Chemical pipeline leakage monitoring and positioning method, system, medium and product fused with artificial intelligence

Abstract

This application provides a method, system, medium, and product for monitoring and locating leaks in chemical pipelines that integrates artificial intelligence, relating to the field of intelligent monitoring technology. It includes: a current monitoring node receiving a first cascaded verification wave transmitted by a first upstream node through the pipeline medium. This verification wave contains at least pipeline fingerprint information. After receiving the verification wave, the node analyzes the pipeline fingerprint information and uses this information to detect leaks in the pipeline section between the current node and the first upstream node. If the detection passes, the node adaptively updates the pipeline fingerprint information based on the actual propagation characteristics of the verification wave, generating a second cascaded verification wave and sending it to the first downstream node. If the detection fails, an abnormal location alarm signal is generated and uploaded to the monitoring center, and the transmission of cascaded verification waves to downstream nodes is stopped. Implementing this method can improve the real-time performance of detection and accurately pinpoint the leak area.

Description

Technical Field

[0001] This application relates to the field of intelligent monitoring technology, and in particular to a method, system, medium, and product for monitoring and locating leaks in chemical pipelines that integrates artificial intelligence. Background Technology

[0002] Chemical pipelines, serving as vital arteries for transporting oil, natural gas, and various hazardous chemicals, are typically characterized by long distances, wide geographical spans, and complex environments along their routes. Due to their long-term service, these pipelines are highly susceptible to leakage accidents due to corrosion from the internal media, erosion from the external environment, and geological subsidence. Once a leak occurs, it not only causes severe resource waste and environmental pollution but may also trigger serious safety incidents such as fires and explosions. Therefore, constructing a highly reliable and real-time pipeline leak monitoring system is of paramount importance for ensuring the safety of chemical production and environmental protection.

[0003] In existing long-distance pipeline monitoring technologies, the mainstream approaches are the "negative pressure wave method" or "distributed optical fiber sensing (DAS)". Specifically, existing technologies mainly rely on external, independent communication links. For example, after monitoring nodes collect pressure or vibration data, they must upload the data to a central pipeline monitoring system for centralized processing by laying accompanying optical cables or using 4G / 5G / satellite networks. In leak detection, existing systems typically use a "passive listening + static model" approach, where nodes only passively receive abnormal signals within the pipeline and calculate the leak point based on a factory-preset ideal pipeline hydraulic model (fixed flow resistance coefficient and sound velocity value).

[0004] However, the aforementioned existing technologies have significant technical bottlenecks in practical long-distance pipeline applications. These technologies heavily rely on external independent communication links for data transmission and centralized analysis. This not only increases system construction and maintenance costs due to fiber optic cable laying and wireless communication networking, but also makes them prone to communication interruptions in complex geographical environments. This results in delayed monitoring data transmission and untimely leak detection, failing to meet the real-time monitoring needs of long-distance pipelines. Summary of the Invention

[0005] This application provides a method, system, medium, and product for monitoring and locating leaks in chemical pipelines that integrates artificial intelligence, which can be used to achieve segmented and precise monitoring and cascaded closed-loop transmission of leak detection in long-distance chemical pipelines, thereby improving the real-time performance and accuracy of leak monitoring.

[0006] Firstly, this application provides a method for monitoring and locating leaks in chemical pipelines that integrates artificial intelligence, applied to a pipeline monitoring system. This system includes multiple monitoring nodes cascaded along the pipeline. The method includes: receiving a first cascaded verification wave transmitted by a first upstream node through the pipeline medium, the first cascaded verification wave including at least pipeline fingerprint information; upon receiving the first cascaded verification wave, parsing the pipeline fingerprint information using an intelligent feature extraction algorithm, the pipeline fingerprint information including pipeline physical characteristic information; using the pipeline fingerprint information to perform intelligent leak detection on the current pipeline section between the current node and the first upstream node; if the leak detection passes, then based on the actual propagation characteristics of the first cascaded verification wave within the current pipeline section, performing online adaptive learning and updating of the pipeline fingerprint information to generate a second cascaded verification wave to be sent to the first downstream node; if the leak detection fails, then generating an abnormal location alarm signal and uploading it to the monitoring center, while simultaneously stopping the transmission of cascaded verification waves to the first downstream node.

[0007] By adopting the above technical solution, the monitoring nodes in the system receive cascaded verification waves from upstream nodes through the pipeline medium. Based on the pipeline fingerprint information within the waves, they conduct section leak detection. No external communication link is needed to transmit monitoring data; the core functionality utilizes the pipeline medium itself for signal transmission and detection analysis, avoiding the cost of building and maintaining external links and preventing communication interruptions in complex environments. Simultaneously, after successful detection, the fingerprint information is adaptively updated and a verification wave is sent downstream, achieving cascaded, segment-by-segment detection along the pipeline. This allows leak detection to be completed synchronously with the transmission of the verification wave, significantly improving the real-time performance of the detection. Furthermore, the segment-by-segment verification method can accurately pinpoint the leak section, solving the technical pain points of data lag and untimely judgment in traditional centralized processing models.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, before the step of receiving the first cascaded verification wave sent by the first upstream node through the pipeline medium, the method further includes: obtaining the cascaded transmission strategy of the current monitoring area, the cascaded transmission strategy including a unidirectional cascaded transmission mode or a bidirectional converged transmission mode; the cascaded transmission strategy configuration method includes: the pipeline monitoring system obtaining historical operation and maintenance data of the pipeline to be monitored, the historical operation and maintenance data including at least the number of pipeline segment maintenance and service years; dividing the pipeline to be monitored into multiple monitoring areas according to the historical operation and maintenance data, and determining the cascaded transmission strategy in each monitoring area.

[0009] By adopting the above technical solution, the system first divides monitoring areas based on historical pipeline operation and maintenance data, and then configures appropriate one-way cascading or two-way convergence transmission modes for each area. Historical operation and maintenance data can accurately reflect the actual operating conditions of each pipe section, such as maintenance frequency and service life, and is the core basis for dividing areas and configuring strategies. For high-risk pipe sections with long service life and frequent maintenance, a more accurate two-way convergence mode is configured, while low-risk pipe sections adopt an efficient one-way cascading mode. This achieves accurate matching between the transmission strategy and the actual operating conditions of the pipe sections, avoiding resource waste or insufficient detection caused by using a single mode for the entire pipeline. This makes the resource allocation of cascading monitoring more reasonable, while adapting to the monitoring needs of different pipe sections, improving the adaptability and monitoring targeting of the entire system under complex pipeline routes.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, before the step of receiving the first cascaded verification wave sent by the first upstream node through the pipeline medium, the method further includes: determining whether a heartbeat packet signal sent by the first upstream node is received, the heartbeat packet signal being sent by the first upstream node after determining that the pipeline between the first upstream node and the second upstream node has passed leak detection; if the heartbeat packet signal is received, starting the listening timer; if the heartbeat packet signal is not received, entering a sleep waiting state.

[0011] By adopting the above technical solution, the monitoring node first determines whether to receive the upstream heartbeat packet. The heartbeat packet serves as a feedback signal indicating that the upstream interval detection has passed, providing a clear triggering basis for the current node's working status. The listening timer only starts upon receiving the heartbeat packet; otherwise, the node enters sleep mode, preventing it from continuously monitoring at full power, effectively reducing node energy consumption and improving the battery-powered node's endurance. Simultaneously, the heartbeat packet triggering mechanism keeps the monitoring work of each node synchronized with the upstream cascading rhythm, avoiding resource consumption and signal interference caused by invalid node listening. This makes the cascading detection link's working sequence more orderly, ensuring the continuity of cascading verification wave transmission and detection.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, the step of receiving the first cascaded verification wave sent by the first upstream node through the pipeline medium specifically includes: when the heartbeat packet signal is received, starting the listening timer for the first cascaded verification wave; if the first cascaded verification wave is not received within the preset transmission delay window, determining that there is a medium transmission blockage abnormality in the current pipeline section, generating an interruption alarm signal and uploading it to the monitoring center.

