A natural gas supply network double metering switching and leakage safety cut-off system

Abstract

The application belongs to the field of natural gas supply, and particularly relates to a natural gas supply pipe network double-metering switching and leakage safety cut-off system, which comprises a double-metering switching subsystem and a leakage safety cut-off subsystem; the double-metering switching subsystem comprises a main metering channel module and a standby metering channel module, the main metering channel module and the standby metering channel module are connected with a dynamic data acquisition module, the dynamic data acquisition module is connected with a health degree diagnosis module, the health degree diagnosis module is connected with a historical performance analysis module, and the historical performance analysis module is connected with a dynamic credibility evaluation model; by adopting the main and standby double-metering channels with different measurement principles and combining real-time health diagnosis, historical performance analysis and the dynamic credibility evaluation model, the system can intelligently judge and automatically switch to a more reliable metering data source, so that the metering work is uninterrupted and the data is accurate and reliable when a single channel fails or performance declines.

Description

Technical Field

[0001] This invention relates to the field of natural gas supply technology, and in particular to a dual metering switching and leakage safety cut-off system for natural gas supply pipeline networks. Background Technology

[0002] With the widespread application of natural gas in industrial and residential sectors, the accuracy of gas pipeline metering and the safety of its operation have become core concerns for the industry. In terms of metering, existing trade-transfer metering stations typically employ a single metering loop. If critical instruments (such as flow meters and temperature / pressure transmitters) malfunction, experience performance drift, or require offline verification, metering interruptions or data distortion may occur, potentially leading to trade disputes and economic losses. Although some systems have backup metering channels, the switching between primary and backup systems often relies on manual judgment or simple thresholds, lacking intelligent assessment of the real-time health status of instruments, historical performance trends, and data reliability. This can easily lead to incorrect or untimely switching, and the switching process may impact the stability of downstream gas supply.

[0003] In terms of safety, traditional pipeline leak monitoring mainly relies on independent sensors (such as combustible gas detectors) or periodic manual inspections, which suffers from problems such as slow response, difficulty in location, and high false alarm rates. When a leak occurs, safety shut-off strategies are often rather crude, such as directly cutting off the upstream main line, resulting in an excessively large gas outage area, affecting a large number of innocent users, and causing unnecessary economic losses and social impact. Existing systems lack the ability to fuse direct monitoring signals with dynamic predictions from pipeline hydraulic models, and also lack a risk-level-based, precise shut-off mechanism. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of the prior art by proposing a dual metering switching and leakage safety cut-off system for natural gas supply pipelines.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A natural gas supply pipeline dual-metering switching and leakage safety shut-off system includes a dual-metering switching subsystem and a leakage safety shut-off subsystem.

[0007] The dual-metering switching subsystem includes a main metering channel module and a backup metering channel module. The main metering channel module and the backup metering channel module are connected to a dynamic data acquisition module. The dynamic data acquisition module is connected to a health diagnosis module. The health diagnosis module is connected to a historical performance analysis module. The historical performance analysis module is connected to a dynamic reliability assessment model. The dynamic reliability assessment model is connected to an intelligent switching decision module. The intelligent switching decision module is connected to a self-calibration triggering and execution module. The self-calibration triggering and execution module is connected to a metering data arbitration and output module. The metering data arbitration and output module is connected to a switching process valve control module and a user gas consumption pattern analysis module. The user gas consumption pattern analysis module is connected to a multi-rule billing model library module. The multi-rule billing model library module is connected to a dynamic billing simulation module. The dynamic billing simulation module is connected to a fairness assessment module. The fairness assessment module is connected to a billing model intelligent arbitration module.

[0008] The leakage safety cutoff subsystem includes a multi-source leakage monitoring module, which is connected to a pipeline hydraulic model module. The pipeline hydraulic model module is connected to a real-time flow-pressure imbalance analysis module. The real-time flow-pressure imbalance analysis module is connected to a multi-source risk fusion analysis engine module. The multi-source risk fusion analysis engine module is connected to a hierarchical safety strategy library module. The hierarchical safety strategy library module is connected to an intelligent safety decision module. The intelligent safety decision module is connected to a hierarchical cutoff execution module. The hierarchical cutoff execution module is connected to an emergency bypass and release module.

[0009] Preferably, the main metering channel module, as the preferred path for daily metering, adopts a high-pressure, large-diameter ultrasonic flow meter that has been calibrated for actual flow. Its matching temperature and pressure transmitters are directly installed on the measurement pipe sections upstream and downstream of the flow meter to perform real-time temperature and pressure compensation for natural gas, converting the operating flow rate into the volumetric flow rate under standard conditions or directly outputting the mass flow rate through the built-in calculation unit. The backup metering channel module, as a redundancy and comparison path, selects a Coriolis mass flow meter with a different measurement principle than the main channel. It can directly measure the mass flow rate of the gas itself, is not affected by temperature and pressure changes, and is connected in parallel to an independent temperature and pressure transmitter for density calculation and cross-validation.

