Gas pipeline leakage early warning system, method and equipment based on dynamic waves

Abstract

The invention relates to the technical field of pipeline early warning, in particular to a gas pipeline leakage early warning system, method and equipment based on dynamic waves, and the system comprises a detection sensor, a pipe explosion detection unit and a leakage early warning analysis unit; the detection sensor detects pressure data and dynamic wave data of the pipeline; the pipe explosion detection unit calculates the pressure drop rate of the pipeline based on the pressure data, and when the pressure drop rate is larger than a preset first threshold value, a preliminary trigger signal is generated; after receiving the preliminary trigger signal, the leakage early warning analysis unit analyzes whether leakage exists or not based on the dynamic wave data and a built-in AI model, and sends a verification result signal to the pipe explosion detection unit; and when receiving the verification result signal, the pipe explosion detection unit sends out early warning information and controls the state of the pipeline valve based on the verification result signal. Thus, through a double criterion mechanism of pressure triggering and dynamic wave AI verification, the valve false closing rate can be greatly reduced, meanwhile, single threshold dependence is broken through, and early warning is efficiently and accurately achieved.

Description

Technical Field

[0001] This invention relates to the field of pipeline early warning technology, specifically to a gas pipeline leakage early warning system, method, and device based on dynamic waves. Background Technology

[0002] As an important clean energy source, the safe and stable operation of natural gas pipelines is crucial for energy supply and public safety. Pipeline leaks, especially pipe ruptures, can lead to catastrophic consequences. Therefore, efficient and reliable leak detection and emergency shut-off technologies are core components of ensuring pipeline safety. Currently, this field mainly relies on the following three types of technical solutions: Mechanical pressure drop detection: This method senses pressure changes through a spring mechanism. While simple in structure, it is inaccurate, prone to malfunctions, and has a slow response (≥60 seconds). It cannot distinguish between pressure fluctuations and actual leaks.

[0003] SCADA remote monitoring system: collects pressure data and uploads it to the control center for analysis. This solution relies on network communication, which has the risks of transmission delay, data error and network interruption. In addition, valve closing decisions usually require manual intervention and have poor real-time performance.

[0004] Local electronic pipe burst detection unit (mainstream solution): This solution processes pressure signals locally within the valve chamber, automatically determines the cause, and outputs a valve shut-off command. However, this solution has significant drawbacks: the system is typically a single-device design without redundancy, meaning equipment failure will directly lead to functional inoperability; its core relies on a single static threshold for "pressure drop rate" for judgment, which is difficult to set due to the complex and variable pipeline conditions—setting it too low makes it susceptible to fluctuations in normal operating conditions, leading to false valve shut-off and causing huge production losses (false alarm rate can reach over 40%); setting it too high may prevent valve shut-off in the event of a real leak, causing a safety accident.

[0005] Therefore, existing technologies, especially mainstream local detection solutions, are insufficient in terms of system reliability, threshold adaptability, and anti-maloperation performance. There is an urgent need for a new pipeline safety protection technology that can achieve high reliability, intelligent judgment, and effectively balance safety and availability. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to provide a gas pipeline leakage early warning system, method and device based on dynamic waves, so as to overcome the problems of poor reliability, weak threshold adaptive capability and insufficient anti-maloperation performance in the current natural gas pipeline transmission process.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, this application provides a gas pipeline leakage early warning system based on dynamic waves, including: a detection sensor, a pipe burst detection unit, and a leakage early warning analysis unit; The detection sensor is used to detect the pressure data and dynamic wave data of the pipeline, and the dynamic wave data includes dynamic infrasound data. The burst pipe detection unit is communicatively connected to the detection sensor and is used to calculate the pressure drop rate of the pipeline based on the pressure data, and generate a preliminary trigger signal when the pressure drop rate is greater than a preset first threshold. The leakage early warning analysis unit is communicatively connected to the detection sensor. After receiving the initial trigger signal, it analyzes whether there is a leak based on dynamic wave data and the built-in AI model, and sends a verification result signal to the burst pipe detection unit. The burst pipe detection unit is also used to issue early warning information and control the status of pipeline valves based on the verification result signal when it receives the verification result signal.

