Intelligent safety monitoring device and method for gas valve well based on multi-parameter sensing

Abstract

The application provides a kind of gas valve well intelligent safety monitoring device and method based on multi-parameter sensing, belongs to the technical field of internet of things equipment, specifically includes: based on the temperature monitoring data of optimization monitoring target, the determination of the opening processing scheme of the leak monitoring sensor of optimization monitoring target is carried out, the gas valve well and the associated valve well are divided into the same combination, according to the opening processing scheme of the leak monitoring sensor of optimization monitoring target in the combination, based on the pressure monitoring data in the gas valve well in the combination, the determination of the safety monitoring method of leak monitoring sensor in the combination is carried out, the reliability of the identification and processing of leakage condition is improved.

Description

Technical Field

[0001] This invention belongs to the field of Internet of Things (IoT) device technology, and particularly relates to an intelligent safety monitoring device and method for gas valve wells based on multi-parameter sensing. Background Technology

[0002] Gas valve wells are key facilities in urban gas pipeline networks. They contain control valves to regulate or cut off gas flow and are commonly found in sidewalks, green belts, and other areas. The well covers are usually marked with the word "Gas" as a warning. During operation, gas valve wells require monitoring and processing of multi-dimensional data, including gas leakage and pipeline vibration. A similar technical solution is provided in CN201810235027.7, "A Monitoring Device and Method for Gas Valve Wells." However, the above technical solution has the following technical problems:

[0003] Because the leakage monitoring devices in gas valve wells lack effective external power supply, the reliability of real-time monitoring cannot be guaranteed. Therefore, determining a safety-coordinated monitoring strategy for gas valve wells based on the correlation between pressure changes and abnormal temperature changes is a pressing technical problem. This strategy aims to ensure the monitoring reliability of at least some gas valve wells even when the reliability of sensors with drastic temperature changes is poor, thereby improving troubleshooting efficiency.

[0004] Therefore, there is an urgent need for an intelligent safety monitoring device and method for gas valve wells based on multi-parameter sensing. Summary of the Invention

[0005] To achieve the objectives of this invention, the following technical solution is adopted:

[0006] Specifically, this application provides an intelligent safety monitoring method for gas valve wells based on multi-parameter sensing, which includes:

[0007] S1 uses the monitoring data of the sensor equipment in the gas valve well as a basis to determine the correlation of the pressure monitoring data in the gas valve well, determines the associated valve wells of the gas valve well based on the correlation, and determines the optimized monitoring target of the leakage monitoring sensor in the gas valve well based on the temperature monitoring data of the associated valve wells of the gas valve well.

[0008] S2 determines the activation scheme of the leakage monitoring sensor of the optimized monitoring target based on the temperature monitoring data of the optimized monitoring target, divides the gas valve well and the associated valve well into the same group, and determines the safety monitoring method of the leakage monitoring sensor in the group based on the pressure monitoring data in the gas valve well of the group according to the activation scheme of the leakage monitoring sensor of the optimized monitoring target in the group.

[0009] The beneficial effects of this invention are as follows:

[0010] Based on the temperature monitoring data of the optimized monitoring target, the activation scheme of the leakage monitoring sensor of the optimized monitoring target is determined. This realizes the determination of the activation scheme from the perspective of the frequency of ambient temperature changes. This not only reduces the power consumption of the optimized monitoring target with relatively stable ambient temperature, but also improves the reliability of the monitoring data of the optimized monitoring target with more drastic ambient temperature changes.

[0011] Based on the activation scheme of the leak monitoring sensor for the optimized monitoring target in the combination and the pressure monitoring data in the gas valve well in the combination, the safety monitoring method of the leak monitoring sensor in the combination is determined. This ensures that the gas valve well can be controlled in a timely manner when the pressure monitoring data changes, thus ensuring the reliability of the monitoring process. At the same time, by further combining the activation scheme of the leak monitoring sensor for the optimized monitoring target in the combination, the battery availability of the associated valve well is also accurately assessed. This enables the determination of the safety monitoring method of the gas valve well based on the battery availability of the associated valve well, thereby further ensuring the reliability of the monitoring process of the gas valve well.

[0012] Furthermore, the sensor device includes a pressure monitoring device and a leak monitoring sensor.

[0013] It should be noted that the leak detection sensor determines the leak status by monitoring the concentration of methane in the air.

[0014] Furthermore, the correlation of the pressure monitoring data in the gas valve well is determined based on the changes in the pressure monitoring data of the gas pipeline in the gas valve well and the changes in the pressure monitoring data of the gas pipeline in other gas valve wells.

[0015] Furthermore, the method for determining the associated valve well of the gas valve well is as follows:

[0016] Based on the aforementioned correlation, determine the changes in pressure monitoring data of gas pipelines in other gas valve wells when the pressure monitoring data of the gas pipeline in the gas valve well changes.

[0017] Based on the aforementioned changes, determine the time period of change in the pressure monitoring data of the gas pipeline in the gas valve well, and the correlation between the time periods of change in the pressure monitoring data in the other gas valve wells. Based on the correlation, determine the associated time periods within the time periods of change.

[0018] Using the associated change period data, it is determined whether the other gas valve wells are associated valve wells.

[0019] Furthermore, the method for determining the activation scheme of the leakage monitoring sensor of the optimized monitoring target is as follows:

[0020] Using the temperature monitoring data of the optimized monitoring target, determine the duration percentage of the optimized monitoring target in different ambient temperature ranges;

[0021] Based on the maximum value of the duration percentage and the ambient temperature range data that meets the requirements for the duration percentage, the activation process of the leakage monitoring sensor for the optimized monitoring target is determined.

[0022] It is understood that when the maximum value of the duration percentage is greater than the preset percentage threshold, the activation processing scheme of the leakage monitoring sensor of the optimized monitoring target is determined to be the preset activation scheme.