[0010] Preferably, the dynamic data acquisition module synchronously triggers and reads the digital output signals or analog signals of all instruments in the main and backup channels at millisecond or second intervals to ensure that the data used for comparison reflects the fluid state at the same moment, avoiding errors introduced by time asynchrony. After the data is tagged with a unified time stamp, it is sent to the subsequent processing module. The equipment health diagnosis module parses the internal diagnostic code of the flow meter, monitors whether the transmitter loop current is within the normal range of 4-20mA, whether there is a disconnection or saturation, and counts the communication error rate, packet loss rate, and response timeout count. Through a rule base or simple model, it outputs a real-time health index of 0-100 points for each instrument.

[0011] Preferably, the historical performance analysis module periodically performs statistical analysis on the historical data of each metering channel, calculates its average standard deviation, zero-point drift trend, and distribution histogram of deviation from another channel within a certain period, and generates a "long-term stability profile" of the channel to determine whether it is in a period of performance degradation; the dynamic reliability assessment model module receives real-time data, health indicators, and historical performance profiles as input, and performs real-time calculations using a multi-factor weighted scoring algorithm or a lightweight machine learning model.

[0012] Preferably, the intelligent switching decision module continuously compares the credibility scores of the two channels, and by default selects the channel with the higher score as the effective measurement output. Only when the difference between the two scores exceeds the set threshold of 0.2 and the score of the lower-scoring channel is lower than the absolute threshold of 0.6, will the instruction to "switch to the higher-scoring channel" be generated.

[0013] If both scores are high but the instantaneous flow difference exceeds the trade handover tolerance, a "major difference alarm" will be generated, triggering the self-calibration process, rather than switching easily, to avoid accidental switching due to brief disturbances.

[0014] Preferably, the execution logic of the self-calibration triggering and execution module is as follows: gradually fine-tuning the regulating valve on the backup channel pipeline to force the flow rate through the two channels to remain consistent for a short period of time, observing the values ​​of the two indicators to determine whether there is a correctable system deviation, or calibrating the deviation curve. The entire process is recorded and a calibration report is generated. The measurement data arbitration and output module selects the original data of the main or backup channel, or takes the weighted average of the two under certain conditions, according to the instructions of the intelligent switching decision module, as the "authoritative measurement value" of this system for publication. The published data packet clearly includes the data source channel, real-time credibility score and timestamp.

[0015] Preferably, after receiving the switching command, the valve control module executes a preset "open first, then close" or "cross-gradual" valve control sequence to ensure minimal downstream flow and pressure fluctuations during the switching process, achieving a seamless or minimally disruptive switching and ensuring stable gas supply to users. The user gas usage pattern analysis module extracts features from the received authoritative metering data and automatically identifies user gas usage patterns using clustering algorithms or pre-trained classification models. The multi-rule billing model library module stores various structured billing models. The dynamic billing simulation and fairness assessment module takes detailed flow data of the current billing period as input, substitutes all applicable billing models in parallel for simulation calculation, and obtains a set of fees. The built-in fairness assessment algorithm scores from both the gas supplier and user perspectives. The billing model intelligent arbitration and output module comprehensively considers the fairness assessment results, the contractual pricing method change clauses, and preset business objectives to output the final billing recommendation, which includes: a recommended billing model, a detailed fee list based on the model, and a comparison analysis of fees with other major models. This result can be directly used to generate bills or serve as an important data basis for revising long-term gas supply contracts.

[0016] Preferably, the multi-source leakage monitoring module converts the original sound wave, concentration, and potential signals into digital signals and uploads them. The pipeline hydraulic model module calculates the theoretical pressure and flow values ​​of each node in the pipeline network in real time by solving fluid dynamics equations based on authoritative inlet flow and pressure data and downstream user demand predictions, forming a dynamic "pressure / flow distribution map".

[0017] Preferably, the real-time flow-pressure imbalance analysis module compares the theoretical values ​​calculated by the hydraulic model with the actual measured values ​​of key nodes in real time, calculates the "pressure imbalance" of key nodes and the "flow imbalance" of pipe sections. Continuous and expanding negative imbalance is a strong indication signal of pipeline leakage. The multi-source risk fusion analysis engine module receives direct leakage signals from the multi-source leakage monitoring module and indirect imbalance signals from the real-time flow-pressure imbalance analysis module. The engine spatiotemporally correlates these events on the electronic map, uses Bayesian networks or evidence theory algorithms to integrate all evidence, calculates a comprehensive leakage probability, and initially locates the possible leakage range. Combined with the environmental data around the leakage point, the risk level of the leakage event is assessed.