[0008] Furthermore, in some embodiments of this application, the detection sensor includes a pressure sensor, a dynamic wave sensor, and a composite sensor; The pressure sensor is used to detect the pressure data of the pipeline; the dynamic wave sensor is used to detect the dynamic wave data of the pipeline; the composite sensor is used to simultaneously detect the pressure data and dynamic wave data of the pipeline.

[0009] Furthermore, in some embodiments of this application, the dynamic wave sensor is a MEMS sensor or a piezoelectric ceramic sensor.

[0010] Furthermore, in some embodiments of this application, the verification result signal includes: confirmed leakage, suspected leakage, and no leakage; The pipe burst detection unit issues an early warning signal and controls the status of the pipeline valves based on the verification result signal, including: When the verification result signal confirms leakage, and it is determined that the current pressure drop rate exceeds the second threshold, a valve closing command is generated based on preset delay information to drive a preset actuator to close the pipeline valve. When the verification result signal indicates a suspected leak, an alarm message is issued and no valve closing command is generated. When the verification result signal is non-leakage, no alarm message is issued and no valve closing command is generated.

[0011] Furthermore, in some embodiments of this application, the leakage early warning analysis unit is also used to calculate the estimated location of the leakage point based on dynamic wave data; The estimated location of the leak point is used by the leak early warning analysis unit to send the verification result signal to the target pipe burst detection unit, or by the pipe burst detection unit to control the state of the target pipeline valve.

[0012] Furthermore, in some embodiments of this application, the AI ​​model is a model based on convolutional pulse detection or a quantum neural network classifier.

[0013] Furthermore, in some embodiments of this application, the leakage early warning analysis unit includes: a signal processing module, a feature extraction module, and a pattern recognition module, when the AI ​​model is a model based on convolutional pulse detection; The signal processing module is used to perform noise filtering on the dynamic wave data to eliminate mechanical vibration interference. The feature extraction module is used to extract leakage features from the noise-filtered data based on the time-frequency decomposition principle; The pattern recognition module is used to classify the extracted leakage features based on the convolutional pulse detection algorithm to determine whether a leakage exists and to generate the verification result signal.

[0014] Secondly, this application provides a gas pipeline leakage early warning method based on dynamic waves, applied to the system described above, including: Real-time monitoring of pipeline pressure and dynamic wave data; The pressure drop rate of the pipeline is calculated based on the pressure data, and a preliminary trigger signal is generated when the pressure drop rate is greater than a preset first threshold. After confirming the initial trigger signal, the system analyzes whether there is a leak based on dynamic wave data and AI model, generates a verification result signal, and issues a warning message and controls the status of pipeline valves based on the verification result signal.

[0015] Furthermore, in some embodiments of this application, the step of analyzing whether leakage exists based on dynamic wave data and AI models, and generating a verification result signal, includes: The dynamic wave data is subjected to noise filtering to eliminate mechanical vibration interference; Leakage features are extracted from noise-filtered data based on the time-frequency decomposition principle; The extracted leakage features are classified based on the convolutional pulse detection algorithm to determine whether leakage exists, and the verification result signal is generated. The verification result signal includes: confirmed leakage, suspected leakage, and no leakage. The issuance of early warning information and control of pipeline valve status based on the verification result signal includes: When the verification result signal confirms leakage, and it is determined that the current pressure drop rate exceeds the second threshold, a valve closing command is generated based on preset delay information to drive a preset actuator to close the pipeline valve. When the verification result signal indicates a suspected leak, an alarm message is issued and no valve closing command is generated. When the verification result signal is non-leakage, no alarm message is issued and no valve closing command is generated.

