Gas pipeline leakage intelligent monitoring system and monitoring method based on Internet of Things

Abstract

The invention relates to the technical field of gas application safety, and particularly discloses an intelligent gas pipeline leakage monitoring method based on the Internet of Things, a compensation node adjusts monitoring parameters based on preset pipeline topological data, and pipeline characteristics such as equivalent volume, pipe diameter and leakage risk area of a failure node can be accurately matched; the accuracy of leakage flow calculation and grade judgment is ensured to be consistent with that of an original node, and missing report and false report caused by parameter mismatching are avoided; moreover, the compensation node has the monitoring and valve closing functions, the valve closing action can be directly executed after leakage judgment is completed, cross-node cooperative waiting is not needed, and the instruction transmission link is reduced. According to the scheme, through the collaborative design of adjacent node linkage networking, failure rapid compensation and nearby accurate valve closing, safety control of no blind area of failure, no delay of valve closing and no monitoring error is realized, the problems of monitoring interruption and untimely valve closing due to single node failure are prevented, and the monitoring reliability and the safety protection capability are improved.

Description

Technical Field

[0001] This invention relates to the field of gas application safety technology, and in particular to an intelligent monitoring system and method for gas pipeline leaks based on the Internet of Things. Background Technology

[0002] Natural gas, as a green and clean energy source, has been widely used; however, its flammable and explosive nature leads to frequent indoor leaks. Currently, gas leak monitoring after the meter mainly relies on manual inspections, gas alarms, and IoT smart gas meters. However, gas alarms have limited installation locations, and manual inspections are inefficient and have blind spots. In view of this, Chinese patent CN113075899A proposes a solution that uses the central processor of an IoT smart gas meter to periodically or by command control of the valve control module to close the valve, forming a closed pipeline downstream of the valve. Pressure and temperature monitoring units detect temperature and pressure changes in the closed pipeline downstream of the valve within a specific time period. Based on the gas state equation, the central processor calculates and determines whether a gas leak exists, triggering an alarm or closing the valve. Furthermore, it classifies and manages gas leaks according to their severity, achieving comprehensive monitoring of all gas facilities downstream of the valve, regardless of the leak location, and eliminating blind spots.

[0003] In practical applications, the distributed layout of pipeline networks and the dense node deployment coexist. With a wide coverage area, nodes need to be densely deployed at key locations such as main and branch pipes and inlets to balance large-scale coverage with precise local monitoring. Operational stability and random fluctuations also coexist. Under normal operating conditions, gas pressure and flow are relatively stable, but differences in user gas usage times and changes in ambient temperature can cause random fluctuations in parameters. Rigid safety requirements and diverse monitoring difficulties coexist. The flammable and explosive nature of gas requires zero omissions in monitoring, while the diverse pipe diameters, complex pipe routes, and numerous environmental interferences (oil fumes, humidity) increase the difficulty of monitoring. System connectivity and single-point failure risks coexist. Nodes achieve data interaction through networking, but there are potential single-point failure risks such as hardware failures and signal interruptions, requiring the assurance of monitoring continuity even in failure states. Because the gas meter's built-in monitoring module in the above technical solution relies on a single node, if the single node fails due to hardware failure, signal interruption, or other reasons, leakage monitoring will be interrupted. At the same time, each node operates independently, lacking linkage mechanisms, communication coordination, and functional compensation, making it difficult to form a monitoring network with full coverage. Once a node fails, its coverage area becomes a safety blind spot, and it is also difficult to quickly switch the monitoring subject, resulting in poor reliability of gas leakage monitoring and difficulty in meeting the safety protection needs of complex gas usage environments.

[0004] Therefore, there is an urgent need for an intelligent gas pipeline leak monitoring system and method that can improve the reliability of gas leak monitoring by having the ability to link adjacent nodes, compensate for failures, and conduct collaborative communication. Summary of the Invention

[0005] This invention provides an intelligent monitoring system and method for gas pipeline leaks based on the Internet of Things, which can improve the reliability of gas leak monitoring.

[0006] To solve the above-mentioned technical problems, this application provides the following technical solution: An IoT-based intelligent monitoring method for gas pipeline leaks is applied to a monitoring system comprising multiple nodes, each node including a central processing unit, an encrypted communication module, a communication gateway, a valve control module, and a gas monitoring module. The method includes the following steps: Step 1, establish the linkage relationship between adjacent nodes: multiple nodes complete the network through the communication gateway, and adjacent nodes exchange working status, monitoring data and leakage information based on the encrypted communication protocol. The gas monitoring module collects the gas monitoring data of the nodes. Step 2, Node Status Self-Check and Failure Reporting: The central processing unit of each node checks the operating status, signal transmission status and monitoring data validity of its own node. When an operating fault, signal interruption or data abnormality is detected, the node is determined to be faulty and a failure notification is sent to neighboring nodes through the encrypted communication module. Step 3, Neighboring Node Compensation Response: After receiving the failure notification, the neighboring node verifies the working status of the failed node through the communication module and confirms the coverage area of ​​the failed node. It then selects the node that is closest to the failed node and is working normally. The node that is closest to the failed node and is working normally automatically starts the compensation mechanism, takes over the monitoring task of the failed node, retrieves the preset pipeline topology data and adjusts the monitoring parameters, and collects the gas monitoring data of the failed node. Step 4, Leakage Flow Calculation and Level Determination: The central processing unit of the compensation node receives the gas monitoring data of the failed node, calculates the gas leakage flow through the gas state; a first threshold and a second threshold are preset, the gas consumption pattern of the failed node is obtained, the first threshold and the second threshold are dynamically corrected in combination with the gas consumption pattern, and the leakage level is determined according to the corrected first threshold and the second threshold. Based on the leakage level, a valve shut-off control command and an early warning sending command are generated. Step 5, Linkage Control and Alarm: The valve control module of the compensation node executes the valve closing control command, and the encrypted communication module of the compensation node executes the early warning sending command.

