Pipeline leakage monitoring method and device, terminal equipment and storage medium

Abstract

This application relates to the field of water supply network monitoring and leakage control technology, and provides a pipeline leakage monitoring method, device, terminal equipment, and storage medium. The method includes: acquiring first time-series noise data of the pipeline uploaded by multiple first monitoring devices; identifying leaks in the pipeline based on the first time-series noise data to obtain at least one second monitoring device that triggers a leakage event; wherein the second monitoring device is included within the first monitoring device; acquiring the first pipe segment monitored by each second monitoring device and the corresponding first material information of the first pipe segment; and calculating the probability distribution of the leakage point and the leakage impact area based on the first material information and second time-series noise data corresponding to each first pipe segment. This can improve the accuracy of leak point location and visually present the leakage range using a probability cloud map, realizing intelligent, visual, and efficient operation and maintenance of water supply pipeline leakage monitoring.

Description

Technical Field

[0001] This application belongs to the field of water supply network monitoring and leakage control technology, and particularly relates to pipeline leakage monitoring methods, devices, terminal equipment and storage media. Background Technology

[0002] Leakage in urban underground water supply networks is a common problem in the industry. Currently, the mainstream approach is to use acoustic / vibration monitoring equipment to collect pipeline noise and locate leaks through threshold alarms or time-of-flight methods based on fixed sound wave velocities. However, these technologies do not differentiate the impact of different pipe materials such as cast iron, steel pipes, PE, and PVC on the propagation speed and attenuation characteristics of leaking sound waves. They uniformly use fixed acoustic parameters for calculation, resulting in a systematic error of tens to hundreds of meters in leak location.

[0003] Meanwhile, traditional equipment can only output suspected coordinates or simple over-limit alarms, and cannot generate intuitive leakage impact range. Furthermore, it is not associated with the Geographic Information System (GIS) of the pipeline network or deeply integrated with asset information. Its intelligence and adaptability are weak, and the algorithm and hardware are highly coupled, making it difficult to adapt to complex pipeline networks and high-noise environments. It also cannot provide accurate and effective guidance for maintenance decisions. Summary of the Invention

[0004] This application provides a pipeline leakage monitoring method, device, terminal equipment, and storage medium, which can improve the accuracy of leak location and intuitively present the leakage range with a probability cloud map, realizing intelligent, visual, and efficient operation and maintenance of water supply pipeline leakage monitoring.

[0005] In a first aspect, embodiments of this application provide a pipeline leakage monitoring method, including: Acquire first-series noise data of the pipeline uploaded by multiple first monitoring devices; wherein the first monitoring devices are deployed at different nodes of a pipeline network composed of multiple pipelines; Based on the first time-series noise data, the pipeline is leaked to identify at least one second monitoring device that triggers the leak event; wherein the second monitoring device is included in the first monitoring device; Obtain the first pipe segment monitored by each second monitoring device and the first material information corresponding to the first pipe segment; wherein, the first pipe segment is a part of the pipe in the pipe network; The probability distribution of leakage points and the area affected by leakage are calculated based on the first material information and the second time-series noise data corresponding to each first pipe segment.

[0006] In this embodiment, multiple first monitoring devices are first deployed at different nodes of the pipeline network to acquire first-series noise data for each pipeline. Then, based on the noise data, leakage events are identified, and second monitoring devices that trigger alarms are selected. Next, the material information of the first pipe segment monitored by these devices is acquired. Finally, combining the pipe segment material and leakage noise data, the probability distribution of leakage points and the area affected by leakage within the pipeline network are accurately calculated, achieving intelligent determination of the leakage location and range. This method uses the linkage of noise data from multiple devices for leakage identification, eliminating environmental interference and false triggers, ensuring the high reliability of the second monitoring device that triggers the leakage event. Simultaneously, calculating leakage based on the pipe segment material allows the system to adapt to complex and varied pipeline material structures, significantly enhancing the overall adaptability and anti-interference capability of the detection system.

[0007] In one possible implementation of the first aspect, a second monitoring device that triggers a leakage event is obtained by identifying a leak in the pipeline based on first time-series noise data, including: Time-frequency analysis was performed on the first time-series noise data uploaded by each first monitoring device to obtain time-frequency characteristics; If the time-frequency characteristics match the preset characteristics, the first monitoring device will be identified as the second monitoring device that triggers the water leakage event; where the preset characteristics are the exclusive characteristics corresponding to the water leakage signal.

[0008] In this embodiment, by accurately analyzing the time-frequency data of the time-series noise and matching it with the specific features of the leak, the automatic and highly accurate determination of the leak event can be achieved, effectively eliminating environmental noise interference, significantly reducing the false alarm / missed alarm rate of the leak, and laying a reliable foundation for subsequent accurate positioning.

[0009] In one possible implementation of the first aspect, the first time-series noise data includes the device coordinates of the first monitoring device; the second time-series noise data includes the device coordinates of the second monitoring device; obtaining the first pipe segment monitored by each second monitoring device and the first material information corresponding to the first pipe segment includes: Based on the coordinates of the second monitoring device, the pipe segment whose distance from the second monitoring device is within a preset threshold is obtained; Extract the material information corresponding to the first pipe segment from the preset database to obtain the first material information.

[0010] In this embodiment, by accurately matching the device coordinates with neighboring pipes and extracting material information, the monitoring device and the corresponding pipe section are quickly and accurately bound together, providing reliable pipeline network basic data support for the subsequent construction of material adaptive acoustic models and improvement of leakage location accuracy.

