Intelligent gas leakage diagnosis method and system based on gas kinetic energy power generation

Abstract

The application relates to the field of gas leakage detection, in particular to an intelligent gas leakage diagnosis method and system based on gas kinetic energy power generation. The method comprises the following steps: acquiring a detector basic data set of a plurality of gas kinetic energy power generation detectors, constructing a kinetic energy consumption chain and a power generation voltage attenuation chain based on the data set, generating a pipe network topology atlas and a pipe network double-chain characteristic information set; based on the data set, operation state data flow and original metering data flow returned by the detector in real time, performing collaborative compensation analysis on the kinetic energy consumption chain and the state interference chain, generating a real flow information set of a plurality of nodes of the pipe network; based on the information set and a mapping rule of a production process leakage tolerance threshold, performing gas mode feature separation and abnormal loss diagnosis, generating a potential leakage event information set; and based on the information set, performing array collaborative evidence checking and energy consumption mutation analysis, generating and outputting a gas leakage diagnosis report. In the gas leakage diagnosis process, the application can prevent safety accidents caused by leakage.

Description

Technical Field

[0001] This application relates to the field of gas leak detection, and in particular to an intelligent gas leak diagnosis method and system based on gas kinetic energy power generation. Background Technology

[0002] In the field of industrial gas pipeline safety monitoring, accurate and real-time diagnosis of gas leaks is a core technology for ensuring production safety, achieving energy conservation and emission reduction, and preventing major accidents. Its level of intelligence is directly related to the reliable operation and resource utilization efficiency of continuous industrial processes, and is a key link in the intelligent and safety early warning system of modern process industries.

[0003] However, existing gas leak monitoring methods lack a mechanism for collaborative modeling and in-depth utilization of the physical laws governing systemic energy transfer and signal attenuation in pipeline networks when facing complex industrial sites. This not only makes it difficult to effectively separate normal process fluctuations from actual leak anomalies, but also easily leads to false alarms and missed alarms, resulting in insufficient reliability of the monitoring system and an inability to provide accurate decision support for proactive safety protection. Summary of the Invention

[0004] This application provides an intelligent gas leak diagnosis method and system based on gas kinetic energy power generation to solve the above-mentioned technical problems.

[0005] In a first aspect, this application provides an intelligent gas leak diagnosis method based on gas kinetic energy power generation. The method includes: acquiring a basic dataset of several gas kinetic energy power generation detectors; constructing a kinetic energy consumption chain and a power generation voltage attenuation chain based on the basic dataset, generating a pipeline network topology map and a pipeline network dual-chain characteristic information set; performing collaborative compensation analysis of the kinetic energy consumption chain and the state interference chain based on the pipeline network topology map, the pipeline network dual-chain characteristic information set, and the real-time operational status data stream and raw metering data stream transmitted by the detectors, generating a real flow information set of multiple nodes in the pipeline network; performing gas consumption pattern feature separation and abnormal loss diagnosis based on the mapping rules between the real flow information set and the leakage tolerance threshold of the production process, generating a potential leak event information set; and performing array collaborative evidence verification and energy consumption mutation analysis based on the potential leak event information set, generating and outputting a gas leak diagnosis report.

[0006] The above technical solutions significantly improve the accuracy of leak diagnosis and effectively reduce the rates of false positives and false negatives; enable real-time dynamic monitoring to quickly detect potential leaks and provide a window of opportunity for timely response; adapt to complex pipeline environments and flexibly match different process and media requirements; reduce manual intervention, lower operation and maintenance costs, and improve pipeline management efficiency; prevent safety accidents caused by leaks and reduce gas waste, balancing safety benefits and energy-saving value, and contributing to the refined operation of pipeline networks.

[0007] Optionally, the generation of the pipeline network topology map and the pipeline network double-chain characteristic information set includes: the detector basic dataset includes the identification code, installation location information, calibrated power generation consumption characteristic parameters, and calibrated power generation voltage response characteristic parameters of each detector; based on the installation location information of all detectors and combined with the flow direction rules of fluid in the pipeline network, constructing the pipeline network topology map characterizing the upstream and downstream connection relationship between detectors; based on the pipeline network topology map and the power generation consumption characteristic parameters of all detectors, constructing the kinetic energy consumption chain describing the law of gradual consumption of gas kinetic energy as it is transferred along the pipeline network; based on the pipeline network topology map and the power generation voltage response characteristic parameters of all detectors, constructing the power generation voltage decay chain describing the law of gradual decay of the detector's power generation voltage along the pipeline network flow direction; and constructing the pipeline network double-chain characteristic information set according to the kinetic energy consumption chain and the power generation voltage decay chain.

[0008] Optionally, the construction process of the kinetic energy consumption chain includes: determining the upstream and downstream sequence of the detectors in the pipeline network based on the pipeline network topology map, and initializing the total input kinetic energy of the gas as an energy benchmark, starting from the air inlet of the first detector; modeling each detector as a kinetic energy conversion node with nonlinear loss characteristics according to the power generation consumption characteristic parameters, wherein the residual gas kinetic energy output by this node is the input kinetic energy minus the specific proportion of kinetic energy consumed in its power generation and the fixed loss caused by the local resistance of the pipeline; starting from the energy benchmark, iteratively performing the nonlinear loss analysis on each detector node along the upstream and downstream sequence, thereby generating a digital twin chain model describing the step-by-step, nonlinear dissipation of gas kinetic energy during the flow through the entire detector network, as the kinetic energy consumption chain.

[0009] Optionally, the construction process of the power generation voltage attenuation chain includes: based on the pipeline network topology map and the upstream and downstream sequence, the power generation process of each detector is equivalent to an equivalent voltage source excited by the inlet gas state, and its output voltage is described by a nonlinear function defined by the power generation voltage response characteristic parameter; taking the equivalent voltage source as the basic node, according to the connection relationship between nodes in the pipeline network topology map, a flow resistance transfer coefficient characterizing the pressure and kinetic energy loss during the gas flow process in the pipeline is introduced on the pipe segment between two adjacent basic nodes, and the equivalent voltage source and the flow resistance transfer coefficient are connected in series to construct a cascaded network model from the beginning to the end of the pipeline network; in the cascaded network model, the output voltage of the upstream detector is attenuated after flowing through the pipe segment characterized by the flow resistance transfer coefficient, and serves as the equivalent inlet excitation of the downstream adjacent detector; starting from the initial voltage state of the detector at the beginning of the pipeline network, the flow resistance attenuation and voltage response are analyzed sequentially along the upstream and downstream sequence, and a physical information network describing the step-by-step and coupled attenuation of the power generation voltage signal along the pipeline network topology is generated through iterative transmission, which serves as the power generation voltage attenuation chain.

[0010] Optionally, generating a true flow information set for multiple nodes in the pipeline network includes: performing kinetic energy conservation analysis and compensation on each detector node in the pipeline network based on the kinetic energy consumption chain and the original metering data stream, generating a node energy flow estimation sequence; performing state interference analysis and compensation on each detector node in the pipeline network based on the power generation voltage decay chain and the operating status data stream, generating a link state flow estimation sequence; and performing spatiotemporal alignment and evidence fusion on the node energy flow estimation sequence and the link state flow estimation sequence, eliminating systematic errors in single-chain analysis through collaborative compensation, and generating a true flow information set for multiple nodes in the pipeline network.

[0011] Optionally, the generation of the node energy flow estimation sequence includes: using the power generation consumption characteristic parameters calibrated by each detector node in the kinetic energy consumption chain and the local resistance loss of the pipeline as known constraints, establishing an energy balance equation for each detector node, where the input variable is the residual gas kinetic energy output by the upstream node, and the output variables are the power generation energy consumption, pipeline loss, and residual kinetic energy output downstream of this node; using the real-time power generation in the original metering data stream as the observed value of the energy consumption term in the equation, and solving the equation simultaneously from the beginning along the pipeline topology map to obtain the node theoretical flow sequence; introducing a chain-like collaborative compensation factor based on the energy residual between adjacent basic nodes to address the imbalance in the energy equations between nodes, and iteratively correcting the node theoretical flow sequence through a backpropagation algorithm; and outputting the final node energy flow estimation sequence with the goal of minimizing the corrected global energy balance residual.

[0012] Optionally, the generation of the link state flow estimation sequence includes: extracting multi-dimensional interference features from the original voltage time-series signal in the operating state data stream, wherein the interference features include at least a mixed interference mode formed by grid coupling noise, electromagnetic transient disturbances, and sensor temperature drift; based on the generation voltage attenuation chain, constructing a voltage-current resistance transmission benchmark model for each pipe segment under an ideal state without interference, and inputting the extracted mixed interference mode into the voltage-current resistance transmission benchmark model, generating corresponding pseudo-attenuation components through forward simulation, and removing the pseudo-attenuation components from the original voltage signal in reverse to achieve primary filtering of structural interference; for the unstructured random interference remaining after primary filtering, introducing a pre-trained interference feature fingerprint database for matching and identification, and using an associated adaptive compensation operator to specifically correct the voltage observation values ​​of the affected nodes; substituting the filtered and corrected node voltage states into the generation voltage attenuation chain for reverse recursive calculation, and iteratively optimizing and outputting the link state flow estimation sequence by constraining the physical consistency of link voltage attenuation.

[0013] Optionally, generating the potential leakage event information set includes: constructing a process mode feature library containing dynamic gas consumption modes of different production processes, where each mode maps to a dynamic leakage tolerance threshold curve that changes over time or production stage, rather than a single fixed threshold; performing a spatiotemporal sliding comparison between the actual flow information set and the dynamic leakage tolerance threshold curve corresponding to the currently activated process mode to identify flow anomalies exceeding the instantaneous tolerance threshold, and recording their spatial location, timestamp, and magnitude of exceedance; performing multi-level threshold cross-validation and persistence analysis on the flow anomalies to filter out legitimate fluctuations caused by brief process switching or start-up / shutdown, and selecting them as potential leakage events; and integrating the potential leakage events to generate a structured potential leakage event information set.

