe section can be obtained directly or indirectly by installing a high-frequency pressure transmitter, a smart pressure recorder or an ultrasonic water meter.

[0058] Acoustic sensors, leak detectors, or distributed fiber optic monitoring systems can be used to collect sound wave data;

[0059] Pipeline segment parameters can be obtained through pipeline GIS systems, design drawings, on-site surveys, and historical archives.

[0060] The data processing module 11 includes a pressure fluctuation submodule 110, a pipe segment division submodule 111, a sliding window submodule 112, a pressure decay quantum module 113, and a leakage pipe segment submodule 114.

[0061] The pressure fluctuation submodule 110 is used to determine whether there is pressure fluctuation in the target pipe section based on the pressure wave data of the target pipe section.

[0062] Before conducting pipeline monitoring or analysis, a specific section of the pipeline to be monitored, known as the target section, needs to be identified within the entire network. The starting point of the target section is the inlet, and the ending point is the outlet.

[0063] Pressure wave data refers to the dynamic signal of pressure change over time collected by pressure sensors when pressure disturbances caused by sudden pressure changes propagate in the pipeline during fluid flow.

[0064] The pressure fluctuation submodule 110 is used to determine whether there is pressure fluctuation in the target pipe section based on the pressure wave data of the target pipe section, including:

[0065] If the pressure difference between the inlet end and the outlet end of the target pipe section is less than the preset pressure difference, then there is no pressure fluctuation in the target pipe section.

[0066] Otherwise, there will be pressure fluctuations in the target pipe section.

[0067] The preset pressure difference range can be determined based on the length of the target pipe section; the longer the target pipe section, the larger the preset pressure difference can be. If there is no pressure fluctuation, the acoustic and pressure wave data of the target pipe section are reacquired.

[0068] The pipe segment division submodule 111 is used to divide the target pipe segment into several sub-segments based on the pressure wave data of the target pipe segment when pressure fluctuations occur, including:

[0069] Based on the pressure wave data of the target pipe section, determine the pressure change amplitude at each point in the target pipe section:

[0070]

[0071] in, For the target pipe section The magnitude of pressure changes at each point For the target pipe section Pressure wave values ​​at each point For the target pipe section Pressure wave values ​​at each point Greater than or equal to 1;

[0072] When the pressure change at a point exceeds the preset change range, sub-pipe sections are defined.

[0073] When the pressure change at a point exceeds a preset range, the pipeline is divided into sub-sections. The preset range can be determined based on the actual conditions of the pipeline.

[0074] When the pressure change at a point exceeds the preset change range, the point is taken as the endpoint, and the section between the endpoints of the previous section is taken as the sub-pipe section (if there is no previous endpoint, the inlet end is taken as the endpoint).

[0075] By calculating the pressure change amplitude at each point based on pressure wave data and dividing the pipeline into sub-segments, it is possible to accurately identify areas of pressure change in the pipeline, realizing intelligent and refined segmentation of the pipeline network. This not only improves the accuracy of subsequent pressure decay analysis but also enhances the sensitivity to abnormal events, providing a reliable basic unit for sliding window scanning and early warning of leaks, and improving the automation and accuracy of pipeline network status monitoring.

[0076] The sliding window submodule 112 is used to combine several sub-segments according to the sliding window to obtain several sliding sub-segments, including:

[0077] Determine the size of the sliding window;

[0078] Based on the size of the sliding window, several sub-segments are combined sequentially according to their arrangement order to obtain several sliding sub-segments.

[0079] The size of the sliding window can be set to 3 or 4, preferably 3. For example, the target pipe segment can be divided into sub-segments 1, 2, 3, 4...6, 7, where sub-segments 1, 2, and 3 are one sliding sub-segment, and sub-segments 2, 3, and 4 are another sliding sub-segment.

[0080] By combining basic sub-segments using a sliding window approach to form multiple overlapping sliding sub-segments, continuous and comprehensive scanning of the target segment can be achieved. Because the boundaries of the sub-segments are determined by pressure abrupt change points during the initial segmentation, there is a possibility of missing some cross-boundary anomalies or failing to determine the optimal analysis scale. Using a sliding window approach allows for the progressive coverage of all possible local combinations, avoiding the omission of potential anomaly regions due to fixed segmentation, thus improving the comprehensiveness and sensitivity of the detection.