[0011] In one possible implementation of the first aspect, the probability distribution of leakage points and the leakage-affected area are calculated based on the first material information and second time-series noise data corresponding to each first pipe segment, including: The acoustic parameters corresponding to the first pipe segment are extracted based on the first material information; among which, the acoustic parameters include signal propagation speed and signal attenuation coefficient; Construct a sound wave propagation model for each first pipe segment based on its acoustic parameters. The probability distribution of leak points and the area affected by leakage are calculated based on multiple sound wave propagation models and second-series noise data.

[0012] In this embodiment, by extracting the specific acoustic parameters of pipe sections of different materials and constructing differentiated sound wave propagation models, the traditional unified model is replaced. This can accurately restore the real propagation and attenuation law of leakage noise in pipes of different materials, and greatly improve the accuracy and reliability of calculating the probability distribution of leakage points and delineating the leakage impact area.

[0013] In one possible implementation of the first aspect, the timing noise includes a timestamp; the probability distribution of the leakage point and the leakage-affected area are determined based on multiple sound wave propagation models and second timing noise data, including: Calculate the time difference between every two second monitoring devices based on the timestamps contained in each second time-series noise data; The coordinates of potential leak points are calculated based on multiple time differences and the signal propagation speed of each first pipe segment. The probability distribution of leak points and the area affected by leakage are calculated based on multiple sound wave propagation models and the coordinates of potential leak points.

[0014] In this embodiment, the coordinates of potential leaks are calculated by using the time difference between multiple devices and the propagation speed of different materials. The probability is then reversed and the range is calculated by combining a differentiated acoustic wave propagation model. This achieves high-precision leak location based on multi-node fusion, which effectively improves the accuracy and reliability of the leak probability distribution and the delineation of the affected area.

[0015] In one possible implementation of the first aspect, the probability distribution of the leak point and the area affected by the leak are calculated based on multiple sound wave propagation models and the coordinates of the potential leak point, including: The potential leak point pipe section is determined from the first pipe section based on the coordinates of the potential leak point; Using the coordinates of the potential leak point as the starting point of the sound source, the propagation and attenuation process of the leak noise is simulated according to the acoustic propagation model corresponding to the pipe section with the potential leak point, and the noise propagation and attenuation results are obtained. The probability distribution of leak points and the area affected by leakage are obtained based on the noise propagation attenuation results.

[0016] In this embodiment, by accurately locating potential leaking pipe sections and simulating noise propagation attenuation using corresponding material models, the theoretical calculation results are compared and verified with the measured noise, thus achieving a high-reliability determination of the probability distribution of leak points and accurate delineation of the leakage-affected area.

[0017] In one possible implementation of the first aspect, the method further includes: The probability distribution of leak points and the area affected by leakage are overlaid onto the corresponding geographic information map of the pipeline network to obtain the monitoring results. The monitoring results are sent to the user terminal to guide pipeline operation and maintenance.

[0018] In this embodiment of the application, by overlaying the probability distribution of leakage points and the area affected by leakage onto the geographic information map of the pipeline network, the distribution and impact range of potential leakage hazards can be displayed intuitively, improving the efficiency of operation and maintenance investigation and providing visual support for accurate maintenance decisions.

[0019] Secondly, embodiments of this application provide a pipeline leakage monitoring device, comprising: The data acquisition module is used to acquire first time-series noise data of the pipeline uploaded by multiple first monitoring devices; wherein, the first monitoring devices are deployed at different nodes of the pipeline network composed of multiple pipelines; The data identification module is used to identify water leakage in the pipeline based on the first time-series noise data, and to obtain at least one second monitoring device that triggers the water leakage event; wherein the second monitoring device is included in the first monitoring device; The pipeline material acquisition module is used to acquire the first pipe segment monitored by each second monitoring device and the first material information corresponding to the first pipe segment; wherein, the first pipe segment is a part of the pipeline in the pipeline network; The leakage calculation module is used to calculate the probability distribution of leakage points and the leakage impact area based on the first material information and second time-series noise data corresponding to each first pipe segment.

[0020] Thirdly, embodiments of this application provide a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the pipeline leakage monitoring method as described in any of the first aspects above.

[0021] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the pipeline leakage monitoring method as described in any of the first aspects above.

[0022] Fifthly, embodiments of this application provide a computer program product that, when run on a terminal device, causes the terminal device to perform pipeline leakage monitoring as described in any of the first aspects above.

[0023] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic flowchart of the pipeline leakage monitoring method provided in the embodiments of this application; Figure 2 This is a schematic diagram of the data identification process provided in the embodiments of this application; Figure 3 This is a schematic diagram of the process for obtaining pipe segment material information provided in an embodiment of this application; Figure 4 This is a flowchart illustrating the calculation of the leakage probability distribution and leakage area provided in the embodiments of this application; Figure 5 This is a flowchart illustrating the calculation of the leakage probability distribution and leakage area provided in the embodiments of this application; Figure 6 This is a flowchart illustrating the calculation of the leakage probability distribution and leakage area provided in the embodiments of this application; Figure 7 This is a schematic diagram of the overall structure of the pipeline leakage monitoring method provided in the embodiments of this application; Figure 8 This is a structural block diagram of the pipeline leakage monitoring device provided in the embodiments of this application; Figure 9 This is a schematic diagram of the structure of the terminal device provided in the embodiments of this application. Detailed Implementation

[0026] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0027] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0028] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0029] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0030] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0031] References to "one embodiment" or "some embodiments" in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized.

[0032] Leakage in urban underground water supply networks is a common problem in the industry. Currently, the mainstream approach is to use acoustic / vibration monitoring equipment to collect pipeline noise and locate leaks through threshold alarms or time-of-flight methods based on fixed sound wave velocities. However, these technologies do not differentiate the impact of different pipe materials such as cast iron, steel pipes, PE, and PVC on the propagation speed and attenuation characteristics of leaking sound waves. They uniformly use fixed acoustic parameters for calculation, resulting in a systematic error of tens to hundreds of meters in leak location.