[0014] Optionally, generating and outputting the gas leak diagnostic report includes: for each event in the potential leak event information set, performing node geographic location tracing, upstream and downstream event correlation analysis, and multi-timescale leak intensity evolution assessment to generate a multi-dimensional leak feature vector; inputting the multi-dimensional leak feature vector into predefined leak mode classification and risk assessment rules to output its corresponding leak mode category, credibility score, and recommended handling priority; and based on the assessment results of all events, generating and outputting the gas leak diagnostic report containing leak point location, mode diagnosis, severity level, evolution trend, and handling recommendations.

[0015] Secondly, this application provides an intelligent gas leak diagnosis system based on gas kinetic energy power generation. The system includes: a map and dual-chain construction module, used to acquire the basic dataset of several gas kinetic energy power generation detectors, construct a kinetic energy consumption chain and a power generation voltage attenuation chain based on the basic dataset, and generate a pipeline network topology map and a pipeline network dual-chain characteristic information set; a flow information analysis module, used to perform collaborative compensation analysis of the kinetic energy consumption chain and the state interference chain based on the pipeline network topology map, the pipeline network dual-chain characteristic information set, and the real-time operation status data stream and original metering data stream transmitted by the detectors, and generate a real flow information set of multiple nodes in the pipeline network; a leak event analysis module, used to perform gas consumption pattern feature separation and abnormal loss diagnosis based on the mapping rules between the real flow information set and the production process leak tolerance threshold, and generate a potential leak event information set; and a leak report generation module, used to perform array collaborative evidence verification and energy consumption mutation analysis based on the potential leak event information set, and generate and output a gas leak diagnosis report. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram illustrating an application scenario provided in one embodiment of this application;

[0018] Figure 2 A flowchart illustrating an embodiment of the intelligent gas leak diagnosis method based on gas kinetic energy power generation provided in this application;

[0019] Figure 3 This is a schematic diagram of the structure of an intelligent gas leak diagnosis system based on gas kinetic energy power generation, provided in an embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0021] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0022] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0023] Existing gas leak monitoring methods, when faced with complex industrial sites, lack a mechanism for collaborative modeling and in-depth utilization of the physical laws governing systemic energy transfer and signal attenuation in pipeline networks. This not only makes it difficult to effectively separate normal process fluctuations from actual leak anomalies, but also easily leads to false alarms and missed alarms, resulting in insufficient reliability of the monitoring system and an inability to provide accurate decision support for proactive safety protection.

[0024] Based on this, this application provides an intelligent gas leak diagnosis method and system based on gas kinetic energy power generation. First, a gas kinetic energy power generation detector is used to collect physical data from multiple nodes in the pipeline network, constructing a digital twin of the kinetic energy consumption chain and voltage decay chain to form a benchmark model characterizing the inherent physical laws of the system. Then, through dual-chain collaborative compensation analysis, real-time operating data is integrated to eliminate measurement errors and environmental interference in the digital space, accurately reproducing the actual gas flow rate at each node of the pipeline network. On this basis, a dynamic leak tolerance threshold linked to the production process is introduced to intelligently separate normal gas consumption fluctuations and preliminarily identify potential leak events. Finally, by performing multi-node spatial correlation verification and time-series energy mutation analysis on potential events, a comprehensive and accurate gas leak diagnosis report with precise location, classification, and attribution is generated and output to factory personnel. This solution significantly improves the accuracy of leak diagnosis, effectively reducing the rate of false positives and false negatives; it enables real-time dynamic monitoring, quickly capturing potential leaks and providing a window of opportunity for timely response; it adapts to complex pipeline environments and can flexibly match different process and media requirements; it reduces manual intervention, lowers operation and maintenance costs, and improves pipeline management efficiency; it can prevent safety accidents caused by leaks, reduce gas waste, and balance safety benefits with energy-saving value, thus contributing to the refined operation of pipeline networks.

[0025] Figure 1 This is a schematic diagram illustrating an application scenario provided by this application. In the process of gas leak diagnosis, the method provided in this application can both prevent safety accidents caused by leaks and reduce gas waste, balancing safety benefits and energy-saving value, and contributing to the refined operation of pipeline networks.

[0026] Specifically, the method of this application is applied to any server that communicates with a gas kinetic energy generator detector. The server obtains the basic dataset provided by the detector. First, the gas kinetic energy generator detector collects physical data from multiple nodes in the pipeline network, constructing a digital twin-based kinetic energy consumption chain and voltage decay chain to form a benchmark model characterizing the inherent physical laws of the system. Then, through dual-chain collaborative compensation analysis, real-time operating data is integrated to eliminate measurement errors and environmental interference in the digital space, accurately reproducing the actual gas flow rate at each node of the pipeline network. Based on this, a dynamic leakage tolerance threshold linked to the production process is introduced to intelligently separate normal gas consumption fluctuations and preliminarily identify potential leakage events. Finally, through multi-node spatial correlation verification and time-series energy mutation analysis of potential events, a comprehensive and accurate gas leakage diagnosis report with precise location, classification, and attribution is generated and output to factory personnel.

[0027] For specific implementation details, please refer to the following examples.

[0028] Figure 2This is a flowchart illustrating an embodiment of an intelligent gas leak diagnosis method based on gas kinetic energy power generation. The method of this embodiment can be applied to servers in the above scenarios. Figure 2 As shown, the method includes:

[0029] S201. Obtain the basic dataset of several gas kinetic energy power generation detectors, construct the kinetic energy consumption chain and power generation voltage decay chain based on the basic dataset of detectors, and generate a pipeline network topology map and a pipeline network dual-chain characteristic information set.

[0030] A gas kinetic energy generator detector can be an online monitoring device installed at key nodes of industrial gas transmission pipelines, which uses the kinetic energy of the flowing gas in the pipeline to drive a micro generator to generate electricity.

[0031] The basic dataset for the detector can be obtained from equipment manufacturers, field calibration, or historical data fitting. It includes the identification code of each detector, precise physical installation location information, calibrated power generation consumption characteristic parameters, and calibrated power generation voltage response characteristic parameters. The data comes from the gas kinetic energy power generation detector.

[0032] The kinetic energy consumption chain can be constructed based on physical laws (such as the conservation of energy), describing the gradual and nonlinear decay of gas kinetic energy as it flows through the pipeline network due to power generation consumption by various detectors and pipeline friction.

[0033] The voltage decay chain of power generation can be constructed based on the principles of circuits and fluid dynamics. It is a cascaded network physical model that describes the coupling attenuation of voltage signals generated by each detector along the pipeline topology during transmission due to factors such as flow resistance in the pipeline section.

[0034] A pipeline topology map can be a visualized or data-driven topology model built based on the actual laying structure, node distribution, and pipeline connection relationships of a gas pipeline network. It is used to clearly present the overall layout of the pipeline network and the relationships between each node.

[0035] The pipeline network dual-chain characteristic information set can be a digital information set that includes the two core physical law models of the energy consumption chain and the power generation voltage decay chain.

[0036] Specifically, in the field of industrial gas pipeline leak monitoring, the core challenge of accurate diagnosis lies in identifying genuine abnormal losses from complex operating noise and legitimate process fluctuations. Traditional methods often rely on isolated pressure or flow sensors for threshold alarms, lacking a holistic understanding of the pipeline network as a "system," and failing to distinguish whether a drop in sensor readings is due to increased downstream gas consumption, pipeline blockage, or a leak. This leads to high false alarm or false negative rates, resulting in low reliability of the monitoring system. To address this fundamental problem, a gas kinetic energy generator detector has been creatively introduced as a sensing unit. Its unique advantage lies in its ability to directly and in-situ convert the gas flow state (kinetic energy) inside the pipeline into observable electrical signals (power generation and voltage). The necessity of this step lies in the fact that it establishes a "physical digital foundation" for the entire intelligent diagnostic system.

[0037] S202. Based on the pipeline network topology map, the pipeline network dual-chain characteristic information set, and the real-time operation status data stream and original metering data stream transmitted by the detector, a collaborative compensation analysis of the kinetic energy consumption chain and the state disturbance chain is performed to generate a real flow information set of multiple nodes in the pipeline network.

[0038] The operating status data stream can be the raw voltage timing signal data stream that the detector collects and uploads in real time, mainly reflecting its electrical operating status.

[0039] The raw metering data stream can be a time-series data stream of raw power generation that is measured and uploaded in real time by the detector and reflects its power generation capacity.

[0040] Collaborative compensation analysis can be a data fusion and correction algorithm that uses both the energy conservation chain model and the signal attenuation chain model to cross-validate and compensate for systematic errors and random disturbances in the observed data.

[0041] The true flow information set can be a high-confidence estimated data set that approximates the actual gas flow at each node of the pipeline network as closely as possible after collaborative compensation analysis.

[0042] Specifically, although the first step establishes an ideal physical model, in actual industrial settings, the observation data (power generation and voltage) from the detectors are severely contaminated. The power generation readings in the raw metering data stream may be inaccurate due to generator efficiency fluctuations and measurement errors; the voltage signal in the operating status data stream is even more susceptible to multi-dimensional interference from grid coupling noise, electromagnetic transient disturbances, and sensor temperature drift. Directly using this noisy raw data for leakage assessment will inevitably lead to erroneous conclusions. The necessity of this step lies in its design of a "dual-chain collaborative" intelligent data cleaning and correction mechanism, aiming to reconstruct a reliable system state from unreliable observations. Its core idea is to utilize the dual constraints provided by the "kinetic energy consumption chain" and the "power generation voltage decay chain," two independent physical perspectives, to iteratively correct the data.

[0043] S203. Based on the mapping rules between the real flow information set and the leakage tolerance threshold of the production process, perform gas usage pattern feature separation and abnormal loss diagnosis to generate a potential leakage event information set.