[0081] Furthermore, the construction of sliding sub-segments makes subsequent pressure decay modeling and anomaly screening more flexible and robust. Each sliding sub-segment can independently calculate its normal and actual pressure decay characteristics, and suspicious areas can be identified by comparing the excess decay amount. The moving analysis unit approach not only enhances the ability to identify localized minor leaks, but also provides a unified structure for multi-source fusion analysis of acoustic and pressure wave data, ultimately accurately locating the pipe segments that need to be checked, significantly improving the automation level and location accuracy of pipeline network leak early warning.

[0082] The pressure decay quantum module 113 is used to determine the normal pressure decay amount of each sliding sub-segment, and based on the normal pressure decay amount and the actual pressure decay amount of the sliding sub-segment, the sliding sub-segments are screened and eliminated to obtain a number of sliding sub-segments to be checked.

[0083] The pressure decay quantum module 113 is used to determine the normal pressure decay amount for each sliding sub-segment, including:

[0084]

[0085] in, For the first The typical pressure drop of each sliding sub-pipe section, For the first The initial pressure wave value of each sliding sub-pipe segment, The angular frequency of the pressure wave. For the kinematic viscosity of the fluid in the pipe, For fluid dynamic viscosity, For fluid density, The average inner diameter of the sliding sub-tube section. For the first The length of each sliding sub-pipe segment For the thermal diffusivity of the fluid, The geometric diffusion coefficient is... It is a natural constant.

[0086] The initial pressure wave value refers to the pressure wave value at the inlet section of the sliding sub-pipe. The normal pressure attenuation refers to the degree to which the pressure wave naturally attenuates during propagation under normal operating conditions due to factors such as fluid viscosity, thermal diffusion, and geometric diffusion.

[0087] The pressure decay quantum module 113 is also used to screen and eliminate sliding sub-segments based on the conventional pressure decay and actual pressure decay of the sliding sub-segment, resulting in several sliding sub-segments to be verified, including:

[0088] Based on the normal pressure decay, actual pressure decay, and length of the sliding sub-section, determine the excess pressure decay of the sliding sub-section:

[0089]

[0090] in, For the first The excess pressure attenuation of each sliding sub-pipe section To preset the standard pipe section length, For the first The length of each sliding sub-pipe segment For the first The actual pressure attenuation of each sliding sub-pipe section;

[0091] If the excess pressure attenuation of the sliding sub-pipe segment is less than the preset attenuation threshold, the sliding sub-pipe segment is removed; otherwise, the sliding sub-pipe segment is retained and designated as a sliding sub-pipe segment to be checked.

[0092] By comparing the actual pressure attenuation of the sliding sub-section with the conventional pressure attenuation, the excess pressure attenuation is calculated. The aim is to identify sections where the pressure loss is significantly greater than the theoretical value. Compared to the initial threshold screening, this second screening identifies potentially leaky, blocked, or structurally defective areas. A length normalization factor is introduced to eliminate the influence of length differences between different sliding sub-sections, uniformly converting the attenuation to a standard length for fair comparison. If the excess attenuation is less than the threshold, the pressure loss of that section is normal and it can be judged as normal and removed; otherwise, it is retained as a sliding sub-section to be verified and proceeds to subsequent detailed acoustic testing.

[0093] In addition, the sliding window generates multiple overlapping sub-combinations, enabling continuous scanning of the entire pipe segment and avoiding the omission of cross-boundary anomalies due to fixed segmentation. Even if the leak point is located near the boundary of a certain sub-pipe segment, it will be covered by multiple sliding sub-pipe segments, improving the robustness and sensitivity of detection.

[0094] The leakage pipe section submodule 114 is used to determine the leakage sliding sub-pipe section from the sliding sub-pipe section to be checked based on the acoustic wave data of the sliding sub-pipe section to be checked, including:

[0095] Short-time Fourier transform and continuous wavelet transform are performed on the acoustic data of the sliding sub-segment to be checked (the acoustic data can be denoised beforehand).

[0096] Based on the acoustic wave data of the sliding sub-segment to be checked, determine the spectral distribution of the sliding sub-segment to be checked;

[0097] The acoustic data of the sliding sub-segment to be checked is decomposed into several IMF components;

[0098] Based on the results of the short-time Fourier transform and continuous wavelet transform of the sliding sub-segment to be checked, the spectral distribution, and several IMF components, it is determined whether the sliding sub-segment to be checked is a leaking sliding sub-segment.