[0016] Thirdly, this application provides a gas pipeline leak early warning device based on dynamic waves, including a processor and a memory, wherein the processor is connected to the memory: The processor is used to call and execute the program stored in the memory; The memory is used to store the program, which is at least used to execute the above-described gas pipeline leakage early warning method based on dynamic waves.

[0017] The gas pipeline leakage early warning system, method, and equipment based on dynamic waves provided in this application have the following beneficial effects: Significantly reduce valve malfunction rate: Through a dual criterion mechanism of pressure triggering and dynamic wave AI verification, it effectively filters pressure fluctuations under normal operating conditions, reduces the malfunction rate, and avoids losses from unplanned production stoppages.

[0018] Breaking the dependence on a single threshold: The first threshold is only used for sensitivity triggering, and the core leakage judgment is completed by the AI ​​model, which solves the problem that traditional solutions are prone to false triggering when the threshold is set too low and prone to leakage when the threshold is set too high, and adapts to complex working conditions.

[0019] Provides redundant safety paths: Pressure and dynamic wave detection verify each other, forming a double insurance, reducing the risk of system failure due to single point of failure, and improving overall reliability.

[0020] Achieve early intelligent warning: Dynamic wave analysis can identify minute leaks and provide early warnings before the pressure reaches the subsequent valve shut-off threshold, buying time for emergency response and upgrading from passive shutdown to proactive safety management. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of the structure of a gas pipeline leakage early warning system based on dynamic waves provided in an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the principle of the gas pipeline leakage early warning method based on dynamic waves provided in this embodiment of the invention. Figure 3 This is a structural diagram of a gas pipeline leakage early warning system based on dynamic waves provided in an embodiment of the present invention; Figure 4 This is a flowchart illustrating the gas pipeline leakage early warning method based on dynamic waves provided in an embodiment of the present invention. Figure 5 This is a schematic diagram of the structure of a gas pipeline leakage early warning device based on dynamic waves provided in an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0024] Figure 1 This is a schematic diagram of the structure of the gas pipeline leakage early warning system based on dynamic waves provided in an embodiment of the present invention. Please refer to [link / reference]. Figure 1 This embodiment may include a detection sensor, a pipe burst detection unit, and a leakage early warning analysis unit. The detection sensor is used to detect the pressure data and dynamic wave data of the pipeline, and the dynamic wave data includes dynamic infrasound data.

[0025] The pipe burst detection unit communicates with the detection sensor to calculate the pressure drop rate of the pipeline based on pressure data, and generates an initial trigger signal when the pressure drop rate exceeds a preset first threshold. The leak early warning analysis unit also communicates with the detection sensor to analyze the presence of a leak based on dynamic wave data and a built-in AI model after receiving the initial trigger signal, and sends a verification result signal to the pipe burst detection unit. Upon receiving the verification result signal, the pipe burst detection unit also issues early warning information and controls the status of pipeline valves based on the verification result signal.

[0026] Specifically, in this application, the detection sensor may include a pressure sensor, a dynamic wave sensor (such as a MEMS sensor or a piezoelectric ceramic sensor), and a composite sensor; wherein, the pressure sensor is used to detect the pressure data of the pipeline, the dynamic wave sensor is used to detect the dynamic wave data of the pipeline, and is used for calculation and analysis by the leak early warning analysis unit; while the composite sensor can simultaneously detect the pressure data and dynamic wave data of the pipeline, and all three sensors operate independently.

[0027] The pressure data detected by the pressure sensor and the composite sensor are used by the burst pipe detection unit to calculate the pressure drop rate of the pipeline and generate a preliminary trigger signal when the pressure drop rate exceeds a preset first threshold. The dynamic wave sensor detected by the dynamic wave sensor and the composite sensor is used by the leak early warning analysis unit to analyze whether a leak exists and to calculate the estimated location of the leak point.