[0044] The leakage tolerance threshold for a production process can be a knowledge base or algorithm that defines the dynamic range of allowable gas flow fluctuations corresponding to different production processes and equipment start-up and shutdown phases, rather than a fixed value.

[0045] Gas consumption pattern feature separation can refer to separating the characteristics of normal gas consumption patterns by analyzing the flow change patterns of each node in the pipeline network under different time periods and operating conditions based on time series data of real flow information sets.

[0046] Abnormal loss diagnosis can refer to comparing real-time actual flow data with normal gas usage pattern characteristics and production process leakage tolerance thresholds after completing the separation of gas usage pattern characteristics, identifying abnormal flow conditions that exceed the normal range or tolerance thresholds, and determining whether the abnormality is a loss phenomenon caused by gas leakage.

[0047] The potential leak event information set can be a structured list of one or more abnormal events that have been initially identified as suspected leaks. Each event includes information such as its location (node), time, abnormal traffic volume, and the extent to which it exceeds the threshold.

[0048] Specifically, after obtaining real flow information from multiple nodes in the pipeline network, judging leaks solely by comparing single flow values ​​makes it difficult to distinguish between normal gas usage pattern changes and actual leaks causing flow anomalies. This is another key challenge faced by existing technologies. Gas demand in pipeline networks is influenced by various factors, including production processes, resulting in complex usage patterns. For example, industrial pipelines experience significant increases in gas consumption during peak production periods and substantial decreases during nighttime shutdowns; urban gas pipelines see peak consumption during mealtimes, with relatively stable flow rates at other times. These normal usage pattern changes lead to significant fluctuations in flow data. Without in-depth analysis of these patterns, they can easily be misjudged as leaks. To address these issues, a leak tolerance threshold for the production process is obtained. Combined with the pipeline network's operating conditions (such as production load, seasonal variations, and gas demand fluctuations), a mapping rule between real flow information and the tolerance threshold is established, clarifying the threshold adjustment standards and anomaly judgment criteria under different operating conditions. Through the mapping rule of the production process leak tolerance threshold, the judgment criteria can be dynamically adjusted according to the process requirements and gas medium characteristics of different pipeline networks, improving the method's versatility and adaptability.

[0049] S204. Based on the potential leak event information set, perform array collaborative evidence verification and energy consumption mutation analysis to generate and output a gas leak diagnosis report.

[0050] Array-based collaborative evidence verification can be a verification logic based on spatial correlation. It checks whether a potential leak event has reasonable propagation across the pipeline topology (e.g., the flow / pressure of all nodes downstream of the leak point should show correlation anomalies), rather than an isolated sensor failure.

[0051] Energy consumption mutation analysis can be a time series-based deep analysis that examines whether the power generation (corresponding to gas kinetic energy) of the associated nodes has undergone a mutation pattern (such as instantaneous drop or continuous slow drop) that conforms to the physical characteristics of leakage when an event occurs.

[0052] A gas leak diagnostic report can be a structured output document that includes the confirmed location of the leak, an assessment of the severity of the leak, a confidence level, possible causes, and recommendations for maintenance priorities.

[0053] Specifically, events within the potential leak event information set still require final confirmation to rule out false positives caused by a few extreme process scenarios or sensor common-mode failures. This step is necessary because it simulates the multi-dimensional comprehensive judgment process used by experts in making final decisions. By introducing array-based collaborative evidence verification and energy consumption mutation analysis, each potential event is assigned "credibility." Array-based collaborative evidence verification utilizes the pipeline topology, requiring that a real leak event must trigger a series of chain reactions in space, forming a chain of evidence. For example, the power generation of the detector upstream of the leak point may not change significantly, while the power generation of its downstream adjacent detector should decrease significantly, and this decrease should propagate along the flow direction. If only one node alarms without any related responses from upstream and downstream, it may be determined to be a fault of the node's own instrument. Energy consumption mutation analysis examines the shape of the power curve from a time dimension; real leaks usually correspond to specific mutation patterns. Combining the strength of evidence from both spatiotemporal perspectives, the system comprehensively scores and finally confirms each potential event.

[0054] The method provided in this embodiment first utilizes a gas kinetic energy generator detector to collect physical data from multiple nodes in the pipeline network, constructing a digital twin-based kinetic energy consumption chain and voltage decay chain to form a benchmark model characterizing the inherent physical laws of the system. Subsequently, through dual-chain collaborative compensation analysis, real-time operating data is integrated to eliminate measurement errors and environmental interference in the digital space, accurately reproducing the actual gas flow rate at each node of the pipeline network. Based on this, a dynamic leakage tolerance threshold linked to the production process is introduced to intelligently separate normal gas consumption fluctuations and preliminarily identify potential leakage events. Finally, by performing multi-node spatial correlation verification and time-series energy mutation analysis on potential events, a comprehensive gas leakage diagnosis report with accurate location, classification, and attribution is generated and output to factory personnel. This solution significantly improves the accuracy of leak diagnosis, effectively reducing the rate of false positives and false negatives; it enables real-time dynamic monitoring, quickly capturing potential leaks and providing a window of opportunity for timely response; it adapts to complex pipeline environments and can flexibly match different process and media requirements; it reduces manual intervention, lowers operation and maintenance costs, and improves pipeline management efficiency; it can prevent safety accidents caused by leaks, reduce gas waste, and balance safety benefits with energy-saving value, thus contributing to the refined operation of pipeline networks.

[0055] In some embodiments, the detector basic dataset includes the identification code, installation location information, calibrated power generation consumption characteristic parameters, and calibrated power generation voltage response characteristic parameters of each detector; based on the installation location information of all detectors and combined with the flow direction rules of fluid in the pipeline network, a pipeline network topology map characterizing the upstream and downstream connection relationships between detectors is constructed; based on the pipeline network topology map and the power generation consumption characteristic parameters of all detectors, a kinetic energy consumption chain describing the law of gradual consumption of gas kinetic energy as it is transferred along the pipeline network is constructed; based on the pipeline network topology map and the power generation voltage response characteristic parameters of all detectors, a power generation voltage decay chain describing the law of gradual decay of the detector's power generation voltage along the pipeline network flow direction is constructed; and based on the kinetic energy consumption chain and the power generation voltage decay chain, a pipeline network dual-chain characteristic information set is constructed.

[0056] The power generation consumption characteristic parameters can be determined under standard laboratory conditions or during on-site calibration, reflecting the energy loss pattern when the detector converts gas kinetic energy into electrical energy. The power generation voltage response characteristic parameters can be calibrated under standard conditions, characterizing the relationship between gas kinetic energy and the detector's output voltage. The power generation voltage attenuation chain can be a chain-like data model describing the gradual attenuation of the detector's output voltage along the pipeline network due to factors such as pipeline pressure loss and signal transmission interference.

[0057] Specifically, traditional gas leak diagnosis technology has a core flaw: it fails to establish a precise correlation between the detection equipment and the pipeline structure, treats the data collected by detectors at different locations as equal, ignores the inherent resistance differences of different pipe sections, the differences in power generation consumption characteristics of individual detectors, and the natural attenuation law of voltage along the pipeline, resulting in the inability to distinguish between "inherent losses of the pipeline" and "abnormal losses from leaks". This often leads to the problem of misjudging normal kinetic energy losses at remote nodes as leaks, or missing or misjudging minor leak signals due to inherent errors. To address the above issues, this step first involves comprehensively collecting the basic dataset of the detectors, including the unique identification code of each detector (e.g., SN20240501), installation location information (e.g., pipeline node N3, geographical coordinates X30.123°Y120.456°, the DN150 pipe segment to which it belongs, and upstream and downstream nodes N2 and N4 respectively), calibrated power generation consumption characteristic parameters (e.g., kinetic energy conversion loss rate 15%, pipeline local resistance coefficient 0.08), and calibrated power generation voltage response characteristic parameters (e.g., kinetic energy-voltage conversion coefficient 0.3V / (kJ·s)). -1 Based on all installation location information and the gas flow rules from the gas source to the user, each detector node is connected with arrowed line segments, and the pipe length (e.g., 500m) and pipe diameter are labeled to construct a pipeline topology map representing the upstream and downstream connection relationship. Subsequently, based on this map and power generation consumption characteristic parameters, with the total gas input kinetic energy (e.g., 120kJ) at the inlet of the first detector as the energy benchmark, each detector is modeled as a nonlinear loss node, and the kinetic energy consumption is iteratively analyzed along the upstream and downstream sequence to generate a kinetic energy consumption chain. At the same time, based on the map and the upstream and downstream sequence, the detector is equivalent to a voltage source excited by the gas state (e.g., initial output voltage 18V), and the flow resistance transfer coefficient (e.g., 0.75) is introduced into the adjacent node pipe segments to construct a cascaded network model in series. The voltage decay is iteratively analyzed along the flow direction to generate a power generation voltage decay chain. Finally, the core characteristic parameters of the two chains are integrated to form a structured pipeline network dual-chain characteristic information set.

[0058] The method provided in this embodiment clearly presents the spatial distribution and connection relationship of the detectors through the pipeline network topology map, which solves the problem of the disconnect between the detection data and the pipeline network structure in traditional methods. This enables subsequent data analysis to be carried out accurately in combination with the characteristics of the pipeline network, and greatly improves the pertinence of data processing.

[0059] In some embodiments, based on the pipeline network topology map, the upstream and downstream sequence of the detectors in the pipeline network is determined, and the total input kinetic energy of the gas is initialized as the energy benchmark, starting from the air inlet of the first detector. According to the power generation consumption characteristic parameters, each detector is modeled as a kinetic energy conversion node with nonlinear loss characteristics. The residual gas kinetic energy output by this node is the input kinetic energy minus the specific proportion of kinetic energy consumed in power generation and the fixed loss caused by the local resistance of the pipeline. Starting from the energy benchmark, nonlinear loss analysis is iteratively performed on each detector node along the upstream and downstream sequence to generate a digital twin chain model describing the step-by-step, nonlinear dissipation of gas kinetic energy during the flow of the entire detector network, as the kinetic energy consumption chain.

