A pipeline leak monitoring system and method based on flow isolation devices

Abstract

This application discloses a pipeline leak monitoring system and method based on mobile isolation devices, including a mobile monitoring node group and a central processing platform. The mobile monitoring node group consists of multiple isolation devices that move throughout the pipeline with the transported medium. Each isolation device integrates a pressure detection unit, an acoustic detection unit, a flow detection unit, a positioning unit, a data transmission unit, and a power supply unit. These units are used to collect multi-dimensional monitoring data of their location in real time and upload it to the central processing platform, which then determines whether the data is normal. This pipeline leak monitoring system utilizes existing isolation devices in sequential transport processes as mobile monitoring nodes. These nodes can reach any position in the pipeline with the medium, achieving full coverage without blind spots. By using a pressure wave time-difference positioning algorithm combined with the real-time position data of the mobile monitoring nodes, the positioning accuracy can reach the meter level, meeting the precise positioning requirements for pipeline leak repair.

Description

Technical Field

[0001] This application relates to the field of pipeline operation monitoring technology, and in particular to a pipeline leakage monitoring system and method based on a flow isolation device. Background Technology

[0002] Pipeline transportation is a core fluid transport method in fields such as petroleum, chemical industry, and urban water supply. During use, pipelines inevitably face problems such as aging and human-caused damage, which can easily lead to leakage accidents. This not only wastes media resources but may also cause significant safety hazards such as environmental pollution. Therefore, pipeline leakage fault diagnosis is of great significance for reducing economic losses and environmental pollution. Summary of the Invention

[0003] To improve the sensitivity and location accuracy of pipeline leak monitoring, this application provides a pipeline leak monitoring system and method based on a flow isolation device.

[0004] Firstly, this application provides a pipeline leakage monitoring system based on a mobile isolation device, employing the following technical solution: A pipeline leak monitoring system based on a flow isolation device includes: Mobile monitoring node group: It consists of multiple isolation devices. Each isolation device is deployed sequentially at the first station of the pipeline at a preset time interval. It flows along the entire pipeline with the transported medium. Each isolation device integrates a pressure detection unit, an acoustic detection unit, a flow detection unit, a positioning unit, a data transmission unit, and a power supply unit to collect and upload multi-dimensional monitoring data of its location in real time. Central processing platform: includes data receiving module, data preprocessing module, adjacent node differential judgment module, acoustic pressure joint locking module, gradient consistency verification module, time window cumulative judgment module, working condition filtering module, positioning calculation module and alarm handling module; The data receiving module is used to receive real-time data uploaded by all mobile monitoring nodes and to perform time synchronization and alignment processing on the data. The data preprocessing module is used to filter and denoise the raw data and remove outliers. It uses a Kalman filter algorithm to smooth the pressure data and a bandpass filter algorithm to process the acoustic data. The adjacent node differential judgment module is used to monitor the pressure difference between two adjacent flow monitoring nodes in real time, and uses a cross-comparison of four sets of pressure data to determine whether there is a suspected leak. The acoustic pressure joint locking module is used to retrieve acoustic sensor data for joint verification after pressure anomaly is determined, requiring that pressure anomaly and leakage characteristic acoustic wave signal appear synchronously. The gradient consistency verification module is used to calculate the pressure drop gradient values ​​of two flow monitoring nodes and verify whether the deviation between the two gradient values ​​is within a preset range. The time window accumulation determination module is used to verify the duration and trend of abnormal signals and eliminate instantaneous fluctuation interference. The operating condition filtering module is used to identify synchronous pressure changes along the entire pipeline and eliminate false alarms caused by normal operating condition adjustments. The positioning calculation module is used to calculate the precise location coordinates of the leak point based on the real-time location data of the two mobile monitoring nodes and the time difference of arrival of the pressure wave. The alarm handling module is used to trigger a tiered alarm process, push handling instructions, and generate a leak event report.