[0018] Preferably, the hierarchical safety strategy library module stores standardized response plans for different risk levels and scenarios. The intelligent safety decision module receives the "event location and level" output by the risk fusion engine, matches the corresponding response plan from the strategy library, fine-tunes the plan according to real-time operating conditions, generates a specific and executable sequence of safety control instructions, and sends it to the hierarchical cut-off execution module. The hierarchical cut-off execution module strictly executes the instructions of the intelligent safety decision module, and operates the valves sequentially and quickly according to the principle of "from near to far, from branch to trunk": first, it closes the branch valves that are most likely to isolate the leak point; if this is ineffective or the leak expands, it closes the regional ring network valves; in extreme cases, it finally triggers the upstream main line emergency cut-off valve to achieve hierarchical control and minimize the gas outage area.

[0019] The beneficial effects of the natural gas supply pipeline dual metering switching and leakage safety cut-off system described in this invention are as follows:

[0020] By employing primary and backup dual metering channels with different measurement principles, and combining real-time health diagnosis, historical performance analysis, and dynamic reliability assessment models, the system can intelligently judge and automatically switch to a more reliable metering data source, ensuring uninterrupted metering work and accurate and reliable data even when a single channel fails or its performance degrades.

[0021] The intelligent switching decision module arbitrates based on multi-dimensional evaluation results, avoiding malfunctions caused by brief disturbances. During the switching process, the valve control module executes an optimized control sequence, achieving seamless or minimal-disturbance switching between primary and backup channels, ensuring the stability of gas supply to downstream users.

[0022] When significant discrepancies occur between the two channels of data, the system can trigger a self-calibration process to proactively identify and correct system deviations, thereby improving the long-term accuracy of the metering system. All data, events, and operations are encrypted and stored, forming a complete and tamper-proof data traceability chain, providing a solid basis for billing arbitration and incident analysis.

[0023] By analyzing users' gas consumption patterns and using multi-rule billing models for simulation and fairness assessment, the system can provide more scientific and fairer billing scheme suggestions for both gas suppliers and users, helping to optimize business models.

[0024] It innovatively combines multi-source direct monitoring signals (sound waves, concentration, etc.) with real-time flow-pressure imbalance analysis based on hydraulic models. Through a multi-source risk fusion analysis engine, it significantly improves the early detection capability of small-flow, slow leaks and enables rapid preliminary location of leak points.

[0025] Based on the risk level and location information of the leak, the system intelligently matches graded safety strategies and executes precise shut-off operations "from near to far, from branch to trunk," limiting the gas outage to the area affected by the leak to the greatest extent possible, thereby reducing the impact on normal users and unnecessary economic losses.

[0026] The system deeply integrates the two core functions of metering and monitoring with safety management, forming a collaborative and organic whole. It extensively applies intelligent technologies such as data analysis, machine learning (e.g., lightweight model evaluation), and digital twins (hydraulic models), significantly improving the automation and intelligent management level of pipeline network operations. Attached Figure Description

[0027] Figure 1 This is a block diagram of a natural gas supply pipeline dual metering switching and leakage safety cut-off system proposed in this invention;

[0028] Figure 2 This is a block diagram of the dual metering switching subsystem of a natural gas supply pipeline dual metering switching and leakage safety cut-off system proposed in this invention.

[0029] Figure 3 This is a block diagram of the leakage safety cut-off subsystem of a natural gas supply pipeline dual metering switching and leakage safety cut-off system proposed in this invention;

[0030] Figure 4 This is a block diagram of Embodiment 2 of the natural gas supply pipeline dual metering switching and leakage safety cut-off system proposed in this invention;

[0031] Figure 5 This is a block diagram of Embodiment 3 of a natural gas supply pipeline dual metering switching and leakage safety cut-off system proposed in this invention. Detailed Implementation

[0032] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0033] Example 1

[0034] Reference Figures 1-3 A natural gas supply pipeline dual-metering switching and leakage safety shut-off system includes a dual-metering switching subsystem and a leakage safety shut-off subsystem.

[0035] The dual-metering switching subsystem includes a main metering channel module and a backup metering channel module. The main metering channel module and the backup metering channel module are connected to a dynamic data acquisition module. The dynamic data acquisition module is connected to a health diagnosis module. The health diagnosis module is connected to a historical performance analysis module. The historical performance analysis module is connected to a dynamic reliability assessment model. The dynamic reliability assessment model is connected to an intelligent switching decision module. The intelligent switching decision module is connected to a self-calibration triggering and execution module. The self-calibration triggering and execution module is connected to a metering data arbitration and output module. The metering data arbitration and output module is connected to a switching process valve control module and a user gas consumption pattern analysis module. The user gas consumption pattern analysis module is connected to a multi-rule billing model library module. The multi-rule billing model library module is connected to a dynamic billing simulation module. The dynamic billing simulation module is connected to a fairness assessment module. The fairness assessment module is connected to a billing model intelligent arbitration module.