[0007] The basic principle and beneficial effects of this solution are as follows: If a single node fails due to hardware malfunction or signal interruption, the pipeline area it covers becomes a monitoring blind spot. Manual maintenance or equipment replacement involves time lags, easily leading to leaks and safety incidents. This solution establishes a linkage between adjacent nodes, allowing each node to exchange its working status. A failed node can quickly report its failure status, and the nearest healthy node immediately activates a compensation mechanism to take over the monitoring task, achieving seamless transition from failure to compensation, completely eliminating monitoring blind spots and ensuring continuous leak monitoring. Furthermore, the distributed deployment characteristics of gas pipelines dictate that the installation spacing between adjacent nodes meets pipeline safety management requirements. The valve control modules corresponding to adjacent nodes are typically located on the same branch pipeline or near the main pipeline. When a leak occurs in the area covered by the failed node, the compensation node, as the closest monitoring entity, has a valve control module whose control range highly overlaps with the pipeline coverage area of ​​the failed node. Closing the valve of the compensation node can directly cut off the gas supply to the failed node, blocking the risk of leakage.

[0008] After the nodes are networked, they interact to monitor data. When a node fails, multiple rounds of verification confirm the actual failure and lock the coverage area. The nearest normal node is selected to compensate, which can avoid monitoring blind spots. The compensation node retrieves topology data to adjust parameters, which can adapt to pipeline characteristics and ensure the relevance of the collected data. The leakage flow rate is calculated based on the gas state equation and compressibility factor, which has high accuracy. The threshold is dynamically corrected by gas usage patterns to adapt to different gas usage scenarios and eliminate interference from normal gas usage fluctuations. In this way, a closed loop is formed from data acquisition, failure compensation, parameter adaptation, accurate calculation to threshold optimization, which realizes accurate identification of leaks.

[0009] In this solution, the compensation node adjusts the monitoring parameters based on the preset pipeline topology data, which can accurately match the pipeline characteristics of the failed node, such as equivalent volume, pipe diameter, and leakage risk area. This ensures that the accuracy of leakage flow calculation and level determination is consistent with the original node, avoiding missed or false alarms due to parameter mismatch. Moreover, the compensation node has both monitoring and valve shut-off functions. After completing the leakage determination, it can directly execute the valve shut-off action without waiting for cross-node coordination, reducing the command transmission links. By employing a tiered valve closure and precise reporting mechanism, the spread of leak impact is effectively prevented: After a node fails, the compensation node, acting as the core of branch monitoring, first closes the corresponding branch valve through its own valve control module, quickly cutting off the leaking branch and preventing the gas leak from spreading to the main line or other branches. Simultaneously, the compensation node reports leak information, including leak location, level, and branch valve closure status, to the superior branch and main line systems via an encrypted communication module. Upon receiving the report, the main line system can determine whether to further close the main line valve based on the leak level. This achieves rapid response at the branch level while ensuring overall control of the main line through tiered reporting, preventing local leaks from triggering overall pipeline safety risks and forming a closed-loop protection system where branch closures are prioritized and the main line provides a safety net. Compared to separate monitoring and valve closure, or reliance on a single node, this monitoring-determination-valve closure compensation mechanism provides dual safety assurance and improves monitoring reliability. Meanwhile, compared to the single-node valve shut-off function being tied to the monitoring function, where a node failure not only interrupts monitoring but also renders its corresponding valve shut-off command unexecutable, even if the remote management terminal receives an alarm, it still requires manual or other unrelated nodes to shut off the valve, resulting in a long response delay, the compensation node in this solution, while taking over the monitoring task, has direct valve shut-off authority. Its valve control action targets the leak area covered by the failed node, effectively cutting off the gas supply to the failed node by shutting off the nearest valve. This avoids the design flaw of binding monitoring and valve shut-off to the same node, ensuring that the leak area can be quickly shut off regardless of whether the original node fails, improving safety redundancy and reducing the risk of valve shut-off failure due to the failure of a single node.

[0010] In summary, this solution achieves safe control with no blind spots in failure, no delay in valve closure, and no monitoring errors through the collaborative design of adjacent node linkage networking, rapid failure compensation, and precise valve closure nearby. It prevents monitoring interruptions and untimely valve closures due to the failure of a single node, and improves monitoring reliability and safety protection capabilities.

[0011] Furthermore, in step 3, the working status of the failed node is verified through the communication module, and the coverage area of ​​the failed node is confirmed. This includes: adjacent nodes sending a preset number of status verification commands to the failed node through the encrypted communication module at preset command intervals. If no valid response is received or a failure confirmation feedback is received, the node is confirmed to be truly failed. The preset pipeline topology data is retrieved and the topology boundary of the failed node is parsed to obtain the coverage area of ​​the failed node.