[0013] By adopting the above technical solution, if no verification wave is received within the window, an abnormality in media transmission is directly determined and an alarm is triggered. This directly links the abnormal transmission delay of the verification wave to the pipeline blockage problem, achieving real-time monitoring of the pipeline media transmission status. Compared to the traditional mode that only detects leaks, this adds an abnormality determination dimension of transmission blockage, making the monitoring of pipeline operation status more comprehensive and enabling rapid detection of media transmission interruptions, timely alarm issuance, and avoidance of subsequent pipeline operation risks caused by blockages.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, the step of using the pipeline fingerprint information to perform intelligent leak detection on the current pipeline section between the current node and the first upstream node specifically includes: obtaining the reception time and received signal strength when the first cascaded verification wave is received; parsing the pipeline fingerprint information to obtain the transmission time and reference transmission strength when the first upstream node sends the first cascaded verification wave; calculating the difference between the reference transmission strength and the received signal strength to obtain the actual signal attenuation, and calculating the time difference between the reception time and the transmission time to obtain the actual transmission delay; determining the theoretical attenuation threshold and theoretical delay range of the current pipeline section based on the pipe segment length and medium wave velocity in the pipeline physical feature information; if the actual signal attenuation is greater than the theoretical attenuation threshold, or the actual transmission delay exceeds the theoretical delay range, then it is determined that the current pipeline section has failed the leak detection.

[0015] By employing the above technical solution, the monitoring node acquires the reception time and intensity of the verification wave, along with the upstream transmitted baseline parameters, and calculates the actual signal attenuation and transmission delay. These parameters directly reflect the actual propagation state of the verification wave within the pipeline section and are the core quantitative indicators for leak detection. Furthermore, by combining the physical characteristics in the pipeline fingerprint to determine theoretical thresholds and ranges, the actual propagation parameters are compared with the theoretical values ​​for judgment. This transforms leak detection from traditional passive signal monitoring to precise comparison of quantitative parameters, avoiding errors from subjective judgment. Simultaneously, abnormal signal attenuation and transmission delay are typical characteristics of pipeline leaks. This dual-indicator judgment significantly improves the accuracy of leak detection, effectively reducing missed and false positives.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, the step of performing online adaptive learning and updating of the pipeline fingerprint information based on the actual propagation characteristics of the first cascaded verification wave within the current pipeline interval to generate a second cascaded verification wave and send it to the first downstream node specifically includes: calculating the received signal-to-noise ratio and spectral distortion rate of the first cascaded verification wave as channel quality parameters reflecting the physical environment characteristics of the current pipeline interval; adjusting the transmit power of the second cascaded verification wave according to the received signal-to-noise ratio based on a preset transmit power adjustment strategy to obtain a target transmit power; adjusting the modulation scheme of the second cascaded verification wave according to the spectral distortion rate based on a preset modulation scheme adjustment strategy to obtain a target modulation scheme; and generating the second cascaded verification wave and sending it to the first downstream node based on the target transmit power and the target modulation scheme.

[0017] By adopting the above technical solution, the monitoring node extracts the received signal-to-noise ratio and spectral distortion rate of the verification wave as channel quality parameters. These parameters can accurately reflect the impact of the physical environment of the current pipeline section on signal propagation. Then, based on these parameters, the transmission power and modulation scheme of the downstream verification wave are adjusted to adapt the transmission power to the signal attenuation level of the channel and the modulation scheme to the spectral distortion state of the channel, achieving dynamic matching between the signal transmission parameters and the current pipeline channel environment. Compared with traditional fixed-parameter signal transmission methods, dynamic adjustment makes the propagation of the verification wave more stable in subsequent sections, improving signal transmission quality and reception success rate, and ensuring the continuity and stability of the cascaded detection link.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, before the step of generating the second cascaded verification wave based on the target transmit power and the target modulation method and sending it to the first downstream node, the method further includes: determining whether the target transmit power is greater than a preset maximum safe power threshold; if so, setting the transmit power of the second cascaded verification wave to the maximum safe power threshold; determining whether the target modulation method is lower than a preset minimum transmission rate modulation level; if so, maintaining the modulation method of the second cascaded verification wave at the minimum transmission rate modulation level; and when the maximum safe power threshold limit or the minimum transmission rate modulation level limit is triggered, marking the link saturation state in the pipeline fingerprint information for the monitoring center to perform an effectiveness assessment of the cascaded link.

[0019] By adopting the above technical solution, the monitoring node imposes threshold limits on the transmission power and modulation method before generating the downstream verification wave. The maximum safe power threshold avoids the potential impact of excessive power on the pipeline medium and pipe wall, while the minimum transmission rate modulation level ensures the basic transmission efficiency of the verification wave. Simultaneously, when the threshold limit is triggered, the link saturation state is marked in the pipeline fingerprint, providing the monitoring center with accurate feedback on the link's operational status. This solution, based on dynamic parameter adjustment, adds dual constraints of safety and efficiency. It avoids the safety risks associated with parameter adjustment while ensuring the basic performance of cascaded detection. Furthermore, it allows the monitoring center to monitor the link status in real time, providing accurate data support for subsequent link optimization and maintenance.

[0020] In a second aspect, this application provides a pipeline monitoring system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code including computer instructions, which the one or more processors call to cause the pipeline monitoring system to perform the methods described in the first aspect and any possible implementation thereof.

[0021] Thirdly, this application provides a computer-readable storage medium including instructions that, when executed on a pipeline monitoring system, cause the pipeline monitoring system to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, this application provides a computer program product, including a computer program that, when run on a pipeline monitoring system, causes the pipeline monitoring system to perform the method described in the first aspect and any possible implementation thereof.

[0023] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0024] 1. By adopting the above technical solution, which uses the pipeline medium as the signal transmission carrier and relies on pipeline fingerprint information to carry out cascaded segment-by-segment leak detection, and adaptively updates the fingerprint information to transmit it downstream after the detection is passed, the technical problems of high construction and maintenance costs, easy communication interruption, data transmission lag, and untimely leak judgment caused by relying on external independent communication links in the existing technology are effectively solved. Thus, it realizes real-time segment-by-segment detection without external links, accurately locates the leak section, greatly improves the real-time performance and reliability of leak detection, and reduces the technical effect of system construction and maintenance costs.

[0025] 2. By adopting the above technical solution, the system first divides monitoring areas based on historical pipeline operation and maintenance data, and then configures appropriate one-way cascading or two-way convergence transmission modes for each area. Historical operation and maintenance data can accurately reflect the actual operating conditions of each pipe section, such as maintenance frequency and service life, and is the core basis for dividing areas and configuring strategies. For high-risk pipe sections with long service life and frequent maintenance, a more accurate two-way convergence mode is configured, while low-risk pipe sections adopt an efficient one-way cascading mode. This achieves accurate matching between the transmission strategy and the actual operating conditions of the pipe section, avoiding resource waste or insufficient detection caused by using a single mode for the entire pipeline. This makes the resource allocation of cascading monitoring more reasonable, while adapting to the monitoring needs of different pipe sections, improving the adaptability and monitoring targeting of the entire system under complex pipeline routes.