[0036] The leakage safety cutoff subsystem includes a multi-source leakage monitoring module, which is connected to a pipeline hydraulic model module. The pipeline hydraulic model module is connected to a real-time flow-pressure imbalance analysis module. The real-time flow-pressure imbalance analysis module is connected to a multi-source risk fusion analysis engine module. The multi-source risk fusion analysis engine module is connected to a hierarchical safety strategy library module. The hierarchical safety strategy library module is connected to an intelligent safety decision module. The intelligent safety decision module is connected to a hierarchical cutoff execution module. The hierarchical cutoff execution module is connected to an emergency bypass and discharge module.

[0037] In this embodiment, the main metering channel module, as the preferred path for daily metering, adopts a high-pressure, large-diameter ultrasonic flow meter that has been calibrated for actual flow. Its matching temperature and pressure transmitters are directly installed on the measurement pipe sections upstream and downstream of the flow meter to perform real-time temperature and pressure compensation for natural gas, converting the operating flow rate into the volumetric flow rate under standard conditions or directly outputting the mass flow rate through the built-in calculation unit. The backup metering channel module, as a redundancy and comparison path, selects a Coriolis mass flow meter with a different measurement principle than the main channel. It can directly measure the mass flow rate of the gas and is not affected by temperature and pressure changes. It is connected in parallel to an independent temperature and pressure transmitter for density calculation and cross-validation.

[0038] In this embodiment, the dynamic data acquisition module synchronously triggers and reads the digital output signals or analog signals of all instruments in the main and backup channels at millisecond or second intervals to ensure that the data used for comparison reflects the fluid state at the same moment, avoiding errors introduced by time asynchrony. After the data is tagged with a unified time stamp, it is sent to the subsequent processing module. The equipment health diagnosis module parses the internal diagnostic code of the flow meter, monitors whether the transmitter loop current is within the normal range of 4-20mA, whether there is a disconnection or saturation, and counts the communication error rate, packet loss rate, and response timeout count. Through a rule base or simple model, it outputs a real-time health index of 0-100 points for each instrument.

[0039] In this embodiment, the historical performance analysis module periodically performs statistical analysis on the historical data of each metering channel, calculates its average standard deviation, zero-point drift trend, and distribution histogram of deviation from another channel within a certain period, and generates a "long-term stability profile" of the channel to determine whether it is in a period of performance degradation; the dynamic reliability assessment model module receives real-time data, health indicators, and historical performance profiles as input, and performs real-time calculations using a multi-factor weighted scoring algorithm or a lightweight machine learning model.

[0040] In this embodiment, the intelligent switching decision module continuously compares the credibility scores of the two channels and selects the channel with the higher score as the effective measurement output by default. Only when the difference between the two scores exceeds the set threshold of 0.2 and the score of the lower-scoring channel is lower than the absolute threshold of 0.6, will the instruction to "switch to the higher-scoring channel" be generated.

[0041] If both scores are high but the instantaneous flow difference exceeds the trade handover tolerance, a "major difference alarm" will be generated, triggering the self-calibration process, rather than switching easily, to avoid accidental switching due to brief disturbances.

[0042] In this embodiment, the execution logic of the self-calibration triggering and execution module is as follows: gradually fine-tune the regulating valve on the backup channel pipeline to force the flow rate through the two channels to remain consistent for a short period of time, observe the values ​​of the two indicators to determine whether there is a correctable system deviation, or calibrate the deviation curve. The entire process is recorded and a calibration report is generated. The measurement data arbitration and output module selects the original data of the main or backup channel according to the instructions of the intelligent switching decision module, or takes the weighted average of the two under certain conditions as the "authoritative measurement value" of this system and publishes it. The published data packet clearly includes the data source channel, real-time credibility score and timestamp.

[0043] In this embodiment, after receiving the switching command, the valve control module executes a preset "open first, then close" or "cross-gradual" valve control sequence to ensure minimal downstream flow and pressure fluctuations during the switching process, achieving a seamless or minimally disruptive switching and ensuring stable gas supply to users. The user gas usage pattern analysis module extracts features from the received authoritative metering data and automatically identifies user gas usage patterns using clustering algorithms or pre-trained classification models. The multi-rule billing model library module stores various structured billing models. The dynamic billing simulation and fairness assessment module takes detailed flow data of the current billing period as input, substitutes all applicable billing models in parallel for simulation calculation, and obtains a set of fees. The built-in fairness assessment algorithm scores from both the gas supplier and user perspectives. The billing model intelligent arbitration and output module comprehensively considers the fairness assessment results, the contractual pricing method change clauses, and preset business objectives to output the final billing recommendation, which includes: a recommended billing model, a detailed fee list based on the model, and a comparison analysis of fees with other major models. This result can be directly used to generate bills or serve as important data for revising long-term gas supply contracts.

[0044] In this embodiment, the multi-source leakage monitoring module converts the original sound wave, concentration, and potential signals into digital signals and uploads them. The pipeline hydraulic model module calculates the theoretical pressure and flow values ​​of each node in the pipeline network in real time by solving the fluid dynamics equations based on authoritative inlet flow and pressure data and downstream user demand predictions, forming a dynamic "pressure / flow distribution map".