[0012] The beneficial effects are: dual confirmation of node failure effectively distinguishes between temporary signal fluctuations and hardware failures, avoids false triggering of compensation, and improves the accuracy of failure determination; it accurately locates the monitoring segment boundary of the failed node, ensuring that the compensation node only monitors the target area and avoids resource waste.

[0013] Furthermore, in step 3, the node that is closest to the failed node and is functioning normally is selected, including: the adjacent node that is functioning normally obtains its own installation coordinates and the installation coordinates of the failed node based on the preset pipeline topology data, calculates the straight-line distance between itself and the failed node, and selects the functioning normally node with the smallest straight-line distance as the compensation node.

[0014] The beneficial effects are: ensuring that the selected node is the normal node closest to the failed node, which is conducive to shutting off the leaking pipeline by closing the valve nearby, shortening the valve closing response path, and avoiding disorderly competition for compensation rights among multiple nodes; improving the efficiency of compensation initiation and strengthening the reliability and stability of failure compensation.

[0015] Furthermore, in step 3, taking over the monitoring tasks of the failed node includes: after receiving the compensation start command sent by the communication gateway, the target compensation node sends a data synchronization request to the communication gateway through the encrypted communication module to obtain the historical monitoring data, preset monitoring parameter configuration, and topology parameters of the covered pipeline of the failed node; the central processing unit of the compensation node performs trend analysis on the synchronized historical monitoring data, extracts the fluctuation range of monitoring data and the adaptation law of leakage judgment threshold when the failed node is working normally, and adjusts its own monitoring parameters in combination with the topology parameters of the covered pipeline; the compensation node starts the gas monitoring module and collects data at key monitoring points of the covered pipeline of the failed node according to the adjusted monitoring parameters; the compensation node sends a takeover confirmation signal to the communication gateway and all adjacent nodes through the encrypted communication module, synchronizes the current monitoring parameters and the first collected monitoring data, until the failed node returns to normal.

[0016] The beneficial effects are as follows: by synchronizing the historical data, parameter configuration, and topology information of the failed nodes, and combining trend analysis to extract monitoring patterns, the replacement nodes can quickly adapt to the failure scenarios, avoid monitoring gaps during the takeover process, and ensure monitoring continuity; the monitoring parameters are adjusted in a targeted manner to ensure that the accuracy of the replacement monitoring is consistent with that of the original nodes, thereby improving the data matching degree; and the synchronized takeover status and monitoring data facilitate remote management and fault tracing.

[0017] Furthermore, in step 3, the monitoring parameters are adjusted based on the preset pipeline topology data, including: after receiving the compensation start command, the compensation node sends a topology data retrieval request to the communication gateway through the encrypted communication module; after verifying the legality of the request, the communication gateway returns the preset pipeline topology data corresponding to the failed node; the central processing unit of the compensation node parses the topology data to obtain the equivalent volume, pipe diameter, and historical monitoring parameter benchmark values ​​of the covered pipeline; the detection time is matched according to the equivalent volume, the data acquisition frequency is adjusted according to the pipe diameter, the gas compression factor is determined using the historical monitoring parameter benchmark values ​​in the topology data, and the threshold correction coefficient of the failed node is synchronized to complete the adjustment of the monitoring parameters.

[0018] The beneficial effects are: ensuring the safe and compliant retrieval of topology data, avoiding data leakage or tampering caused by illegal operations; achieving precise matching between monitoring parameters and pipeline characteristics, reducing leakage flow calculation errors; dynamically adjusting parameters to improve detection accuracy, balancing equipment energy consumption, optimizing the parameter matching efficiency of compensation nodes, and enhancing the reliability of failure compensation.

[0019] Further, in step 4, the gas consumption pattern of the failed node is obtained, and the first and second thresholds are dynamically adjusted based on the gas consumption pattern. This includes: the compensation node sends a gas consumption data retrieval request to the communication gateway through the encrypted communication module; after the communication gateway verifies the legality, it feeds back the historical gas consumption data of the failed node; the central processing unit of the compensation node divides the historical gas consumption data into daily gas consumption periods, off-peak gas consumption periods, and zero gas consumption periods, and calculates the gas consumption fluctuation coefficient for each period; a threshold correction coefficient is set based on the gas consumption period and the fluctuation coefficient; wherein, the first threshold is the minimum allowable leakage flow rate baseline value for equipment detection error, and the second threshold is the maximum allowable safe leakage flow rate baseline value for the most unfavorable location; the current gas consumption status of the area covered by the failed node is monitored, and if the current period switches from the preset gas consumption period, the threshold correction coefficient is automatically recalculated and the first and second thresholds are updated.

[0020] The beneficial effects are as follows: by dividing the gas usage period and setting the correction coefficient according to the fluctuation coefficient, the threshold can be dynamically adapted, avoiding misjudgment during peak hours and missed judgment during zero gas usage periods by fixed thresholds, thus reducing the misjudgment rate of leakage detection; by monitoring the switching of gas usage periods and automatically updating the threshold, the threshold can be accurately matched with the user's gas usage pattern, thus improving the flexibility of the threshold.