[0028] In practical applications, pressure sensors can be pressure transmitters, and multiple pressure transmitters can be set for a single pipeline section, such as three (two at both ends and one in the middle). These three pressure transmitters can operate independently simultaneously (subsequent calculations and analyses can be dynamically performed based on a preset redundancy mechanism, such as a 2oo3 mechanism). Dynamic wave sensors can be MEMS infrasound sensor arrays or piezoelectric ceramic sensors. MEMS infrasound sensor arrays offer advantages such as low cost and moderate accuracy, making them suitable for pressure monitoring scenarios in most gas pipelines. Piezoelectric ceramic sensors, on the other hand, can avoid signal drift or sensitivity degradation that may occur with MEMS infrasound sensors under ultra-high pressure or extreme operating conditions. With their higher mechanical stability and wider dynamic response range, they maintain a detection accuracy of ±0.2Pa even under ultra-high pressure conditions, effectively improving the reliability of the system under extreme conditions.

[0029] Understandably, by using the above settings, multiple pressure transmitters, dynamic acoustic sensors, and composite sensors can be used to detect data for the same pipe section, enabling joint calculation and analysis from multiple data sources. This avoids subsequent errors caused by an abnormality in a single sensor, thereby improving the accuracy of early warning.

[0030] In other embodiments of this application, at least one biochemical sensor, such as a methane spectral sensor (used to compare with a preset threshold to generate a preliminary trigger signal), can be added to the pipeline to directly detect the concentration of leaking gas when there is leakage outside the pipeline (such as soil infiltration) but the pressure change inside the pipeline is not significant. This achieves dual verification of the sensor inside the pipeline and the gas detection outside the pipeline, and improves the detection rate of minor external leaks, making it suitable for environmentally sensitive areas.

[0031] Figure 2 This is a schematic diagram illustrating the principle of the gas pipeline leakage early warning method based on dynamic waves provided in this embodiment of the invention. Figure 2 As shown in this application, after generating an initial trigger signal based on the pressure drop rate, the leakage early warning analysis unit analyzes whether a leakage exists based on dynamic wave data and a built-in AI model, including determining different verification result signals and taking different actions for different feature matching degrees.

[0032] The working mode in which the leakage early warning analysis unit analyzes the presence of leaks based on dynamic wave data and a built-in AI model can be called the pilot mode. For example... Figure 2 As shown, in some embodiments of this application, a non-pilot mode can also be added and flexibly activated as needed based on actual detection requirements. That is, the pipe burst detection unit calculates the pressure drop rate of the pipeline based on pressure data, and when the pressure drop rate is greater than other preset thresholds, it directly issues a warning message and controls the status of the pipeline valve.

[0033] In practical applications, the system can adopt a dual-mode control strategy, namely pilot mode and non-pilot mode, to achieve the optimal balance between safety and reliability through differentiated parameter matrices.

[0034] For example, in pilot mode, the system enables a dynamic wave verification mechanism. The trigger threshold for the voltage drop rate, i.e., the first threshold, can be set to a relatively low range of 0.1-0.15 MPa / min, and an early warning can be issued within a delay window of 3-300 seconds. This improves detection sensitivity while significantly reducing the risk of false triggering through secondary verification. The sensitivity of the acoustic wave can be dynamically adjusted between levels 1 and 5 to effectively suppress interference from on-site impact noise.

[0035] In non-pilot mode, the system will execute traditional protection logic, and the pressure drop rate threshold can be increased to 0.15-4.0 MPa / min, and the response delay can be shortened to 1-3 seconds, ensuring that rapid and safe cutoff can still be achieved based on pressure changes in the event of computing unit malfunction or communication interruption.

[0036] By coordinating the design of dual-mode parameters, the system can significantly reduce the false alarm rate through the intelligent verification mechanism of the pilot mode, and provide a reliable last line of defense when necessary through the non-pilot mode, thereby achieving the best balance between safe response and operational stability under complex working conditions.