[0005] Optionally, the isolation device includes an isolation body and a support body, both of which are flexible rubber structures, with the support body disposed inside the isolation body; a reinforcing ring is arranged on the inner wall of the isolation body, and multiple reinforcing ribs are arranged on the outer wall of the support body, with the reinforcing ribs fixedly connected to the reinforcing ring.

[0006] Optionally, the pressure detection unit includes a first pressure sensor and a second pressure sensor installed at opposite ends inside the isolation body; The acoustic detection unit is a miniature acoustic sensor installed inside the isolation body; The flow detection unit is a miniature flow sensor installed in a groove on the outer wall of the isolation body; The positioning unit is a wireless positioning module with a built-in beacon.

[0007] Secondly, this application provides a pipeline leakage monitoring method based on a flow isolation device, employing the following technical solution: A pipeline leak monitoring method based on a flow isolation device includes the following steps: S1. Node deployment: Multiple isolation devices are deployed sequentially at the first station of the pipeline at preset time intervals. Each isolation device serves as a mobile monitoring node, flowing along the pipeline with the transported medium throughout the entire process. The distance between two adjacent mobile monitoring nodes is controlled by the deployment time interval and the medium flow rate, so that the mobile monitoring nodes form a uniformly distributed mobile monitoring network throughout the entire pipeline. The isolation device is the isolation device described in any one of claims 1 to 2. S2. Data Acquisition: Each mobile monitoring node acquires multi-dimensional monitoring data of its location in real time, including the pressure of the medium in front of the node, the pressure of the medium behind the node, acoustic signals, local medium flow velocity, and real-time location information of the node. All data is uploaded to the central processing platform wirelessly. The central processing platform is the central processing platform as described in any one of claims 1 to 2. S3. Adjacent Node Differential Determination: The central processing platform monitors the pressure difference between two adjacent flow monitoring nodes in real time. When the pressure difference exceeds a preset threshold, it is initially determined that there is a suspected leak in the pipe section between the two nodes. The determination of the pressure difference between two adjacent flow monitoring nodes adopts a cross-comparison method of four sets of pressure data, namely, obtaining the pressure sensor data behind the upstream node, the pressure sensor data in front of the downstream node, and the pressure sensor data in front of the upstream node and the pressure sensor data behind the downstream node. The four sets of data form a cross-validation matrix. When the difference between the pressure behind the upstream node and the pressure in front of the downstream node exceeds the threshold, and the pressure in front of the upstream node and the pressure behind the downstream node remain normally synchronized, it is confirmed that there is a suspected leak in the section between the nodes. S4. Acoustic Pressure Joint Lock-in: After determining a suspected leak in step S3, the central processing platform retrieves the acoustic sensor data of the suspected node from the two mobile monitoring nodes within the interval. If the acoustic sensor of at least one of the two nodes detects a leak-characteristic acoustic signal, and the arrival time of the acoustic signal matches the occurrence time of the pressure anomaly, the leak is locked in. If the acoustic sensor does not detect a leak-characteristic acoustic signal, the alarm is not triggered temporarily, and monitoring continues. S5. Gradient Consistency Verification: After the leak is determined in step S4, the central processing platform performs gradient consistency verification on the pressure data of the two mobile monitoring nodes. That is, it calculates the pressure drop gradient value of the pressure sensor behind the upstream node and the pressure sensor in front of the downstream node in the same time period. If the deviation between the two gradient values ​​does not exceed the preset deviation threshold, the verification is passed, confirming that the pressure abnormality is caused by the same leak event. If the deviation between the two gradient values ​​exceeds the preset deviation threshold, it is determined to be a single sensor failure or local disturbance, and no alarm is triggered. S6. Accumulated Time Window Judgment: After passing the gradient consistency verification in step S5, the central processing platform performs accumulated time window judgment on the abnormal signal. It requires that the abnormal signal continuously exceeds the preset duration threshold and the pressure change shows a monotonic trend. After eliminating the interference of instantaneous pressure fluctuations, the leakage event is finally confirmed. S7. Operating Condition Filtering: During the execution of steps S3 to S6, the central processing platform simultaneously performs operating condition filtering, that is, real-time monitoring of the pressure data of all flow monitoring nodes along the entire pipeline. If the pressure of multiple consecutive node pairs drops synchronously, it is determined to be a normal operating condition adjustment of the pipeline and no leakage alarm is triggered. Only when the abnormal pressure change is limited to a single node pair range will it enter the subsequent judgment process. S8. Leakage point location: After confirming the leakage event, the central processing platform uses the real-time location data of the suspected node on the two mobile monitoring nodes in the interval, combined with the pressure wave arrival time recorded by each of the two nodes, to calculate the precise location coordinates of the leak point using the pressure wave time difference location algorithm. The location calculation formula is: distance from the leak point to the upstream node = (node ​​spacing + pressure wave propagation speed × pressure wave arrival time difference between upstream and downstream nodes) ÷ 2, where the pressure wave propagation speed is determined based on the density and elastic modulus of the transported medium. S9. Alarm Handling: After the location calculation is completed, the central processing platform automatically triggers the hierarchical alarm process, classifies the alarm level according to the severity of the leak, and pushes the location information of the leak section, the coordinates of the leak point, the estimated leakage amount and the suggested handling instructions to the on-duty personnel. At the same time, it archives all data of the leak event and generates a complete leak event report.