[0033] Meanwhile, traditional equipment can only output suspected coordinates or simple over-limit alarms, and cannot generate intuitive leakage impact range. Furthermore, it is not linked to the pipeline network GIS or deeply integrated with asset information. Its intelligence and adaptability are weak, and the algorithm and hardware are highly coupled, making it difficult to adapt to complex pipeline networks and high-noise environments. It also cannot provide accurate and effective guidance for maintenance decisions.

[0034] To address the aforementioned technical issues, this application provides a pipeline leakage monitoring method. By deeply binding intelligent monitoring equipment with asset information such as pipeline material, coordinates, and pipe diameter, and through noise artificial intelligence (AI) identification, coordinate-pipe segment matching, material adaptive acoustic model construction, and multi-node fusion positioning, a leakage probability cloud map / contour map is finally generated and overlaid with a pipeline map for output. This significantly improves positioning accuracy and maintenance guidance, achieving intelligent, visual, and high-precision leakage monitoring.

[0035] This application provides an intelligent water leakage monitoring device for pipeline networks, which is deployed at pipeline nodes such as fire hydrants and valves. As the sensing endpoint of the system, it completes noise signal acquisition and data uploading. This hardware has four key functions: Acoustic Acquisition: Built-in acoustic / vibration sensors collect real-time temporal noise data generated by pipeline leaks; Spatial Positioning: Integrated high-precision global positioning system module, or supports precise device positioning by selecting and writing installation coordinates from a geographic information system map via an application; Pipeline Attribute Binding: Binds the device to specific pipe segments via QR codes, near-field communication, radio frequency identification, or configuration software, associating asset information such as pipe material, diameter, and burial depth; Data Communication and Reporting: Packages the device number, coordinates, timestamp, pipeline asset number, and noise data through the communication module and uploads them to the platform periodically or triggeredly.

[0036] See Figure 1 This is a schematic flowchart of the pipeline leakage monitoring method provided in the embodiments of this application. It is intended as an example and not a limitation. The method may include the following steps: S101, acquire the first time-series noise data of the pipeline uploaded by multiple first monitoring devices; wherein, the first monitoring devices are deployed at different nodes of the pipeline network composed of multiple pipelines.

[0037] In this embodiment of the application, in a water supply network composed of multiple pipe sections, monitoring devices (i.e., the aforementioned intelligent monitoring devices for the network) are installed at different nodes. The platform uniformly receives the raw pipe noise data uploaded in real time by these devices and recorded in chronological order, providing a basic data source for subsequent leak identification and location.

[0038] For example, in an urban water supply network consisting of multiple pipes of different materials and diameters, multiple leakage monitoring devices are deployed at different key nodes such as fire hydrants, valve wells, and water meter wells. Each monitoring device continuously collects noise signals generated by water pressure, fluid flow, or leakage in the pipe at its node through built-in acoustic / vibration sensors, and forms time-series noise data in chronological order. Subsequently, the devices synchronously upload the time-series noise data they have collected to the system platform through communication modules. The platform completes the unified reception, processing, and temporary storage of the data uploaded by multiple devices, forming a multi-source raw data set that can be used for leakage signal identification, pipe segment matching, and leakage range prediction.

[0039] S102, based on the first time-series noise data, the pipeline is identified to identify water leakage, and at least one second monitoring device that triggers the water leakage event is obtained; wherein, the second monitoring device is included in the first monitoring device.

[0040] In this embodiment, leakage signals are identified based on time-series noise data uploaded by multiple monitoring devices. Valid monitoring devices that are determined to trigger leakage events are selected from all monitoring devices. These valid devices are a subset of the original set of monitoring devices.

[0041] In one embodiment, see Figure 2 This is a schematic diagram of the data identification process provided in the embodiments of this application, such as... Figure 2 As shown, step S102 includes: S201, Perform time-frequency analysis on the first time-series noise data uploaded by each first monitoring device to obtain time-frequency characteristics.

[0042] In this embodiment of the application, the noise signal collected by each device that changes over time is converted into "time + frequency" feature information, which makes it easier for the algorithm to distinguish between water leakage sound and interference sound.

[0043] For example, the system platform sequentially performs time-frequency analysis on the first time-series noise data uploaded by each monitoring device using methods such as spectrum analysis and wavelet transform. This decomposes the original one-dimensional sound signal, which only changes with time, into energy distribution and waveform characteristics at different time points and frequency bands. In this way, time-frequency features that can distinguish between water leakage signals, environmental interference, water flow noise, and valve operation noise are extracted, providing calculable and distinguishable effective feature basis for subsequent AI water leakage identification.

[0044] S202, if the time-frequency characteristics meet the preset characteristics, then the first monitoring device is determined as the second monitoring device that triggers the water leakage event; wherein, the preset characteristics are the exclusive characteristics corresponding to the water leakage signal.

[0045] In this embodiment of the application, AI is used to perform pattern recognition on time and frequency features to determine whether it is a sound of water leakage; if it matches the specific features of water leakage, the device is marked as the second monitoring device that "triggered a water leakage event".

[0046] For example, the system uses AI to intelligently compare and classify the time-frequency features extracted by each first monitoring device with the unique features of the leakage signal, such as frequency, energy, and waveform, which are obtained through training on a large number of leakage samples. When the algorithm determines that the current time-frequency features belong to the category of leakage signal and meet the preset matching conditions, it automatically marks the corresponding first monitoring device as the second monitoring device that triggered the leakage event, thus completing the AI ​​determination from noise signal to leakage event.