[0037] Furthermore, in some embodiments of this application, the verification result signal generated by the leakage early warning analysis unit includes: confirmed leakage, suspected leakage, and no leakage. Specifically, when the verification result signal is confirmed leakage (feature matching degree greater than or equal to 95%), and the current pressure drop rate exceeds a second threshold, a valve closing command is generated based on preset delay information to drive a preset actuator to close the pipeline valve; when the verification result signal is suspected leakage (feature matching degree less than 95% but greater than or equal to 80%), an alarm message is issued but no valve closing command is generated; when the verification result signal is no leakage (feature matching degree less than 80%, determined to be non-leakage, no action is taken), no alarm message is issued and no valve closing command is generated (corresponding prompt information can be generated for manual screening).

[0038] Thus, the system first confirms whether a leak exists. If a leak is confirmed but is only minor (i.e., the pressure drop rate does not exceed the second threshold), only a warning is issued. For major leaks (i.e., the pressure drop rate exceeds the second threshold), a warning signal for a serious pipeline leak can be issued in advance. Then, based on a preset delay mechanism (e.g., after a few minutes of delay after issuing the warning signal), the automatic valve shut-off function of the standard pipe burst electronic unit, i.e., the pipe burst detection unit mentioned in this application, is executed.

[0039] Furthermore, in some embodiments of this application, the leakage early warning analysis unit is also used to calculate the estimated location of the leakage point based on dynamic wave data; the estimated location of the leakage point is used for the leakage early warning analysis unit to send the verification result signal to the target burst pipe detection unit, or for the burst pipe detection unit to control the state of the target pipeline valve.

[0040] Specifically, the leak early warning analysis unit can calculate the estimated location of the leak point based on dynamic wave data over a period of time, or dynamic wave data from multiple dynamic wave sensors at the same time, or even dynamic wave data from multiple dynamic wave sensors over a period of time. Then, the estimated location information is sent to the corresponding pipe burst detection unit. (In practical applications, based on the division of pipe segments, detection sensors and pipe burst detection units can be set on different pipes to detect the pressure data and dynamic wave data of the corresponding pipes through the detection sensors, and to issue early warning information and control the status of the corresponding pipe valves through the pipe burst detection unit.)

[0041] Based on this, if it is determined that the leak is in another pipe (or pipe section) rather than in this pipe (or pipe section), the corresponding burst pipe detection unit of the valve chamber can also issue an early warning signal and provide information that the leak location cannot be determined, but will not trigger the valve shut-off action.

[0042] In addition, in some other embodiments of this application, multiple valves can be controlled by one burst pipe detection unit. In this case, the estimated location of the leak point calculated by the leak early warning analysis unit can be directly sent to the burst pipe detection unit (of course, the burst pipe detection unit is also corresponding to the pipeline location) so that the burst pipe detection unit can control the state of the target pipeline valve.

[0043] Furthermore, in some embodiments of this application, the AI ​​model may be a model based on convolutional pulse detection.

[0044] Specifically, in this embodiment, the leakage early warning analysis unit includes the following virtual functional modules: signal processing module, feature extraction module, and pattern recognition module.

[0045] The system includes several modules: a signal processing module for noise filtering of dynamic wave data to eliminate mechanical vibration interference (i.e., industrial noise stripping), such as adaptive notch filtering to eliminate mechanical vibration interference in the 20-200Hz frequency range; a feature extraction module for extracting leakage features from the noise-filtered data based on time-frequency decomposition principles; and a pattern recognition module for classifying the extracted leakage features using a convolutional pulse detection algorithm to determine the presence of leakage and generate a verification result signal.

[0046] In practical applications, the above-mentioned convolutional pulse detection algorithm is used to determine leakage based on time-frequency domain features. Low-latency edge inference can be achieved on ordinary computing hardware, meaning that computing equipment can be set up on-site.