[0026] 3. By adopting the above technical solution, which uses parameters such as the transmission and reception time and signal strength of the cascaded verification wave to calculate the actual signal attenuation and transmission delay, and then combines the physical characteristics in the pipeline fingerprint to determine the theoretical threshold and range, and conducts leak detection through quantitative parameter comparison, the technical problem of strong subjectivity, large error, and easy misjudgment and missed judgment caused by the passive listening + static model in the existing technology is effectively solved. Thus, the leak detection is transformed into precise quantitative comparison, improving the accuracy and objectivity of leak detection, effectively reducing missed judgment and misjudgment, and achieving the technical effect of accurate leak determination. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating a chemical pipeline leak monitoring and location method integrating artificial intelligence, as described in this application embodiment.

[0028] Figure 2 This is another flowchart illustrating the chemical pipeline leak monitoring and location method integrating artificial intelligence in this application embodiment;

[0029] Figure 3 This is a schematic diagram of the physical device structure of a pipeline monitoring system in an embodiment of this application. Detailed Implementation

[0030] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to and includes any or all possible combinations of one or more of the listed items.

[0031] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0032] For ease of understanding, the system framework provided in this embodiment is described below. The pipeline monitoring system of this application includes multiple chemical pipelines to be monitored. Several monitoring nodes are set at preset locations on the pipelines. Each node is equipped with a signal receiving module, a signal parsing module, a data calculation module, and a signal transmitting module. It can independently complete signal capture, parsing, leak detection, and verification wave transmission operations. All nodes are cascaded along the pipeline's transport direction, with adjacent nodes corresponding to each other, collectively covering the entire monitored pipeline section, achieving comprehensive and segmented monitoring of the pipeline. Simultaneously, each node establishes a communication connection with the monitoring center, enabling real-time uploading of abnormal alarm signals and monitoring data, ensuring the uniformity of the overall system control.

[0033] The following describes the process of the method provided in this implementation, using the system described above as an example. Please refer to [link / reference]. Figure 1 This is a flowchart illustrating a chemical pipeline leak monitoring and location method integrating artificial intelligence, as described in this application embodiment.

[0034] S101. Receive the first cascaded verification wave sent by the first upstream node through the pipeline medium, wherein the first cascaded verification wave includes at least pipeline fingerprint information.

[0035] In this context, the first upstream node refers to the monitoring node adjacent to the current node and located upstream of the pipeline in the transport direction. It is the transmitting end of the current node receiving the cascade verification wave, used to collect monitoring data of its own pipeline segment and generate the cascade verification wave. For example, if the pipeline is laid along the ABCD direction, the first upstream node of monitoring node B is monitoring node A. The pipeline medium refers to the fluid substances such as oil, natural gas, and various hazardous chemicals transported in chemical pipelines. Here, it serves as the transmission carrier for the cascade verification wave, replacing the traditional external communication link. The first cascade verification wave is the signal wave generated and transmitted by the first upstream node, used to realize pipeline section leak detection, information transmission, and cascade verification. It carries core information related to pipeline operation and can be transmitted without an external link through the pipeline medium. Pipeline fingerprint information refers to the core data set used to characterize the inherent characteristics and operating status of the pipeline itself. It is the basis for carrying out leak detection, signal verification, and parameter updates, and includes at least pipeline physical characteristic information.

[0036] This step is executed when the current node has completed its preliminary preparations (such as confirming the cascading transmission strategy, receiving the upstream heartbeat packet, and starting the listening timer) and is in a normal listening state. Its core purpose is to collect pre-processing information for pipeline section leakage detection, and it is the starting step of the entire cascading detection process.

[0037] During execution, upon confirming receipt of the heartbeat signal from the first upstream node, the current node immediately initiates the monitoring and timing function for the first cascade verification wave, while maintaining precise monitoring of signals within the pipeline medium. This heartbeat signal is sent by the first upstream node after completing and verifying a leak detection of the pipe section between itself and its own upstream node (the second upstream node). Its transmission signifies that there are no abnormalities in the upstream monitoring process of the first upstream node, and the first cascade verification wave will subsequently propagate using the medium itself transported within the pipeline (without requiring external optical cables, 4G / 5G, or other independent communication links). The current node, through its onboard signal receiving module, directionally monitors signals propagating in the pipeline medium that match the characteristics of the first cascade verification wave, while simultaneously managing a preset transmission delay window using the monitoring and timing function.

[0038] If the current node fails to capture the first cascaded verification wave sent by the first upstream node within the preset transmission delay window, and irrelevant factors such as signal interference and its own receiving module failure have been ruled out, then it is immediately determined that there is a media transmission blockage anomaly in the current pipeline section between the current node and the first upstream node. A standardized interruption alarm signal is then generated, which contains core information such as the current node number, the current pipeline section location, the timing timeout duration, and the anomaly judgment criteria. This signal is then quickly uploaded to the monitoring center in a preset manner to provide accurate information for subsequent anomaly investigation and emergency response. If the first cascaded verification wave is successfully captured within the transmission delay window, then the listening timer is terminated, and the subsequent verification wave analysis and leakage detection steps are initiated.

[0039] The effect of this step is that it replaces the traditional method of relying on external independent communication links to transmit monitoring data, and uses the pipeline medium itself to achieve signal transmission. This not only reduces the construction and maintenance costs of external links, but also avoids the problem of communication interruption in complex environments, providing core data support for subsequent leak detection and ensuring the real-time nature of the detection process.

[0040] In some embodiments, after the current node starts running and before executing S101 to receive the first cascade verification wave, preparatory steps for the entire cascade detection process can be implemented to clarify the cascade transmission mode of its own monitoring area, ensure that the reception and transmission of subsequent cascade verification waves comply with the system's preset rules, and ensure that cascade detection is carried out in an orderly manner.

[0041] Specifically, this step consists of two core parts: configuring the cascading transmission strategy (executed by the pipeline monitoring system) and acquiring the current node's strategy (executed by the current node). First, the strategy configuration process: The pipeline monitoring system first comprehensively acquires historical operation and maintenance data for each segment of the pipeline to be monitored through a preset data acquisition module, focusing on collecting the number of maintenance visits and service life of each segment. It can also collect supplementary data such as pipe material, medium type, and roadside environment. The collected historical operation and maintenance data is then organized and filtered, eliminating invalid and distorted data to ensure the accuracy and usability of the data.

[0042] Subsequently, based on the compiled historical operation and maintenance data, the system classifies and assesses the operational risks of each pipe segment. The assessment criteria are: the more maintenance frequency and the longer the service life, the higher the operational risk of the pipe segment, and vice versa. According to the risk classification results, the pipeline to be monitored is divided into multiple monitoring areas. Usually, continuous pipe segments with similar risk levels are divided into one monitoring area to avoid insufficient strategy adaptability due to excessively large differences in pipe segment risk within a single area. Finally, a suitable cascading transmission strategy is determined for each monitoring area: for monitoring areas with low risk levels (few maintenance frequency, short service life, and stable operating conditions), a one-way cascading transmission mode is configured, which can reduce node operation energy consumption and improve detection efficiency; for monitoring areas with high risk levels (frequent maintenance frequency, long service life, and complex operating conditions), a two-way convergence transmission mode is configured, which can realize two-way segment-by-segment detection, improve the accuracy and timeliness of leak detection, and facilitate data aggregation within the area, making it convenient for the system to coordinate and monitor. Secondly, the current node acquires the policy process: After the current node starts up, it sends a policy acquisition request to the system through its own preset communication link with the pipeline monitoring system (temporary low-power wireless communication can be used during the initialization phase). The request includes its own node number and installation location information. After receiving the request, the pipeline monitoring system determines the monitoring area to which the current node belongs based on its location information, and then sends the cascading transmission policy (one-way or two-way convergence mode) configured for that area to the current node. After receiving the policy information, the current node stores the policy information in the local cache module.