[0008] Optionally, the time interval between two adjacent isolation devices in step S1 is determined based on the pipeline flow rate and the required monitoring accuracy. The calculation formula is: deployment interval = target monitoring distance ÷ medium flow rate.

[0009] Optionally, the leakage characteristic acoustic signal mentioned in step S4 is a specific frequency band acoustic wave generated by the high-speed jet of the medium at the leakage point. Its frequency range is pre-calibrated according to the pipe diameter, medium type and pressure level, and stored in the acoustic feature library of the central processing platform.

[0010] Optionally, the preset deviation threshold in step S5 is 15%, that is, when the deviation between two pressure drop gradient values ​​does not exceed 15%, it is determined to pass the verification.

[0011] Optionally, the preset duration threshold in step S6 is 30 seconds, meaning that the abnormal signal must last for more than 30 seconds and the pressure change must show a monotonic trend in order to be determined as a valid leakage signal.

[0012] Optionally, the formula for calculating the pressure wave propagation velocity in step S8 is: , where K is the bulk modulus of the conveying medium and ρ is the density of the conveying medium.

[0013] In summary, the beneficial technical effects of this application are as follows: the pipeline leakage monitoring system based on the mobile isolation device does not rely on a fixed monitoring network, but only uses the existing isolation device in the sequential conveying process as a mobile monitoring node. It can reach any position in the pipeline with the medium, achieving full coverage of the pipeline without dead angles. By using the pressure wave time difference positioning algorithm combined with the real-time position data of the mobile monitoring node, the positioning accuracy can reach the meter level, meeting the precise positioning requirements for pipeline leakage repair. Attached Figure Description

[0014] Figure 1This is a framework diagram of a pipeline leakage monitoring system based on a flow isolation device in an embodiment of this application; Figure 2 This is a schematic diagram of the internal structure of the isolation device in the embodiments of this application; Figure 3 This is a flowchart of a pipeline leakage monitoring method based on a flow isolation device in an embodiment of this application.

[0015] Explanation of reference numerals in the attached drawings: 1. Isolator; 2. Support; 3. Reinforcing ring; 4. Reinforcing rib; 5. Equalizing ring; 6. Connecting rib; 7. Pressure detection unit; 8. Acoustic detection unit; 9. Flow detection unit; 10. Positioning unit. Detailed Implementation

[0016] The present application will be further described in detail below with reference to the accompanying drawings.

[0017] This application discloses a pipeline leakage monitoring system based on a mobile isolation device.

[0018] Reference Figure 1 and Figure 2 The pipeline leak monitoring system includes a mobile monitoring node group and a central processing platform. The mobile monitoring node group consists of multiple isolation devices, each including an isolation body 1 and a support body 2. Both the isolation body 1 and the support body 2 are made of rubber, with the support body 2 located inside the isolation body 1. The main body of the isolation body 1 is cylindrical, with hemispherical ends, while the support body 2 is spherical.