[0060] The upstream and downstream sequence can be the order of detector nodes determined by the gas flow direction in the pipeline topology map, i.e., the sequential distribution order of detectors as the gas flows from the gas source to the user. The energy benchmark can be a set standard value of the total input kinetic energy of the gas at the inlet of the first detector, serving as the initial calculation benchmark for the entire kinetic energy consumption chain. Nonlinear loss characteristics refer to the non-proportional variation of gas kinetic energy loss during transmission with factors such as gas flow rate, pipe resistance, and detector energy consumption; that is, the loss rate dynamically adjusts with changing operating conditions. The kinetic energy conversion node can be a functional model node abstracted from a single gas kinetic energy generator detector, used to simulate the conversion and loss process of gas kinetic energy by the detector. A specific proportion of kinetic energy can be the kinetic energy consumed by the detector at a fixed proportion when converting gas kinetic energy into electrical energy; this proportion is determined by the power generation consumption characteristic parameters. Local pipeline resistance can be the force hindering gas flow caused by factors such as pipe material, pipe diameter changes, and interface structure when the gas flows through the pipe section where the detector is located. A digital twin chain model can be a chain data model that highly simulates the actual pipeline kinetic energy transfer process, and can map the step-by-step loss state of gas kinetic energy along the pipeline in real time.

[0061] Specifically, traditional gas leak diagnosis technology has a key flaw in quantifying kinetic energy loss: it generally treats gas kinetic energy loss as a linear relationship and uses a global average loss rate for overall estimation. This not only severs the synergistic effect between the power generation consumption of the detector and the local resistance loss of the pipe section, but also ignores the differences in loss patterns under different pipe sections and different operating conditions. Furthermore, it lacks a unified energy calculation benchmark, resulting in an overestimation of kinetic energy at near-end nodes and an underestimation at far-end nodes. It is unable to distinguish between inherent losses and leak anomalies, thus causing deviations in flow calculation and misjudgments or omissions in leak diagnosis. To address the above issues, this step first uses the existing pipeline topology map (e.g., including upstream and downstream connections from node A to node F) to identify the upstream and downstream sequence of the detectors according to the gas flow direction from the gas source to the user (e.g., node A → node B → node C → node D → node E → node F). Starting from the inlet of node A, the total gas input kinetic energy is set as the energy benchmark (e.g., 60 kJ / s), which can be taken from the pipeline design rated parameters or on-site measured calibration values. Based on the calibration power generation consumption characteristic parameters of each detector (e.g., 11% of the input kinetic energy is consumed at low flow rates and 23% at high flow rates at node B) and the corresponding local resistance parameters of the pipe section, each detector is abstracted as having nonlinear losses. The characteristic kinetic energy conversion node outputs residual gas kinetic energy = input kinetic energy - specific proportion of kinetic energy consumed for power generation - fixed loss due to local resistance in the pipeline (e.g., the fixed loss of the pipe segment where node C is located is 3 kJ / s). Starting from the energy benchmark, nonlinear loss analysis is iteratively performed on each node along the upstream and downstream sequence. First, the input kinetic energy of node A (60 kJ / s) is calculated, and its proportion of kinetic energy consumed for power generation and fixed loss are subtracted to obtain the output residual kinetic energy (e.g., 52 kJ / s). Then, this value is used as the input kinetic energy of node B, and the loss calculation process is repeated to derive the input, loss, and output values ​​of all nodes in sequence. Finally, a digital twin chain model describing the stepwise, nonlinear dissipation of gas kinetic energy is generated, i.e., the kinetic energy consumption chain.

[0062] The method provided in this embodiment determines the upstream and downstream sequence based on the pipeline network topology, sets a unified energy benchmark, and models the detector as a kinetic energy conversion node with nonlinear loss. This overcomes the shortcomings of traditional technologies, such as linear assumptions, isolated analysis, and lack of benchmarks. It accurately quantifies the stepwise nonlinear dissipation law of kinetic energy along the pipeline network, providing a reliable benchmark for subsequent flow compensation analysis based on kinetic energy conservation, and fundamentally improving the accuracy of flow calculation and leak diagnosis.

[0063] In some embodiments, based on the pipeline network topology and upstream and downstream sequences, the power generation process of each detector is equivalent to an equivalent voltage source excited by the inlet gas state, and its output voltage is described by a nonlinear function defined by the power generation voltage response characteristic parameter. Taking the equivalent voltage source as the basic node, according to the connection relationship between nodes in the pipeline network topology, a flow resistance transfer coefficient characterizing the pressure and kinetic energy loss during the gas flow process in the pipeline is introduced on the pipe segment between two adjacent basic nodes. The equivalent voltage source and the flow resistance transfer coefficient are connected in series to construct a cascaded network model from the beginning to the end of the pipeline network. In the cascaded network model, the output voltage of the upstream detector is attenuated after flowing through the pipe segment characterized by the flow resistance transfer coefficient and serves as the equivalent inlet excitation of the downstream adjacent detector. Starting from the initial voltage state of the detector at the beginning of the pipeline network, the flow resistance attenuation and voltage response are analyzed sequentially along the upstream and downstream sequences. Through iterative transmission, a physical information network describing the step-by-step and coupled attenuation of the power generation voltage signal along the pipeline network topology is generated as the power generation voltage attenuation chain.

[0064] An equivalent voltage source can be a functional model that abstracts the power generation process of the detector. Its output voltage reflects the conversion relationship between gas kinetic energy and electrical energy, and is excited and regulated by the inlet gas state (such as kinetic energy and pressure). The power generation voltage response characteristic parameters can be parameters calibrated under standard operating conditions, characterizing the correspondence between gas kinetic energy and the detector's output voltage, including the kinetic energy-voltage conversion coefficient, response sensitivity, and voltage output saturation threshold. The nonlinear function can be a mathematical expression describing the non-proportional correspondence between the equivalent voltage source's output voltage and the inlet gas state; its parameters are determined by the power generation voltage response characteristic parameters. The flow resistance transfer coefficient can be a quantitative parameter characterizing the influence of pressure loss and kinetic energy loss on the voltage signal during gas flow within the pipe section, reflecting the attenuation law of voltage transmission along the pipe section. The cascaded network model can be a chain network structure formed by connecting all equivalent voltage sources according to the pipe network topology and cascading them through the flow resistance transfer coefficient, used to simulate the transmission and attenuation process of the voltage signal along the pipe network. The equivalent inlet excitation can be the input excitation signal of the upstream voltage signal, after attenuation through the pipe section, acting on the downstream adjacent detector (equivalent voltage source), thus determining the output voltage level of the downstream detector. Flow resistance attenuation can be the amplitude reduction effect of the voltage signal caused by the flow resistance of the pipe section. Voltage response can be the process by which the equivalent voltage source outputs the corresponding voltage based on the inlet excitation signal. The physical information network can be a digital network model that integrates the physical characteristics of the pipeline network (pipeline flow resistance, node connections) with the voltage signal transmission law, capable of accurately mapping the actual voltage attenuation process.

[0065] Specifically, traditional gas leak diagnosis techniques have key shortcomings in voltage signal analysis: they view the output voltage of each detector in isolation, failing to correlate it with the pipeline topology, ignoring the impact of pressure and kinetic energy losses caused by pipe flow resistance on voltage attenuation, and often using linear assumptions to describe voltage transmission, which is inconsistent with the nonlinear response characteristics of detector power generation. Furthermore, they fail to consider the coupling effect of upstream voltage attenuation on downstream inlet excitation, resulting in voltage data that cannot accurately reflect gas kinetic energy, leading to systematic errors such as underestimation of voltage at distant nodes and overestimation at near nodes, directly affecting the accuracy of flow rate estimation. To address these issues, this step, based on the constructed pipeline topology and the determined upstream and downstream sequence (e.g., node 1 → node 2 → node 3 → node 4), equates the power generation process of each detector to an equivalent voltage source excited by the inlet gas state, referencing its calibrated power generation voltage response characteristic parameters (e.g., kinetic energy-voltage conversion coefficient 0.35V / (kJ·s)). -1 The output voltage is described using a nonlinear function (e.g., the voltage increases exponentially in the low kinetic energy range and tends to saturate in the high kinetic energy range). Using an equivalent voltage source as the basic node, and based on the node connection relationship, a flow resistance transfer coefficient (e.g., 0.78) characterizing pressure and kinetic energy loss is introduced into the pipe section between adjacent nodes (e.g., the DN180 pipe section between node 2 and node 3). The equivalent voltage source and the flow resistance transfer coefficient are connected in series to construct a cascaded network model from the beginning to the end of the pipeline. The initial voltage state of the detector at the beginning of the pipeline is set (e.g., 22V). When the output voltage of the upstream node flows through the pipe section, it is attenuated according to the flow resistance transfer coefficient (e.g., 22V × 0.78 = 17.16V). After attenuation, it is used as the equivalent inlet excitation of the downstream node. The flow resistance attenuation and voltage response are iteratively analyzed along the upstream and downstream sequences. The input excitation and output voltage of nodes 2, 3, and 4 are calculated sequentially. Finally, a physical information network describing the step-by-step, coupled attenuation of the generated voltage signal is generated, i.e., the generated voltage attenuation chain.

[0066] The method provided in this embodiment, by equating the detector to a nonlinear equivalent voltage source, introduces the flow resistance transfer coefficient to construct a cascaded network, iteratively analyzes the flow resistance attenuation and voltage response, accurately captures the step-by-step coupling attenuation law of voltage along the pipeline network, provides a reliable inherent voltage attenuation benchmark for subsequent state disturbance analysis and flow compensation, and ensures data authenticity and diagnostic accuracy.