[0043] The effect of this step is to achieve precise configuration and node acquisition of the cascading transmission strategy, avoiding the problems of resource waste (using a complex mode for low-risk pipe sections) or insufficient detection (using a simple mode for high-risk pipe sections) caused by using a single transmission mode for the entire pipeline. By dividing areas and configuring strategies based on historical operation and maintenance data, the transmission mode is precisely adapted to the actual operational risks of the pipe section, improving the targeting and adaptability of cascading detection. At the same time, the current node acquires the strategy in advance, ensuring that the transmission and detection process of the subsequent cascading verification wave conforms to the overall system plan, avoiding cascading chaos and detection failure caused by unclear strategies, providing a pre-emptive guarantee for the real-time and accuracy of subsequent leak detection, and further optimizing the system's operating efficiency and detection reliability.

[0044] In some embodiments, after the current node completes the acquisition of the cascade transmission strategy, but before entering the formal reception stage of the first cascade verification wave, and while the chemical pipeline leakage monitoring system is in normal cascade operation, during the process of each node carrying out pre-synchronization and monitoring preparation according to the preset strategy, the current node first starts its dedicated heartbeat packet signal receiving module. This module only performs directional listening to the heartbeat packet signal characteristics of the first upstream node, continuously detecting whether a heartbeat packet signal with matching characteristics is propagating in the pipeline medium. At the same time, it is clear that the trigger condition for the generation of the heartbeat packet signal is "the first upstream node completes the leakage detection of the pipeline section between the first upstream node and the second upstream node, and determines that the detection is passed". That is, the transmission of the heartbeat packet signal indicates that there is no abnormality in the upstream monitoring link of the first upstream node, and the first cascade verification wave will be sent subsequently. If the current node identifies a matching heartbeat signal during continuous monitoring, it immediately determines that a heartbeat signal has been received and simultaneously starts the monitoring timer. This function opens a transmission delay window of preset duration. Within this window, the current node will maintain full-function signal monitoring, waiting to receive the first cascaded verification wave sent by the first upstream node. The start time of the monitoring timer is the moment the heartbeat signal is received, and the timing duration matches the physical characteristics of the pipeline and the propagation characteristics of the medium. If the current node fails to identify a matching heartbeat signal within the preset heartbeat monitoring period, it immediately determines that a heartbeat signal has not been received and enters a sleep waiting state. It then disables the dedicated monitoring module for the first cascaded verification wave, retaining only the low-power reception function for the heartbeat signal. In this state, the node will reduce power consumption until it receives the heartbeat signal from the first upstream node, at which point it will wake up from the sleep waiting state, start the monitoring timer, and enter the verification wave reception preparation stage. Furthermore, if the current node receives other abnormal signals while in the sleep waiting state, it will immediately exit sleep mode and trigger an early warning of anomalies.

[0045] This step, through pre-synchronous detection of heartbeat packets, achieves matching of the monitoring rhythm between the current node and the first upstream node. This ensures that the current node only initiates the full-function mode of receiving verification waves after confirming that the upstream is normal, avoiding ineffective continuous high-power listening, significantly reducing the overall energy consumption of the monitoring node, and improving the system's energy efficiency and endurance. At the same time, by setting the start of the listening timer and the transmission delay window, a clear time range is defined for the subsequent reception of the first cascaded verification wave, facilitating the subsequent determination of whether there are any abnormalities in the pipeline's medium transmission, further improving the pre-anomaly investigation step of leak monitoring. The setting of the sleep waiting state is adapted to the application scenario of long-distance pipeline monitoring nodes without external power supply, solving the power consumption management problem of field nodes and ensuring the long-term stable operation of the system.

[0046] S102. When the first cascaded verification wave is received, the pipeline fingerprint information is analyzed by the intelligent feature extraction algorithm. The pipeline fingerprint information includes pipeline physical feature information.

[0047] Among them, pipeline physical characteristic information refers to the basic information in pipeline fingerprint information that reflects the physical properties of chemical pipelines themselves. It is the core basis for carrying out leak detection, including but not limited to pipe section length, pipe inner diameter, pipe wall thickness, pipe material, pipe section flow resistance coefficient, medium wave velocity, reference signal attenuation coefficient, etc.

[0048] This step is executed the instant the current node successfully receives the first cascaded verification wave sent by the first upstream node, with no additional time delay. The execution scenario is the signal preprocessing stage after the current node completes the reception of the verification wave and before conducting pipeline section leak detection. Upon confirming successful reception of the first cascaded verification wave, the current node immediately initiates its built-in signal parsing program. Using a preset intelligent feature extraction algorithm, it decodes the signal structure of the verification wave layer by layer. First, it separates the carrier signal and the valid data signal of the verification wave. Then, it extracts the pipeline fingerprint information in a preset format from the valid data signal. After extraction, the pipeline fingerprint information undergoes further information splitting and identification to clarify the various pipeline physical feature information it contains. This information is then standardized and stored, providing directly callable standardized data for subsequent leak detection using pipeline fingerprint information. If problems such as missing pipeline fingerprint information, incorrect format, or information distortion are found during the parsing process, it will be directly judged as a signal anomaly, triggering the early warning mechanism.

[0049] This step achieves rapid extraction and storage of effective monitoring data by accurately analyzing and standardizing the pipeline fingerprint information in the cascaded verification wave, providing data support for accurate calculations in subsequent leak detection. At the same time, through anomaly verification during the analysis process, the impact of invalid data on leak detection results is avoided in advance, thus improving the accuracy of leak detection.

[0050] S103. Use the pipeline fingerprint information to perform intelligent leak detection on the current pipeline section between the current node and the first upstream node;

[0051] This step focuses on targeted section leak detection using pipeline fingerprint information to achieve precise segmented monitoring of chemical pipelines. Specific details will be described in subsequent steps S201-S205, and will not be repeated here.

[0052] In some embodiments,

[0053] S104. If the leak detection passes, based on the actual propagation characteristics of the first cascade verification wave in the current pipeline section, the pipeline fingerprint information is updated online through adaptive learning to generate a second cascade verification wave and send it to the first downstream node.

[0054] The actual propagation characteristics refer to the actual signal characteristics exhibited by the first cascade verification wave as a carrier of the pipeline medium during its propagation within the current pipeline section, including but not limited to actual signal attenuation, actual transmission delay, received signal-to-noise ratio, and spectral distortion rate. The second cascade verification wave refers to the new cascade verification wave generated by the current node after adjusting the transmission parameters based on the updated pipeline fingerprint information and the actual propagation characteristics. The first downstream node refers to the adjacent monitoring node located directly downstream of the current node along the chemical pipeline transportation direction, and is the direct receiving end of the second cascade verification wave generated by the current node.

[0055] This step is executed when the current node completes the leak detection of the current pipeline section and clearly determines that the leak detection has passed. The execution scenario is when there is no leak in the current pipeline section, the pipeline medium propagation status is normal, and the current node has obtained valid verification wave actual propagation characteristic parameters.

[0056] The current node first summarizes all actual propagation characteristic parameters of the first-cascade verification wave within the current pipeline section, verifies the validity of these parameters, and eliminates invalid or distorted parameters. Then, it retrieves the original pipeline fingerprint information. If the leak detection passes, it indicates that the current pipeline environment is in a normal state but may have experienced minor changes in physical characteristics. At this point, the system initiates an adaptive update algorithm based on machine learning. The verified actual propagation characteristic parameters are used as positive sample data to perform online learning and updating of the pipeline fingerprint information, which serves as the system's baseline model parameters. By comparing the actual propagation characteristic parameters with the corresponding theoretical parameters in the pipeline fingerprint information, any deviations in the pipeline physical characteristics are dynamically corrected. Simultaneously, the positive sample data from this monitoring is added to the pipeline fingerprint information. Through this iterative learning process, the system can "remember" the latest state of the pipeline, eliminate the risk of false alarms caused by environmental drift, and complete a comprehensive adaptive update of the original pipeline fingerprint information. This ensures that the updated pipeline fingerprint information accurately reflects the actual physical and operational state of the current pipeline section, enabling continuous model evolution.