[0047] The above method, through precise time-frequency analysis of time-series noise data and matching with water leakage-specific features, can achieve automatic and highly accurate determination of water leakage events, effectively eliminate environmental noise interference, significantly reduce the false alarm / missed alarm rate of water leakage, and lay a reliable foundation for subsequent accurate positioning.

[0048] S103, obtain the first pipe segment monitored by each second monitoring device and the first material information corresponding to the first pipe segment; wherein, the first pipe segment is a part of the pipe in the pipeline network.

[0049] In this embodiment of the application, the system obtains the monitored pipe segment and the corresponding material parameters of each second monitoring device that triggers the leakage event. The pipe segment is a local pipe unit in the entire pipe network and is used to construct a material adaptive acoustic model.

[0050] In one embodiment, see Figure 3 This is a schematic diagram of the process for obtaining pipe segment material information provided in an embodiment of this application, such as... Figure 3 As shown, step S103 includes: S301, based on the equipment coordinates of the second monitoring device, query the pipes whose distance from the second monitoring device is within a preset threshold, and obtain the first pipe section.

[0051] In this embodiment of the application, taking the coordinates of the monitoring device that triggered the leak as the center, all pipes within a set range from the device are searched on the system's pipe network map, and these pipes are selected as the first pipe segment.

[0052] For example, the system first obtains the coordinates of the second monitoring device that has triggered a water leakage event, and constructs a spatial query range with the coordinates as the center point according to a preset distance threshold. In the pipeline geographic information system, the system retrieves the location information of all pipelines, filters out the pipelines whose center point and the distance between the device coordinates do not exceed the preset threshold, and determines these pipelines that meet the spatial distance conditions as the first pipe segment (the pipeline closest to the device can be determined as the first pipe segment) for subsequent material matching and acoustic model calculation.

[0053] S302, extract the material information corresponding to the first pipe segment from the preset database to obtain the first material information.

[0054] In this embodiment of the application, the material (steel pipe / PE / cast iron, etc.) corresponding to the first pipe segment selected earlier is directly retrieved from the pre-built pipeline database of the system. This material is called the first material information.

[0055] For example, the system pre-establishes and stores a pipeline network database containing information such as the location, number, material, and diameter of each pipeline. After determining the first pipe segment, the system uses the number or spatial location of the first pipe segment as the search condition to perform a matching query in the preset database, extracts the material data that uniquely corresponds to the first pipe segment, and uses this material data as the first material information to provide parameter basis for the subsequent construction of a material-adaptive sound wave propagation and attenuation model.

[0056] The above method accurately matches the neighboring pipes by device coordinates and extracts material information, realizing the rapid and accurate binding of monitoring equipment with the corresponding pipe section. This provides reliable pipeline network basic data support for the subsequent construction of material adaptive acoustic models and improvement of leakage location accuracy.

[0057] S104, calculate the probability distribution of leakage points and the area affected by leakage based on the first material information and the second time-series noise data corresponding to each first pipe segment.

[0058] In this embodiment of the application, based on the material of each pipe section and combined with the noise data (i.e., the second time-series noise data) collected by the leakage monitoring equipment, the location of the most likely leakage point in the pipe network and the extent to which the leakage will affect are calculated.

[0059] In the above method, leakage identification is performed by linking noise data from multiple devices, which can eliminate environmental interference and false triggering, and ensure that the second monitoring device that triggers the leakage event has high reliability. At the same time, leakage calculation is performed based on the pipe segment material, which enables the system to adapt to complex and varied pipe network material structures, greatly enhancing the adaptability and anti-interference ability of the overall detection system.

[0060] In one embodiment, see Figure 4This is a flowchart illustrating the calculation of leakage probability distribution and leakage area provided in an embodiment of this application, as shown below. Figure 4 As shown, step S104 includes: S401, extract the acoustic parameters corresponding to the first pipe segment based on the first material information; wherein, the acoustic parameters include signal propagation speed and signal attenuation coefficient.

[0061] In this embodiment of the application, based on the material of the pipe, the two key acoustic calculation parameters corresponding to this section of the pipe, namely the sound wave propagation speed and the signal attenuation coefficient, are automatically determined.

[0062] For example, the system pre-establishes a database of correspondences between different materials and acoustic parameters. After obtaining the material information of the first pipe section, it matches and extracts the unique acoustic parameters of the pipe section from the parameter database according to the material type, including the speed of sound propagation in the pipe and the attenuation coefficient of sound as the distance decreases, providing accurate physical parameter support for subsequent leak location and probability distribution calculation.

[0063] S402, construct the sound wave propagation model corresponding to the first pipe segment based on the acoustic parameters corresponding to each first pipe segment.

[0064] In this embodiment of the application, a sound wave propagation calculation model that best conforms to the real physical laws is established for each section of the pipe according to its unique acoustic parameters, instead of using a uniform model.

[0065] For example, the system constructs a unique acoustic propagation model for each pipe segment based on acoustic parameters such as the material, sound wave propagation speed, and signal attenuation coefficient of each segment. This model can accurately reflect the propagation speed, attenuation law, and energy change characteristics of the leaking sound in the pipe of that material, replacing the traditional unified fixed model and providing an accurate algorithmic basis for subsequent precise calculation of the location, probability distribution, and scope of leakage.

[0066] S403 calculates the probability distribution of leakage points and the area affected by leakage based on multiple sound wave propagation models and second-series noise data.

[0067] In this embodiment, based on the material of each pipe section, multiple sets of differentiated sound wave propagation models are constructed, combined with the time-series leakage noise signals collected by multiple devices that trigger leakage events, and the location of the leak is calculated by a multi-node fusion algorithm, such as the optimized TDOA algorithm, intensity attenuation model or hybrid algorithm. The probability distribution of the leakage points in the pipeline network is calculated, and the leakage impact area that the leakage noise can propagate and cover is determined.