[0067] In some embodiments, based on the kinetic energy consumption chain and the original metering data stream, kinetic energy conservation analysis and compensation are performed on each detector node in the pipeline network to generate a node energy flow estimation sequence; based on the power generation voltage decay chain and the operating status data stream, state interference analysis and compensation are performed on each detector node in the pipeline network to generate a link state flow estimation sequence; the node energy flow estimation sequence and the link state flow estimation sequence are spatiotemporally aligned and evidence fused, and the systematic error of single-chain analysis is eliminated through collaborative compensation to generate a real flow information set of multiple nodes in the pipeline network.

[0068] Kinetic energy conservation analysis and compensation is an analytical method based on the law of energy conservation, combined with the inherent loss law of the kinetic energy consumption chain, to correct errors in the kinetic energy-related data in the original metering data stream. Its purpose is to eliminate systematic errors caused by inherent kinetic energy losses in the pipeline network. The node energy flow estimation sequence, obtained after kinetic energy conservation analysis and compensation, is a continuous data sequence characterizing the energy input, conversion, loss, and output of each detector node. It reflects the energy flow law at the node level and can then be used to infer the flow status. State disturbance analysis and compensation is an analytical method based on the inherent attenuation law of the generator voltage decay chain, identifying interference factors (such as electromagnetic interference, temperature drift, grid noise, etc.) in the operating state data stream and correcting voltage-related data. Its purpose is to eliminate random errors caused by external disturbances. The link state flow estimation sequence, obtained after state disturbance analysis and compensation, is a continuous data sequence characterizing voltage transmission, state changes, and corresponding flow correlations at the pipeline network link level. Collaborative compensation to eliminate single-chain analysis involves a comprehensive, multi-level comparison, correlation, and integrated analysis of the "theoretical characteristic information" output from the "dual-chain" model and the "actual characteristic information" obtained from the field.

[0069] Specifically, traditional flow calculation technology relies on a single data source. Relying solely on kinetic energy data is easily affected by external interference, and relying solely on voltage data makes it difficult to eliminate system errors caused by inherent losses in the pipeline network. Furthermore, it lacks multi-dimensional data collaborative analysis and cannot utilize data complementarity to correct errors, resulting in distorted flow data and directly causing misjudgments or missed diagnoses of leaks. To address the above issues, this step, based on the kinetic energy consumption chain, uses the calibrated power generation consumption characteristic parameters of each node (e.g., kinetic energy conversion loss rate of 15%) and the fixed loss of local pipeline resistance (e.g., 3 kJ / s) as constraints to establish an energy balance equation for each detector. The real-time power generation in the original metering data stream (e.g., 20 kW) is used as the observed value of the energy consumption term. The theoretical flow sequence of each node is obtained by simultaneously solving these equations along the beginning of the pipeline network. A chain-like collaborative compensation factor based on energy residuals is introduced, and after iterative correction, a node energy flow estimation sequence is generated. Based on the power generation voltage attenuation chain, mixed interference modes such as grid coupling noise in the operating status data stream are extracted. Structural interference is filtered out using a voltage-flow resistance transmission benchmark model, and random interference is corrected by matching with a pre-trained interference feature fingerprint database. A link state flow estimation sequence is then generated through reverse recursion. The two sequences are spatiotemporally aligned according to the same timestamp and the same node, and weights are assigned according to credibility (e.g., 0.6 and 0.4) for evidence fusion to eliminate single-chain errors and generate a set of real flow information for multiple nodes in the pipeline network.

[0070] The method provided in this embodiment eliminates systematic errors by compensating for the kinetic energy conservation of the energy consumption chain and the original metering data; it also eliminates random errors by compensating for the state interference of the power generation voltage decay chain and the operating status data; and finally eliminates single-link systematic errors through spatiotemporal alignment and evidence fusion, thereby obtaining accurate and true flow data, laying a core foundation for subsequent abnormal loss diagnosis.

[0071] In some embodiments, the power generation consumption characteristic parameters calibrated by each detector node in the kinetic energy consumption chain and the local resistance loss of the pipeline are used as known constraints. An energy balance equation is established for each detector node, where the input variable is the residual gas kinetic energy output by the upstream node, and the output variables are the power generation energy consumption, pipeline loss, and residual kinetic energy output downstream of this node. The real-time power generation in the original metering data stream is used as the observed value of the energy consumption term in the equation, and the equation is solved simultaneously from the beginning along the pipeline topology to obtain the node theoretical flow sequence. To address the imbalance of energy equations between adjacent basic nodes, a chain-like collaborative compensation factor based on the energy residual between nodes is introduced, and the node theoretical flow sequence is iteratively corrected through a backpropagation algorithm. With the goal of minimizing the corrected global energy balance residual, the final node energy flow estimation sequence is output.

[0072] The energy balance equation can be a mathematical equation describing the relationship between energy input, conversion, loss, and output of a single detector node, with the core condition being "Input kinetic energy = Power generation energy consumption + Pipeline local resistance loss + Downstream output residual kinetic energy". The observed value of the energy consumption term can be measured data used to verify and correct the energy balance equation, i.e., real-time power generation in the original metering data stream. The node theoretical flow sequence can be a preliminary flow data sequence obtained by simultaneously solving the energy balance equations of each node, before compensation and correction, reflecting the theoretical estimation result of the node flow. The imbalance in the energy equations between adjacent basic nodes can refer to the phenomenon that the residual kinetic energy output by the previous node is not equal to the input kinetic energy of the next node, caused by modeling errors, parameter calibration deviations, etc. The energy residual between nodes can be the difference between the energy input and output of adjacent nodes, quantitatively representing the degree of energy imbalance. The chain-like collaborative compensation factor can be a dynamic correction parameter designed based on the energy residual between nodes, used to adjust the energy distribution relationship of each node in reverse to achieve global energy balance. The backpropagation algorithm can be a modified algorithm that iterates backward from the end of the pipeline network to the beginning, adjusting the parameters of the node energy balance equation by distributing the energy residual to each preceding node. The global energy balance residual can be the sum of the energy residuals of all nodes in the entire pipeline network, characterizing the degree of balance of energy flow throughout the entire link.

[0073] Specifically, traditional node energy flow calculation techniques have key flaws: they model only isolated nodes, ignoring the energy transfer correlation between nodes caused by the chain-like characteristics of the pipeline network, allowing deviations from a single node to accumulate and amplify along the link; they lack a full-link collaborative compensation mechanism, and local adjustments can easily introduce new deviations; the simultaneous solution order is chaotic, and calibration parameters are treated as fixed values, resulting in a disconnect from actual operating conditions, leading to energy imbalances between nodes and significant deviations in the theoretical flow sequence. To address these issues, this step relies on the kinetic energy consumption chain, extracting the power generation consumption characteristic parameters calibrated at each detector node (e.g., 13% kinetic energy conversion loss rate) and the fixed loss of local pipeline resistance (e.g., 2.9 kJ / s) as known constraints. An energy balance equation is established for each node, specifying the input as the upstream residual kinetic energy and the output as power generation energy consumption, pipeline losses, and downstream output kinetic energy. Real-time power generation (e.g., 19 kW) is extracted from the original metering data stream as the energy consumption observation value. Simultaneous equations are solved along the pipeline topology (e.g., node 1 → node 2 → node 3) to obtain the node's theoretical flow sequence. For adjacent nodes... Energy residuals (e.g., 0.7 kJ / s) are introduced using a chain-like collaborative compensation factor. This factor is iteratively corrected using a backpropagation algorithm until the global energy balance residual is minimized, generating a node energy flow estimation sequence. Based on the generation voltage attenuation chain, wavelet analysis is used to extract mixed interference modes such as grid coupling noise (e.g., 50 Hz harmonics) and electromagnetic transient disturbances from the operating state data stream. A voltage-current resistance transmission benchmark model is constructed, and pseudo-attenuation components (e.g., 0.4 V) are simulated and inversely removed. Random interference is then corrected by matching with a pre-trained interference feature fingerprint database. The corrected voltage is substituted into the attenuation chain for inverse recursion, generating a link state flow estimation sequence. Spatiotemporal alignment with nodes is achieved using the same timestamp. Fusion weights of 0.6 and 0.4 are assigned based on data stability to eliminate single-chain systematic errors, ultimately generating a set of real flow information for multiple nodes in the pipeline network.

[0074] The method provided in this embodiment ensures the physical authenticity of the energy balance equation by using the calibration parameters of the kinetic energy consumption chain and pipeline resistance loss as constraints; it solves the equations sequentially along the beginning of the pipeline network to match the gas flow path; it introduces the energy residual between nodes and the chain-like collaborative compensation factor, and achieves dynamic correction of the entire link through the backpropagation algorithm. With the goal of minimizing the global energy balance residual, it accurately outputs the node energy flow sequence, providing reliable node data for dual-link fusion.

[0075] In some embodiments, multi-dimensional interference features are extracted from the original voltage time-series signal in the operating status data stream. The interference features include at least a mixed interference mode formed by grid coupling noise, electromagnetic transient disturbances, and sensor temperature drift. Based on the generation voltage attenuation chain, a voltage-current resistance transmission benchmark model for each pipe segment under an ideal state without interference is constructed. The extracted mixed interference mode is input into the voltage-current resistance transmission benchmark model, and the corresponding pseudo-attenuation component is generated through forward simulation. The pseudo-attenuation component is then removed from the original voltage signal in reverse to achieve the initial filtering of structural interference. For the unstructured random interference remaining after the initial filtering, a pre-trained interference feature fingerprint database is introduced for matching and identification. An associated adaptive compensation operator is used to specifically correct the voltage observation values ​​of the affected nodes. The filtered and corrected node voltage states are substituted into the generation voltage attenuation chain for reverse recursive calculation. By constraining the physical consistency of the link voltage attenuation, the link state flow estimation sequence is iteratively optimized and output.