[0019] The inner wall of the isolator 1 has several reinforcing rings 3 arranged radially in the middle region, and the outer wall of the support 2 has multiple reinforcing ribs 4 arranged in a circle. The reinforcing ribs 4 are fixed to the reinforcing rings 3, so that the support 2 is connected to the inside of the isolator 1.

[0020] With the central region of the isolator 1, which is equipped with reinforcing rings 3, as the boundary, the inner walls of the hemispherical ends of the isolator 1 are respectively provided with equalizing rings 5. The sides of the support body 2 facing the equalizing rings 5 ​​at both ends are respectively provided with connecting ribs 6 extending to the equalizing rings 5. The middle section of the connecting ribs 6 is set as a continuous bending structure to form an elastic buffer part, which can offset the instantaneous load caused by the impact of the medium.

[0021] The outer wall of the isolator 1 is independently provided with a first air inlet, and the support 2 inside the isolator 1 is connected to an independent air inlet tube. The end of the air inlet tube extends through the outer wall of the isolator 1, forming a second air inlet on the outside of the isolator 1 that is independent of the first air inlet.

[0022] The isolation device integrates a pressure detection unit 7, an acoustic detection unit 8, a flow detection unit 9, a positioning unit 10, a data transmission unit, and a power supply unit.

[0023] The pressure detection unit 7 includes a first pressure sensor installed on the front end face of the isolator 1 and a second pressure sensor installed on the rear end face of the isolator 1. Both pressure sensors are completely built into the rubber housing of the isolator 1 and do not protrude from the outer surface of the isolator 1. The gap between the sensor and the rubber housing is treated with a vulcanization sealing process.

[0024] The acoustic detection unit 8 is a miniature acoustic sensor installed in the cavity inside the isolation body 1, with an operating frequency band covering 20Hz to 20kHz.

[0025] The flow detection unit 9 is a miniature flow sensor installed in a groove on the outer wall of the isolator 1, with the detection end face flush with the outer wall of the isolator 1.

[0026] The positioning unit 10 is a wireless positioning module with a built-in beacon, which uses ultra-low frequency electromagnetic waves to penetrate the pipe wall and transmit positioning signals outward.

[0027] The power supply unit is a high-energy-density lithium battery pack with an explosion-proof sealed encapsulation structure.

[0028] The central processing platform includes a data receiving module, a data preprocessing module, an adjacent node differential determination module, an acoustic pressure joint locking module, a gradient consistency verification module, a time window cumulative determination module, a working condition filtering module, a positioning calculation module, and an alarm handling module.

[0029] The data receiving module is used to receive real-time data uploaded by all mobile monitoring nodes and perform time synchronization and alignment processing on the data.

[0030] The data preprocessing module is used to filter and denoise the raw data and remove outliers. It uses the Kalman filter algorithm to smooth the pressure data and the bandpass filter algorithm to process the acoustic data.

[0031] The adjacent node differential judgment module is used to monitor the pressure difference between two adjacent flow monitoring nodes in real time, and uses a cross-comparison of four sets of pressure data to determine whether there is a suspected leak.

[0032] The acoustic pressure joint locking module is used to retrieve acoustic sensor data for joint verification after pressure anomaly is detected, requiring that pressure anomaly and leakage characteristic acoustic wave signals appear synchronously.

[0033] The gradient consistency verification module is used to calculate the pressure drop gradient values ​​of two flow monitoring nodes and verify whether the deviation between the two gradient values ​​is within the preset range.

[0034] The time window accumulation judgment module is used to verify the duration and trend of abnormal signals and eliminate instantaneous fluctuation interference.

[0035] The operating condition filtering module is used to identify synchronous pressure changes throughout the pipeline and eliminate false alarms caused by normal operating condition adjustments.

[0036] The positioning calculation module is used to calculate the precise location coordinates of the leak point based on the real-time location data of the two mobile monitoring nodes and the time difference of arrival of the pressure wave.