[0068] The above method extracts the specific acoustic parameters of pipe sections of different materials and constructs differentiated sound wave propagation models to replace the traditional unified model. It can accurately restore the real propagation and attenuation law of leakage noise in pipes of different materials, and greatly improve the accuracy and reliability of calculating the probability distribution of leakage points and delineating the leakage impact area.

[0069] In one embodiment, see Figure 5 This is a flowchart illustrating the calculation of leakage probability distribution and leakage area provided in an embodiment of this application, as shown below. Figure 5 As shown, step S403 includes: S501 calculates the time difference between every two second monitoring devices based on the timestamps contained in each second time-series noise data.

[0070] In this embodiment of the application, the arrival time difference of any two devices receiving the same leak sound is calculated by using the precise timestamp of the noise signal (second time-series noise data) uploaded by each leak monitoring device (second monitoring device), which is used to locate the leak point in the future.

[0071] For example, the system extracts the high-precision timestamp of the leakage signal arriving at each device from the second-series noise data uploaded by each second monitoring device. Then, it pairs all the second monitoring devices, subtracting the timestamp of one device from the timestamp of the other to obtain the time difference between the arrival times of the same leakage sound. This provides crucial data for subsequent leak location calculations based on the time difference. For instance, if device A's timestamp is 10:00:00.125 and device B's timestamp is 10:00:00.200, then the time difference between them is 0.200. 0.125 = 0.075 seconds.

[0072] S502 calculates the coordinates of potential leak points based on multiple time differences and the signal propagation speed of each first pipe segment.

[0073] In this embodiment, by utilizing the time difference of sound arrival between multiple sets of devices and combining it with the sound wave propagation speed corresponding to the material of each pipe section, a geometric positioning algorithm is used to deduce the spatial coordinates of potential leak points, such as: For example, in pipe segment L1 where device A is located: sound velocity =1450m / s (steel pipe); Pipe section L2 where equipment B is located: sound velocity =1100m / s (PE pipe); Device A coordinates: (0,0); Device B coordinates: (100,0); The leak signal arrived at time A (timestamp: t_A = 0.000s); The leak signal arrived at time B (timestamp: t_B = 0.030s). Time difference Δt = 0.030s Let the distance from the leak point P to A be . (At L1, velocity) The distance to B is (At L2, velocity) ).

[0074] Due to time constraints:

[0075] Geometric constraints (A and B are 100m apart on the x-axis): (Simplified collinearity) Solving the system of equations simultaneously, we get: ≈62.3m (steel pipe section) ≈37.7m (PE section) Corresponding coordinates: Potential leak point P≈(62.3,0) S503 calculates the probability distribution of leak points and the area affected by leakage based on multiple sound wave propagation models and the coordinates of potential leak points.

[0076] In this embodiment of the application, based on multiple sound wave propagation models corresponding to different pipe segment materials, and combined with the calculated coordinates of potential leak points, the probability distribution of each location in the pipe network being a real leak point is calculated, as well as the range to which the sound of leakage can propagate (the area affected by the leakage).

[0077] In the above method, the coordinates of potential leak points are calculated by using the time difference of multiple devices and the propagation speed of different materials. The probability back-inference and range calculation are performed by combining the differentiated sound wave propagation model. This achieves high-precision leak location based on multi-node fusion, which effectively improves the accuracy and reliability of the probability distribution of leak points and the delineation of the affected area.

[0078] In one embodiment, see Figure 6 This is a flowchart illustrating the calculation of leakage probability distribution and leakage area provided in an embodiment of this application, as shown below. Figure 6 As shown, step S503 includes: S601, determine the pipe section with potential leakage points from the first pipe section based on the coordinates of the potential leakage points.

[0079] In this embodiment of the application, based on the calculated coordinates of the potential leak point, it is determined in the pipeline network which segment the point falls on, and this segment of pipeline is identified as the potential leak point segment.

[0080] For example, the system uses the coordinates of a potential leak point as the location basis. Within the existing set of first pipe segments, it determines the pipe segment to which the coordinate point belongs through spatial location matching and geometric judgment. That is, it determines which first pipe segment the point falls within, and finally marks that pipe segment as the pipe segment where the potential leak point is located, i.e., the potential leak point pipe segment. For example, first pipe segment L1 has coordinates from (0,0) to (100,0) and is a steel pipe; first pipe segment L2 has coordinates from (100,0) to (200,0) and is a PE pipe; the system calculates the coordinates of the potential leak point: (60,0), and judges that (60,0) is between the starting point (0,0) and the ending point (100,0) of L1. Therefore, the potential leak point pipe segment is determined to be L1.

[0081] S602 uses the coordinates of the potential leak point as the starting point of the sound source, and simulates the propagation and attenuation process of the leak noise according to the acoustic propagation model corresponding to the pipe section of the potential leak point, and obtains the noise propagation and attenuation results.

[0082] In this embodiment, the coordinates of the potential leak point are taken as the starting point of the sound. The specific sound wave propagation model of the pipe section where it is located is used to simulate how the leak sound propagates in the pipe and how it weakens with distance, and finally a complete set of noise propagation attenuation results are obtained.

[0083] The system uses the calculated coordinates of potential leak points as the starting point of the noise source, retrieves the acoustic propagation model (including sound wave propagation speed, attenuation coefficient, etc.) corresponding to the pipe segment where the potential leak point is located, simulates the propagation process of the leak noise from the source segment by segment along the pipeline path, calculates the noise intensity attenuation at different distances and locations, and finally outputs the theoretical noise amplitude and arrival time at each monitoring point location to form a complete noise propagation attenuation result.

[0084] S603, based on the noise propagation attenuation results, obtains the probability distribution of leakage points and the area affected by leakage.