[0045] In this embodiment, the real-time flow-pressure imbalance analysis module compares the theoretical values ​​calculated by the hydraulic model with the actual measured values ​​of key nodes in real time, calculates the "pressure imbalance" of key nodes and the "flow imbalance" of pipe sections. Continuous and expanding negative imbalance is a strong indication signal of pipeline leakage. The multi-source risk fusion analysis engine module receives direct leakage signals from the multi-source leakage monitoring module and indirect imbalance signals from the real-time flow-pressure imbalance analysis module. The engine correlates these events in time and space on the electronic map, uses Bayesian networks or evidence theory algorithms to integrate all evidence, calculates a comprehensive leakage probability, and initially locates the possible leakage range. Combined with the environmental data around the leakage point, the risk level of the leakage event is assessed.

[0046] In this embodiment, the hierarchical safety strategy library module stores standardized response plans for different risk levels and scenarios. The intelligent safety decision module receives the "event location and level" output by the risk fusion engine, matches the corresponding response plan from the strategy library, fine-tunes the plan according to real-time operating conditions, generates a specific and executable sequence of safety control instructions, and sends it to the hierarchical cut-off execution module. The hierarchical cut-off execution module strictly executes the instructions of the intelligent safety decision module, and operates the valves sequentially and quickly according to the principle of "from near to far, from branch to trunk": first, it closes the branch valves that are most likely to isolate the leak point; if this is ineffective or the leak expands, it closes the regional ring network valves; in extreme cases, it finally triggers the upstream main line emergency cut-off valve to achieve hierarchical control and minimize the gas outage area.

[0047] Example 2

[0048] Reference Figure 4 The difference between this embodiment and Embodiment 1 is that: a natural gas supply pipeline dual-metering switching and leakage safety shut-off system includes a dual-metering switching subsystem and a leakage safety shut-off subsystem.

[0049] The dual-metering switching subsystem includes a main metering channel module and a backup metering channel module. These modules are connected to a dynamic data acquisition module, which in turn is connected to a health status diagnosis module. The health status diagnosis module is connected to a historical performance analysis module, which is connected to a dynamic reliability assessment model. The dynamic reliability assessment model is connected to an intelligent switching decision module, which is connected to a self-calibration triggering and execution module. This self-calibration triggering and execution module is connected to a metering data arbitration and output module, which is connected to a switching process valve control module and a user interface. The gas usage pattern analysis module is connected to the multi-rule billing model library module, which in turn is connected to the dynamic billing simulation module. The dynamic billing simulation module is connected to the fairness assessment module, which is connected to the billing model intelligent arbitration module. The main metering channel module and the backup metering channel module are connected to the metering data traceability and storage module. The module performs time-stamped, full-chain encrypted storage of the original data, diagnostic process data, switching event logs, calibration records, and final metering results after arbitration for the main and backup channels, providing an immutable data foundation for billing disputes, performance analysis, and accident tracing.

[0050] The leakage safety cutoff subsystem includes a multi-source leakage monitoring module, which is connected to a pipeline hydraulic model module. The pipeline hydraulic model module is connected to a real-time flow-pressure imbalance analysis module. The real-time flow-pressure imbalance analysis module is connected to a multi-source risk fusion analysis engine module. The multi-source risk fusion analysis engine module is connected to a hierarchical safety strategy library module. The hierarchical safety strategy library module is connected to an intelligent safety decision module. The intelligent safety decision module is connected to a hierarchical cutoff execution module. The hierarchical cutoff execution module is connected to an emergency bypass and discharge module.

[0051] Example 3

[0052] Reference Figure 5 The difference from Embodiment 1 is that: a natural gas supply pipeline dual-metering switching and leakage safety shut-off system includes a dual-metering switching subsystem and a leakage safety shut-off subsystem.

[0053] The dual-metering switching subsystem includes a main metering channel module and a backup metering channel module. The main metering channel module and the backup metering channel module are connected to a dynamic data acquisition module. The dynamic data acquisition module is connected to a health diagnosis module. The health diagnosis module is connected to a historical performance analysis module. The historical performance analysis module is connected to a dynamic reliability assessment model. The dynamic reliability assessment model is connected to an intelligent switching decision module. The intelligent switching decision module is connected to a self-calibration triggering and execution module. The self-calibration triggering and execution module is connected to a metering data arbitration and output module. The metering data arbitration and output module is connected to a switching process valve control module and a user gas consumption pattern analysis module. The user gas consumption pattern analysis module is connected to a multi-rule billing model library module. The multi-rule billing model library module is connected to a dynamic billing simulation module. The dynamic billing simulation module is connected to a fairness assessment module. The fairness assessment module is connected to a billing model intelligent arbitration module.