[0021] Furthermore, in step 4, the gas leakage flow rate is calculated based on the gas state, including: the central processing unit of the compensation node extracts the collected monitoring data, determines the initial pressure P1, termination pressure P2, initial temperature T1, and termination temperature T2 of the closed pipeline, and synchronously presets the equivalent volume V0 of the closed pipeline in the pipeline topology data and the gas compressibility factor Z1 at the initial time and the gas compressibility factor Z2 at the termination time; the gas leakage volume ΔV is calculated using the following formula: In the formula, ΔV is the volume of gas leakage within the detection time t; The formula for calculating the gas leakage flow rate Q is as follows: Where Q is the gas leakage flow rate.

[0022] Furthermore, in step 4, if T1-T2≤0.5K, the formula simplifies to... ΔV= .

[0023] Furthermore, in step 4, the validity of the leakage flow rate Q is verified: it is determined whether the leakage flow rate Q is within the preset reasonable flow range of 0≤Q≤Q_max; if not, the temperature and pressure data are automatically re-acquired and recalculated; if yes, the final gas leakage flow rate Q is output. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating an embodiment of an IoT-based intelligent monitoring method for gas pipeline leaks. Detailed Implementation

[0025] The following detailed description illustrates the specific implementation method: This invention relates to an intelligent monitoring method for gas pipeline leaks based on the Internet of Things, as shown in the appendix. Figure 1 As shown, this is applied to a monitoring system comprising multiple nodes. Each node includes a central processing unit, an encrypted communication module, a communication gateway, a valve control module, and a gas monitoring module. The monitoring system contains 30 nodes. The central processing unit is an STM32H743, the encrypted communication module is a LoRa module supporting AES-256 encryption, the communication gateway is an MG510, the valve control module is an electric ball valve controller, and the gas monitoring module integrates a pressure sensor MPX5700, a temperature sensor DS18B20, and a gas concentration sensor MQ-4.

[0026] The specific implementation process is as follows: Step 1: Establishing Interconnection Between Adjacent Nodes: Multiple nodes form a network through a communication gateway. Adjacent nodes exchange operating status, monitoring data, and leakage information based on an encrypted communication protocol. The gas monitoring module collects gas monitoring data from the nodes. Nodes form a star network through the communication gateway, with an adjacent node spacing of 5 meters. Based on a preset AES-256 encrypted communication protocol, they exchange operating status (e.g., normal / fault), monitoring data (pressure, temperature, gas concentration), and leakage information every 2 seconds. The gas monitoring module collects data with a pressure acquisition accuracy of ±0.01MPa, a temperature acquisition accuracy of ±0.1℃, and a gas concentration acquisition accuracy of ±50ppm.

[0027] Step 2, Node Status Self-Check and Failure Reporting: The central processing unit (CPU) of each node checks its own operating status, signal transmission status, and the validity of monitoring data. When an operational fault, signal interruption, or data anomaly is detected, the node is deemed to have failed and a failure notification is sent to neighboring nodes via the encrypted communication module. Every second, the CPU of each node checks its own operating status (such as CPU utilization, sensor power supply voltage), signal transmission status (received signal strength ≥ -70dBm is normal), and the validity of monitoring data (pressure fluctuation ≤ 0.05MPa, temperature fluctuation ≤ 1℃ is normal). When node N15 detects that the pressure sensor power supply voltage is below 3.3V (operational fault) and the signal transmission strength is ≤ -85dBm for three consecutive times (signal interruption), the node is deemed to have failed and a failure notification is sent to neighboring nodes N14, N16, N13, and N17 via the encrypted communication module. If a failed node (such as N15) is unable to communicate with neighboring nodes due to signal interruption, hardware failure, or other reasons, and the neighboring nodes do not receive the failure notification within a preset timeout period (such as 10 seconds), the entire network status inspection mechanism will be automatically triggered. The communication gateway polls the status of all nodes at a frequency of 3 seconds per poll. When a node fails to respond for three consecutive polls, it is determined that the node has failed and its identifier ID is locked. The communication gateway then retrieves the node's topology data, determines its coverage area and associated neighboring nodes, and sends a compensation instruction to the associated nodes via encrypted communication, selecting the nearest normal node to initiate compensation. At the same time, the gateway synchronizes the failure information with the upstream main network system to ensure rapid takeover and risk management of the branch network, preventing the failure of nodes from being left unattended due to communication interruptions.

[0028] Step 3, Neighboring Node Compensation Response: After receiving the failure notification, the neighboring node verifies the working status of the failed node through the communication module and confirms the coverage area of ​​the failed node. It then selects the node that is closest to the failed node and is working normally. The node that is closest to the failed node and is working normally automatically starts the compensation mechanism, takes over the monitoring task of the failed node, retrieves the preset pipeline topology data and adjusts the monitoring parameters, and collects the gas monitoring data of the failed node.

[0029] After receiving the failure notification, nodes N14, N16, N13, and N17 verify the working status and coverage of N15, and select the nearest working node (such as N16). Node N16 automatically starts the compensation mechanism, takes over the monitoring tasks of N15, retrieves the preset pipeline topology data and adjusts its own monitoring parameters, and collects gas monitoring data of the pipelines covered by N15.