[0085] In this embodiment of the application, based on the noise propagation attenuation results obtained by simulation, the propagation and attenuation law of leakage noise in pipe networks of specific materials is further simulated, and finally a probability distribution cloud map or contour map centered on the potential leakage point can be generated to intuitively show the probability of leakage and the range of leakage, providing a visualized probability distribution of leakage points and the area affected by leakage.

[0086] For example, the system first calls the acquired noise propagation attenuation results (including data such as the theoretical arrival time and amplitude attenuation of noise at different locations), and combines them with the specific material of the pipe section at the potential leak point and the corresponding sound wave propagation model to simulate the propagation process and attenuation change of the leak noise from the potential leak point along the pipe path in the pipe network of that material. The system focuses on calculating the noise intensity, propagation delay and matching error with the actual monitored noise at each spatial location in the pipe network.

[0087] Subsequently, based on the magnitude of the matching error, a corresponding leak probability is assigned to each location (the smaller the error, the higher the probability), and the boundary of the leak-affected area is defined according to the preset effective detection attenuation threshold. Finally, these probability data are associated with the spatial coordinates of the pipeline network, and a probability distribution cloud map (using different shades of color to represent high and low probabilities) or contour map (using different lines to represent areas with the same probability or the same attenuation amount) centered on the potential leak point is generated through a visualization algorithm. This visually presents the probability distribution of the leak point and the range of the leak-affected area, providing a visual basis for subsequent leak location and treatment.

[0088] In the above method, by accurately locating potential leaking pipe sections and simulating noise propagation attenuation using corresponding material models, the theoretical calculation results are compared and verified with the measured noise, thus achieving a high-reliability determination of the probability distribution of leak points and accurate delineation of the leakage-affected area.

[0089] In one embodiment, the method further includes: The probability distribution of leakage points and the area affected by leakage are overlaid onto the corresponding geographic information map of the pipeline network to obtain monitoring results; the monitoring results are then sent to user terminals to guide pipeline network operation and maintenance.

[0090] In this embodiment of the application, the calculated probability distribution of leakage points (the probability of leakage) and the area affected by leakage (the coverage of leakage) are overlaid on the actual geographic information map of the pipeline network to form a complete monitoring result. The result is then sent to the user terminal to provide accurate guidance for the daily operation and maintenance and leakage repair of the pipeline network.

[0091] For example, the system first retrieves the geographic information map of the pipeline network (including the actual geographic coordinates, location distribution, and segment number of each pipe segment), and then accurately overlays the previously generated probability distribution of leak points (such as probability cloud map and contour map) and the boundary of the leakage impact area onto the geographic information map according to the spatial coordinate correspondence, so that the probability of leak points and the scope of impact correspond one-to-one with the actual geographical location of the pipeline network, forming a complete monitoring result that includes geographic information, probability of leak points, and impact area.

[0092] Subsequently, the monitoring results (including map overlay and core data) are sent to the user terminals (such as mobile phones, computers, and maintenance tablets) of maintenance personnel through a preset communication module. After receiving the data, the terminal can intuitively display the location of the leak, the probability of leakage, and the scope of impact, and clearly mark the pipe sections that need to be investigated and repaired. This guides maintenance personnel to carry out pipeline inspections and leak repairs efficiently, thereby improving the accuracy and efficiency of maintenance.

[0093] In the above method, by overlaying the probability distribution of leakage points and the area affected by leakage onto the geographic information map of the pipeline network, the distribution and impact range of potential leakage hazards can be displayed intuitively, improving the efficiency of operation and maintenance investigation and providing visual support for accurate maintenance decisions.

[0094] See Figure 7 This is a schematic diagram of the overall structure of the pipeline leakage monitoring method provided in the embodiments of this application, as shown below. Figure 7 As shown, it includes: 1. Acquire noise equipment data Core function: Collecting raw monitoring data, serving as the input source for the entire process. The system uses pipeline monitoring equipment deployed on the pipeline network to collect real-time temporal noise data (including sound waveforms, timestamps, equipment coordinates, etc.) within the pipeline, providing raw signals for subsequent leak identification.

[0095] 2. Noise Feature Extraction and Event Detection Core function: To filter out valid signals suspected of water leakage from massive amounts of noise. The system performs signal processing on the collected raw noise to extract the characteristics of water leakage noise (such as frequency, amplitude, waveform characteristics, etc.), and uses algorithms to distinguish normal operation noise (such as water flow, ambient noise) from abnormal water leakage noise, thus completing the preliminary detection of water leakage events and filtering out the second time-series noise data that triggers the alarm and the corresponding monitoring equipment (i.e., the second detection equipment).

[0096] 3. Coordinate-pipeline feature database query and adaptation Core function: To establish a connection between monitoring equipment and the actual pipeline network and match pipeline attributes. Based on the coordinates of the monitoring equipment that triggered the alarm (i.e., the second monitoring equipment), a spatial query is performed in the preset pipeline feature database (GIS system) to filter out the pipelines near the equipment (the first pipe segment), and extract the characteristic information of these pipelines such as material, diameter, and location to complete the binding and adaptation between the equipment and the pipe segment.

[0097] 4. Construction of Material-Adaptive Acoustic Model Core Function: To customize exclusive acoustic wave calculation models for pipes of different materials, solving the accuracy problem of traditional uniform models. Based on the pipe material matched in the previous step (steel pipe, PE pipe, cast iron pipe, etc.), acoustic parameters such as sound wave propagation speed and signal attenuation coefficient of the corresponding material are extracted from the parameter library. An individual matching acoustic wave propagation model is built for each pipe section to truly reflect the propagation law of leakage sound in pipes of different materials.