[0023] Secondly, this application provides an intelligent safety monitoring device for gas valve wells based on multi-parameter sensing, employing the aforementioned intelligent safety monitoring method for gas valve wells based on multi-parameter sensing, specifically including:

[0024] Temperature monitoring devices, leak detection sensors, pressure monitoring devices, and communication devices;

[0025] The temperature monitoring device is responsible for monitoring and processing the ambient temperature inside the gas valve well; the leak monitoring sensor is responsible for monitoring and processing gas leaks; the pressure monitoring device is responsible for monitoring and processing the pressure of the gas pipeline; and the communication device is responsible for uploading and processing the monitoring data from the temperature monitoring device, the leak monitoring sensor, and the pressure monitoring device.

[0026] Other features and advantages will be set forth in the following description, and the objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0027] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0028] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings;

[0029] Figure 1 This is a flowchart of an intelligent safety monitoring method for gas valve wells based on multi-parameter sensing;

[0030] Figure 2 This is a flowchart illustrating the method for determining the associated valve wells of a gas valve well;

[0031] Figure 3 This is a flowchart illustrating a method for optimizing the determination of monitoring targets for leak monitoring sensors in gas valve wells;

[0032] Figure 4 This is a framework diagram of an intelligent safety monitoring device for gas valve wells based on multi-parameter sensing. Detailed Implementation

[0033] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0034] Example 1

[0035] like Figure 1 As shown, this application provides an intelligent safety monitoring method for gas valve wells based on multi-parameter sensing, specifically including:

[0036] S1 uses the monitoring data of the sensor equipment in the gas valve well as a basis to determine the correlation of the pressure monitoring data in the gas valve well, determines the associated valve wells of the gas valve well based on the correlation, and determines the optimized monitoring target of the leakage monitoring sensor in the gas valve well based on the temperature monitoring data of the associated valve wells of the gas valve well.

[0037] Furthermore, the sensor device includes a pressure monitoring device and a leak monitoring sensor.

[0038] It should be noted that the leak detection sensor determines the leak status by monitoring the concentration of methane in the air.

[0039] Furthermore, the correlation of the pressure monitoring data in the gas valve well is determined based on the changes in the pressure monitoring data of the gas pipeline in the gas valve well and the changes in the pressure monitoring data of the gas pipeline in other gas valve wells.

[0040] The core decision-making objective of this method is to identify and determine other valve wells (i.e., associated valve wells) that are operationally correlated with the target gas valve well, based on the interconnected changes in pressure monitoring data from multiple gas valve wells. The core logic involves analyzing pressure variations over time to identify other valve wells whose pressure changes are highly synchronized with those of the target well. This establishes the physical and operational relationships between nodes in the pipeline network, providing a basis for leak location, pressure control, and pipeline network analysis.

[0041] Specifically, such as Figure 2 As shown, the method for determining the associated valve well of the gas valve well is as follows:

[0042] Based on the aforementioned correlation, determine the changes in pressure monitoring data of gas pipelines in other gas valve wells when the pressure monitoring data of the gas pipeline in the gas valve well changes.

[0043] Keyword explanation:

[0044] Unit of time: The basic unit of time for data aggregation and analysis, such as 30 minutes.

[0045] Pressure monitoring data for a specific time period: refers to the average value of pressure monitoring data collected at all times within that time period (such as a 30-minute window).

[0046] Based on the aforementioned changes, determine the time period of change in the pressure monitoring data of the gas pipeline in the gas valve well, and the correlation between the time periods of change in the pressure monitoring data in the other gas valve wells. Based on the correlation, determine the associated time periods within the time periods of change.

[0047] Fluctuation period: refers to the period in which pressure changes significantly. The specific criterion is as follows: if the absolute value of the difference between the average pressure Pi of a certain period (the i-th period) and the average pressure P_{i-1} of the previous period (the (i-1)-th period) is greater than the average pressure P_{i-1} of the previous period, then the ratio of this difference to a preset threshold (e.g., 0.03), i.e., |Pi - P_{i-1}| / P_{i-1} > 0.03, then the i-th period is determined to be a fluctuation period.

[0048] Using relative rates of change (ratios) instead of absolute differences avoids the problem of inconsistent threshold settings due to differences in baseline pressure across different pipelines and regions. A threshold of 0.03 (3%) aims to filter out normal, minor pressure fluctuations (such as small fluctuations in user gas consumption or measurement noise), thus focusing on significant pressure changes caused by events such as leaks, pressure regulation, and large user start-ups / shutdowns. Dividing continuous data into time periods and averaging them smooths out instantaneous disturbances, highlights trend changes, and makes correlation analysis more robust.

[0049] Example: For a valve well V1, the continuous average pressure over a 30-minute period is as follows (unit: kPa):

[0050] Time period 1: 300.0, Time period 2: 301.5, Time period 3: 290.0, Time period 4: 289.8;

[0051] Calculate the rate of change:

[0052] Time period 2 vs Time period 1: |301.5-300.0| / 300.0 = 0.005<0.03, which is a non-variable time period.

[0053] Time period 3 vs Time period 2: |290.0-301.5| / 301.5 ≈ 0.038>0.03, indicating a fluctuating time period.

[0054] Time period 4 vs Time period 3: |289.8-290.0| / 290.0 ≈ 0.0007<0.03, which is a non-variable time period.

[0055] Therefore, V1 was identified as a period of pressure variation in time period 3.

[0056] Correlation changes: refers to the temporal correspondence between the pressure changes of the target gas valve well (well A) and other gas valve wells (well B).

[0057] Related periods of change: These refer to the periods during which well A and well B are simultaneously within their respective defined "periods of change". That is, within the same time window (such as a 30-minute period), the pressure change rate of well A exceeds 3%, and the pressure change rate of well B also exceeds 3%.

[0058] This is the core of correlation determination. If the pressure in two valve wells frequently changes significantly at the same time, it strongly suggests that there is a hydraulic connection between them, that they are affected by the same pressure source or the same event (such as being upstream and downstream of a main pipeline, having connected branches, or being controlled by the same pressure regulating station). Simply analyzing the similarity of pressure change trends (such as both rising or both falling) is insufficient; it is necessary to require a high degree of consistency in the "moment" at which the changes occur in order to rule out mere coincidence.