[0030] In step 3, the working status of the failed node is verified through the communication module, and the coverage area of ​​the failed node is confirmed. This includes: adjacent nodes sending a preset number of status verification commands to the failed node through the encrypted communication module at preset command intervals. If no valid response is received or a failure confirmation feedback is received, the node is confirmed to be truly failed. Preset pipeline topology data is retrieved and the topology boundary of the failed node is parsed to obtain the coverage area of ​​the failed node. After receiving the failure notification from N15, adjacent nodes N14, N16, N13, and N17 send a preset number of status verification commands (3 times) to node N15 through their respective encrypted communication modules at preset command intervals (1 second). Each command includes a hardware self-test request, a signal link detection request, and a monitoring data feedback request. If node N15 fails to return a valid response for 3 consecutive times, or returns 2 or more failure confirmation feedbacks (such as hardware fault code 0x001), then N15 is confirmed to be truly failed. Subsequently, each adjacent node sends a data retrieval request to the communication gateway through the encrypted communication module to retrieve the preset pipeline topology data.

[0031] In step 3, the nearest working node to the failed node is selected. This includes: working adjacent nodes obtaining their own installation coordinates and the failed node's installation coordinates based on preset pipeline topology data, calculating the straight-line distance between them, and selecting the working node with the smallest straight-line distance as the replacement node. First, working adjacent nodes are screened. Nodes N14, N16, N13, and N17 confirm that they are all working nodes by exchanging their own working status data. Each working node obtains its own installation coordinates and the installation coordinates of node N15 based on preset pipeline topology data, and calculates the distance between each node and N15: assuming the distance between N14 and N15 is 0.5 meters, the distance between N16 and N15 is 0.5 meters, the distance between N13 and N15 is 0.5 meters, and the distance between N17 and N15 is 0.5 meters, the node with the smallest distance, N16, is selected as the replacement node.

[0032] In step 3, the monitoring task of taking over the failed node includes: after receiving the compensation start command sent by the communication gateway, the target compensation node sends a data synchronization request to the communication gateway through the encrypted communication module to obtain the historical monitoring data, preset monitoring parameter configuration, and topology parameters of the covered pipeline of the failed node; the central processing unit of the compensation node performs trend analysis on the synchronized historical monitoring data, extracts the fluctuation range of monitoring data and the adaptation law of leakage judgment threshold when the failed node is working normally, and adjusts its own monitoring parameters in combination with the topology parameters of the covered pipeline; the compensation node starts the gas monitoring module and collects data at the key monitoring points of the covered pipeline of the failed node according to the adjusted monitoring parameters; the compensation node sends a takeover confirmation signal to the communication gateway and all adjacent nodes through the encrypted communication module, synchronizes the current monitoring parameters and the first collected monitoring data, until the failed node returns to normal.

[0033] After receiving the compensation start command from the communication gateway, the target compensation node N16 sends a data synchronization request to the communication gateway through the encrypted communication module. After verifying the legitimacy of the request, the communication gateway returns relevant data from node N15: historical monitoring data for the past 2 hours, preset monitoring parameter configuration, and topology parameters of the covered pipeline. The central processing unit of node N16 performs trend analysis on the synchronized historical monitoring data, extracts the fluctuation range of monitoring data during normal operation of N15, and extracts the leakage judgment threshold adaptation pattern (e.g., the threshold decreases by 20% during periods of zero gas consumption). Combined with topology parameters (e.g., equivalent volume 15L, pipe diameter 20mm), it adjusts its original detection time from 4 minutes to 5 minutes, maintaining a sampling frequency of once every 2 seconds. Node N16 starts the gas monitoring module and, according to the adjusted monitoring parameters, collects data from key monitoring points in the N15 covered pipeline (distance from the original N15 monitoring points ≤ 0.3 meters), obtaining data such as initial pressure P1 = 0.12MPa and initial temperature T1 = 25.5℃. Finally, node N16 sends a takeover confirmation signal to the communication gateway and all adjacent nodes through the encrypted communication module, synchronizing the current monitoring parameters (detection time 5 minutes, acquisition frequency 1 time / 2 seconds) and the first acquired monitoring data (P1=0.12MPa, T1=25.5℃, gas concentration 10ppm), and continues to perform monitoring tasks until node N15 is repaired and restored to normal.

[0034] In step 3, the monitoring parameters are adjusted based on the preset pipeline topology data, including: after receiving the compensation start command, the compensation node sends a topology data retrieval request to the communication gateway through the encrypted communication module. After verifying the legality of the request, the communication gateway returns the preset pipeline topology data corresponding to the failed node; the central processing unit of the compensation node parses the topology data to obtain the equivalent volume, pipe diameter, and historical monitoring parameter benchmark values ​​of the covered pipeline; the detection time is matched according to the equivalent volume, the data acquisition frequency is adjusted according to the pipe diameter, the gas compression factor is determined using the historical monitoring parameter benchmark values ​​in the topology data, and the threshold correction coefficient of the failed node is synchronized to complete the adjustment of the monitoring parameters.