[0098] 5. Node fusion localization and probability range generation Core Function: Multi-device data fusion for accurate calculation of leak location and impact range. Based on the time difference of multiple monitoring devices and the acoustic models of each pipe section, the coordinates of potential leak points are calculated using algorithms such as TDOA (Time Difference of Origin). Then, noise propagation is simulated through forward simulation and compared with measured data to generate a probability distribution of leak points (leak probability heatmap). At the same time, the area affected by the leak is determined based on the attenuation threshold, thus completing the accurate location and range definition of the leak.

[0099] 6. Overlay the predicted leakage range onto the pipeline network GIS map. Core function: Visualize output results to guide operation and maintenance. The calculated probability distribution of leak points and the area affected by leakage are accurately overlaid onto the geographic information system (GIS) map of the pipeline network according to spatial coordinates to form intuitive monitoring results. The results are then sent to the operation and maintenance terminal, clearly marking the location of the leak, the probability level, and the scope of impact, to guide operation and maintenance personnel to carry out precise inspections and repairs.

[0100] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0101] Corresponding to the pipeline leakage monitoring method in the above embodiment, Figure 8 This is a structural block diagram of the pipeline leakage monitoring device provided in the embodiments of this application. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0102] Reference Figure 2 The device 8 includes: The data acquisition module 81 is used to acquire the first time-series noise data of the pipeline uploaded by multiple first monitoring devices; wherein the first monitoring devices are deployed at different nodes of the pipeline network composed of multiple pipelines; The data identification module 82 is used to identify water leakage in the pipeline based on the first time-series noise data, and to obtain at least one second monitoring device that triggers the water leakage event; wherein the second monitoring device is included in the first monitoring device; The pipe material acquisition module 83 is used to acquire the first pipe segment monitored by each second monitoring device and the first material information corresponding to the first pipe segment; wherein, the first pipe segment is a part of the pipe in the pipe network; The leakage calculation module 84 is used to calculate the probability distribution of leakage points and the leakage impact area based on the first material information and second time-series noise data corresponding to each first pipe segment.

[0103] Optionally, the data recognition module 82 is also used for: Time-frequency analysis was performed on the first time-series noise data uploaded by each first monitoring device to obtain time-frequency characteristics; If the time-frequency characteristics match the preset characteristics, the first monitoring device will be identified as the second monitoring device that triggers the water leakage event; where the preset characteristics are the exclusive characteristics corresponding to the water leakage signal.

[0104] Optionally, the pipe material acquisition module 83 is also used for: Based on the coordinates of the second monitoring device, the pipe segment whose distance from the second monitoring device is within a preset threshold is obtained; Extract the material information corresponding to the first pipe segment from the preset database to obtain the first material information.

[0105] Optionally, the leakage calculation module 84 is also used for: The acoustic parameters corresponding to the first pipe segment are extracted based on the first material information; among which, the acoustic parameters include signal propagation speed and signal attenuation coefficient; Construct a sound wave propagation model for each first pipe segment based on its acoustic parameters. The probability distribution of leak points and the area affected by leakage are calculated based on multiple sound wave propagation models and second-series noise data.

[0106] Optionally, the leakage calculation module 84 is also used for: Calculate the time difference between every two second monitoring devices based on the timestamps contained in each second time-series noise data; The coordinates of potential leak points are calculated based on multiple time differences and the signal propagation speed of each first pipe segment. The probability distribution of leak points and the area affected by leakage are calculated based on multiple sound wave propagation models and the coordinates of potential leak points.

[0107] Optionally, the leakage calculation module 84 is also used for: The potential leak point pipe section is determined from the first pipe section based on the coordinates of the potential leak point; Using the coordinates of the potential leak point as the starting point of the sound source, the propagation and attenuation process of the leak noise is simulated according to the acoustic propagation model corresponding to the pipe section with the potential leak point, and the noise propagation and attenuation results are obtained. The probability distribution of leak points and the area affected by leakage are obtained based on the noise propagation attenuation results.

[0108] Optionally, the pipeline leakage monitoring device 8 also includes a leakage result output module 85, used for: The probability distribution of leak points and the area affected by leakage are overlaid onto the corresponding geographic information map of the pipeline network to obtain the monitoring results. The monitoring results are sent to the user terminal to guide pipeline operation and maintenance.

[0109] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0110] in addition, Figure 8 The pipeline leakage monitoring device shown can be a software unit, a hardware unit, or a combination of software and hardware built into existing terminal equipment. It can also be integrated into the terminal equipment as an independent component, or exist as a standalone terminal equipment.

[0111] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0112] Figure 9 This is a schematic diagram of the structure of the terminal device provided in the embodiments of this application. For example... Figure 9 As shown, the terminal device 9 of this embodiment includes: at least one processor 90 ( Figure 9 (Only one is shown in the image) a processor, a memory 91, and a computer program 92 stored in the memory 91 and executable on at least one processor 90. When the processor 90 executes the computer program 92, it implements the steps in any of the above embodiments of the pipeline leakage monitoring method.

[0113] The terminal device can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. This terminal device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that... Figure 9 This is merely an example of terminal device 9 and does not constitute a limitation on terminal device 9. It may include more or fewer components than shown in the figure, or combine certain components, or different components, such as input / output devices, network access devices, etc.

[0114] The processor 90 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0115] In some embodiments, memory 91 may be an internal storage unit of terminal device 9, such as a hard disk or memory of terminal device 9. In other embodiments, memory 91 may be an external storage device of terminal device 9, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD) card, flash card, etc., equipped on terminal device 9. Furthermore, memory 91 may include both internal storage units and external storage devices of terminal device 9. Memory 91 is used to store operating system, application programs, boot loader, data, and other programs, such as program code of computer programs. Memory 91 may also be used to temporarily store data that has been output or will be output.