[0099] After performing Short Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT), the following can be obtained: frequency characteristics of the leakage sound: confirming whether abnormal energy appears in the preset frequency band (usually 500-2000 Hz); time period of leakage: determining whether it is a continuous leakage or an instantaneous event; time-frequency structure of the signal: distinguishing leakage sound from environmental noise such as traffic and pumping stations; signs of early minute leakage: CWT can capture short-time pulses that are difficult to detect by STFT; and providing input for EMD, PSD, correlation analysis, etc.

[0100] Based on the results of the short-time Fourier transform and continuous wavelet transform of the sliding sub-segment to be checked, its spectral distribution, and several IMF components, it is determined whether the sliding sub-segment to be checked is a leaking sliding sub-segment, including:

[0101] If the PSD energy of the sliding sub-segment to be checked is greater than the energy threshold in the preset frequency band, the duration is greater than the preset duration, and the IMF component shows an upward trend in the time sequence (i.e., the high-frequency IMF energy or amplitude increases with time), then the sliding sub-segment to be checked is a leaking sliding sub-segment.

[0102] The pipe section early warning module 12 is used to determine the repairable section of the leaking sliding sub-pipe section based on the acoustic wave data, pressure wave data, and pipe section parameters, and to mark the repairable section from the image of the target pipe section for three-dimensional display, including:

[0103]

[0104]

[0105]

[0106] in, The location of the area to be repaired. To predict leak points using sound waves, To predict leak points using pressure waves, To estimate the variance of sound waves, To estimate the variance of the pressure wave, To disclose the length of the sliding sub-pipe section, The speed of sound in the pipe. The average inner diameter, The average wall thickness is... The elastic modulus of the pipe. Bulk modulus For constraint coefficients, The time difference between the arrival of the sound wave at both ends of the leaking sliding sub-pipe section. The propagation speed of the pressure wave in the pipeline, The time difference between the arrival of the pressure wave at both ends of the leaking sliding sub-pipe section. This is the preset length.

[0107] Once the location of the section to be repaired is obtained, it can be marked on the image of the target pipe section and uploaded to the client for display so that relevant personnel can review it.

[0108] The variance of the sound wave prediction or the variance of the pressure wave prediction can be determined based on the cross-correlation peak sharpness, empirical models based on the signal-to-noise ratio (SNR), the introduction of sound velocity uncertainty, or measured statistical methods, etc. There are no restrictions here.

[0109] This invention calculates the location of the leak point using both acoustic correlation and pressure wave reflection methods, and dynamically estimates the location variance based on the signal quality of each method. Subsequently, a variance fusion strategy is employed, giving greater weight to the more accurate results in the final location calculation, effectively suppressing deviations caused by noise or operating conditions affecting a single method. Finally, by combining pipe segment parameters to accurately model the sound velocity and wave propagation characteristics, and introducing engineering margins, a scientifically reasonable repair zone is formed. This invention not only improves location accuracy but also enhances the system's adaptability to complex environments, achieving a closed loop from coarse alarm to precise repair. After obtaining the specific zone, the specific leak point and zone can be sent to the client, triggering an alarm.

[0110] In summary, this solution achieves efficient and accurate identification of pipeline leak early warning and repair zones through a multi-level collaborative mechanism of pressure screening, acoustic wave precision judgment, and fusion positioning. First, pressure wave data is dynamically used to divide sub-pipe segments and an analysis unit is generated using a sliding window. Normal pipe segments are quickly eliminated by comparing the normal and actual pressure attenuation, significantly narrowing the key monitoring range. Next, suspicious sliding sub-pipe segments to be checked are detected using high-sensitivity acoustic waves, and leak confirmation is achieved by combining time-frequency analysis, spectral characteristics, and IMF components. Finally, the dual-source positioning results of acoustic and pressure waves are fused, and the repair zone is accurately delineated based on variance-weighted optimization and considering engineering margins. This invention balances efficiency and accuracy, effectively reducing false alarm rates and maintenance costs, and improving the automation, intelligence, and practicality of pipeline leak management.

[0111] This invention combines basic sub-segments using a sliding window approach to form multiple overlapping sliding sub-segments, enabling continuous and comprehensive scanning of the target segment. Because the boundaries of sub-segments are determined by pressure abrupt change points during the initial segmentation, there is a risk of missing some cross-boundary anomalies or failing to determine the optimal analysis scale. By using a sliding window, all possible local combinations can be covered in a progressive, segment-by-segment manner, avoiding the omission of potential anomaly regions due to fixed segmentation, thus improving the comprehensiveness and sensitivity of the detection.