[0076] The raw voltage timing signal can be a continuously changing voltage acquisition signal from the operating status data stream without any interference processing, directly reflecting the generator voltage output of the detector. Multi-dimensional interference feature extraction involves using signal processing techniques to separate the characteristic parameters (such as frequency, amplitude, duration, and variation patterns) of different types of interference signals from the raw voltage timing signal. Grid coupling noise can be periodic, low-amplitude interference in the raw voltage timing signal caused by grid voltage fluctuations coupled into the detector circuitry through the power supply line. Electromagnetic transient disturbances can be instantaneous, high-amplitude electromagnetic interference caused by the start-up and shutdown of surrounding electrical equipment, wireless signal radiation, etc., causing sudden fluctuations in the raw voltage timing signal. Sensor temperature drift can be the performance drift of the detector sensor due to changes in ambient temperature, reflected in the voltage signal as a slow, gradual deviation. The generator voltage attenuation chain can be a physical information network describing the step-by-step, coupled attenuation of the generator voltage signal along the pipeline topology, including core parameters such as the voltage-current resistance transmission law of pipe sections and the voltage response characteristics of nodes. The voltage-current resistance transmission benchmark model can be an ideal model simulating the transmission and attenuation of voltage signals along pipeline segments without any external interference, based on the generation voltage attenuation chain. The pseudo-attenuation component can be a false voltage attenuation signal corresponding to the interference, generated through forward simulation after inputting a mixed interference mode into the voltage-current resistance transmission benchmark model; its variation pattern is consistent with the interference mode. Unstructured random interference can be sudden and irregular interference without a fixed pattern (such as random electromagnetic transient disturbances), which cannot be completely replicated through model simulation and requires correction through feature matching. The pre-trained interference feature fingerprint database can be a database containing various unstructured random interference feature templates, built based on a large amount of historical interference data and experimental simulated interference data. Iterative optimization can be a process of repeatedly verifying the physical consistency of the voltage signal through backward recursive calculation; if deviations exist, the adaptive compensation operator parameters are adjusted or the voltage value is corrected until the consistency requirements are met.

[0077] Specifically, traditional voltage signal interference processing techniques have key flaws: they use a single filtering algorithm to treat all interference indiscriminately, failing to distinguish the essential differences between structural interference (such as grid coupling noise and sensor temperature drift) and unstructured random interference (such as electromagnetic transient disturbances), resulting in incomplete interference removal; furthermore, they disregard the physical characteristics of the generation voltage attenuation chain, causing the filtered data to violate voltage transfer laws and lack physical consistency; and they lack a clear correspondence between interference and correction schemes, leading to blind correction that can result in over-adjustment or under-adjustment, ultimately distorting the link status data. To address these issues, this step uses Fourier transform and wavelet analysis to extract multi-dimensional interference features from the original voltage time-series signal in the operating status data stream, separating the mixed interference mode formed by grid coupling noise (such as 50Hz power frequency harmonics), electromagnetic transient disturbances (such as sudden 2.5V pulses), and sensor temperature drift (such as 0.1V shift per hour); based on the generation voltage attenuation chain, parameters such as the pipe segment current resistance transfer coefficient (such as 0.78) are extracted to construct an interference-free ideal state voltage-current resistance transfer benchmark model. The mixed interference mode is then input into the model for forward simulation to generate pseudo-attenuation. The component to be reduced (e.g., 0.3V) is removed from the original voltage signal in reverse order, completing the initial filtering of structural interference. For the remaining unstructured random interference, a pre-trained interference feature fingerprint database is called for matching and identification (e.g., matching strong electromagnetic pulse features). An associated adaptive compensation operator is used to correct the voltage observation value of the affected node (e.g., 6.8V) to a reasonable range (e.g., 5.2V). The corrected node voltage state is substituted into the generator voltage attenuation chain for reverse recursive calculation, verifying and constraining the physical consistency of the link voltage attenuation. After iterative optimization, the link state flow estimation sequence is output.

[0078] The method provided in this embodiment extracts interference features from multiple dimensions, filters out structural interference in layers, corrects unstructured interference, and then constrains physical consistency through reverse recursion to accurately output the link state flow sequence, providing high-quality link data for dual-link fusion and ensuring the accuracy of real traffic calculation.

[0079] In some embodiments, a process mode feature library is constructed, which includes dynamic gas consumption modes of different production processes. Each mode maps to a dynamic leakage tolerance threshold curve that changes over time or production stage, rather than a single fixed threshold. The actual flow rate information set is compared with the dynamic leakage tolerance threshold curve corresponding to the currently activated process mode in a spatiotemporal sliding manner to identify flow anomalies that exceed the instantaneous tolerance threshold, and their spatial location, timestamp, and exceedance magnitude are recorded. Multi-level threshold cross-validation and continuous analysis are performed on the flow anomalies to filter out legitimate fluctuations caused by brief process switching or start-up / shutdown and screen them as potential leakage events. Potential leakage events are integrated to generate a structured potential leakage event information set.

[0080] The process mode feature library can be a structured database that integrates the dynamic gas consumption patterns of different production processes. It includes gas consumption pattern characteristics (such as flow rate change cycles and fluctuation ranges) corresponding to various processes, along with associated dynamic leakage tolerance threshold curves. The dynamic leakage tolerance threshold curve can be a leakage allowance threshold curve that dynamically changes over time or production stages, rather than a fixed value. Its threshold value is dynamically adjusted according to the gas load and safety requirements of the corresponding process. Spatiotemporal sliding comparison is a method of comparing real-time flow data from a centralized real-time flow information database with the dynamic leakage tolerance threshold curve point by point, using a time sliding window (e.g., 1 minute / window) and spatial node dimensions. Flow anomalies are pipeline node data points where, after spatiotemporal sliding comparison, the real-time flow value exceeds the dynamic tolerance threshold at the corresponding time point. Legitimate fluctuations are flow fluctuations caused by non-leakage factors such as brief process switching, equipment start-up and shutdown, or sudden reasonable gas consumption demands. Their characteristics include short duration, conformity to process logic, and no breach of multi-level thresholds. Potential leakage events are abnormal events confirmed as suspected leaks after multi-level threshold cross-validation and continuous analysis, excluding legitimate fluctuations.

[0081] Specifically, traditional leak diagnosis technologies use a single, fixed leak tolerance threshold, ignoring the dynamic gas consumption characteristics of different production processes. This makes them unsuitable for adapting to variations in flow fluctuations at different stages and times. A threshold that is too high can mask minor leaks, while a threshold that is too low can misjudge legitimate fluctuations such as process switching and equipment start-up / shutdown as leaks. Furthermore, relying solely on flow rate values ​​at a single point in time lacks in-depth analysis and multi-level verification of gas consumption patterns, leading to high false positive and false negative rates. Additionally, scattered and unstructured abnormal information complicates subsequent verification. To address these issues, this step first constructs a process mode feature library containing dynamic gas consumption patterns for different production processes. For example, in continuous chemical production, the flow fluctuation cycle is 8 hours with a fluctuation range of ±10%, corresponding to a dynamic leak tolerance threshold curve of 5-8 m³ / h. For urban residential gas, the peak flow occurs during mealtimes with a fluctuation range of ±20%, corresponding to a threshold curve of 3-6 m³ / h. Real-time flow data for each node is extracted from the actual flow information set (e.g., node X's real-time flow is 7.5 m³ / h). The currently active process mode (e.g., the chemical production reaction stage) is determined through the production scheduling system, and the corresponding dynamic threshold curve is retrieved. The system uses a 1-minute sliding window to compare real-time flow with threshold curves in a spatiotemporal manner, identifying flow anomalies exceeding instantaneous thresholds (e.g., 6.8 m³ / h). It records the spatial location (node ​​X, coordinates X30.345°Y120.678°), timestamp, and the magnitude of the exceedance (0.7 m³ / h). The system performs multi-level threshold cross-validation on the anomalies, considering instantaneous, short-term cumulative, and medium-term trend data. Legitimate fluctuations lasting 10 seconds due to process switching are filtered out, identifying potential leakage events. Finally, the core information of these events is integrated to generate a structured set of potential leakage event information.

[0082] The method provided in this embodiment, based on the construction of a process mode feature library containing dynamic threshold curves, combined with spatiotemporal sliding comparison, multi-level verification and continuous analysis, accurately distinguishes between legitimate fluctuations and leakage anomalies, generates a structured potential leakage event information set, provides clear targets for subsequent verification, and ensures the accuracy and efficiency of diagnosis.

[0083] In some embodiments, for each event in the potential leak event information set, node geographic location tracing, upstream and downstream event correlation analysis, and multi-timescale leak intensity evolution assessment are performed to generate a multi-dimensional leak feature vector. The multi-dimensional leak feature vector is input into predefined leak mode classification and risk assessment rules to output its corresponding leak mode category, confidence score, and recommended handling priority. Based on the assessment results of all events, a gas leak diagnosis report containing leak point location, mode diagnosis, severity level, evolution trend, and handling recommendations is generated and output.

[0084] Multi-timescale leakage intensity evolution assessment can be an evaluation process that analyzes the intensity change trend of leakage events (e.g., continuous enhancement, gradual weakening, periodic fluctuations) at different time scales (e.g., instantaneous, short-term, medium-term). Multi-dimensional leakage feature vectors can be feature data vectors formed by integrating multi-dimensional parameters such as node geographical location, upstream and downstream correlation status, multi-timescale leakage intensity, threshold exceedance, and duration, used to comprehensively characterize the core attributes of leakage events. Leakage mode categories can be the specific leakage type to which a potential leakage event belongs, determined according to predefined classification rules. Credibility scoring can be based on the verification criteria of leakage events (e.g., number of upstream and downstream coordination anomalies, intensity evolution stability, feature vector matching degree), quantifying the reliability of the event as a real leakage (e.g., 0-100 points). Recommended handling priorities can be a sequence of operation and maintenance handling (e.g., Level 1, Level 2, Level 3) determined by combining leakage mode category, risk level, impact range, and pipeline importance.