[0035] After receiving the compensation start command sent by the communication gateway, the compensation node N16 sends a topology data retrieval request to the communication gateway through the encrypted communication module. The request includes the unique identifier ID of node N16 and the failure identifier of node N15. After verifying the legality of the request, the communication gateway returns the preset pipeline topology data corresponding to node N15, including the equivalent volume of the covered pipeline V0=15L, the pipe diameter D=20mm, and the historical monitoring parameter benchmark values ​​(gas compression factor Z1=0.98, Z2=0.97, threshold correction coefficient k=1.0). The central processor of node N16 parses the topology data and matches the detection time according to the equivalent volume: since 10L < 15L ≤ 25L, the detection time t is set to 5 minutes; the data acquisition frequency is adjusted according to the pipe diameter: since 15mm < 20mm ≤ 30mm, the acquisition frequency is set to 1 time / 2 seconds; using the historical monitoring parameter benchmark values ​​in the topology data, the gas compression factor Z1=0.98 and Z2=0.97 are determined, and the threshold correction coefficient k=1.0 of node N15 is synchronized to complete the full-dimensional adjustment of monitoring parameters, forming a dedicated monitoring parameter configuration adapted to the pipeline covered by N15.

[0036] Step 4, Leakage Flow Calculation and Level Determination: The central processing unit of the compensation node receives the gas monitoring data of the failed node, calculates the gas leakage flow based on the gas status, pre-sets a first threshold and a second threshold, obtains the gas consumption pattern of the failed node, dynamically corrects the first threshold and the second threshold based on the gas consumption pattern, and determines the leakage level based on the corrected first threshold and the second threshold. Based on the leakage level, it generates valve closing control commands and early warning sending commands.

[0037] The central processing unit of node N16 receives the collected monitoring data and calculates the gas leakage flow rate through the gas state equation. It pre-sets a first threshold Q1=0.01L / s (the minimum leakage flow rate allowed by equipment detection error) and a second threshold Q2=0.1L / s (the maximum safe leakage flow rate allowed at the most unfavorable location). It obtains the gas consumption pattern of node N15 over the past 30 days, dynamically corrects Q1 and Q2, determines the leakage level, and generates corresponding instructions.

[0038] In step 4, the gas usage pattern of the failed node is obtained, and the first and second thresholds are dynamically adjusted based on the gas usage pattern. This includes: the compensation node sends a gas usage data retrieval request to the communication gateway through the encrypted communication module; after the communication gateway verifies the legitimacy, it returns the historical gas usage data of the failed node; the central processing unit of the compensation node divides the historical gas usage data into daily gas usage periods, off-peak gas usage periods, and zero gas usage periods, and calculates the gas usage fluctuation coefficient for each period; a threshold correction coefficient is set based on the gas usage period and the fluctuation coefficient; wherein, the first threshold is the minimum allowable leakage flow rate baseline value for equipment detection error, and the second threshold is the maximum allowable safe leakage flow rate baseline value for the most unfavorable location; the current gas usage status of the area covered by the failed node is monitored, and if the current period switches from the preset gas usage period, the threshold correction coefficient is automatically recalculated and the first and second thresholds are updated.

[0039] The compensation node N16 sends a gas consumption data retrieval request to the communication gateway via an encrypted communication module. The request includes the unique identifier ID of node N15. After verifying the validity, the communication gateway returns historical gas consumption data for node N15 over the past 30 days, including the daily gas consumption period distribution, average gas consumption for each period, peak gas consumption, and periods of zero gas consumption. The central processing unit of node N16 analyzes the historical gas consumption data, dividing it into daily gas consumption periods, off-peak gas consumption periods, and periods of zero gas consumption, and calculates the gas consumption fluctuation coefficient for each period: daily gas consumption period fluctuation coefficient ≈ 0.33, off-peak gas consumption period fluctuation coefficient ≈ 0.33, and zero gas consumption period fluctuation coefficient ≈ 0.05. The threshold correction coefficient k is set based on the fluctuation coefficient: k=1.2 for daily gas consumption periods, k=1.0 for off-peak gas consumption periods, and k=0.8 for zero gas consumption periods. Given the first threshold Q1=0.01L / s and the second threshold Q2=0.1L / s, the corrected thresholds are calculated according to the formulas Q1'=Q1×k and Q2'=Q2×k: Q1'=0.012L / s and Q2'=0.12L / s for daily gas consumption periods, Q1'=0.01L / s and Q2'=0.1L / s for off-peak gas consumption periods, and Q1'=0.008L / s and Q2'=0.08L / s for zero gas consumption periods. Furthermore, Q1' is set to have a maximum limit of 0.015L / s and Q2' to have a maximum limit of 0.13L / s. When the current period transitions from a low-peak gas consumption period to a regular gas consumption period, node N16 automatically recalculates the correction coefficient k=1.2 and updates the first and second thresholds to ensure that the thresholds dynamically match the gas consumption patterns.

[0040] In step 4, the gas leakage flow rate is calculated based on the gas state, including: the central processing unit of the compensation node extracts the collected monitoring data, determines the initial pressure P1, termination pressure P2, initial temperature T1, and termination temperature T2 of the closed pipeline, and synchronizes the equivalent volume V0 of the closed pipeline and the gas compressibility factor Z1 and termination temperature Z2 in the preset pipeline topology data; the gas leakage volume ΔV is then calculated using the following formula: In the formula, ΔV is the volume of gas leakage within the detection time t.