[0059] Example: Continuing the previous example, V1 is in a fluctuating period in time period 3. Simultaneously analyzing the data for another valve well, V2, we find that the pressure change rate of V2 also exceeds 3% in time period 3 (i.e., V2 is also in a fluctuating period in time period 3). Therefore, time period 3 is a fluctuating correlation period between V1 and V2.

[0060] Using the associated change period data, it is determined whether the other gas valve wells are associated valve wells.

[0061] It is understood that the change-related time period refers to the time period during which the gas valve well and other gas valve wells simultaneously belong to the change-related time period.

[0062] Specifically, when the number of the variable associated time periods meets the requirements, the other gas valve wells are determined to be associated valve wells.

[0063] The number of variable correlation periods meets the requirement: the number of variable correlation periods between target well A and other wells B must be a very high proportion relative to the total number of variable periods of well A itself. A specific standard is: number of variable correlation periods / total number of variable periods of well A > 0.9.

[0064] Setting a high percentage threshold (e.g., 90%) ensures the robustness and consistency of the correlation. Occasionally, simultaneous changes might be coincidental, but if almost every time a target well experiences a significant pressure change, another well also experiences a significant change simultaneously, then this correlation is systematic and repeatable, and highly likely reflects a true physical connection or tight operational coupling. This method can effectively distinguish between directly correlated adjacent wells and distant wells that are only indirectly affected by large system fluctuations.

[0065] Example: Suppose we are conducting a 10-day analysis of valve well V1 (480 30-minute intervals in total).

[0066] V1 itself was identified as having 20 "variable time periods". Analyzing another valve well, V2, it was found that V2 was also in 19 of these 20 variable time periods. That is, the number of variable-related time periods is 19.

[0067] The calculated percentage is 19 / 20 = 0.95 > 0.9. Therefore, V2 is determined to be the associated valve well of V1.

[0068] Specifically, such as Figure 3 As shown, the method for determining the optimized monitoring target of the leak monitoring sensor in the gas valve well is as follows:

[0069] Specifically, based on the interconnected network of gas valve wells and the impact of ambient temperature changes on the reliability of methane leak monitoring, this method intelligently identifies gas valve wells requiring enhanced monitoring as "optimized monitoring targets." The core logic is that significant temperature changes in associated valve wells alter the detection environment of the methane sensor (e.g., affecting gas diffusion rate, sensor sensitivity, or background noise), thus impacting the reliability of leak monitoring. Therefore, enhanced monitoring of target wells connected to these temperature-anomaly-related wells is necessary. This allows for the exclusion of optimized monitoring targets if the monitoring data of the optimized targets is normal, prioritizing the investigation of temperature-anomaly-related wells and improving the efficiency of leak detection. This method quantifies the weight of temperature changes in associated wells and sets judgment conditions for "single-point severe anomaly" or "multi-point cumulative anomaly," enabling precise identification of high-risk nodes.

[0070] Gas diffusion and accumulation: Increased ambient temperature usually accelerates the movement of gas molecules, which may cause leaked methane to diffuse and dilute more quickly, reducing its accumulation concentration near the sensor and increasing the risk of small leaks being missed. Decreased temperature may make the gas more likely to accumulate, but it may also affect the sensor's performance.

[0071] Sensor performance deviation: The sensitivity of many gas sensors (such as catalytic combustion type and semiconductor type) is affected by ambient temperature, which may cause reading drift, changes in response speed or false alarms.

[0072] Background noise changes: Temperature changes may be accompanied by changes in atmospheric pressure and humidity, or affect other biochemical processes in the well (such as the decomposition of organic matter), thereby changing the background methane level and affecting the signal-to-noise ratio.

[0073] Changes in leakage pattern: Changes in soil or pipe temperature may affect the rate or pattern of micro-leaks in the pipe.

[0074] Relying solely on methane concentration thresholds for leak detection may lead to "monitoring blind spots" or an increase in false alarms during periods of rapid temperature change. This method uses temperature anomalies in associated valve wells as an early warning indicator of "monitoring reliability risk." When the temperature of associated wells changes significantly, it signifies increased energy consumption for both the well itself and strongly correlated target wells; therefore, these wells need to be marked as "optimized monitoring targets."

[0075] Based on the associated valve well data of the gas valve well, the number of associated valve wells of the gas valve well is determined;

[0076] Related valve well data: based on Figure 2 The list and number of associated valve wells for the target gas valve well determined in the embodiment.

[0077] Number of associated valve wells: refers to the total number of other valve wells that have a strong pressure linkage with the target valve well (the percentage of the time period of change linkage > 0.9).

[0078] This is the "network size" dimension of risk assessment. The more associated valve wells there are, the more "central" or complex the target well is in the pipeline network. Once an anomaly occurs in its associated network, the impact may be wider, thus requiring closer monitoring. Furthermore, the quantity also forms the basis for subsequent calculations of cumulative weights.

[0079] Example: Target valve well V-101, according to Figure 2 The method identifies three associated valve wells: V-102, V-104, and V-107. Therefore, the total number of associated valve wells is 3.

[0080] Based on the temperature monitoring data of the associated valve wells, determine the changes in the temperature monitoring data of the associated valve wells, and determine the temperature change weight value of the associated valve wells based on the changes;

[0081] Temperature monitoring data fluctuations: This specifically refers to temperature changes that may significantly impact the reliability of methane sensor monitoring. Examples include rapid temperature rises and falls, temperatures exceeding the sensor's calibration range, or temperatures reaching critical points known to affect local leakage patterns.

[0082] Temperature Variation Weight (Wi): This quantifies the impact of temperature changes in the associated well on monitoring reliability. Its calculation should reflect the potential impact on reliability. For example: Wi = |Temperature Change Rate| * Duration Coefficient (rapid changes have a greater impact); Wi = f(Current Temperature, Sensor's Optimal Operating Temperature Range) (the greater the deviation from the optimal range, the greater the weight); Combining historical data, calculate the Z-score of temperature relative to seasonal norms; the greater the anomaly, the higher the weight.