[0112] Furthermore, the construction of sliding sub-segments makes subsequent pressure decay modeling and anomaly screening more flexible and robust. Each sliding sub-segment can independently calculate its normal and actual pressure decay characteristics, and suspicious areas can be identified by comparing the excess decay amount. The moving analysis unit approach not only enhances the ability to identify localized minor leaks, but also provides a unified structure for multi-source fusion analysis of acoustic and pressure wave data, ultimately accurately locating the pipe segments that need to be checked, significantly improving the automation level and location accuracy of pipeline network leak early warning.

[0113] This invention calculates the leak location using both acoustic correlation and pressure wave reflection methods, and dynamically estimates the location variance based on the signal quality of each method. Subsequently, a variance fusion strategy is employed, giving greater weight to the more accurate results in the final location calculation, effectively suppressing deviations caused by noise or operating conditions affecting a single method. Finally, by combining pipe section parameters to accurately model the sound velocity and wave propagation characteristics, and introducing engineering margins, a scientifically reasonable repair zone is formed. This invention not only improves location accuracy but also enhances the system's adaptability to complex environments, achieving a closed loop from coarse alarm to precise repair.

[0114] Since the electronic device described in this embodiment is an electronic device used to implement the information processing method in the embodiments of the present invention, those skilled in the art can understand the specific implementation methods and various variations of the electronic device in this embodiment based on the information processing method described in the embodiments of the present invention. Therefore, how the electronic device implements the method in the embodiments of the present invention will not be described in detail here. Any electronic device used by those skilled in the art to implement the information processing method in the embodiments of the present invention falls within the scope of protection of the present invention.

[0115] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0116] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A pipe section early warning system based on an ultrasonic water meter, characterized in that, The method comprises the following steps: a data acquisition module is configured to acquire acoustic wave data, pressure wave data and pipe segment parameters of each pipe segment in a target pipe network; a data processing module comprises a pressure fluctuation submodule, a pipe segment division submodule, a sliding window submodule, a pressure attenuation submodule and a leaking pipe segment submodule, wherein the pressure fluctuation submodule is configured to determine whether a target pipe segment has pressure fluctuation based on the pressure wave data of the target pipe segment; the pipe segment division submodule is configured to divide the target pipe segment into a plurality of sub-pipe segments based on the pressure wave data of the target pipe segment when the target pipe segment has pressure fluctuation; the sliding window submodule is configured to combine the plurality of sub-pipe segments to obtain a plurality of sliding sub-pipe segments based on a sliding window; the pressure attenuation submodule is configured to determine a regular pressure attenuation of each sliding sub-pipe segment, and filter and remove the sliding sub-pipe segments based on the regular pressure attenuation and an actual pressure attenuation of the sliding sub-pipe segments to obtain a plurality of to-be-checked sliding sub-pipe segments; and the leaking pipe segment submodule is configured to determine a leaking sliding sub-pipe segment from the to-be-checked sliding sub-pipe segments based on acoustic wave data of the to-be-checked sliding sub-pipe segments; a pipe segment early warning module is configured to determine a to-be-repaired interval of the leaking sliding sub-pipe segment based on the acoustic wave data, the pressure wave data and the pipe segment parameters of the leaking sliding sub-pipe segment, and mark the to-be-repaired interval in an image of the target pipe segment for three-dimensional display; wherein the determination of the to-be-repaired interval of the leaking sliding sub-pipe segment based on the acoustic wave data, the pressure wave data and the pipe segment parameters of the leaking sliding sub-pipe segment comprises: wherein, is the location of the interval to be repaired, is the estimated leak point for acoustic waves, is the estimated leak point for pressure waves, is the estimated variance for acoustic waves, is the estimated variance for pressure waves, is the length of the leak slip sub-segment, is the speed of sound in the pipe, is the average internal diameter, is the average wall thickness, is the modulus of elasticity of the pipe material, is the bulk modulus, is the constraint coefficient, is the time difference for acoustic waves to reach both ends of the leak slip sub-segment, is the speed of pressure wave propagation in the pipe, is the time difference for pressure waves to reach both ends of the leak slip sub-segment, is the predetermined length.

2. The ultrasonic water meter based pipe segment early warning system of claim 1, wherein, the pipe segment division submodule is configured to divide the target pipe segment into a plurality of sub-pipe segments based on the pressure wave data of the target pipe segment when the target pipe segment has pressure fluctuation, comprising: determining a pressure variation amplitude of each point in the target pipe segment based on the pressure wave data of the target pipe segment; wherein, is a pressure variation amplitude of a point in the target pipe section, is a pressure variation amplitude of a point in the target pipe section, is a pressure wave value of a point in the target pipe section, is a pressure wave value of a point in the target pipe section, is a pressure wave value of a point in the target pipe section, is a pressure wave value of a point in the target pipe section, is greater than or equal to 1; when the pressure variation amplitude of the point is greater than a preset variation amplitude, a sub-pipe segment is divided.