[0085] Specifically, traditional gas leak diagnosis reports only list basic information about potential events in a scattered manner, lacking leak patterns, evolution trends and upstream and downstream correlation analysis, and there is no credibility score and handling priority. This makes it difficult for maintenance personnel to distinguish between real leaks and low-credibility events, which can easily lead to waste of resources or delays in handling high-risk leaks. At the same time, key information needs to be sorted out by themselves, which is inefficient. To address the above issues, this step extracts event data (such as event 2, node N8, exceeding the limit by 2.1 m³ / h) from the potential leak event information set, and completes the node's geographical location tracing based on the pipeline network GIS system (coordinates X30.567°Y120.890°). Combining the pipeline network topology map, it analyzes the real-time data of upstream and downstream related nodes (upstream N7, downstream N9), and finds a coordinated anomaly of a 1.5 m³ / h decrease in flow rate at downstream N9 during the same period. Based on the instantaneous, short-term, and medium-term time scales, it is determined that the leak intensity shows a "continuously stable" evolution trend, and integrates and generates a multi-dimensional leak feature vector. This vector is input into a predefined rule base and matched as "interface loosening leak," with a credibility score of 88 points, and a recommended priority of level two for handling. Finally, it integrates the location, pattern, risk, evolution trend, and handling recommendations of all events to generate and output a gas leak diagnosis report.

[0086] The method provided in this embodiment generates multi-dimensional feature vectors based on geographic location tracing, correlation analysis, and evolution assessment of potential events. Combined with predefined rules, it clarifies leakage patterns, credibility, and handling priorities, and finally outputs a structured report, transforming scattered information into an operation and maintenance action guide to ensure the accuracy and efficiency of handling.

[0087] Figure 3 This is a schematic diagram of the structure of an intelligent gas leak diagnosis system based on gas kinetic energy power generation according to an embodiment of this application, as shown below. Figure 3 As shown, the intelligent gas leak diagnosis system 300 based on gas kinetic energy power generation in this embodiment includes: a map and dual-chain construction module 301, a flow information analysis module 302, a leak event analysis module 303, and a leak report generation module 304.

[0088] The network topology and dual-chain construction module 301 is used to acquire the basic dataset of several gas kinetic energy power generation detectors, construct the kinetic energy consumption chain and the power generation voltage attenuation chain based on the basic dataset, and generate a pipeline network topology map and a pipeline network dual-chain characteristic information set. The flow information analysis module 302 is used to perform collaborative compensation analysis of the kinetic energy consumption chain and the state interference chain based on the pipeline network topology map, the pipeline network dual-chain characteristic information set, and the real-time operation status data stream and original metering data stream transmitted by the detectors, and generate a real flow information set of multiple nodes in the pipeline network. The leakage event analysis module 303 is used to perform gas consumption pattern feature separation and abnormal loss diagnosis based on the mapping rules between the real flow information set and the production process leakage tolerance threshold, and generate a potential leakage event information set. The leakage report generation module 304 is used to perform array collaborative evidence verification and energy consumption mutation analysis based on the potential leakage event information set, and generate and output a gas leakage diagnosis report.

[0089] Optionally, the mapping and dual-chain construction module 301, when generating the pipeline network topology map and pipeline network dual-chain characteristic information set, is specifically used for: the detector basic dataset including the identification code, installation location information, calibrated power generation consumption characteristic parameters, and calibrated power generation voltage response characteristic parameters of each detector; based on the installation location information of all detectors and combined with the flow direction rules of fluid in the pipeline network, constructing the pipeline network topology map characterizing the upstream and downstream connection relationship between detectors; based on the pipeline network topology map and the power generation consumption characteristic parameters of all detectors, constructing the kinetic energy consumption chain describing the law of gradual consumption of gas kinetic energy as it is transferred along the pipeline network; based on the pipeline network topology map and the power generation voltage response characteristic parameters of all detectors, constructing the power generation voltage decay chain describing the law of gradual decay of detector power generation voltage along the pipeline network flow direction; and constructing the pipeline network dual-chain characteristic information set according to the kinetic energy consumption chain and the power generation voltage decay chain.

[0090] Optionally, the map and dual-chain construction module 301, during the construction process based on the kinetic energy consumption chain, is specifically used for: determining the upstream and downstream sequence of the detectors in the pipeline network based on the pipeline network topology map, and initializing the total input kinetic energy of the gas as an energy benchmark, starting from the air inlet of the first detector; modeling each detector as a kinetic energy conversion node with nonlinear loss characteristics according to the power generation consumption characteristic parameters, wherein the residual gas kinetic energy output by this node is the input kinetic energy minus the specific proportion of kinetic energy consumed in its power generation and the fixed loss caused by the local resistance of the pipeline; starting from the energy benchmark, iteratively performing the nonlinear loss analysis on each detector node along the upstream and downstream sequence, thereby generating a digital twin chain model describing the step-by-step, nonlinear dissipation of gas kinetic energy during the flow through the entire detector network, as the kinetic energy consumption chain.

[0091] Optionally, the map and double-chain construction module 301, during the construction process based on the power generation voltage decay chain, is specifically used for: based on the pipeline topology map and the upstream and downstream sequences, equating the power generation process of each detector to an equivalent voltage source excited by the inlet gas state, the output voltage of which is described by a nonlinear function defined by the power generation voltage response characteristic parameters; using the equivalent voltage source as a basic node, and according to the connection relationship between nodes in the pipeline topology map, introducing a characteristic of pressure and kinetic energy loss during the gas flow process in the pipeline on the pipe segment between two adjacent basic nodes. A flow resistance transfer coefficient is used, and the equivalent voltage source is connected in series with the flow resistance transfer coefficient to construct a cascaded network model from the beginning to the end of the pipeline network. In the cascaded network model, the output voltage of the upstream detector is attenuated after flowing through the pipe segment represented by the flow resistance transfer coefficient, and serves as the equivalent inlet excitation for the downstream adjacent detector. Starting from the initial voltage state of the detector at the beginning of the pipeline network, the flow resistance attenuation and voltage response are analyzed sequentially along the upstream and downstream sequence. Through iterative transmission, a physical information network describing the step-by-step and coupled attenuation of the generated voltage signal along the pipeline network topology is generated, which serves as the generated voltage attenuation chain.

[0092] Optionally, when generating the real flow information set for multiple nodes in the pipeline network, the flow information analysis module 302 is specifically used for: performing kinetic energy conservation analysis and compensation on each detector node in the pipeline network based on the kinetic energy consumption chain and the original metering data stream, generating a node energy flow estimation sequence; performing state interference analysis and compensation on each detector node in the pipeline network based on the power generation voltage decay chain and the operating status data stream, generating a link state flow estimation sequence; performing spatiotemporal alignment and evidence fusion on the node energy flow estimation sequence and the link state flow estimation sequence, eliminating the systematic error of single-chain analysis through collaborative compensation, and generating the real flow information set for multiple nodes in the pipeline network.

[0093] Optionally, the flow information analysis module 302, when generating the node energy flow estimation sequence, specifically performs the following: using the power generation consumption characteristic parameters calibrated by each detector node in the kinetic energy consumption chain and the local resistance loss of the pipeline as known constraints, establishes an energy balance equation for each detector node, where the input variable is the residual gas kinetic energy output by the upstream node, and the output variables are the power generation energy consumption, pipeline loss, and residual kinetic energy output downstream of this node; using the real-time power generation in the original metering data stream as the observed value of the energy consumption term in the equation, and solving the equation simultaneously from the beginning along the pipeline topology map to obtain the node theoretical flow sequence; for the imbalance of energy equations between adjacent basic nodes, introducing a chain-like collaborative compensation factor based on the energy residual between nodes, and iteratively correcting the node theoretical flow sequence through a backpropagation algorithm; with the goal of minimizing the corrected global energy balance residual, outputting the final node energy flow estimation sequence.

[0094] Optionally, the flow information analysis module 302, when generating the link state flow estimation sequence, specifically performs the following: extracts multi-dimensional interference features from the original voltage time-series signal in the operating state data stream, wherein the interference features include at least a mixed interference mode formed by grid coupling noise, electromagnetic transient disturbances, and sensor temperature drift; based on the generation voltage attenuation chain, constructs a voltage-current resistance transmission benchmark model for each pipe segment under an ideal state without interference, and inputs the extracted mixed interference mode into the voltage-current resistance transmission benchmark model, generates corresponding pseudo-attenuation components through forward simulation, and removes the pseudo-attenuation components from the original voltage signal in reverse, thereby achieving primary filtering of structural interference; for the unstructured random interference remaining after primary filtering, introduces a pre-trained interference feature fingerprint database for matching and identification, and uses an associated adaptive compensation operator to specifically correct the voltage observation values ​​of the affected nodes; substitutes the filtered and corrected node voltage states into the generation voltage attenuation chain for reverse recursive calculation, and iteratively optimizes and outputs the link state flow estimation sequence by constraining the physical consistency of link voltage attenuation.

[0095] Optionally, the leakage event analysis module 303 is specifically used for: constructing a process mode feature library containing dynamic gas consumption modes of different production processes, where each mode maps to a dynamic leakage tolerance threshold curve that changes with time or production stage, rather than a single fixed threshold; performing a spatiotemporal sliding comparison between the real flow information set and the dynamic leakage tolerance threshold curve corresponding to the currently activated process mode to identify flow anomalies exceeding the instantaneous tolerance threshold, and recording their spatial location, timestamp, and exceedance magnitude; performing multi-level threshold cross-validation and persistence analysis on the flow anomalies to filter out legitimate fluctuations caused by brief process switching or start-up / shutdown, and selecting them as potential leakage events; and integrating the potential leakage events to generate a structured potential leakage event information set.