[0083] The weighting values ​​no longer merely represent the physical degree of temperature "abnormality," but rather focus on the potential impact of this abnormality on the reliability of core safety monitoring functions (leakage monitoring). This allows subsequent decisions to more accurately serve the fundamental goal of ensuring monitoring effectiveness.

[0084] Example: Suppose that the weighting is determined by the rate of temperature change (°C / hour) and the degree of deviation from the optimal operating temperature (20°C).

[0085] Corresponding well V-102: The temperature dropped sharply from 15°C to 5°C within 1 hour, with a rate of change of -10°C / h. The current temperature is 5°C. Rate of change component: |-10| = 10, deviation component: |5-20| = 15, combined weight W1 = 10 + 15 = 25;

[0086] Correlated well V-104: Temperature stabilizes at 18°C ​​(near the optimal range), rate of change 0°C / h. W2 ≈ 0. Correlated well V-107: Temperature slowly rises from 25°C to 28°C, rate of change 3°C / h, current temperature 28°C. Rate of change component: 3, deviation component: |28-20| = 8, combined weight W3 = 3 + 8 = 11.

[0087] By using different temperature variation weight values ​​of associated valve wells, it is determined whether the gas valve well is an optimal monitoring target for the leak monitoring sensor.

[0088] It should be noted that when there is an associated valve well with a temperature change weight value greater than the preset weight value in the gas valve well, the gas valve well is determined to be the optimized monitoring target of the leak monitoring sensor.

[0089] Whether the temperature changes in the associated wells have become, or may become, severe enough to affect the reliability of leak monitoring, thus necessitating enhanced monitoring of the associated target wells.

[0090] First-level assessment: The reliability risk of monitoring a single associated well is extremely high.

[0091] If the temperature of a related well undergoes an extreme change (e.g., rapid cooling causing a sharp drop in sensor sensitivity, or high temperature causing a surge in background noise), the leak monitoring of that well itself may temporarily "malfunction" or become inaccurate. Because the target well and this well are strongly correlated (pressure linkage), the risk of a leak occurring in the target well area but being missed by a monitoring system that is malfunctioning in the related well increases. Therefore, the target well must be immediately upgraded to an optimized monitoring target. By improving the monitoring reliability of the optimized target, the scope of leak investigation can be reduced.

[0092] Example: Assume the preset weight value (W_critical) is set to 20. Check the above weights: W1=25>20. The condition is met. Therefore, the gas valve well V-101 is directly identified as the optimized monitoring target for the leak detection sensor. This is because the monitoring reliability of its associated well V-102 may have been severely compromised due to the sudden temperature drop.

[0093] Additionally, it can be understood that when there are no associated valve wells with a temperature change weight value greater than the preset weight value for the gas valve well, if the sum of the temperature change weight values ​​of the associated valve wells of the gas valve well is greater than the preset change weight threshold, then the gas valve well is determined to be the optimized monitoring target of the leak monitoring sensor.

[0094] The cumulative risk to monitoring reliability from multiple associated wells needs to be assessed. Even when no single associated well exhibits extreme risk to monitoring reliability, the simultaneous occurrence of moderate temperature anomalies in multiple associated wells can lead to an overall decline in the reliability of leak monitoring across the entire local pipeline network. If the target well is located within this network of declining reliability, the likelihood of its leak going undetected increases. Therefore, it is necessary to upgrade the monitoring level of the target well to address the network-level degradation in monitoring reliability, thereby reducing the difficulty of troubleshooting leaks.

[0095] Example: (Continuing from the first level of the "no" branch) Assume W1=18 (<20), W2=0, W3=15.

[0096] Calculate the weighted sum: Sum_W = 18 + 0 + 15 = 33. Assuming the preset variable weight threshold (Sum_W_threshold) is 30, since 33 > 30, the condition is met. Therefore, the gas valve well V-101 is determined as the optimized monitoring target for the leak monitoring sensor.

[0097] This method upgrades the traditional monitoring strategy based on direct evidence of leakage (methane concentration) to a proactive, preventative strategy that incorporates the risk of the monitoring system's own condition by introducing the core dimension of "the impact of temperature changes on the reliability of leak monitoring." Its core value lies in: 1. Ensuring the effectiveness of the monitoring system: Strengthening monitoring in advance when environmental conditions may weaken monitoring capabilities prevents leaks due to monitoring failure. 2. Achieving a closed-loop risk management system: Focusing not only on "whether the pipeline leaks" but also on "whether leaks can be reliably detected," reflecting a more comprehensive risk management approach. 3. Dynamically adapting to environmental changes: Enabling the monitoring strategy to dynamically adjust with meteorological conditions and seasonal changes, improving the system's adaptability and robustness. 4. Optimizing emergency resource preparation: When areas where monitoring reliability is reduced due to temperature are identified, inspection teams or maintenance resources can be deployed in advance, shortening emergency response time. 5. Deepening data-driven decision-making: Further exploring the value of temperature data, transforming it from a simple environmental parameter into a key indicator for assessing the health and risk level of the monitoring system.

[0098] S2 determines the activation scheme of the leakage monitoring sensor of the optimized monitoring target based on the temperature monitoring data of the optimized monitoring target, divides the gas valve well and the associated valve well into the same group, and determines the safety monitoring method of the leakage monitoring sensor in the group based on the pressure monitoring data in the gas valve well of the group according to the activation scheme of the leakage monitoring sensor of the optimized monitoring target in the group.