[0057] After the pipeline fingerprint information is updated, the current node uses the updated pipeline fingerprint information as the core content and adjusts the transmission parameters such as the transmission power and modulation method of the verification wave according to the actual propagation characteristics. It generates a second-level verification wave according to the preset signal format. Finally, the second-level verification wave is transmitted to the first downstream node distributed along the pipeline using the pipeline medium as the propagation carrier. This provides signal and data support for the first downstream node to carry out pipeline section leakage detection between itself and the current node. The entire generation and transmission process is completed using the pipeline medium and does not require an external communication link.

[0058] In some embodiments, when the adaptive update of the pipeline fingerprint information has been completed and the second cascade verification wave is about to be generated, the current node needs to optimize the transmission parameters of the second cascade verification wave according to the actual propagation characteristics of the first cascade verification wave to ensure the stable transmission of the cascade monitoring signal.

[0059] Specifically, the current node first activates the signal feature analysis module, retrieves the full signal data of the first cascaded verification wave, and extracts the power values ​​of the effective signal segment and the power values ​​of the noise signal segment using a preset signal-to-noise ratio (SNR) calculation algorithm. The ratio of these two values ​​is then calculated to obtain the received SNR. Simultaneously, the actual spectrum of the verification wave is acquired and fitted using a spectrum analysis algorithm. This spectrum is then compared band by band with the original spectrum characteristics transmitted by the first upstream node. The mean deviation rates of the amplitude, phase, and frequency of each band are calculated to obtain the spectral distortion rate. After standardizing and calibrating the received SNR and spectral distortion rate, these parameters are used as channel quality parameters reflecting the physical environment characteristics of the current pipeline section and stored in the local parameter library. This parameter can intuitively reflect the degree of influence of the pipeline medium, the physical state of the pipeline section, and the environment along the pipeline on signal propagation.

[0060] Subsequently, the current node retrieves the system's preset transmit power adjustment strategy. This strategy includes multiple receive signal-to-noise ratio (SNR) ranges, and each range is matched with a corresponding power adjustment coefficient (e.g., when the SNR is high, a low power adjustment coefficient is matched to appropriately reduce the transmit power; when the SNR is low, a high power adjustment coefficient is matched to appropriately increase the transmit power). The calculated SNR is substituted into the strategy, and the corresponding power adjustment coefficient is matched. The target transmit power is obtained by multiplying the base transmit power of the second-cascaded verification wave of the current node by the adjustment coefficient, ensuring that the transmit power is compatible with the channel's anti-interference capability. Next, the current node retrieves the system's preset modulation method adjustment strategy. This strategy includes multiple spectral distortion rate (SCR) ranges, and each range is matched with a corresponding modulation method (e.g., when the SCR is low, a high-order digital modulation method is matched to improve signal transmission efficiency; when the SCR is high, a low-order digital modulation method or analog modulation method is matched to improve signal distortion resistance). The calculated SCR is substituted into the strategy, and the corresponding modulation method is matched as the target modulation method, ensuring that the modulation method is compatible with the channel signal distortion level. After determining the target transmit power and target modulation method, the current node starts the verification wave generation module. Using the adaptively updated pipeline fingerprint information as the core effective data, the data is encapsulated according to the preset signal frame structure. Then, the encapsulated baseband signal is modulated according to the target modulation method to generate a modulated signal. Subsequently, the modulated signal is amplified according to the target transmit power to complete the overall generation of the second-cascade verification wave. After generation, the current node transmits the second-cascade verification wave to the first downstream node in a directional manner using the pipeline medium as the propagation carrier. During the transmission process, the signal is kept in line with the propagation characteristics of the pipeline medium, and no additional external communication links are involved, ensuring that the signal is transmitted to the downstream node quickly and stably.

[0061] Throughout the process, all calculation data, parameter matching results, and transmission parameter configuration information will be recorded in real time as the basis for subsequent pipeline fingerprint information updates and strategy optimization. If the received signal-to-noise ratio or spectral distortion rate exceeds the preset normal range, or if the transmission power or modulation method fails to match, the current node will trigger a channel warning, mark the channel abnormality information in the generated second-level cascaded verification wave, and upload it synchronously to the monitoring center.

[0062] This step quantifies the channel quality of the current pipeline section by calculating the received signal-to-noise ratio and spectral distortion rate, providing a clear quantitative basis for adjusting the transmission parameters of the second-cascaded verification wave. This avoids problems such as signal propagation failure and distortion caused by the mismatch between fixed transmission parameters and the actual channel state. Based on a preset strategy, the transmission power and modulation method are dynamically adjusted according to the channel quality parameters, achieving adaptive matching between the transmission parameters and the characteristics of the channel's physical environment. This significantly improves the stability, anti-interference, and anti-distortion capabilities of the second-cascaded verification wave propagating in the downstream pipeline section, ensuring the effective transmission of the cascaded monitoring signal. Simultaneously, the second-cascaded verification wave is generated and transmitted according to the optimized target parameters, ensuring that downstream nodes can accurately receive and analyze the verification wave information. This provides a reliable signal foundation for leak detection in the downstream pipeline section, further improving the signal transmission link of the cascaded leak monitoring system and enhancing the continuity and reliability of the entire pipeline monitoring system.

[0063] In some embodiments, after the current node has determined the target transmit power and target modulation scheme of the second-cascaded verification wave based on the channel quality parameters, and before formally generating the second-cascaded verification wave, the current node can first retrieve the preset maximum safe power threshold and minimum transmission rate modulation level from the local system configuration module, start the transmit parameter safety verification procedure, and first perform threshold verification of the transmit power: compare the determined target transmit power with the maximum safe power threshold. If the value of the target transmit power is greater than the threshold, immediately trigger the power threshold limiting mechanism, abandon the original target transmit power, and directly set the final transmit power of the second-cascaded verification wave to the maximum safe power threshold, ensuring that the transmit power is always within the system safety control range, avoiding severe disturbance of the pipeline medium, overload damage to the monitoring node transmit module, or noise interference to other monitoring signals in the pipeline caused by excessive power. If the target transmit power is less than or equal to the maximum safe power threshold, the original target transmit power remains unchanged, and the power threshold limiting is not triggered.

[0064] After completing the transmit power verification, the current node immediately performs modulation method level verification: First, it identifies the transmission rate level corresponding to the determined target modulation method and compares it with the preset minimum transmission rate modulation level. If the transmission rate level of the target modulation method is lower than the minimum level, the modulation level limitation mechanism is immediately triggered, the original target modulation method is abandoned, and the final modulation method of the second cascaded verification wave is maintained at the minimum transmission rate modulation level. This ensures that the verification wave has a basic effective transmission rate, and that core data such as pipeline fingerprint information can be completely received and accurately parsed by the first downstream node. This avoids insufficient signal transmission rate, data loss, or parsing failure due to excessively low modulation level, and ensures the continuity of data transmission in the cascaded monitoring link. If the transmission rate level of the target modulation method is higher than or equal to the minimum transmission rate modulation level, the original target modulation method remains unchanged, and the modulation level limitation is not triggered.