[0096] Optionally, the leak report generation module 304 is specifically used to: perform node geographic location tracing, upstream and downstream event correlation analysis, and multi-timescale leak intensity evolution assessment on each event in the potential leak event information set, generating a multi-dimensional leak feature vector; input the multi-dimensional leak feature vector into predefined leak mode classification and risk assessment rules, and output its corresponding leak mode category, credibility score, and recommended handling priority; based on the assessment results of all events, generate and output the gas leak diagnosis report containing leak point location, mode diagnosis, severity level, evolution trend, and handling recommendations.

[0097] The system in this embodiment can be used to execute the methods of any of the above embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

Claims

1. A smart gas leak diagnosis method based on gas kinetic energy power generation, characterized in that, include: Acquire the basic dataset of several gas kinetic energy power generation detectors, construct the kinetic energy consumption chain and the power generation voltage decay chain based on the basic dataset, and generate a pipeline network topology map and a pipeline network dual-chain characteristic information set; Based on the pipeline network topology map, the pipeline network dual-chain characteristic information set, and the real-time operation status data stream and original metering data stream transmitted back by the detector, a collaborative compensation analysis of the kinetic energy consumption chain and the state disturbance chain is performed to generate a real flow information set of multiple nodes in the pipeline network. Based on the mapping rules between the real flow information set and the leakage tolerance threshold of the production process, gas consumption pattern feature separation and abnormal loss diagnosis are performed to generate a potential leakage event information set. Based on the potential leak event information set, array collaborative evidence verification and energy consumption mutation analysis are performed to generate and output a gas leak diagnosis report.

2. The method according to claim 1, characterized in that, The generated pipeline network topology map and pipeline network double-chain characteristic information set include: The basic dataset of the detectors includes the identification code of each detector, installation location information, calibrated power generation consumption characteristic parameters, and calibrated power generation voltage response characteristic parameters. Based on the installation location information of all detectors and combined with the flow direction rules of fluid in the pipeline network, the pipeline network topology map characterizing the upstream and downstream connection relationship between detectors is constructed. Based on the pipeline topology map and the power generation consumption characteristic parameters of all detectors, the kinetic energy consumption chain is constructed to describe the law of gradual consumption of gas kinetic energy as it is transmitted along the pipeline. Based on the pipeline network topology map and the power generation voltage response characteristic parameters of all detectors, a power generation voltage decay chain is constructed to describe the gradual decay law of the power generation voltage of the detectors along the pipeline network flow direction. Based on the energy consumption chain and the power generation voltage decay chain, the pipeline network dual-chain characteristic information set is constructed.

3. The method according to claim 2, characterized in that, The construction process of the kinetic energy consumption chain includes: Based on the pipeline topology map, the upstream and downstream sequence of the detector in the pipeline is determined, and the total gas input kinetic energy is initialized as the energy reference, starting from the air inlet of the first detector. Based on the power generation consumption characteristic parameters, each detector is modeled as a kinetic energy conversion node with nonlinear loss characteristics. The residual gas kinetic energy output by this node is the input kinetic energy minus the specific proportion of kinetic energy consumed in its power generation and the fixed loss caused by the local resistance of the pipeline. Starting from the energy benchmark, the nonlinear loss analysis is iteratively performed on each detector node along the upstream and downstream sequence to generate a digital twin chain model describing the stepwise and nonlinear dissipation of gas kinetic energy as it flows through the entire detector network, which serves as the kinetic energy consumption chain.

4. The method according to claim 3, characterized in that, The process of constructing the power generation voltage decay chain includes: Based on the pipeline topology map and the upstream and downstream sequence, the power generation process of each detector is equivalent to an equivalent voltage source excited by the inlet gas state, and its output voltage is described by a nonlinear function defined by the power generation voltage response characteristic parameter. Using the equivalent voltage source as the basic node, and based on the connection relationship between nodes in the pipeline topology map, a flow resistance transfer coefficient that characterizes the pressure and kinetic energy loss during gas flow in the pipeline is introduced on the pipe segment between two adjacent basic nodes. The equivalent voltage source and the flow resistance transfer coefficient are connected in series to construct a cascaded network model from the beginning to the end of the pipeline. In the cascaded network model, the output voltage of the upstream detector is attenuated after flowing through the pipe segment characterized by the flow resistance transfer coefficient, and serves as the equivalent inlet excitation for the downstream adjacent detector. Starting from the initial voltage state of the detector at the beginning of the pipeline network, the flow resistance attenuation and voltage response are analyzed sequentially along the upstream and downstream sequence. Through iterative transmission, a physical information network describing the gradual and coupled attenuation of the power generation voltage signal along the pipeline network topology is generated, which serves as the power generation voltage attenuation chain.

5. The method according to claim 4, characterized in that, The generated set of real flow information for multiple nodes in the pipeline network includes: Based on the energy consumption chain and the original metering data stream, energy conservation analysis and compensation are performed on each detector node in the pipeline network to generate a node energy flow estimation sequence. Based on the power generation voltage attenuation chain and the operating status data stream, state interference analysis and compensation are performed on each detector node in the pipeline network to generate a link state flow estimation sequence. The node energy flow estimation sequence and the link state flow estimation sequence are spatiotemporally aligned and evidence fused. The systematic error of single-link analysis is eliminated through collaborative compensation, and a real flow information set of multiple nodes in the pipeline network is generated.

6. The method according to claim 5, characterized in that, The generated node energy flow estimation sequence includes: Using the power generation consumption characteristic parameters calibrated by each detector node in the energy consumption chain and the local resistance loss of the pipeline as known constraints, an energy balance equation is established for each detector node, where the input variable is the residual gas kinetic energy output by the upstream node, and the output variables are the power generation energy consumption, pipeline loss and residual kinetic energy output downstream of this node. The real-time power generation in the original metering data stream is used as the observed value of the energy consumption term in the equation, and the equations are solved simultaneously from the beginning along the pipeline topology to obtain the theoretical flow sequence of the nodes. To address the imbalance in the energy equations between adjacent basic nodes, a chain-like collaborative compensation factor based on the energy residuals between nodes is introduced, and the theoretical flow sequence of the nodes is iteratively corrected using a backpropagation algorithm. With the goal of minimizing the corrected global energy balance residual, the final nodal energy flow estimation sequence is output.

7. The method according to claim 5, characterized in that, The generated link state flow estimation sequence includes: Multi-dimensional interference features are extracted from the raw voltage timing signal in the operating status data stream. The interference features include at least a mixed interference mode formed by grid coupling noise, electromagnetic transient disturbances and sensor temperature drift. Based on the power generation voltage attenuation chain, a voltage-current resistance transmission benchmark model for each pipe segment under an ideal state without interference is constructed. The extracted mixed interference mode is input into the voltage-current resistance transmission benchmark model, and the corresponding pseudo attenuation component is generated through forward simulation. The pseudo attenuation component is then removed from the original voltage signal in reverse to achieve the primary filtering of structural interference. To address the unstructured random interference remaining after primary filtering, a pre-trained interference feature fingerprint database is introduced for matching and identification, and an associated adaptive compensation operator is used to specifically correct the voltage observations of the affected nodes. The filtered and corrected node voltage states are substituted into the generation voltage decay chain for reverse recursive calculation. By constraining the physical consistency of link voltage decay, the link state flow estimation sequence is iteratively optimized and output.

8. The method according to claim 7, characterized in that, The generation of the potential leakage event information set includes: Construct a process mode feature library containing dynamic gas consumption patterns for different production processes. Each mode is mapped to a dynamic leakage tolerance threshold curve that changes over time or production stage, rather than a single fixed threshold. The real flow information set is compared with the dynamic leakage tolerance threshold curve corresponding to the currently activated process mode in a time-space sliding manner to identify flow anomalies that exceed the instantaneous tolerance threshold and record their spatial location, timestamp, and exceedance range. Multi-level threshold cross-validation and continuous analysis are performed on the abnormal flow points to filter out legitimate fluctuations caused by brief process switching or start-up / shutdown and screen them as potential leakage events. The potential leakage events are integrated to generate a structured set of information on the potential leakage events.

9. The method according to claim 8, characterized in that, The generation and output of the gas leak diagnostic report includes: For each event in the potential leakage event information set, perform node geographical location tracing, upstream and downstream event correlation analysis, and multi-timescale leakage intensity evolution assessment to generate a multi-dimensional leakage feature vector; The multi-dimensional leakage feature vector is input into the predefined leakage mode classification and risk assessment rules, and the leakage mode category, credibility score and suggested handling priority are output. Based on the assessment results of all events, a gas leak diagnostic report is generated and output, which includes leak location, pattern diagnosis, severity level, evolution trend and handling recommendations.

10. An intelligent gas leak diagnosis system based on gas kinetic energy power generation, characterized in that, The method applied to any one of claims 1-9 includes: The map and dual-chain construction module is used to acquire the basic dataset of several gas kinetic energy power generation detectors, construct the kinetic energy consumption chain and the power generation voltage decay chain based on the basic dataset of the detectors, and generate a pipeline network topology map and a pipeline network dual-chain characteristic information set. The flow information analysis module is used to perform collaborative compensation analysis of the kinetic energy consumption chain and the state disturbance chain based on the pipeline network topology map, the pipeline network dual-chain characteristic information set, and the real-time operation status data stream and original metering data stream transmitted back by the detector, and to generate a real flow information set of multiple nodes in the pipeline network. The leakage event analysis module is used to perform gas consumption pattern feature separation and abnormal loss diagnosis based on the mapping rules between the real flow information set and the production process leakage tolerance threshold, and to generate a potential leakage event information set. The leak report generation module is used to perform array collaborative evidence verification and energy consumption mutation analysis based on the potential leak event information set, and generate and output a gas leak diagnosis report.

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