[0054] The leakage safety shut-off subsystem includes a multi-source leakage monitoring module, which is connected to a pipeline hydraulic model module. This module is connected to a real-time flow-pressure imbalance analysis module, which is connected to a multi-source risk fusion analysis engine module. The multi-source risk fusion analysis engine module is connected to a hierarchical safety strategy library module, which is connected to an intelligent safety decision-making module. This intelligent safety decision-making module is connected to a hierarchical shut-off execution module, which is connected to an emergency bypass and venting module. The leakage safety shut-off subsystem also includes an accident retrospective and digital twin simulation module. After a leakage or shut-off event occurs, it automatically retrieves stored historical data and performs accident process review and simulation in the digital twin of the pipeline hydraulic model module. This accurately displays the leakage diffusion process and the impact of valve action sequences, enabling in-depth analysis of accident causes, responsibility identification, and emergency plan optimization.

[0055] Experimental Example 1

[0056] Verification of the accuracy, switching logic, and self-calibration function of the dual metrology subsystem

[0057] 1. Experimental objective:

[0058] Verify the effectiveness and accuracy of the primary and backup metering channels' data synchronization acquisition, health diagnosis, dynamic reliability assessment, intelligent switching decision-making, and self-calibration process.

[0059] 2. Test Scenario and Steps:

[0060] Scenario A (Normal and Stable Operation): The pipeline network is in a stable gas supply state, and the flow rate fluctuates gently within the rated range.

[0061] Scenario B (Simulation of performance degradation of backup channel): The power supply voltage of the backup channel (Coriolis flow meter) is artificially reduced to simulate the drift of its sensor performance.

[0062] Scenario C (Simulation of Sudden Failure in Main Channel): A momentary interference is applied to the signal line of the main channel (ultrasonic flow meter) to simulate a sudden change or loss of signal.

[0063] Scenario D (Significant Difference Triggers Self-Calibration): Slightly adjust the valve opening of the backup channel pipeline to artificially create a fixed, small flow deviation between the two channels (on the edge of trade tolerance).

[0064] 3. Key Test Data Recording Table:

[0065] Test time Scene Main channel flow rate (m³ / h) Backup channel flow rate (m³ / h) Main channel health Backup channel health Main channel reliability Backup channel credibility System arbitration output System Action Log 10:00:00 A 1000.5 999.8 98 97 0.95 0.93 Main channel data Stable, main output channel 10:05:00 B 1001.2 995.3(↓) 97 85(↓) 0.94 0.68(↓) Main channel data If the health and reliability of the backup channel decrease, maintain the primary channel. 10:10:00 C Error 998.7 35(↓) 96 0.30(↓) 0.92 Switch to backup channel Main channel failure, resolution > 0.2, triggering bumpless handover. 10:15:00 B recovery 1000.8 996.1 96 90 0.93 0.75 Main channel data The backup channel has been restored, but it remains below the main channel. 10:20:00 D 1000.0 985.0 (Δ=1.5%) 97 96 0.94 0.92 Main channel data + trigger self-calibration When the flow difference exceeds the internal threshold, the self-calibration process is triggered. Self-calibration period D Forced consistency: 990.0 Forced consistency: 990.0 - - - - Calibration value The system detected a fixed deviation of +0.8% in the backup channel; the compensation parameters were updated.

[0066] 4. Experimental Conclusion:

[0067] The system can accurately assess the health status and reliability of the dual channels in real time.

[0068] The intelligent switching decision logic is reliable, and switching is only performed when the confidence difference is significant and the absolute confidence of the low-scoring channel is low, thus avoiding erroneous switching.

[0069] For situations where the switching threshold is not reached but there are significant differences, the self-calibration process can be effectively triggered to identify and compensate for system deviations, thereby improving long-term metrological accuracy.

[0070] Experimental Example 2

[0071] Leakage Safety Cutoff Subsystem Response and Hierarchical Control Verification

[0072] 1. Experimental objective:

[0073] Verify the speed, accuracy, and effectiveness of multi-source leakage monitoring, hydraulic model imbalance analysis, risk fusion location, and graded safety disconnection strategies.

[0074] 2. Test Scenario:

[0075] Two leak points are set up on a branch pipe in the middle reaches of a simulated pipeline network:

[0076] Leakage point 1 (small leak): Simulates a slight internal leak in the valve, with a leakage rate of approximately 0.5% of the total flow rate.

[0077] Leakage Point 2 (Major Leak): Simulates a pipe rupture, with the leakage rate increasing sharply to 5% of the total flow.