[0099] By implementing an enhanced monitoring scheme for optimized monitoring targets with abnormal temperature correlations and volatile pressures, the system ensures highly reliable monitoring data under any operating condition. This allows these target points to be directly excluded from manual investigation during leak events, achieving "exemption from inspection privileges through reliable monitoring." The core logic is to identify critical monitoring points (optimized monitoring targets) whose monitoring reliability is challenged due to abnormal temperature correlations and whose pressures are also prone to fluctuation. By dynamically adjusting the activation scheme of their leak sensors (such as higher frequency and longer pressure-triggered monitoring windows), the real-time performance and confidence level of their monitoring data are significantly improved. When a pipeline leak occurs, these monitoring points, due to their continuous and reliable "health certificate" (normal real-time monitoring data), can be automatically identified as safe points by the system, eliminating the need for on-site manual investigation and allowing emergency response forces to focus entirely on other suspected areas.

[0100] Specifically, based on the historical temperature distribution characteristics of the gas valve well, the most economical and effective activation strategy for its leakage monitoring sensor is adaptively determined to optimize sensor energy consumption and lifespan while ensuring monitoring reliability. The core logic is to analyze the residence time of monitoring points in different temperature ranges to determine the stability of the temperature environment (whether it is concentrated in a narrow range for a long period). For monitoring points with stable temperature environments, a power-saving strategy of "low-frequency routine monitoring + short-term pressure-triggered monitoring" is adopted; for monitoring points with variable temperature environments, a strengthened strategy of "medium-frequency routine monitoring + longer-term pressure-triggered monitoring" is adopted. This logic aims to tailor solutions for monitoring points with different temperature characteristics, achieving the best balance between reliability, response speed, and equipment economy.

[0101] Optimize monitoring targets: These refer to those whose temperature changes due to associated sensors are significant ( Figure 3 This may be because the monitoring environment or the reliability of the sensors themselves has been affected by temperature.

[0102] Pressure is volatile: These points are themselves located in pipe sections with large pressure fluctuations (derived from analysis of historical pressure data).

[0103] The dual challenges are that environmental interference (temperature) combined with variable operating conditions (pressure) make its monitoring data traditionally the most unreliable and the most in need of manual verification.

[0104] Solution: Through an enhanced, adaptive on-processing scheme ( Figure 4 (An enhanced version) to overcome these challenges.

[0105] Objective achieved: Transform the "most unreliable point" into the "most reliable point" through technical means, thereby obtaining an "inspection-free pass" in emergency situations.

[0106] This method concentrates resources on the most challenging monitoring points, using an "overcorrection" approach to elevate the data quality of these points to an extremely high confidence level that requires no manual review. This breaks the traditional cycle of "unreliability necessitates more manual checks," achieving significant savings in labor costs and improved response speed during later emergency responses through higher upfront technological investment (more frequent wake-ups, more intelligent triggers).

[0107] Specifically, the method for determining the activation scheme of the leakage monitoring sensor for the optimized monitoring target is as follows:

[0108] Using the temperature monitoring data of the optimized monitoring target, determine the duration percentage of the optimized monitoring target in different ambient temperature ranges;

[0109] Key parameter descriptions:

[0110] Ambient temperature range division: Divide the temperature into multiple continuous ranges, such as: ≤0°C, (0°C, 10°C], (10°C, 20°C], (20°C, 30°C], >30°C. The division method can be adjusted according to local climate characteristics.

[0111] Statistical period: Usually a representative time period is selected, such as the most recent full quarter or the previous period that is the same as the current season.

[0112] Duration percentage calculation: For each temperature range, calculate the percentage of the total time that the ambient temperature of the optimized monitoring target falls within that range relative to the total duration of the monitoring.

[0113] Different temperatures not only affect the diffusion behavior after a gas leak, but can also affect the performance of the sensor itself (such as sensitivity and zero drift). Understanding the most frequent temperature environment at a monitoring point allows us to predict the monitoring conditions it is most likely to face, thus enabling us to pre-configure the most suitable monitoring schedule. Furthermore, the concentration of temperature distribution reflects environmental stability and is a key basis for selecting different strategies.

[0114] Example: Taking a gas valve well V-001 as an example, collect its autumn (September-November) data. Assume the temperature range division and percentage are as follows: (10°C, 20°C) 72%, (20°C, 30°C) 18%, (0°C, 10°C) 8%, other ranges 2%.

[0115] The maximum percentage of time taken is 72%, corresponding to the interval (10°C, 20°C). The intervals with a percentage of time taken greater than 0.1 are (10°C, 20°C) and (20°C, 30°C).

[0116] Based on the maximum value of the duration percentage and the ambient temperature range data that meets the requirements for the duration percentage, the activation process of the leakage monitoring sensor for the optimized monitoring target is determined.

[0117] It is understood that when the maximum value of the duration percentage is greater than the preset percentage threshold, the activation processing scheme of the leakage monitoring sensor of the optimized monitoring target is determined to be the preset activation scheme.

[0118] Scenario 1: A dominant temperature range exists (maximum value > 0.6), activation scheme: default activation scheme;

[0119] Within the dominant temperature range (i.e., the range with the largest proportion): the leak monitoring sensor is routinely turned on and data is collected according to a preset time cycle (e.g., 3 days). This is a low-frequency, periodic baseline monitoring.

[0120] In all other temperature ranges: the regular 3-day cycle of operation is not performed. However, pressure-triggered monitoring is set as a safety precaution: when a pressure change rate >3% (|P_now - P_prev| / P_now>0.03), the sensor will continue to monitor for the first duration (e.g., 10 minutes) regardless of the current temperature.

[0121] When a monitoring point exists within a narrow temperature range for the vast majority of the time (>60%), its monitoring environment can be considered highly stable. In this stable environment, a lower monitoring frequency (once every 3 days) is sufficient to establish a reliable baseline and detect slowly developing leaks. Simultaneously, the pressure triggering mechanism acts as a "safety net," ensuring that high-intensity monitoring is immediately initiated should any sudden pressure fluctuations at any temperature, potentially caused by a leak, occur. This achieves a balance between energy optimization and safety assurance in a stable environment.

[0122] Specifically in this embodiment: since the maximum value 72% > 60%, V-001 adopts a preset activation scheme.

[0123] When the ambient temperature is between 10°C and 20°C, the sensor is woken up every 3 days, operates for 2 minutes, and collects and uploads methane concentration data.