[0065] After completing dual verification of transmit power and modulation scheme, the current node determines the triggering status of threshold / level limits: if either the maximum safe power threshold limit (i.e., transmit power adjustment) or the minimum transmission rate modulation level limit (i.e., modulation scheme adjustment) is triggered during the above process, or both are triggered, the current node immediately retrieves the adaptively updated pipeline fingerprint information. In its preset status identifier field, it marks the link saturation status according to the system's unified coding rules. The marking content includes key information such as the triggered limit type, original target parameters, adjusted final parameters, and trigger time. After marking, the updated pipeline fingerprint information is updated. The data is then stored locally and simultaneously pushed to the second-generation cascaded verification wave. If no restriction is triggered, there is no need to mark the status of the pipeline fingerprint information; the original information remains unchanged. The link saturation status mark will be transmitted to downstream nodes along with the second-generation cascaded verification wave and simultaneously uploaded to the monitoring center in real time. This allows the monitoring center to conduct an effectiveness assessment of the cascaded link. Based on this mark, combined with the parameter adjustment data and signal propagation characteristics of the entire link, the monitoring center can determine the channel carrying capacity and node hardware adaptability of the current pipeline section, and then formulate targeted link optimization schemes, such as node transmission module maintenance, pipeline section channel optimization, and system configuration parameter updates.

[0066] This step, through dual security and effectiveness verification of transmission power and modulation method, and the setting of strict thresholds and level restrictions, avoids problems such as equipment damage, media disturbance, and data transmission failure caused by unreasonable parameters, ensuring the security of the second-cascade verification wave transmission process and the effectiveness of data transmission. Simultaneously, by accurately marking the link saturation state, it provides the monitoring center with intuitive data on the channel performance of each pipeline section, realizing visualized monitoring of the cascade link's operational status. This solves the problems of existing technologies lacking quantitative identification of link status and precise basis for operation and maintenance optimization, improving the operation and maintenance efficiency and link stability of the entire pipeline monitoring system. Furthermore, the link saturation state mark is transmitted and uploaded along with the pipeline fingerprint information, realizing full-link sharing of status information, providing a preliminary reference for adjusting the transmission parameters of subsequent nodes, and further optimizing the overall coordination of cascade monitoring.

[0067] S105. If the leak detection fails, an abnormal location alarm signal is generated and uploaded to the monitoring center, and the cascading verification wave sent to the first downstream node is stopped.

[0068] This step is executed the instant the current node completes the leak detection of the current pipeline section and clearly determines that the leak detection has failed. The execution scenario is when the current pipeline section is determined to have a medium leak or abnormal medium propagation, and there are no other previous alarm signals covering it. Upon failing a leak detection, the current node immediately initiates an anomaly alarm procedure. First, based on its node number, location information, and the segmentation information of the current pipeline section, it determines the precise geographical location of the abnormal pipeline section. Then, it summarizes all actual monitoring parameters, theoretical thresholds, and comparison results from this leak detection, generating a standardized anomaly location alarm signal according to the signal format preset by the monitoring center. This signal includes anomaly location information, anomaly parameters, judgment criteria, alarm time, and node information. Subsequently, the anomaly location alarm signal is rapidly uploaded to the monitoring center via a preset communication method, ensuring the monitoring center can obtain pipeline anomaly information immediately for subsequent emergency response. Simultaneously with generating and uploading the anomaly location alarm signal, the current node immediately terminates all operations related to the generation and transmission of the second-level cascade verification wave, stops sending any cascade verification waves to the first downstream node, and interrupts the transmission of cascade monitoring signals in that direction. This prevents invalid verification wave signals from interfering with the monitoring of downstream nodes and prevents downstream nodes from generating incorrect detection results due to receiving abnormal verification waves. If the abnormal state of the current pipeline section is subsequently resolved, the cascade verification wave transmission function can only be restored after manual reset or remote command from the monitoring center.

[0069] This step generates accurate anomaly location alarm signals and uploads them quickly, enabling real-time early warning and precise location of pipeline leaks. This solves the problem of untimely leak detection in existing technologies, buys time for emergency response, and reduces the harm of leak accidents. At the same time, by stopping the transmission of cascaded verification waves to downstream nodes, it avoids the cascading transmission of abnormal signals, prevents the spread of erroneous detection results, ensures the detection accuracy of the entire pipeline monitoring system, and improves the overall reliability of the system.

[0070] In this embodiment, since the first and second-level verification waves are transmitted through the pipeline medium, there is no need to rely on an external independent communication link. Furthermore, accurate leak detection of the current pipeline section is achieved through pipeline fingerprint information. Simultaneously, the pipeline fingerprint information is adaptively updated based on the actual propagation characteristics of the verification waves, and alarms are promptly triggered and invalid cascade transmissions are interrupted in case of anomalies. Therefore, the drawbacks of existing technologies, such as high communication costs, easy transmission interruptions, untimely judgments, and poor adaptability of static models, are effectively avoided. This effectively solves the problems of insufficient real-time performance and reliability in long-distance pipeline leak monitoring, as well as high system construction and maintenance costs. Thus, segmented accurate monitoring and cascaded closed-loop transmission of chemical long-distance pipelines are realized, ensuring the timeliness, accuracy, and system stability of leak monitoring, reducing monitoring costs, and providing reliable support for the safe operation and maintenance of chemical pipelines.

[0071] Following the above embodiments, the method provided in this embodiment will now be described in more detail. Please refer to [link / reference]. Figure 2 This is another flowchart illustrating the chemical pipeline leak monitoring and location method integrating artificial intelligence in this application embodiment.

[0072] S201. Obtain the reception time and received signal strength when the first cascaded verification wave is received;

[0073] This step is executed after the current node successfully receives the first cascade verification wave and completes the initial signal identification of the verification wave (confirming that it is a valid verification wave sent by the first upstream node, rather than interference signals inside the pipeline or external noise), and before parsing the pipeline fingerprint information. The execution scenario is that the current pipeline section is in normal operation, the current node has completed the pre-cascade preparation (such as obtaining the cascade transmission strategy, receiving heartbeat packets and starting the listening timer), and has successfully captured the first cascade verification wave. After the current node confirms successful reception of the first cascaded verification wave, it immediately activates its built-in signal acquisition module and timing module, simultaneously performing two operations: First, the timing module accurately records the specific moment of receiving the verification wave. This moment will be used in conjunction with the subsequently acquired transmission moment to calculate the actual transmission time of the verification wave. The timing module uses a time base consistent with the first upstream node (such as network synchronization time) to avoid calculation errors caused by inconsistent time bases. Second, the signal acquisition module performs power detection on the received first cascaded verification wave, accurately acquiring the actual power value of the signal, i.e., the received signal strength. During the acquisition process, the influence of factors such as medium flow noise in the pipeline and external electromagnetic interference needs to be eliminated. The acquired power data is then subjected to simple filtering to ensure the accuracy of the received signal strength.

[0074] S202. Analyze the pipeline fingerprint information to obtain the transmission time and reference transmission intensity when the first upstream node sends the first cascaded verification wave.

[0075] The current node, acting as the execution entity, initiates its built-in signal analysis module to analyze the first cascaded verification wave layer by layer: First, it decodes the signal structure of the verification wave, separating the carrier signal from the valid data signal and eliminating interference components in the carrier signal; second, it extracts the pipeline fingerprint information in a preset format from the valid data signal. During the analysis process, it must be decoded according to the system's preset encoding rules to ensure the integrity and accuracy of the pipeline fingerprint information. If problems such as missing pipeline fingerprint information, incorrect format, or information distortion are found during the analysis process, the analysis operation will be immediately suspended, judged as a signal anomaly, and a pre-warning mechanism will be triggered (such as recording abnormal information and uploading it synchronously to the monitoring center); finally, from the pipeline fingerprint information obtained by the analysis, two core reference parameters are accurately extracted—the transmission time when the first upstream node sends the first cascaded verification wave, and the reference transmission strength of the verification wave. The two parameters are stored in correspondence with the reception time and received signal strength obtained in step S201 to ensure that the four parameters can be called synchronously.