[0047] Figure 3 This is a device structure diagram of the gas pipeline leakage early warning system based on dynamic waves provided in the embodiments of the present invention, that is, the specific hardware device set locally in the local edge computing architecture based on the convolutional pulse detection algorithm, wherein (1) is the display screen of the final leakage detection result, (2) is the operation button for the final leakage detection result, (3) is the display screen of the burst pipe detection, (4) is the operation button for the burst pipe detection, (5) is the start / stop switch of the device, (6) is the leakage early warning analysis unit, (7) is the explosion-proof control box, (8) is the leakage detection electrical control component, (9) is the network communication module, (10) is the electronic burst pipe main control board, (11) is the electronic burst pipe logic controller, (12) is the pressure acquisition module, (13) is the SSD solenoid valve, (14) is the pressure transmitter 1, (15) is the pressure transmitter 2, (16) is the pressure transmitter 3 and (17) is the dynamic wave sensor.

[0048] In addition, in other embodiments of this application, based on the features extracted above, feature recognition and classification can also be performed by a quantum neural network classifier to avoid feature confusion that may occur when using convolutional pulse detection in scenarios with multiple concurrent leak points and complex noise backgrounds, which would lead to a decrease in the recognition rate of small leaks.

[0049] In practical applications, quantum neural network classifiers, thanks to the parallel computing characteristics of quantum states, have a significantly better ability to process high-dimensional features than convolutional pulse detection algorithms, which can improve the identification accuracy of multi-leakage scenarios. However, it requires quantum computing hardware. Therefore, this solution can be applied to large hub stations or valve chambers accessible by cloud computing resources.

[0050] As mentioned above, in the embodiment of the detection scheme using convolutional pulses, an edge computing local decision-making architecture can be adopted to realize leakage judgment and valve closure control through the local computing unit in the valve chamber. In the embodiment of the quantum neural network classifier, because of the high computing power requirements, a cloud-edge collaborative architecture can be built. This also enables scenarios that require joint analysis across valve chambers, where the computing power of a single edge node is limited and it is difficult to integrate multi-node data for macroscopic analysis.

[0051] In practical applications, training a global leakage model in the cloud and distributing it to edge nodes enables cross-valve chamber leakage correlation analysis, which can significantly reduce the false alarm rate. It also supports historical data backtracking and model optimization, making it suitable for areas with frequent geological activity. In this embodiment, because communication between the cloud and the actual edge relies on network continuity, it can automatically degrade to local edge decision-making (i.e., on-site decision-making through convolutional pulse edge computation as described above) when the network is interrupted.

[0052] It is understood that the gas pipeline leakage early warning system based on dynamic waves provided in this application can operate 24 / 7, with its detection sensors, pipe burst detection unit, and leakage early warning analysis unit, unaffected by the environment and independent of actuators, forming an independent and highly reliable pipeline leakage monitoring and early warning system for each valve chamber of the pipeline.

[0053] The gas pipeline leakage early warning system based on dynamic waves provided in this application solves the problem of traditional standard pipe burst detection units lacking redundancy and failing to close valves or closing them erroneously after a fault by using a redundancy mechanism for valve chambers in pipeline sections. Simultaneously, the system combines dynamic wave data verification with pressure drop rate triggering for dual judgment, overcoming the drawbacks of relying solely on a single pressure drop rate threshold for valve closure (i.e., setting it too low easily leads to false valve closure, while setting it too high prevents valve closure from being triggered after a pipeline leak). It also solves the problem of existing technologies relying solely on pressure drop rate thresholds for direct valve closure, resulting in a false trigger rate >40% (caused by natural gas compressor pump start-up / shutdown in long-distance pipelines / upstream and downstream gas pressure fluctuations), and the inability to detect minute leaks in existing technologies. Furthermore, the leak location calculated by the dynamic wave monitoring AI model achieves an accuracy within 50m, overcoming the limitation of standard pipe burst detection in locating leaks. It can effectively monitor gas pipelines in various scenarios, such as the PL-LBC series gas pipelines, for faults and leaks, such as pipeline leaks caused by landslides or geological displacements between two valve chambers, leaks caused by minor sudden changes in pipeline conditions, and sudden pipeline leaks caused by third-party construction. It can prevent accidents and disasters in a timely manner and has become the key to the technical safety of gas pipelines.