[0041] The central processing unit of compensation node N16 extracts the collected monitoring data and determines the initial pressure P1=0.12MPa, termination pressure P2=0.10MPa, initial temperature T1=25.5℃ (298.65K), and termination temperature T2=25.3℃ (298.45K) of the closed pipeline. It also synchronizes the preset pipeline topology data with the equivalent volume V0=15L of the closed pipeline and the gas characteristic parameters (gas compressibility factor Z1=0.98 at the initial moment and gas compressibility factor Z2=0.97 at the termination moment). The data is then used to calculate the gas leakage volume using the gas leakage volume calculation formula: assuming the calculated ΔV=0.45L, this ΔV represents the gas leakage volume within the detection time t=5 minutes. The gas leakage flow rate is then converted from t=5 minutes to 300 seconds using the gas leakage flow rate calculation formula, and substituted into ΔV=0.45L to obtain Q=0.002L / s.

[0042] The formula for calculating the gas leakage flow rate Q is as follows: Where Q is the gas leakage flow rate; If T1-T2≤0.5K, the formula simplifies to ΔV= .

[0043] Verify the validity of the leakage flow rate Q: Determine whether the leakage flow rate Q is within the preset reasonable flow range of 0≤Q≤Q_max: If not, automatically re-collect temperature and pressure data and perform secondary calculation; if yes, output the final gas leakage flow rate Q.

[0044] The maximum permissible leakage flow rate for the pipeline is preset to Q_max = 0.5 L / s. The calculated leakage flow rate Q = 0.002 L / s is verified: if 0 ≤ 0.002 L / s ≤ 0.5 L / s, it meets the preset reasonable flow rate range, and the leakage flow rate Q is deemed valid. The final gas leakage flow rate Q = 0.002 L / s is output as the core basis for subsequent leakage level determination. If the temperature and pressure data collected are abnormal due to a temporary sensor failure, and the calculated Q = -0.001 L / s (less than 0) or Q = 0.6 L / s (greater than Q_max), then the data collection is deemed abnormal. The N16 node automatically re-collects temperature and pressure data and re-executes the leakage volume and flow rate calculation until a leakage flow rate Q within the reasonable range is obtained.

[0045] Step 5, Linkage Control and Alarm: The valve control module of the compensation node executes the valve closing control command, and the encrypted communication module of the compensation node executes the early warning sending command. If a moderate leak is determined, the valve control module of node N16 executes the valve closing control command, closing the electric ball valve of the corresponding branch pipeline; at the same time, it sends an early warning information to the communication gateway, the community property monitoring center, and the user's mobile APP through the encrypted communication module. The early warning information includes the leak location, leak level, and valve closing status.

[0046] In summary, this solution achieves safe control with no blind spots in failure, no delay in valve closure, and no monitoring errors through the collaborative design of adjacent node linkage networking, rapid failure compensation, and precise valve closure nearby. It prevents monitoring interruptions and untimely valve closures due to the failure of a single node, and improves monitoring reliability and safety protection capabilities.

[0047] The above are merely embodiments of the present invention. The invention is not limited to the fields covered by these embodiments. Commonly known structures and characteristics in the solutions are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are able to access all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A smart monitoring system and method for gas pipeline leakage based on the Internet of Things, characterized in that, An application to a monitoring system comprising multiple nodes, each node including a central processing unit, an encrypted communication module, a communication gateway, a valve control module, and a gas monitoring module, comprising the following steps: Step 1, establish the linkage relationship between adjacent nodes: multiple nodes complete the network through the communication gateway, and adjacent nodes exchange working status, monitoring data and leakage information based on the encrypted communication protocol. The gas monitoring module collects the gas monitoring data of the nodes. Step 2, Node Status Self-Check and Failure Reporting: The central processing unit of each node checks the operating status, signal transmission status and monitoring data validity of its own node. When an operating fault, signal interruption or data abnormality is detected, the node is determined to be faulty and a failure notification is sent to neighboring nodes through the encrypted communication module. Step 3, Neighboring Node Compensation Response: After receiving the failure notification, the neighboring node verifies the working status of the failed node through the communication module and confirms the coverage area of ​​the failed node. It then selects the node that is closest to the failed node and is working normally. The node that is closest to the failed node and is working normally automatically starts the compensation mechanism, takes over the monitoring task of the failed node, retrieves the preset pipeline topology data and adjusts the monitoring parameters, and collects the gas monitoring data of the failed node. Step 4, Leakage Flow Calculation and Level Determination: The central processing unit of the compensation node receives the gas monitoring data from the failed node and calculates the gas leakage flow based on the gas status. A first threshold and a second threshold are preset, the gas consumption pattern of the failure node is obtained, the first threshold and the second threshold are dynamically corrected in combination with the gas consumption pattern, and the leakage level is determined according to the corrected first threshold and the second threshold. Based on the leakage level, valve closing control command and early warning sending command are generated. Step 5, Linkage Control and Alarm: The valve control module of the compensation node executes the valve closing control command, and the encrypted communication module of the compensation node executes the early warning sending command.