[0078] 3. Key Test Data Recording Table:

[0079] Event Timeline Monitoring signals Hydraulic model theoretical values ​​vs. measured values Multi-source risk fusion engine output Intelligent security decision making Graded cut-off execution action Downstream impact range T0 Everything is normal Good match Leak probability: <0.1% No action none none T1 (Small leak begins) Acoustic sensor A1 minor alarm The downstream node P1 pressure decreased by 0.05 MPa (slight imbalance). Leakage probability: 45% Generate an L1 level warning and notify the inspection team. No cutting No impact T2 (Major Leakage Occurred) Concentration sensor B2 and acoustic sensor A2 are triggering strong alarms; the pressure imbalance is rapidly escalating. The pressure at P1 suddenly dropped by 0.3 MPa; the upstream flow meter showed an inflow rate greater than the total downstream user consumption. Leakage probability: 98%; Location: Branch pipe Zone-B Matching L3 level emergency response plan Step 1: Close branch valve V-B1 within 30 seconds. Step 2: Monitor pressure; if the leak signal persists. Step 3: Close zone valve V-Zone within 60 seconds. Isolate Zone-B branch, mainnet operation T3 (Extreme Case Simulation) Assuming Zone-B valve fails The leak continues, and the pressure on the main grid continues to decrease. The risk level has been raised to L4 (critical). Activate the highest level of emergency response plan Step 4: Trigger the upstream mainline emergency shut-off valve V-Main When the main gas line is shut down, the emergency bypass module is activated to ensure gas supply to critical users.

[0080] Experimental conclusion:

[0081] The system can provide early warning of minor leaks through a combination of direct monitoring and indirect analysis using hydraulic models.

[0082] The multi-source risk fusion engine can effectively integrate information and improve the probability of leak identification and location accuracy.

[0083] The tiered shut-off strategy is implemented quickly, following the principle of "from near to far, from branch to trunk," to minimize the impact of leaks and ensure the safety and stability of the overall pipeline network.

[0084] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A dual-metering switching and leakage safety shut-off system for a natural gas supply pipeline network, characterized in that, Includes a dual metering switching subsystem and a leakage safety disconnection subsystem: The dual-metering switching subsystem includes a main metering channel module and a backup metering channel module. The main metering channel module and the backup metering channel module are connected to a dynamic data acquisition module. The dynamic data acquisition module is connected to a health diagnosis module. The health diagnosis module is connected to a historical performance analysis module. The historical performance analysis module is connected to a dynamic reliability assessment model. The dynamic reliability assessment model is connected to an intelligent switching decision module. The intelligent switching decision module is connected to a self-calibration triggering and execution module. The self-calibration triggering and execution module is connected to a metering data arbitration and output module. The metering data arbitration and output module is connected to a switching process valve control module and a user gas consumption pattern analysis module. The user gas consumption pattern analysis module is connected to a multi-rule billing model library module. The multi-rule billing model library module is connected to a dynamic billing simulation module. The dynamic billing simulation module is connected to a fairness assessment module. The fairness assessment module is connected to a billing model intelligent arbitration module. The leakage safety cutoff subsystem includes a multi-source leakage monitoring module, which is connected to a pipeline hydraulic model module. The pipeline hydraulic model module is connected to a real-time flow-pressure imbalance analysis module. The real-time flow-pressure imbalance analysis module is connected to a multi-source risk fusion analysis engine module. The multi-source risk fusion analysis engine module is connected to a hierarchical safety strategy library module. The hierarchical safety strategy library module is connected to an intelligent safety decision module. The intelligent safety decision module is connected to a hierarchical cutoff execution module. The hierarchical cutoff execution module is connected to an emergency bypass and release module.

2. The natural gas supply pipeline dual-metering switching and leakage safety shut-off system according to claim 1, characterized in that, The main metering channel module, serving as the preferred path for daily metering, employs a high-pressure, large-diameter ultrasonic flow meter calibrated for actual flow. Its matching temperature and pressure transmitters are directly installed on the upstream and downstream measuring pipe sections of the flow meter to perform real-time temperature and pressure compensation for natural gas, converting the operating flow rate into the volumetric flow rate under standard conditions or directly outputting the mass flow rate through the built-in calculation unit. The backup metering channel module, serving as a redundancy and comparison path, selects a Coriolis mass flow meter with a different measurement principle than the main channel. It can directly measure the mass flow rate of the gas, unaffected by temperature and pressure changes, and is connected in parallel to an independent temperature and pressure transmitter for density calculation and cross-validation.

3. The natural gas supply pipeline dual-metering switching and leakage safety shut-off system according to claim 2, characterized in that, The dynamic data acquisition module synchronously triggers and reads the digital output signals or analog signals of all instruments in the main and backup channels at millisecond or second intervals to ensure that the data used for comparison reflects the fluid state at the same moment, avoiding errors introduced by time asynchrony. After the data is tagged with a unified time stamp, it is sent to the subsequent processing module. The equipment health diagnosis module parses the internal diagnostic code of the flow meter, monitors whether the transmitter loop current is within the normal range of 4-20mA, whether there is a disconnection or saturation, and counts the communication error rate, packet loss rate, and response timeout count. Through a rule base or simple model, it outputs a real-time health index of 0-100 points for each instrument.

4. The natural gas supply pipeline dual-metering switching and leakage safety shut-off system according to claim 3, characterized in that, The historical performance analysis module periodically performs statistical analysis on the historical data of each metering channel, calculates its average standard deviation, zero-point drift trend, and distribution histogram of deviation from another channel within a certain period, and generates a "long-term stability profile" of the channel to determine whether it is in a period of performance degradation; the dynamic reliability assessment model module receives real-time data, health indicators, and historical performance profiles as input, and performs real-time calculations using a multi-factor weighted scoring algorithm or a lightweight machine learning model.