[0054] Based on the same inventive concept, this invention also provides a gas pipeline leakage early warning method based on dynamic waves, applied to the above-mentioned system embodiment. Figure 4 This is a flowchart illustrating the gas pipeline leakage early warning method based on dynamic waves provided in an embodiment of the present invention, as shown below. Figure 4 As shown, the method includes: S101. Real-time monitoring of pipeline pressure data and dynamic wave data.

[0055] S102. Calculate the pressure drop rate of the pipeline based on the pressure data, and generate a preliminary trigger signal when the pressure drop rate is greater than a preset first threshold.

[0056] S103. After confirming the initial trigger signal, analyze whether there is a leak based on dynamic wave data and AI model, generate a verification result signal, and issue early warning information and control the status of pipeline valves based on the verification result signal.

[0057] This includes analyzing the presence of leaks based on dynamic wave data and AI models, and generating verification result signals, including: Noise filtering is applied to the dynamic wave data to eliminate mechanical vibration interference. Leakage features are extracted from the noise-filtered data based on the time-frequency decomposition principle. The extracted leakage features are classified based on the convolutional pulse detection algorithm to determine whether a leak exists and to generate a verification result signal. The verification result signal includes: confirmed leak, suspected leak, and no leak. Based on the verification result signal, an early warning message is issued and the status of the pipeline valve is controlled, including: when the verification result signal is a confirmed leak and the current pressure drop rate exceeds a second threshold, a valve closing command is generated based on preset delay information to drive a preset actuator to close the pipeline valve; when the verification result signal is a suspected leak, an alarm message is issued and no valve closing command is generated; when the verification result signal is a no leak, no alarm message is issued and no valve closing command is generated.

[0058] Regarding the methods in the above embodiments, the specific manner in which each step is performed has been described in detail in the embodiments of the system, and will not be elaborated here.

[0059] The present invention also provides a gas pipeline leakage early warning device based on dynamic waves, used to implement the above-described method embodiments. Figure 5 This is a schematic diagram of the structure of the gas pipeline leakage early warning device based on dynamic waves provided in an embodiment of the present invention, as shown below. Figure 5 As shown, the gas pipeline leak early warning device based on dynamic waves in this embodiment includes a processor and a memory, with the processor connected to the memory. The processor is used to call and execute a program stored in the memory; the memory is used to store the program, which is at least used to execute the gas pipeline leak early warning method based on dynamic waves in the above embodiments.

[0060] The specific implementation scheme of the gas pipeline leakage early warning device based on dynamic waves provided in this application can refer to the implementation scheme of the gas pipeline leakage early warning method based on dynamic waves in any of the above embodiments, and will not be repeated here.

[0061] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0062] It should be noted that in the description of this invention, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this invention, unless otherwise stated, "a plurality of" means at least two.

[0063] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0064] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0065] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0066] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0067] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.

[0068] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0069] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A gas pipeline leakage early warning system based on dynamic waves, characterized in that, include: Detection sensors, pipe burst detection unit, and leak early warning analysis unit; The detection sensor is used to detect the pressure data and dynamic wave data of the pipeline, and the dynamic wave data includes dynamic infrasound data. The burst pipe detection unit is communicatively connected to the detection sensor and is used to calculate the pressure drop rate of the pipeline based on the pressure data, and generate a preliminary trigger signal when the pressure drop rate is greater than a preset first threshold. The leakage early warning analysis unit is communicatively connected to the detection sensor. After receiving the initial trigger signal, it analyzes whether there is a leak based on dynamic wave data and the built-in AI model, and sends a verification result signal to the burst pipe detection unit. The burst pipe detection unit is also used to issue early warning information and control the status of pipeline valves based on the verification result signal when it receives the verification result signal.

2. The gas pipeline leakage early warning system based on dynamic waves according to claim 1, characterized in that, The detection sensors include pressure sensors, dynamic wave sensors, and composite sensors; The pressure sensor is used to detect the pressure data of the pipeline; the dynamic wave sensor is used to detect the dynamic wave data of the pipeline; the composite sensor is used to simultaneously detect the pressure data and dynamic wave data of the pipeline.