3. The ultrasonic water meter based pipe segment early warning system of claim 1, wherein, the pressure attenuation submodule is configured to determine a regular pressure attenuation of each sliding sub-pipe segment, comprising: wherein, is the regular pressure decay amount for the th sliding sub-pipe segment, is the initial pressure wave value for the th sliding sub-pipe segment, is the pressure wave angular frequency, is the pipe fluid kinematic viscosity, is the fluid dynamic viscosity, is the fluid density, is the average inner diameter of the sliding sub-pipe segment, is the length of the th sliding sub-pipe segment, is the fluid thermal diffusivity, is the geometric diffusivity coefficient, is the natural constant.

4. The ultrasonic water meter based pipe segment early warning system of claim 3, wherein, the pressure attenuation submodule is further configured to filter and remove the sliding sub-pipe segments based on the regular pressure attenuation and the actual pressure attenuation of the sliding sub-pipe segments to obtain a plurality of to-be-checked sliding sub-pipe segments, comprising: determining a surplus pressure attenuation of the sliding sub-pipe segment based on the regular pressure attenuation, the actual pressure attenuation and the length of the sliding sub-pipe segment; wherein, is the excess pressure decay amount of the th sliding sub-pipe segment, is a preset standard pipe segment length, is the length of the th sliding sub-pipe segment, is the actual pressure decay amount of the th sliding sub-pipe segment; if the surplus pressure attenuation of the sliding sub-pipe segment is less than a preset attenuation threshold, the sliding sub-pipe segment is removed; otherwise, the sliding sub-pipe segment is retained and taken as a to-be-checked sliding sub-pipe segment.

5. The ultrasonic water meter based pipe segment early warning system of claim 1, wherein, the leaking pipe segment submodule is configured to determine a leaking sliding sub-pipe segment from the to-be-checked sliding sub-pipe segments based on acoustic wave data of the to-be-checked sliding sub-pipe segments, comprising: performing short-time Fourier transform and continuous wavelet transform on the acoustic wave data of the to-be-checked sliding sub-pipe segments; determining a frequency spectrum distribution of the to-be-checked sliding sub-pipe segment based on the acoustic wave data of the to-be-checked sliding sub-pipe segment; decomposing the acoustic wave data of the to-be-checked sliding sub-pipe segment into a plurality of IMF components; Based on the results of short-time Fourier transform and continuous wavelet transform, the frequency spectrum distribution and several IMF components of the to-be-checked sliding sub-pipe section, it is determined whether the to-be-checked sliding sub-pipe section is a leaking sliding sub-pipe section.

6. The ultrasonic water meter based pipe segment early warning system of claim 5, wherein, Based on the results of short-time Fourier transform and continuous wavelet transform, the frequency spectrum distribution and several IMF components of the to-be-checked sliding sub-pipe section, it is determined whether the to-be-checked sliding sub-pipe section is a leaking sliding sub-pipe section, including: If the PSD energy of the to-be-checked sliding sub-pipe section is greater than the energy threshold in the preset frequency band, the duration is greater than the preset duration, and the IMF component has an upward trend in time sequence, the to-be-checked sliding sub-pipe section is a leaking sliding sub-pipe section.

7. The ultrasonic water meter based pipe segment early warning system of claim 1, wherein, The sliding window submodule is used to combine several sub-pipe sections to obtain several sliding sub-pipe sections according to a sliding window, including: Determining the size of the sliding window; According to the size of the sliding window, several sub-pipe sections are combined in turn according to the arrangement order to obtain several sliding sub-pipe sections.

8. The ultrasonic water meter based pipe segment early warning system of claim 1, wherein, The pressure fluctuation submodule is used to determine whether the target pipe section has pressure fluctuation according to the pressure wave data of the target pipe section, including: If the difference between the pressure at the inlet end of the target pipe section and the pressure at the outlet end of the target pipe section is less than the preset pressure difference, the target pipe section does not have pressure fluctuation; Otherwise, the target pipe section has pressure fluctuation.

9. The ultrasonic water meter based pipe segment early warning system of claim 1, wherein, Further comprising: If there is no pressure fluctuation, the acoustic wave data and the pressure wave data of the target pipe section are reacquired.

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