[0076] This step obtains the transmission time and reference transmission intensity by accurately analyzing the pipeline fingerprint information, providing clear benchmark comparison parameters for subsequent calculations of attenuation and transmission delay. This solves the problem of lacking unified benchmark parameters and being unable to accurately quantify signal propagation changes in existing technologies. At the same time, through anomaly verification during the analysis process, the impact of invalid data on leakage detection results is avoided in advance, further improving the accuracy and reliability of leakage detection.

[0077] S203. Calculate the difference between the reference transmission strength and the received signal strength to obtain the actual signal attenuation, and calculate the time difference between the receiving time and the transmitting time to obtain the actual transmission delay.

[0078] The current node initiates its built-in data calculation module and performs calculations step-by-step according to a preset algorithm: First, it calculates the actual signal attenuation, strictly following the formula "reference transmit strength - received signal strength." During the calculation, it ensures that the units of both parameters are consistent. If the calculation result is negative (i.e., the received signal strength is greater than the reference transmit strength), it is considered abnormal data, indicating that the received signal may be affected by external interference or a data acquisition error. This triggers a data verification mechanism, re-acquiring the received signal strength and recalculating. Second, it calculates the actual transmission delay, strictly following the formula "receive time - transmit time." Since the receive and transmit times use a unified time base, the time difference can be directly calculated. If the calculation result is negative (i.e., the receive time is earlier than the transmit time), it is considered a time synchronization anomaly. The timing module is immediately synchronized and calibrated, and the actual transmission delay is recalculated. Third, the calculated actual signal attenuation and actual transmission delay are standardized and stored, and associated with the identification information of the current pipeline section (such as the pipe segment number) to ensure accurate comparison with theoretical parameters later. Simultaneously, all original data and calculation results during the calculation process are recorded for subsequent anomaly tracing and verification.

[0079] This step quantifies the propagation characteristics of the verification wave into specific parameters by accurately calculating the actual signal attenuation and actual transmission delay. This provides a clear quantitative basis for subsequent leak detection and solves the problems of existing technologies that cannot accurately quantify signal propagation changes in pipelines and lack data support for leak detection. At the same time, the accuracy of the quantified parameters is ensured through anomaly verification during the calculation process, further improving the accuracy of leak detection. Moreover, the calculation process is fast and efficient, and the results can be output in real time, meeting the needs of real-time monitoring of long-distance pipelines.

[0080] S204. Based on the pipe segment length and medium wave velocity in the physical characteristic information of the pipeline, determine the theoretical attenuation threshold and theoretical time delay range of the current pipeline section.

[0081] As the executing entity, the current node first accurately retrieves two core inherent parameters from the pipeline fingerprint information obtained in step S202: pipe segment length and medium wave velocity. During the retrieval process, the completeness and accuracy of the two parameters must be confirmed. If the parameters are missing or distorted, the system's preset backup parameters for the pipe segment will be retrieved immediately, and the abnormal parameter information will be uploaded to the monitoring center simultaneously. Subsequently, the built-in theoretical parameter calculation module will be activated. According to the preset hydraulic and signal propagation algorithms, the theoretical attenuation threshold and theoretical time delay range will be determined step by step: First, the theoretical attenuation threshold will be calculated. Combining parameters such as pipe segment length (the longer the pipe segment, the greater the signal attenuation), medium wave velocity (the lower the medium wave velocity, the greater the signal attenuation), and pipe material (such as the different signal attenuation coefficients of steel pipes and plastic pipes), the maximum allowable attenuation of the verification wave when it propagates normally in the current pipeline section will be calculated through the preset algorithm. This is the theoretical attenuation threshold. This threshold reserves a certain reasonable redundancy to avoid misjudgment caused by fluctuations in normal operating conditions.

[0082] The second step is to calculate the theoretical time delay range. First, based on the formula "pipe section length ÷ medium wave velocity", the theoretical time (i.e., the reference time delay) for the verification wave to propagate normally in the current pipeline section is calculated. Then, combined with normal influencing factors such as the flow velocity of the medium in the pipeline, temperature and pressure fluctuations, a reasonable fluctuation range is set, i.e., the theoretical time delay range (reference time delay ± reasonable fluctuation value). The magnitude of the fluctuation value is preset according to the actual working conditions of the pipeline to ensure that the actual transmission delay under normal working conditions can fall within this range.

[0083] Finally, the calculated theoretical attenuation threshold and theoretical delay range are standardized and stored, and correlated with the current pipeline section's identification information, actual signal attenuation, and actual transmission delay for easy comparison and judgment in the future.

[0084] This step determines the theoretical attenuation threshold and theoretical time delay range by using the inherent physical parameters of the pipeline, providing a clear quantitative benchmark for subsequent leak detection. This solves the problem of judgment deviation caused by the use of static fixed models in existing technologies, which cannot be adapted to the inherent characteristics of the pipeline. At the same time, the theoretical parameters are calculated in combination with the actual physical characteristics of the pipeline and reasonable redundancy is reserved, which not only avoids misjudgment but also ensures that abnormal situations can be accurately identified, further improving the accuracy and reliability of leak detection.

[0085] S205. If the actual signal attenuation is greater than the theoretical attenuation threshold, or the actual transmission delay exceeds the theoretical delay range, then the current pipeline section is determined to have failed the leak detection.

[0086] The current node activates its built-in leakage detection module, comparing and analyzing the actual parameters with the theoretical baseline parameters one by one, strictly following the "OR logic" judgment rule. The specific judgment process is as follows: First, compare the actual signal attenuation with the theoretical attenuation threshold to determine if the actual signal attenuation is greater than the theoretical attenuation threshold. If it is greater, it indicates that the propagation loss of the verification wave in the current pipeline section exceeds the normal range, most likely due to abnormal medium flow caused by pipeline leakage, which in turn leads to increased signal attenuation. Second, compare the actual transmission delay with the theoretical delay range to determine if the actual transmission delay exceeds this range (i.e., the actual transmission delay is less than the minimum value of the theoretical delay range, or greater than the maximum value of the theoretical delay range). If it exceeds... If the actual signal attenuation is greater than the theoretical attenuation threshold and the actual transmission delay is within the theoretical delay range, it indicates an abnormal change in the propagation speed of the verification wave. The third step involves making a final judgment based on the above two comparison results: if either "actual signal attenuation is greater than the theoretical attenuation threshold" or "actual transmission delay exceeds the theoretical delay range" is met, the current pipeline section is directly determined to have failed the leak detection, confirming a leak risk or abnormal propagation state. If both comparison results are met (actual signal attenuation ≤ theoretical attenuation threshold, and actual transmission delay within the theoretical delay range), the current pipeline section is determined to have passed the leak detection, confirming no leak and normal operation.

[0087] After the judgment is completed, the current node will immediately record the judgment result, including the judgment conclusion, various comparison parameters, judgment time and other information. If the judgment fails, the subsequent S105 step will be triggered (generate an abnormal location alarm signal and upload it to the monitoring center, and stop sending cascade verification waves to downstream nodes); if the judgment passes, the subsequent S104 step will be triggered (adaptively update the pipeline fingerprint information, generate a second cascade verification wave and send it to the first downstream node).