3. The system according to claim 2, characterized in that, The dynamic wave sensor is a MEMS sensor or a piezoelectric ceramic sensor.

4. The gas pipeline leakage early warning system based on dynamic waves according to claim 1, characterized in that, The verification result signals include: confirmed leakage, suspected leakage, and no leakage; The pipe burst detection unit issues an early warning signal and controls the status of the pipeline valves based on the verification result signal, including: When the verification result signal confirms leakage, and it is determined that the current pressure drop rate exceeds the second threshold, a valve closing command is generated based on preset delay information to drive a preset actuator to close the pipeline valve. When the verification result signal indicates a suspected leak, an alarm message is issued and no valve closing command is generated. When the verification result signal is non-leakage, no alarm message is issued and no valve closing command is generated.

5. The gas pipeline leakage early warning system based on dynamic waves according to claim 1, characterized in that, The leakage early warning analysis unit is also used to calculate the estimated location of the leakage point based on dynamic wave data; The estimated location of the leak point is used by the leak early warning analysis unit to send the verification result signal to the target pipe burst detection unit, or by the pipe burst detection unit to control the state of the target pipeline valve.

6. The gas pipeline leakage early warning system based on dynamic waves according to claim 1, characterized in that, The AI ​​model is either a model based on convolutional pulse detection or a quantum neural network classifier.

7. The gas pipeline leakage early warning system based on dynamic waves according to claim 5, characterized in that, The leakage early warning analysis unit includes: a signal processing module, a feature extraction module, and a pattern recognition module, when the AI ​​model is a model based on convolutional pulse detection; The signal processing module is used to perform noise filtering on the dynamic wave data to eliminate mechanical vibration interference. The feature extraction module is used to extract leakage features from the noise-filtered data based on the time-frequency decomposition principle; The pattern recognition module is used to classify the extracted leakage features based on the convolutional pulse detection algorithm to determine whether a leakage exists and to generate the verification result signal.

8. A gas pipeline leakage early warning method based on dynamic waves, applied to the system described in any one of claims 1-7, characterized in that, include: Real-time monitoring of pipeline pressure and dynamic wave data; The pressure drop rate of the pipeline is calculated based on the pressure data, and a preliminary trigger signal is generated when the pressure drop rate is greater than a preset first threshold. After confirming the initial trigger signal, the system analyzes whether there is a leak based on dynamic wave data and AI model, generates a verification result signal, and issues a warning message and controls the status of pipeline valves based on the verification result signal.

9. The gas pipeline leakage early warning method based on dynamic waves according to claim 8, characterized in that, The analysis based on dynamic wave data and AI models to determine the presence of leakage and the generation of verification result signals include: The dynamic wave data is subjected to noise filtering to eliminate mechanical vibration interference; Leakage features are extracted from noise-filtered data based on the time-frequency decomposition principle; The extracted leakage features are classified based on the convolutional pulse detection algorithm to determine whether leakage exists, and the verification result signal is generated. The verification result signal includes: confirmed leakage, suspected leakage, and no leakage. The issuance of early warning information and control of pipeline valve status based on the verification result signal includes: When the verification result signal confirms leakage, and it is determined that the current pressure drop rate exceeds the second threshold, a valve closing command is generated based on preset delay information to drive a preset actuator to close the pipeline valve. When the verification result signal indicates a suspected leak, an alarm message is issued and no valve closing command is generated. When the verification result signal is non-leakage, no alarm message is issued and no valve closing command is generated.

10. A gas pipeline leakage early warning device based on dynamic waves, characterized in that, It includes a processor and a memory, wherein the processor is connected to the memory: The processor is used to call and execute the program stored in the memory; The memory is used to store the program, which is at least used to execute the gas pipeline leakage early warning method based on dynamic waves as described in any one of claims 1-7.

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