[0037] The alarm handling module is used to trigger tiered alarm processes, push handling instructions, and generate leak event reports.

[0038] Reference Figure 3 A pipeline leak monitoring method based on mobile monitoring nodes includes the following steps: S1. Node Deployment: At the first station of the pipeline, multiple isolation devices are deployed sequentially at preset time intervals. Each isolation device serves as a mobile monitoring node, flowing along the pipeline with the transported medium throughout the entire process. The spacing between two adjacent mobile monitoring nodes is controlled by the deployment time interval and the medium flow rate, so that the mobile monitoring nodes form a uniformly distributed mobile monitoring network throughout the entire pipeline. S2. Data Acquisition: Each mobile monitoring node collects multi-dimensional monitoring data of its location in real time, including the pressure of the medium in front of the node, the pressure of the medium behind the node, acoustic signals, local medium flow velocity, and real-time location information of the node. All data is uploaded to the central processing platform wirelessly. S3. Adjacent Node Differential Determination: The central processing platform monitors the pressure difference between two adjacent flow monitoring nodes in real time. When the pressure difference exceeds a preset threshold, it is initially determined that there is a suspected leak in the pipe section between the two nodes. The determination of the pressure difference between two adjacent flow monitoring nodes adopts a cross-comparison method of four sets of pressure data, namely, obtaining the pressure sensor data behind the upstream node, the pressure sensor data in front of the downstream node, and the pressure sensor data in front of the upstream node and the pressure sensor data behind the downstream node. The four sets of data form a cross-validation matrix. When the difference between the pressure behind the upstream node and the pressure in front of the downstream node exceeds the threshold, and the pressure in front of the upstream node and the pressure behind the downstream node remain normally synchronized, it is confirmed that there is a suspected leak in the section between the nodes. S4. Acoustic Pressure Joint Lock-in: After determining a suspected leak in step S3, the central processing platform retrieves the acoustic sensor data of the suspected node from the two mobile monitoring nodes within the interval. If the acoustic sensor of at least one of the two nodes detects a leak-characteristic acoustic signal, and the arrival time of the acoustic signal matches the occurrence time of the pressure anomaly, the leak is locked in. If the acoustic sensor does not detect a leak-characteristic acoustic signal, the alarm is not triggered temporarily, and monitoring continues. S5. Gradient Consistency Verification: After the leak is determined in step S4, the central processing platform performs gradient consistency verification on the pressure data of the two mobile monitoring nodes. That is, it calculates the pressure drop gradient value of the pressure sensor behind the upstream node and the pressure sensor in front of the downstream node in the same time period. If the deviation between the two gradient values ​​does not exceed the preset deviation threshold, the verification is passed, confirming that the pressure abnormality is caused by the same leak event. If the deviation between the two gradient values ​​exceeds the preset deviation threshold, it is determined to be a single sensor failure or local disturbance, and no alarm is triggered. S6. Accumulated Time Window Judgment: After passing the gradient consistency verification in step S5, the central processing platform performs accumulated time window judgment on the abnormal signal. It requires that the abnormal signal continuously exceeds the preset duration threshold and the pressure change shows a monotonic trend. After eliminating the interference of instantaneous pressure fluctuations, the leakage event is finally confirmed. S7. Operating Condition Filtering: During the execution of steps S3 to S6, the central processing platform simultaneously performs operating condition filtering, which means real-time monitoring of the pressure data of all flow monitoring nodes along the entire pipeline. If the pressure of multiple consecutive node pairs drops simultaneously, it is determined to be a normal pipeline operating condition adjustment, such as pump station start-up and shutdown, valve adjustment, etc., and leakage alarms are not triggered. Only when the abnormal pressure change is limited to a single node pair range will it enter the subsequent judgment process. S8. Leakage point location: After confirming the leakage event, the central processing platform uses the real-time location data of the suspected node on the two mobile monitoring nodes in the interval, combined with the pressure wave arrival time recorded by each of the two nodes, to calculate the precise location coordinates of the leak point using the pressure wave time difference location algorithm. The location calculation formula is: distance from the leak point to the upstream node = (node ​​spacing + pressure wave propagation speed × pressure wave arrival time difference between upstream and downstream nodes) ÷ 2, where the pressure wave propagation speed is determined based on the density and elastic modulus of the transported medium. S9. Alarm Handling: After the location calculation is completed, the central processing platform automatically triggers the hierarchical alarm process, classifies the alarm level according to the severity of the leak, and pushes the location information of the leak section, the coordinates of the leak point, the estimated leakage amount and the suggested handling instructions to the on-duty personnel. At the same time, it archives all data of the leak event and generates a complete leak event report.