[0124] When the temperature is in other ranges (such as when the temperature rises to 25°C on a certain day), the 3-day cycle monitoring is not performed. However, if a sudden change in pressure from 300 kPa to 291 kPa (3% change rate) is detected at this time, the sensor is immediately activated and monitored continuously for 10 minutes.

[0125] It should also be noted that when the maximum value of the duration percentage is not greater than the preset percentage threshold, the leakage monitoring sensor of the optimized monitoring target will be activated in accordance with the second preset activation scheme within the ambient temperature range where the duration percentage meets the requirements.

[0126] The second preset activation scheme determines the main temperature range: all temperature ranges with a duration percentage > 0.1 are defined as the "main temperature range".

[0127] Within the main temperature range: the sensor is turned on regularly according to the second preset time period (e.g., 2 days). This is more frequent than in Option 1.

[0128] In other temperature ranges: the regular cycle activation is not performed. When the pressure change rate is >3%, enhanced monitoring is triggered, and sensor monitoring is activated for a subsequent second duration (e.g., 20 minutes).

[0129] When temperature distribution is dispersed, monitoring points frequently experience different monitoring environments. To maintain sufficient monitoring coverage and establish an adaptive baseline under various common environments (>10%), a higher routine monitoring frequency (once every two days) is required. Simultaneously, considering the potential for greater environmental noise and uncertainty due to temperature variations, the monitoring window after pressure triggering is extended to 20 minutes to provide more time for observation to confirm or eliminate leaks. This reflects the conservative principle of "variable environment, enhanced monitoring, extended window".

[0130] Example: Assume the temperature duration percentage for another valve well V-002 is: (0°C, 10°C) 35%, (10°C, 20°C) 40%, (20°C, 30°C) 20%, others 5%.

[0131] The maximum percentage is 40% < 60%. The main temperature ranges (percentage > 0.1%) are the first three.

[0132] V-002 adopts the second preset opening scheme:

[0133] When the temperature is between (0°C, 10°C), (10°C, 20°C), or (20°C, 30°C), routine monitoring shall be performed every 2 days. When the temperature is in other rare ranges (such as below 0°C) and the pressure variability exceeds 3%, monitoring for 20 minutes shall be triggered.

[0134] This method drives the generation of differentiated monitoring strategies through "temperature profiling analysis," achieving refined and intelligent management of gas leak monitoring. Its core value lies in:

[0135] Significantly optimizes equipment lifecycle and energy consumption: "Reducing the burden" on monitoring points with stable temperature environments (lowering the wake-up frequency) can greatly extend the battery life of battery-powered equipment and reduce maintenance costs; "Empowering" monitoring points with variable environments (appropriately increasing the frequency) ensures their monitoring effectiveness.

[0136] Enhance the intelligence and safety of monitoring response: upgrade the single fixed-cycle monitoring to a two-layer mode of "temperature adaptive cycle monitoring + pressure event triggered monitoring", which not only ensures the economy of daily monitoring, but also strengthens the safety defense line for dealing with sudden working conditions.

[0137] Personalized configuration and improved management efficiency: By eliminating the "one-size-fits-all" parameter settings, the system can automatically calculate and allocate appropriate strategies for hundreds or thousands of monitoring points, greatly reducing the workload of maintenance personnel and improving the scientific nature of overall pipeline network monitoring.

[0138] Enhancing data validity: Periodic baseline measurements in the temperature range where the sensor is most likely to maintain good performance (i.e., the dominant or primary range) help establish a more accurate and stable range of normal values, improving the accuracy of leak detection.

[0139] Specifically, the method for determining the safety monitoring method of the leakage monitoring sensor in the aforementioned combination is as follows:

[0140] Based on the sensor activation strategy distribution and actual energy consumption of each optimized monitoring target within a gas valve well assembly (such as a region or pipeline), the system intelligently determines the appropriate safety monitoring mode for the entire assembly. The core logic is as follows: First, count the number of monitoring points within the assembly using the energy-saving activation scheme (the first preset activation scheme). If this number is dominant, it indicates lower overall energy consumption, supporting a more advanced real-time monitoring mode. If the number is not dominant, further examine the recent actual energy consumption (activation duration). If the actual energy consumption is controllable, real-time monitoring can still be supported. If the energy consumption is too high, it must be downgraded to an emergency monitoring mode triggered only in case of pressure anomalies. This logic aims to dynamically balance the monitoring intensity at the assembly level with system energy consumption constraints, achieving a balance between regional safety and resource sustainability.

[0141] The optimized monitoring targets in the combination are determined based on the constituent data of the optimized monitoring targets in the combination.

[0142] Group: refers to a group of gas valve wells that are logically or physically related, such as wells that belong to the same downstream pressure regulating station, the same geographical area, or are managed by the same maintenance team.

[0143] Optimized monitoring target composition data: refers to the list of all valve wells identified as "optimized monitoring targets" within this group and their attributes.

[0144] First preset activation scheme: refers to the preset activation scheme defined in the aforementioned embodiments, namely, the relatively energy-saving scheme of "activating once every 3 days in the dominant temperature range and monitoring for 10 minutes after pressure triggering".

[0145] Other activation schemes: mainly refers to the second preset activation scheme (which activates once every 2 days in multiple main temperature ranges and monitors for 20 minutes after pressure triggering), which has relatively high energy consumption.

[0146] While the strategies for individual monitoring points have been determined, combined-level monitoring requires a holistic consideration of overall resources (such as regional communication bandwidth, centralized power supply capacity, and backend data processing capabilities) and overall risks. By analyzing the proportion of energy-efficient monitoring points within the combined system, a preliminary assessment can be made at the "strategy configuration" level to determine whether the area has the energy capacity to implement more intensive monitoring.

[0147] Example: Suppose a certain area contains 10 gas valve wells, of which 7 are identified as optimized monitoring targets (numbered O1-O7). Based on... Figure 4 The implementation example specifies activation schemes for each of them: O1, O2, O3, O4, and O5 adopt the first preset activation scheme (energy-saving type). O6 and O7 adopt the second preset activation scheme (higher energy consumption type).