[0088] In this embodiment, since the monitoring node is the main execution entity, the system completes the acquisition of measured parameters, extraction of benchmark parameters, calculation of quantitative parameters, determination of theoretical benchmarks, and final leakage determination step by step. It relies entirely on pipeline fingerprint information and pipeline physical characteristic information, employing a quantitative determination method that compares "measured parameters + theoretical parameters." Furthermore, a data verification mechanism is set up at each step to ensure parameter accuracy. Therefore, it can accurately identify the leakage situation in the current pipeline section, effectively solving the problems of low accuracy, high false positive and false negative rates, lack of quantitative basis for judgment, and inability to adapt to the actual physical characteristics and operating conditions of pipelines in existing technologies. This achieves segmented, precise leakage detection of long-distance chemical pipelines, ensuring that potential leakage hazards can be identified in a timely and accurate manner, providing reliable support for subsequent alarm handling and cascade monitoring link transmission. Simultaneously, it reduces the false judgment cost of the monitoring system, improves the real-time performance, reliability, and practicality of the entire pipeline leakage monitoring system, and ensures the safe and stable operation of chemical pipelines.

[0089] The pipeline monitoring system in the embodiments of this invention is described below from the perspective of hardware processing. Please refer to [link / reference]. Figure 3 This is a schematic diagram of the physical device structure of a pipeline monitoring system in an embodiment of this application.

[0090] It should be noted that, Figure 3 The structure of the pipeline monitoring system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0091] like Figure 3 As shown, the pipeline monitoring system includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 302 or programs loaded from storage section 308 into Random Access Memory (RAM) 303, such as performing the methods described in the above embodiments. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An Input / Output (I / O) interface 305 is also connected to the bus 304.

[0092] The following components are connected to I / O interface 305: input section 306 including audio input devices, push-button switches, etc.; output section 307 including a liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 308 including a hard disk, etc.; and communication section 309 including a network interface card such as a LAN (Local Area Network) card, modem, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0093] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the various functions defined in the present invention.

[0094] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0095] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0096] Specifically, the pipeline monitoring system in this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, it implements the chemical pipeline leakage monitoring and location method integrating artificial intelligence provided in the above embodiment.

[0097] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the pipeline monitoring system described in the above embodiments; or it may exist independently and not assembled into the pipeline monitoring system. The storage medium carries one or more computer programs, which, when executed by a processor of the pipeline monitoring system, cause the pipeline monitoring system to implement the artificial intelligence-integrated chemical pipeline leak detection and location method provided in the above embodiments.

[0098] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. 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 scope of the technical solutions of the embodiments of this application.

[0099] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0100] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for monitoring and locating leaks in chemical pipelines that integrates artificial intelligence, characterized in that, The method, applied to a pipeline monitoring system comprising multiple monitoring nodes cascaded along the pipeline, includes: Receive a first cascaded verification wave sent by the first upstream node through the pipeline medium, wherein the first cascaded verification wave includes at least pipeline fingerprint information; When the first cascaded verification wave is received, the pipeline fingerprint information is analyzed by an intelligent feature extraction algorithm. The pipeline fingerprint information includes pipeline physical feature information. The pipeline fingerprint information is used to perform intelligent leak detection on the current pipeline section between the current node and the first upstream node. If the leak detection passes, the pipeline fingerprint information is updated online adaptively based on the actual propagation characteristics of the first cascaded verification wave in the current pipeline section, so as to generate a second cascaded verification wave and send it to the first downstream node. If the leak detection fails, an abnormal location alarm signal is generated and uploaded to the monitoring center, and the sending of cascaded verification waves to the first downstream node is stopped.

2. The method according to claim 1, characterized in that, Before the step of receiving the first cascaded verification wave transmitted by the first upstream node through the pipeline medium, the method further includes: Obtain the cascading transmission strategy for the current monitoring area, wherein the cascading transmission strategy includes a one-way cascading transmission mode or a two-way convergence transmission mode; The cascading propagation strategy configuration method includes: The pipeline monitoring system acquires historical operation and maintenance data of the pipeline to be monitored, and the historical operation and maintenance data includes at least the number of pipeline segment repairs and the service life. Based on the historical operation and maintenance data, the pipeline to be monitored is divided into multiple monitoring areas, and a cascading transmission strategy is determined for each monitoring area.

3. The method according to claim 1, characterized in that, Before the step of receiving the first cascaded verification wave transmitted by the first upstream node through the pipeline medium, the method further includes: Determine whether a heartbeat signal sent by the first upstream node has been received. The heartbeat signal is sent by the first upstream node after it determines that the pipeline between it and the second upstream node has passed the leak detection. If the heartbeat signal is received, start the monitoring timer. If the heartbeat signal is not received, the system enters a sleep waiting state.

4. The method according to claim 3, characterized in that, The step of receiving the first cascaded verification wave transmitted by the first upstream node through the pipeline medium specifically includes: When the heartbeat packet signal is received, the listening timer for the first cascaded verification wave is started; If the first cascaded verification wave is not received within the preset transmission delay window, it is determined that there is a medium transmission blockage anomaly in the current pipeline section, an interruption alarm signal is generated and uploaded to the monitoring center.

5. The method according to claim 1, characterized in that, The step of using the pipeline fingerprint information to perform intelligent leak detection on the current pipeline section between the current node and the first upstream node specifically includes: Obtain the reception time and received signal strength when the first cascaded verification wave is received; The pipeline fingerprint information is analyzed to obtain the transmission time and reference transmission intensity when the first upstream node sends the first cascaded verification wave; The difference between the reference transmission strength and the received signal strength is calculated to obtain the actual signal attenuation, and the time difference between the receiving time and the transmitting time is calculated to obtain the actual transmission delay; Based on the pipe segment length and medium wave velocity in the pipeline physical characteristic information, the theoretical attenuation threshold and theoretical time delay range of the current pipeline section are determined. If the actual signal attenuation is greater than the theoretical attenuation threshold, or the actual transmission delay exceeds the theoretical delay range, then the current pipeline section is determined to have failed the leak detection.

6. The method according to claim 1, characterized in that, The step of performing online adaptive learning and updating of the pipeline fingerprint information based on the actual propagation characteristics of the first cascaded verification wave within the current pipeline section to generate a second cascaded verification wave to be sent to the first downstream node specifically includes: Calculate the received signal-to-noise ratio and spectral distortion rate of the first cascaded verification wave as channel quality parameters reflecting the physical environment characteristics of the current pipeline section; Based on a preset transmit power adjustment strategy, the transmit power of the second cascaded verification wave is adjusted according to the received signal-to-noise ratio to obtain the target transmit power; Based on a preset modulation scheme adjustment strategy, the modulation scheme of the second cascaded verification wave is adjusted according to the spectral distortion rate to obtain the target modulation scheme; Based on the target transmit power and the target modulation method, the second cascaded verification wave is generated and sent to the first downstream node.

7. The method according to claim 6, characterized in that, Before the step of generating the second cascaded verification wave based on the target transmit power and the target modulation scheme and sending it to the first downstream node, the method further includes: Determine whether the target transmission power is greater than the preset maximum safe power threshold. If so, set the transmission power of the second cascaded verification wave to the maximum safe power threshold. Determine whether the target modulation mode is lower than the preset minimum transmission rate modulation level. If so, maintain the modulation mode of the second cascaded verification wave at the minimum transmission rate modulation level. When the maximum safe power threshold limit or the minimum transmission rate modulation level limit is triggered, the link saturation state is marked in the pipeline fingerprint information so that the monitoring center can evaluate the effectiveness of the cascaded links.

8. A pipeline monitoring system, characterized in that, The pipeline monitoring system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the pipeline monitoring system to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the pipeline monitoring system, the pipeline monitoring system performs the method as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is run on the pipeline monitoring system, it causes the pipeline monitoring system to perform the method as described in any one of claims 1-7.

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