[0039] Furthermore, the time interval between two adjacent isolation devices in step S1 is determined based on the pipeline flow rate and the required monitoring accuracy. The calculation formula is: deployment interval time = target monitoring distance ÷ medium flow rate.

[0040] Furthermore, in step S2, the isolation device flows along the pipeline under the impetus of the conveying medium, simultaneously performing the dual functions of media isolation and leakage monitoring. The first pressure sensor on the isolation device collects the pressure of the medium ahead in real time, the second pressure sensor collects the pressure of the medium behind in real time, the acoustic sensor collects the characteristic acoustic signal of leakage, the flow sensor detects local flow velocity changes, and the positioning unit 10 obtains the precise position of the device in the pipeline in real time. All data is packaged and uploaded to the central processing platform through the data transmission unit.

[0041] Furthermore, the leakage characteristic acoustic wave signal mentioned in step S4 is a specific frequency band acoustic wave generated by the high-speed jet of the medium at the leakage point. Its frequency range is pre-calibrated according to the pipe diameter, medium type and pressure level, and stored in the acoustic feature library of the central processing platform.

[0042] Furthermore, the preset deviation threshold mentioned in step S5 is 15%, that is, when the deviation between two pressure drop gradient values ​​does not exceed 15%, it is determined to pass the verification.

[0043] Furthermore, the preset duration threshold mentioned in step S6 is 30 seconds, that is, the abnormal signal must last for more than 30 seconds and the pressure change must show a monotonic trend in order to be determined as a valid leakage signal.

[0044] Furthermore, the formula for calculating the pressure wave propagation speed in step S8 is as follows: , where K is the bulk modulus of the conveying medium and ρ is the density of the conveying medium.

[0045] This pipeline leak monitoring system does not rely on a fixed monitoring network at all. It only uses the isolation devices already in the sequential transport process as mobile monitoring nodes. There is no need to deploy any fixed sensors, power supply facilities and communication networks along the pipeline. The system construction cost is reduced by more than 70%, and it is particularly suitable for remote areas, deserts, mountains and other scenarios where fixed sensors are difficult to deploy and maintain.

[0046] This specific embodiment is merely an explanation of this application and is not intended to limit it. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

Claims

1. A pipeline leakage monitoring system based on a flow isolation device, characterized in that: include: Mobile monitoring node group: It consists of multiple isolation devices. Each isolation device is deployed sequentially at the first station of the pipeline at a preset time interval and flows along the pipeline with the transported medium. Each isolation device integrates a pressure detection unit (7), an acoustic detection unit (8), a flow detection unit (9), a positioning unit (10), a data transmission unit, and a power supply unit, which are used to collect and upload multi-dimensional monitoring data of the location in real time. Central processing platform: includes data receiving module, data preprocessing module, adjacent node differential judgment module, acoustic pressure joint locking module, gradient consistency verification module, time window cumulative judgment module, working condition filtering module, positioning calculation module and alarm handling module; The data receiving module is used to receive real-time data uploaded by all mobile monitoring nodes and to perform time synchronization and alignment processing on the data. The data preprocessing module is used to filter and denoise the raw data and remove outliers. It uses a Kalman filter algorithm to smooth the pressure data and a bandpass filter algorithm to process the acoustic data. The adjacent node differential judgment module is used to monitor the pressure difference between two adjacent flow monitoring nodes in real time, and uses a cross-comparison of four sets of pressure data to determine whether there is a suspected leak. The acoustic pressure joint locking module is used to retrieve acoustic sensor data for joint verification after pressure anomaly is determined, requiring that pressure anomaly and leakage characteristic acoustic wave signal appear synchronously. The gradient consistency verification module is used to calculate the pressure drop gradient values ​​of two flow monitoring nodes and verify whether the deviation between the two gradient values ​​is within a preset range. The time window accumulation determination module is used to verify the duration and trend of abnormal signals and eliminate instantaneous fluctuation interference. The operating condition filtering module is used to identify synchronous pressure changes along the entire pipeline and eliminate false alarms caused by normal operating condition adjustments. The positioning calculation module is used to calculate the precise location coordinates of the leak point based on the real-time location data of the two mobile monitoring nodes and the time difference of arrival of the pressure wave. The alarm handling module is used to trigger a tiered alarm process, push handling instructions, and generate a leak event report.