[0148] Therefore, the number of optimized monitoring targets for the first preset activation scheme is 5.

[0149] Based on the activation processing scheme of the leakage monitoring sensor for the optimized monitoring target, the number of optimized monitoring targets using different activation processing schemes is determined. Based on the number of optimized monitoring targets using different activation processing schemes, the number of optimized monitoring targets using the first preset activation scheme is determined.

[0150] The safety monitoring method for leakage monitoring sensors in the combination is determined by optimizing the number of monitoring targets according to the first preset activation scheme.

[0151] Furthermore, when the number of optimized monitoring targets of the first preset opening scheme meets the requirements, since the number of optimized monitoring targets with low power consumption of the opening scheme is large, it is determined that the gas valve well can be safely monitored in real time.

[0152] When the number of optimized monitoring targets under the first preset activation scheme meets the requirements. "Meets the requirements" here usually means that the number exceeds a certain threshold, for example: number > (total number of optimized monitoring targets within the combination * proportional threshold) or number > absolute number threshold. Assume the proportional threshold is 0.5 (50%).

[0153] When the number of optimized monitoring targets for the first preset opening scheme meets the requirements, then since the number of optimized monitoring targets with lower power consumption for the opening process is large, it is determined that the gas valve well can be safely monitored in real time.

[0154] If most optimized monitoring points within the system employ energy-saving solutions, it means that the sensors at these points are dormant most of the time, only briefly activating under specific temperature ranges or pressure triggers. This accumulates a considerable "power surplus" for the entire system's power supply (such as a battery pack or solar power system). Utilizing this surplus, all valve wells within the system (including both optimized and non-optimized targets) can be upgraded to a more advanced real-time monitoring mode, where sensors remain constantly open with low power consumption or are intermittently awakened at high frequencies, enabling near-continuous data acquisition. This significantly improves the real-time monitoring and leak detection speed across the entire area.

[0155] Example (continued): Total number of optimized monitoring targets within the combination = 7, number using the first preset scheme = 5. Ratio = 5 / 7 ≈ 71% > 50%, meeting the requirements.

[0156] Decision: All gas valve wells in this portfolio (including O1-O7 and three other non-optimized targets) can be monitored for safety using a "real-time monitoring" mode. For example, the sensors would wake up every 5 minutes to sample and upload data.

[0157] Furthermore, when the number of optimized monitoring targets for the first preset opening scheme does not meet the requirements, the opening duration of different optimized monitoring targets within the most recent preset duration is determined. When the average opening duration of different optimized monitoring targets within the most recent preset duration meets the requirements, the gas valve well can be safely monitored in real time during the next preset duration.

[0158] When the number of energy-saving monitoring points does not meet the requirements, further decisions are made based on actual energy consumption:

[0159] When the number of monitoring points using the first preset scheme is insufficient (such as only 2 in the example above), a direct judgment cannot be made. This is because even if more monitoring points are used using the second preset scheme, the total energy consumption may not be high if their recent actual operating time is very short. Therefore, it is necessary to examine the recent actual energy consumption.

[0160] Metric: The activation duration of different optimized monitoring targets within the most recent preset time period. The preset time period is, for example, "the past 7 days". Activation duration refers to the cumulative time that the sensors of each optimized monitoring target have actually been in an active state (rather than in a dormant state) over the past 7 days.

[0161] Sub-judgment 1: When the average opening time of different optimization monitoring targets within the most recent preset time period meets the requirements, "meets the requirements" means that the average opening time is lower than a certain threshold (for example, the average daily opening time is <30 minutes).

[0162] Decision: During the next preset time period (as in the following week), the gas valve well can still be monitored and handled safely through real-time monitoring.

[0163] This introduces "actual operational data" to correct the predictions made in "strategy configuration." Even if many points employ energy-intensive solutions, their actual energy consumption may be very low if there are no frequent stress-triggered events recently, or if they are not in the temperature range that triggers regular monitoring most of the time. This indicates that the system has the capacity to support real-time monitoring under current operating conditions.

[0164] Example: Assume there are 7 optimized monitoring points within the combination, but only 2 use the first preset scheme (not meeting the quantity requirement). Calculate the actual operating time (in minutes) of each point over the past 7 days: [O1:40, O2:35, O3:120, O4:110, O5:38, O6:200, O7:180]

[0165] Average usage time = (40+35+120+110+38+200+180) / 7 ≈ 103 minutes / week ≈ 14.7 minutes / day.

[0166] Assuming the requirement is an average of <30 minutes per day, then 14.7 minutes < 30 minutes, which meets the requirement. Decision: Next week, this combination can still use the real-time monitoring mode.

[0167] Specifically, when the average opening duration of different optimized monitoring targets within the most recent preset time period does not meet the requirements, it is determined that the gas valve well will only activate the leakage monitoring sensor within the subsequent third time period when a change in pressure monitoring data occurs, thereby performing safety monitoring. In a possible specific embodiment, the third time period is half an hour.

[0168] When the average activation duration of different optimization monitoring targets within the most recent preset time period does not meet the requirements, that is, the actual average energy consumption is too high (e.g., >30 minutes / day).

[0169] Decision: It is determined that the gas valve well will only activate the leak monitoring sensor for a third time period (e.g., half an hour) after a change in pressure monitoring data, thus performing safety monitoring. That is, all wells within the group are downgraded to a pure pressure-triggered mode, all regular periodic activation is cancelled, and the sensor will only be activated for half an hour if any well detects a pressure change rate >3%.

[0170] This is the final energy-saving backup strategy. When the monitoring points within the system not only consume power strategically but also have high actual operating energy consumption, it indicates that the area either has a complex environment (fluctuating temperatures causing frequent entry into the regular monitoring range) or unstable operating conditions (frequent pressure fluctuations causing frequent triggering). At this point, the system is no longer able to support real-time monitoring. To ensure that critical safety functions are not interrupted (battery not depleted), the most extreme energy-saving measures must be taken: cancel all periodic wake-ups and retain only the core "pressure anomaly - start leak detection" linkage function to maximize equipment endurance and safeguard the bottom line of safety.