[0116] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement the steps in the above-described method embodiments.

[0117] This application provides a computer program product that, when run on a terminal device, enables the terminal device to implement the steps described in the various method embodiments above.

[0118] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. A computer-readable medium can include at least: any entity or device capable of carrying computer program code to a device / terminal equipment, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0119] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0120] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0121] In the embodiments provided in this application, it should be understood that the disclosed apparatus / terminal devices and methods can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0122] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0123] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for monitoring pipeline leakage, characterized in that, The method includes: Acquire first time-series noise data of the pipeline uploaded by multiple first monitoring devices; wherein the first monitoring devices are deployed at different nodes of a pipeline network composed of multiple pipelines; Based on the first time-series noise data, the pipeline is identified to detect leaks, and at least one second monitoring device that triggers the leak event is obtained; wherein, the second monitoring device is included in the first monitoring device; Obtain the first material information of the first pipe segment monitored by each of the second monitoring devices and the first pipe segment corresponding to the first pipe segment; wherein, the first pipe segment is a part of the pipe in the pipe network; The probability distribution of leakage points and the area affected by leakage are calculated based on the first material information and the second time-series noise data corresponding to each first pipe segment. The first time-series noise data includes the device coordinates of the first monitoring device; the second time-series noise data includes the device coordinates of the second monitoring device; obtaining the first material information corresponding to the first pipe segment monitored by each of the second monitoring devices includes: Based on the device coordinates of the second monitoring device, the pipe segment whose distance from the second monitoring device is within a preset threshold is obtained; Extract the material information corresponding to the first pipe segment from the preset database to obtain the first material information; The step of calculating the probability distribution of leakage points and the leakage impact area based on the first material information and second time-series noise data corresponding to each first pipe segment includes: The acoustic parameters corresponding to the first pipe segment are extracted based on the first material information; wherein, the acoustic parameters include signal propagation speed and signal attenuation coefficient; Construct a sound wave propagation model for each of the first pipe segments based on the acoustic parameters corresponding to each first pipe segment. The probability distribution of the leakage point and the area affected by leakage are calculated based on multiple sound wave propagation models and the second time-series noise data.

2. The pipeline leakage monitoring method as described in claim 1, characterized in that, The step of identifying a leak in the pipeline based on the first time-series noise data to obtain a second monitoring device that triggers a leak event includes: Time-frequency analysis is performed on the first time-series noise data uploaded by each of the first monitoring devices to obtain time-frequency characteristics; If the time-frequency characteristics match the preset characteristics, then the first monitoring device is identified as the second monitoring device that triggered the water leakage event; wherein, the preset characteristics are the specific characteristics corresponding to the water leakage signal.

3. The pipeline leakage monitoring method as described in claim 2, characterized in that, The time-series noise includes timestamps; determining the probability distribution of leak points and the leakage impact area based on multiple sound wave propagation models and the second time-series noise data includes: Calculate the time difference between every two second monitoring devices based on the timestamps contained in each second time-series noise data; The coordinates of potential leak points are calculated based on the multiple time differences and the signal propagation speed of each first pipe segment. The probability distribution of leak points and the area affected by leakage are calculated based on multiple sound wave propagation models and the coordinates of potential leak points.

4. The pipeline leakage monitoring method as described in claim 3, characterized in that, The calculation of the probability distribution of leak points and the area affected by leakage based on multiple sound wave propagation models and the coordinates of potential leak points includes: The potential leak point pipe section is determined from the first pipe section based on the coordinates of the potential leak point; Using the coordinates of the potential leak point as the starting point of the sound source, the propagation and attenuation process of the leak noise is simulated according to the acoustic propagation model corresponding to the pipe section of the potential leak point, and the noise propagation and attenuation results are obtained. The probability distribution of the leak point and the area affected by the leak are obtained based on the noise propagation attenuation results.

5. The pipeline leakage monitoring method as described in claim 1, characterized in that, The method further includes: The probability distribution of the leak points and the area affected by the leaks are superimposed onto the geographic information map of the pipeline network corresponding to the pipeline network to obtain the monitoring results. The monitoring results are sent to the user terminal to guide pipeline operation and maintenance.

6. A pipeline leakage monitoring device, characterized in that, include: The data acquisition module is used to acquire first time-series noise data of the pipeline uploaded by multiple first monitoring devices; wherein the first monitoring devices are deployed at different nodes of the pipeline network composed of multiple pipelines. The data identification module is used to identify water leakage in the pipeline based on the first time-series noise data, and to obtain at least one second monitoring device that triggers the water leakage event; wherein the second monitoring device is included in the first monitoring device; The pipeline material acquisition module is used to acquire the first material information of the first pipe segment monitored by each of the second monitoring devices and the first pipe segment corresponding to the first pipe segment; wherein, the first pipe segment is a part of the pipeline in the pipeline network; The leakage calculation module is used to calculate the probability distribution of leakage points and the leakage impact area based on the first material information and second time-series noise data corresponding to each first pipe segment. The pipe material acquisition module is also used for: Based on the device coordinates of the second monitoring device, the pipe segment whose distance from the second monitoring device is within a preset threshold is obtained; Extract the material information corresponding to the first pipe segment from the preset database to obtain the first material information; The leakage calculation module is also used for: The acoustic parameters corresponding to the first pipe segment are extracted based on the first material information; wherein, the acoustic parameters include signal propagation speed and signal attenuation coefficient; Construct a sound wave propagation model for each of the first pipe segments based on the acoustic parameters corresponding to each first pipe segment. The probability distribution of the leakage point and the area affected by leakage are calculated based on multiple sound wave propagation models and the second time-series noise data.

7. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 5.

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