2. The pipeline leakage monitoring system based on a flow isolation device according to claim 1, characterized in that: The isolation device includes an isolation body (1) and a support body (2). Both the isolation body (1) and the support body (2) are flexible rubber structures. The support body (2) is located inside the isolation body (1). A reinforcing ring (3) is arranged on the inner wall of the isolation body (1). Multiple reinforcing ribs (4) are arranged on the outer wall of the support body (2). The reinforcing ribs (4) are fixedly connected to the reinforcing ring (3).

3. The pipeline leakage monitoring system based on a flow isolation device according to claim 2, characterized in that: The pressure detection unit (7) includes a first pressure sensor and a second pressure sensor installed at opposite ends inside the isolation body (1); The acoustic detection unit (8) is a miniature acoustic sensor installed inside the isolation body (1); The flow detection unit (9) is a miniature flow sensor installed in a groove on the outer wall of the isolation body (1); The positioning unit (10) is a wireless positioning module with a built-in beacon.

4. A pipeline leakage monitoring method based on a flow isolation device, characterized in that: Includes the following steps: S1. Node deployment: Multiple isolation devices are deployed sequentially at the first station of the pipeline at preset time intervals. Each isolation device serves as a mobile monitoring node, flowing along the pipeline with the transported medium throughout the entire process. The distance between two adjacent mobile monitoring nodes is controlled by the deployment time interval and the medium flow rate, so that the mobile monitoring nodes form a uniformly distributed mobile monitoring network throughout the entire pipeline. The isolation device is the isolation device described in any one of claims 1 to 2. S2. Data Acquisition: Each mobile monitoring node acquires multi-dimensional monitoring data of its location in real time, including the pressure of the medium in front of the node, the pressure of the medium behind the node, acoustic signals, local medium flow velocity, and real-time location information of the node. All data is uploaded to the central processing platform wirelessly. The central processing platform is the central processing platform as described in any one of claims 1 to 2. S3. Adjacent Node Differential Determination: The central processing platform monitors the pressure difference between two adjacent flow monitoring nodes in real time. When the pressure difference exceeds a preset threshold, it is initially determined that there is a suspected leak in the pipe section between the two nodes. The determination of the pressure difference between two adjacent flow monitoring nodes adopts a cross-comparison method of four sets of pressure data, namely, obtaining the pressure sensor data behind the upstream node, the pressure sensor data in front of the downstream node, and the pressure sensor data in front of the upstream node and the pressure sensor data behind the downstream node. The four sets of data form a cross-validation matrix. When the difference between the pressure behind the upstream node and the pressure in front of the downstream node exceeds the threshold, and the pressure in front of the upstream node and the pressure behind the downstream node remain normally synchronized, it is confirmed that there is a suspected leak in the section between the nodes. S4. Acoustic Pressure Joint Lock-in: After determining a suspected leak in step S3, the central processing platform retrieves the acoustic sensor data of the suspected node from the two mobile monitoring nodes within the interval. If the acoustic sensor of at least one of the two nodes detects a leak-characteristic acoustic signal, and the arrival time of the acoustic signal matches the occurrence time of the pressure anomaly, the leak is locked in. If the acoustic sensor does not detect a leak-characteristic acoustic signal, the alarm is not triggered temporarily, and monitoring continues. S5. Gradient Consistency Verification: After the leak is determined in step S4, the central processing platform performs gradient consistency verification on the pressure data of the two mobile monitoring nodes. That is, it calculates the pressure drop gradient value of the pressure sensor behind the upstream node and the pressure sensor in front of the downstream node in the same time period. If the deviation