[0171] Example: Continuing from the previous example, if the average on-time is 45 minutes / day (>30 minutes / day), then the requirement is not met.

[0172] Decision: This system will enter "pressure-triggered monitoring only" mode. Going forward, routine 3-day or 2-day monitoring cycles for all wells (O1-O7) will be suspended. Only when a well detects a pressure change >3% will its leak sensor be activated and operate continuously for 30 minutes (the third duration).

[0173] Example 2

[0174] Secondly, such as Figure 4 As shown, this application provides an intelligent safety monitoring device for gas valve wells based on multi-parameter sensing, employing the aforementioned intelligent safety monitoring method for gas valve wells based on multi-parameter sensing, specifically including:

[0175] Temperature monitoring devices, leak detection sensors, pressure monitoring devices, and communication devices;

[0176] The temperature monitoring device is responsible for monitoring and processing the ambient temperature inside the gas valve well; the leak monitoring sensor is responsible for monitoring and processing gas leaks; the pressure monitoring device is responsible for monitoring and processing the pressure of the gas pipeline; and the communication device is responsible for uploading and processing the monitoring data from the temperature monitoring device, the leak monitoring sensor, and the pressure monitoring device.

[0177] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0178] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0179] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A method for intelligent safety monitoring of gas valve wells based on multi-parameter sensing, characterized in that, Specifically, it includes: Based on the monitoring data of the sensor equipment in the gas valve well, the correlation of the pressure monitoring data in the gas valve well is determined. Based on the correlation, the associated valve wells of the gas valve well are determined. Based on the temperature monitoring data of the associated valve wells of the gas valve well, the optimized monitoring target of the leakage monitoring sensor in the gas valve well is determined. Based on the temperature monitoring data of the optimized monitoring target, the activation scheme of the leakage monitoring sensor of the optimized monitoring target is determined, the gas valve well and the associated valve well are divided into the same group, and based on the activation scheme of the leakage monitoring sensor of the optimized monitoring target in the group, the safety monitoring method of the leakage monitoring sensor in the group is determined based on the pressure monitoring data in the gas valve well in the group. The method for determining the activation scheme of the leakage monitoring sensor of the optimized monitoring target is as follows: Using the temperature monitoring data of the optimized monitoring target, determine the duration percentage of the optimized monitoring target in different ambient temperature ranges; Based on the maximum value of the duration percentage and the ambient temperature range data that meets the requirements for the duration percentage, the activation process of the leakage monitoring sensor for the optimized monitoring target is determined.

2. The intelligent safety monitoring method for gas valve wells based on multi-parameter sensing as described in claim 1, characterized in that, The sensor equipment includes pressure monitoring equipment and leak monitoring sensors.

3. The intelligent safety monitoring method for gas valve wells based on multi-parameter sensing as described in claim 2, characterized in that, The leak detection sensor determines the leakage situation by monitoring the concentration of methane in the air.

4. The intelligent safety monitoring method for gas valve wells based on multi-parameter sensing as described in claim 1, characterized in that, The correlation of the pressure monitoring data in the gas valve well is determined based on the changes in the pressure monitoring data of the gas pipeline in the gas valve well and the changes in the pressure monitoring data of the gas pipeline in other gas valve wells.

5. The intelligent safety monitoring method for gas valve wells based on multi-parameter sensing as described in claim 1, characterized in that, The method for determining the associated valve wells of the gas valve well is as follows: Based on the aforementioned correlation, determine the changes in pressure monitoring data of gas pipelines in other gas valve wells when the pressure monitoring data of the gas pipeline in the gas valve well changes. Based on the aforementioned changes, determine the time period of change in the pressure monitoring data of the gas pipeline in the gas valve well, and the correlation between the time periods of change in the pressure monitoring data of the other gas valve wells. Based on the correlation, determine the associated time periods within the time periods of change. Using the associated change period data, it is determined whether the other gas valve wells are associated valve wells.

6. The intelligent safety monitoring method for gas valve wells based on multi-parameter sensing as described in claim 5, characterized in that, The variable correlation period is the period during which the gas valve well and other gas valve wells are simultaneously included in the variable period.

7. The intelligent safety monitoring method for gas valve wells based on multi-parameter sensing as described in claim 1, characterized in that, When the maximum value of the duration ratio is greater than the preset ratio threshold, the activation processing scheme of the leakage monitoring sensor of the optimized monitoring target is determined to be the preset activation scheme.

8. The intelligent safety monitoring method for gas valve wells based on multi-parameter sensing as described in claim 1, characterized in that, The method for determining the safety monitoring method of the leakage monitoring sensor in the aforementioned combination is as follows: Based on the composition data of the optimized monitoring targets in the combination, determine the proportion of the number of optimized monitoring targets in the combination; Based on the activation processing scheme of the leakage monitoring sensor for the optimized monitoring target, the number of optimized monitoring targets using different activation processing schemes is determined. The safety monitoring method for leakage monitoring sensors in the combination is determined based on the proportion of the number of optimized monitoring targets and the number of optimized monitoring targets using different activation processing schemes.

9. A smart safety monitoring device for gas valve wells based on multi-parameter sensing, employing the smart safety monitoring method for gas valve wells based on multi-parameter sensing as described in any one of claims 1-8, characterized in that, Specifically, it includes: Temperature monitoring devices, leak detection sensors, pressure monitoring devices, and communication devices; The temperature monitoring device is responsible for monitoring and processing the ambient temperature inside the gas valve well; the leak monitoring sensor is responsible for monitoring and processing gas leaks; the pressure monitoring device is responsible for monitoring and processing the pressure of the gas pipeline; and the communication device is responsible for uploading and processing the monitoring data from the temperature monitoring device, the leak monitoring sensor, and the pressure monitoring device.

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