5. A natural gas supply pipeline dual-metering switching and leakage safety shut-off system according to claim 4, characterized in that, The intelligent switching decision module continuously compares the credibility scores of the two channels and selects the channel with the higher score as the effective measurement output by default. Only when the difference between the two scores exceeds the set threshold of 0.2 and the score of the lower-scoring channel is lower than the absolute threshold of 0.6 will the instruction to "switch to the higher-scoring channel" be generated. If both scores are high but the instantaneous flow difference exceeds the trade handover tolerance, a "major difference alarm" will be generated, triggering the self-calibration process, rather than switching easily, to avoid accidental switching due to brief disturbances.

6. A natural gas supply pipeline dual-metering switching and leakage safety shut-off system according to claim 5, characterized in that, The self-calibration triggering and execution module executes the following logic: it gradually fine-tunes the regulating valve on the backup channel pipeline to force the flow rate through the two channels to remain consistent for a short period of time, observes the values ​​of the two indicators to determine whether there is a correctable system deviation, or calibrates the deviation curve. The entire process is recorded and a calibration report is generated. The measurement data arbitration and output module selects the original data of the primary or backup channel, or takes the weighted average of the two under certain conditions, according to the instructions of the intelligent switching decision module, as the "authoritative measurement value" of this system and publishes it. The published data packet clearly includes the data source channel, real-time reliability score and timestamp.

7. A natural gas supply pipeline dual-metering switching and leakage safety shut-off system according to claim 6, characterized in that, Upon receiving the switching command, the valve control module executes a preset "open then close" or "cross-gradual" valve control sequence to ensure minimal downstream flow and pressure fluctuations during the switching process, achieving a seamless or minimally disruptive switching and guaranteeing stable gas supply to users. The user gas usage pattern analysis module extracts features from authoritative metering data and automatically identifies user gas usage patterns using clustering algorithms or pre-trained classification models. The multi-rule billing model library module stores various structured billing models. The dynamic billing simulation and fairness assessment module takes detailed flow data of the current billing period as input, substitutes all applicable billing models in parallel for simulation calculations, and obtains a set of fees. The built-in fairness assessment algorithm scores from both the gas supplier and user perspectives. The billing model intelligent arbitration and output module comprehensively considers the fairness assessment results, contractual pricing method change clauses, and preset business objectives to output the final billing recommendation, including: a recommended billing model, a detailed fee list based on the model, and a cost comparison analysis with other major models. This result can be directly used to generate bills or serve as important data for revising long-term gas supply contracts.

8. A natural gas supply pipeline dual-metering switching and leakage safety shut-off system according to claim 7, characterized in that, The multi-source leakage monitoring module converts the original sound wave, concentration, and potential signals into digital signals and uploads them. The pipeline hydraulic model module calculates the theoretical pressure and flow values ​​of each node in the pipeline network in real time by solving fluid dynamics equations based on authoritative inlet flow and pressure data and downstream user demand predictions, forming a dynamic "pressure / flow distribution map".

9. A natural gas supply pipeline dual-metering switching and leakage safety shut-off system according to claim 8, characterized in that, The real-time flow-pressure imbalance analysis module compares the theoretical values ​​calculated by the hydraulic model with the actual measured values ​​of key nodes in real time, calculating the "pressure imbalance" of key nodes and the "flow imbalance" of pipe sections. Continuous and expanding negative imbalance is a strong indication signal of pipeline leakage. The multi-source risk fusion analysis engine module receives direct leakage signals from the multi-source leakage monitoring module and indirect imbalance signals from the real-time flow-pressure imbalance analysis module. The engine correlates these events in time and space on the electronic map, uses Bayesian networks or evidence theory algorithms to integrate all evidence, calculates a comprehensive leakage probability, and initially locates the possible leakage range. Combined with the environmental data around the leakage point, the risk level of the leakage event is assessed.

10. A natural gas supply pipeline dual-metering switching and leakage safety shut-off system according to claim 9, characterized in that, The hierarchical safety strategy library module stores standardized response plans for different risk levels and scenarios. The intelligent safety decision module receives the "event location and level" output by the risk fusion engine, matches the corresponding response plan from the strategy library, fine-tunes the plan according to real-time operating conditions, generates a specific and executable sequence of safety control instructions, and sends it to the hierarchical cut-off execution module. The hierarchical cut-off execution module strictly executes the instructions of the intelligent safety decision module, operating valves sequentially and quickly according to the principle of "from near to far, from branch to trunk": first, it closes the branch valves most likely to isolate the leak point; if ineffective or the leak expands, it closes the regional ring network valves; in extreme cases, it finally triggers the upstream trunk emergency cut-off valve to achieve hierarchical control and minimize the gas outage area.

Images