between the two gradient values ​​does not exceed the preset deviation threshold, the verification is passed, confirming that the pressure abnormality is caused by the same leak event. If the deviation between the two gradient values ​​exceeds the preset deviation threshold, it is determined to be a single sensor failure or local disturbance, and no alarm is triggered. S6. Accumulated Time Window Judgment: After passing the gradient consistency verification in step S5, the central processing platform performs accumulated time window judgment on the abnormal signal. It requires that the abnormal signal continuously exceeds the preset duration threshold and the pressure change shows a monotonic trend. After eliminating the interference of instantaneous pressure fluctuations, the leakage event is finally confirmed. S7. Operating Condition Filtering: During the execution of steps S3 to S6, the central processing platform simultaneously performs operating condition filtering, that is, real-time monitoring of the pressure data of all flow monitoring nodes along the entire pipeline. If the pressure of multiple consecutive node pairs drops synchronously, it is determined to be a normal operating condition adjustment of the pipeline and no leakage alarm is triggered. Only when the abnormal pressure change is limited to a single node pair range will it enter the subsequent judgment process. S8. Leakage point location: After confirming the leakage event, the central processing platform uses the real-time location data of the suspected node on the two mobile monitoring nodes in the interval, combined with the pressure wave arrival time recorded by each of the two nodes, to calculate the precise location coordinates of the leak point using the pressure wave time difference location algorithm. The location calculation formula is: distance from the leak point to the upstream node = (node ​​spacing + pressure wave propagation speed × pressure wave arrival time difference between upstream and downstream nodes) ÷ 2, where the pressure wave propagation speed is determined based on the density and elastic modulus of the transported medium. S9. Alarm Handling: After the location calculation is completed, the central processing platform automatically triggers the hierarchical alarm process, classifies the alarm level according to the severity of the leak, and pushes the location information of the leak section, the coordinates of the leak point, the estimated leakage amount and the suggested handling instructions to the on-duty personnel. At the same time, it archives all data of the leak event and generates a complete leak event report.

5. The pipeline leakage monitoring method based on a flow isolation device according to claim 4, characterized in that: The time interval between two adjacent isolation devices in step S1 is determined based on the pipeline flow rate and the required monitoring accuracy. The calculation formula is: deployment interval = target monitoring distance ÷ medium flow rate.

6. The pipeline leakage monitoring method based on a flow isolation device according to claim 1, characterized in that: The leakage characteristic acoustic signal mentioned in step S4 is a specific frequency band acoustic wave generated by the high-speed jet of the medium at the leakage point. Its frequency range is pre-calibrated according to the pipe diameter, medium type and pressure level, and stored in the acoustic feature library of the central processing platform.

7. The pipeline leakage monitoring method based on a flow isolation device according to claim 1, characterized in that: The preset deviation threshold mentioned in step S5 is 15%, that is, when the deviation between two pressure drop gradient values ​​does not exceed 15%, it is determined to pass the verification.

8. The pipeline leakage monitoring method based on a flow isolation device according to claim 1, characterized in that: The preset duration threshold mentioned in step S6 is 30 seconds, that is, the abnormal signal must last for more than 30 seconds and the pressure change must show a monotonic trend in order to be determined as a valid leakage signal.

9. A pipeline leakage monitoring method based on a flow isolation device according to claim 1, characterized in that: The formula for calculating the pressure wave propagation velocity in step S8 is: , where K is the bulk modulus of the conveying medium and ρ is the density of the conveying medium.

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