2. The intelligent monitoring method for gas pipeline leakage based on the Internet of Things according to claim 1, characterized in that, In step 3, the working status of the failed node is verified through the communication module, and the coverage area of ​​the failed node is confirmed. This includes: adjacent nodes sending a preset number of status verification commands to the failed node through the encrypted communication module at preset command intervals. If no valid response is received or a failure confirmation feedback is received, the node is confirmed to be truly failed. The preset pipeline topology data is retrieved and the topology boundary of the failed node is parsed to obtain the coverage area of ​​the failed node.

3. The intelligent monitoring method for gas pipeline leakage based on the Internet of Things according to claim 2, characterized in that, In step 3, the node that is closest to the failed node and is in normal working condition is selected, including: the adjacent node in normal working condition obtains its own installation coordinates and the installation coordinates of the failed node based on the preset pipeline topology data, calculates the straight-line distance between the node and the failed node, and selects the node in normal working condition with the smallest straight-line distance as the compensation node.

4. The intelligent monitoring method for gas pipeline leakage based on the Internet of Things according to claim 3, characterized in that, In step 3, the monitoring task of taking over the failed node includes: after receiving the compensation start command sent by the communication gateway, the target compensation node sends a data synchronization request to the communication gateway through the encrypted communication module to obtain the historical monitoring data, preset monitoring parameter configuration, and topology parameters of the covered pipeline of the failed node; the central processing unit of the compensation node performs trend analysis on the synchronized historical monitoring data, extracts the fluctuation range of monitoring data and the adaptation law of leakage judgment threshold when the failed node is working normally, and adjusts its own monitoring parameters in combination with the topology parameters of the covered pipeline; the compensation node starts the gas monitoring module and collects data at the key monitoring points of the covered pipeline of the failed node according to the adjusted monitoring parameters; the compensation node sends a takeover confirmation signal to the communication gateway and all adjacent nodes through the encrypted communication module, synchronizes the current monitoring parameters and the first collected monitoring data, until the failed node returns to normal.

5. The intelligent monitoring method for gas pipeline leakage based on the Internet of Things according to claim 4, characterized in that, In step 3, the monitoring parameters are adjusted based on the preset pipeline topology data, including: after receiving the compensation start command, the compensation node sends a topology data retrieval request to the communication gateway through the encrypted communication module. After verifying the legality of the request, the communication gateway returns the preset pipeline topology data corresponding to the failed node; the central processing unit of the compensation node parses the topology data to obtain the equivalent volume, pipe diameter, and historical monitoring parameter benchmark values ​​of the covered pipeline; the detection time is matched according to the equivalent volume, the data acquisition frequency is adjusted according to the pipe diameter, the gas compression factor is determined using the historical monitoring parameter benchmark values ​​in the topology data, and the threshold correction coefficient of the failed node is synchronized to complete the adjustment of the monitoring parameters.

6. The intelligent monitoring method for gas pipeline leakage based on the Internet of Things according to claim 5, characterized in that, In step 4, the gas usage pattern of the failed node is obtained, and the first and second thresholds are dynamically adjusted based on the gas usage pattern. This includes: the compensation node sends a gas usage data retrieval request to the communication gateway through the encrypted communication module; after the communication gateway verifies the legitimacy, it returns the historical gas usage data of the failed node; the central processing unit of the compensation node divides the historical gas usage data into daily gas usage periods, off-peak gas usage periods, and zero gas usage periods, and calculates the gas usage fluctuation coefficient for each period; a threshold correction coefficient is set based on the gas usage period and the fluctuation coefficient; wherein, the first threshold is the minimum allowable leakage flow rate baseline value for equipment detection error, and the second threshold is the maximum allowable safe leakage flow rate baseline value for the most unfavorable location; the current gas usage status of the area covered by the failed node is monitored, and if the current period switches from the preset gas usage period, the threshold correction coefficient is automatically recalculated and the first and second thresholds are updated.

7. The intelligent monitoring method for gas pipeline leakage based on the Internet of Things according to claim 6, characterized in that, In step 4, the gas leakage flow rate is calculated based on the gas state, including: the central processing unit of the compensation node extracts the collected monitoring data, determines the initial pressure P1, termination pressure P2, initial temperature T1, and termination temperature T2 of the closed pipeline, and synchronizes the equivalent volume V0 of the closed pipeline and the gas compressibility factor Z1 and termination temperature Z2 in the preset pipeline topology data; the gas leakage volume ΔV is then calculated using the following formula: In the formula, ΔV is the volume of gas leakage within the detection time t; The formula for calculating the gas leakage flow rate Q is as follows: Where Q is the gas leakage flow rate.

8. The intelligent monitoring method for gas pipeline leakage based on the Internet of Things according to claim 7, characterized in that, In step 4, if T1-T2≤0.5K, the formula simplifies to: ΔV= .

9. The intelligent monitoring method for gas pipeline leakage based on the Internet of Things according to claim 8, characterized in that, In step 4, the validity of the leakage flow rate Q is verified: it is determined whether the leakage flow rate Q is within the preset reasonable flow range of 0≤Q≤Q_max. If not, the temperature and pressure data are automatically re-acquired and recalculated. If yes, the final gas leakage flow rate Q is output.

10. An intelligent monitoring system for gas pipeline leaks based on the Internet of Things, characterized in that, Used to perform the monitoring method according to any one of claims 1-9.

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