Intelligent gas monitoring method and system

Abstract

The invention discloses an intelligent gas monitoring method and system, and relates to the technical field of gas monitoring. The method comprises the steps that a gas flow sequence of a gas pipeline is monitored and obtained through a gas flow sensor, the gas concentration of a monitoring position is monitored and obtained through a gas concentration sensor, and the monitoring position is located in a target space; air flow characteristic distribution in the target space is obtained, the gas concentration is compensated according to the air flow characteristic distribution, and the compensated gas concentration is obtained; performing gas abnormity verification on the gas flow sequence and the compensated gas concentration to obtain a gas abnormity rate, and performing gas abnormity influence analysis and correction according to the air flow characteristic distribution to obtain a corrected gas abnormity rate; and according to the corrected fuel gas abnormal rate, performing fuel gas abnormal early warning judgment to obtain a fuel gas monitoring result. According to the invention, the accuracy of gas abnormity identification is effectively improved, and the intelligence and reliability of gas monitoring are improved.

Description

Technical Field

[0001] This invention relates to the field of gas monitoring technology, specifically to a smart gas monitoring method and system. Background Technology

[0002] Gas leaks are a common safety hazard in homes and industrial environments. If a leak occurs and is not dealt with promptly, it can lead to serious accidents such as fires, explosions, or poisoning. Current gas monitoring technologies typically use gas concentration sensors to detect gas concentration or gas flow sensors to detect pipeline flow.

[0003] Traditional methods using a single sensor for monitoring often result in data bias. Airflow can affect the distribution of gas concentration, making the gas concentration monitoring data inaccurate and leading to false alarms or missed alarms. Summary of the Invention

[0004] This application provides a gas intelligent monitoring method and system, which aims to solve the technical problems of false alarms and missed alarms in existing gas monitoring technologies.

[0005] In view of the above problems, this application provides a gas intelligent monitoring method and system.

[0006] Firstly, this application provides a method for intelligent gas monitoring, including: The gas flow sequence of the gas pipeline is monitored and obtained through a gas flow sensor, and the gas concentration at the monitoring location is monitored and obtained through a gas concentration sensor, wherein the monitoring location is within the target space. Obtain the airflow characteristic distribution within the target space, and compensate the gas concentration based on the airflow characteristic distribution to obtain a compensated gas concentration; The gas flow sequence and the compensated gas concentration are used to verify gas anomalies and obtain the gas anomaly rate. Based on the air flow characteristic distribution, the gas anomaly impact analysis and correction are performed to obtain the corrected gas anomaly rate. Based on the corrected gas anomaly rate, gas anomaly early warning judgment is performed to obtain gas monitoring results.

[0007] Secondly, this application provides a gas intelligent monitoring system, comprising: The gas concentration monitoring module is used to monitor and obtain the gas flow sequence of the gas pipeline through a gas flow sensor, and to monitor and obtain the gas concentration at the monitoring location through a gas concentration sensor, wherein the monitoring location is within the target space; The gas concentration compensation module is used to acquire the air flow characteristic distribution in the target space, and compensate the gas concentration according to the air flow characteristic distribution to obtain the compensated gas concentration. The gas anomaly verification module is used to verify gas anomalies in the gas flow sequence and the compensation gas concentration to obtain the gas anomaly rate. Based on the air flow characteristic distribution, it performs gas anomaly impact analysis and correction to obtain the corrected gas anomaly rate. The gas anomaly early warning module is used to perform gas anomaly early warning judgment based on the corrected gas anomaly rate and obtain gas monitoring results.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: This application provides a gas intelligent monitoring method and system. It compensates for gas concentration based on airflow characteristics, effectively reducing interference from environmental factors such as ventilation and convection on sensor monitoring results, thus making the detected gas concentration more accurate. Verification is performed using both gas flow rate and gas concentration parameters, eliminating reliance on single sensor data and avoiding false alarms and missed alarms caused by fluctuations in a single signal, thereby improving the reliability of gas anomaly identification. By introducing anomaly impact analysis under airflow conditions and calculating the gas anomaly impact coefficient, the gas anomaly rate is corrected, further enhancing the accuracy of early warning. It has wide applicability and can be used in environments with complex airflow, such as central kitchens, commercial restaurants, and industrial workshops, demonstrating strong versatility. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 A flowchart illustrating a smart gas monitoring method provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a gas intelligent monitoring system provided in an embodiment of this application; The components represented by each number in the attached diagram are explained below: Gas concentration monitoring module 11, gas concentration compensation module 12, gas anomaly verification module 13, gas anomaly early warning module 14. Detailed Implementation

[0011] This application provides a gas intelligent monitoring method and system to address the technical problems of false alarms and missed alarms in existing gas monitoring technologies.

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0013] It should be noted that the terms "comprising" and "having" are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to these processes, methods, products, or devices.

[0014] Example 1, as Figure 1 As shown, this application provides a smart gas monitoring method, the method comprising: S100: The gas flow sequence of the gas pipeline is monitored and obtained through a gas flow sensor, and the gas concentration at the monitoring location is monitored and obtained through a gas concentration sensor, wherein the monitoring location is within the target space.

[0015] In this embodiment, flow rate information within the gas pipeline and gas concentration information within the target space are acquired to provide basic data for subsequent leak risk assessment. For example, if the gas flow rate is greater than 0 and the gas concentration in the monitored space shows an upward trend, it is determined that there may be a gas leak. If the gas flow rate is 0, it indicates that there is no gas flow in the monitored space. In this case, even if there is a small change in concentration, it may be due to residual gas from the past or other external factors, rather than a pipeline leak.

[0016] Specifically, step S100 includes the following sub-steps: When gas flow occurs in the gas pipeline, the gas flow sequence of the gas pipeline is monitored and obtained through a gas flow sensor; The gas concentration at the monitoring location is monitored and obtained through a gas concentration sensor, which includes a methane concentration sensor, and the monitoring location is within the target space.

[0017] In this embodiment, when gas flow occurs in the gas pipeline, a gas flow sensor is used to monitor and acquire the gas flow sequence. For example, a gas flow sensor, such as an ultrasonic flow meter or a diaphragm flow meter, is installed on the main gas pipeline entering the user's home to continuously monitor the volume or velocity of the gas flowing through the pipeline and record it at a fixed high-frequency sampling interval, such as once or ten times per second. These instantaneous flow data points arranged in chronological order constitute the gas flow sequence. The gas flow sequence can reflect the gas consumption and flow state per unit time and is used to determine whether gas usage is abnormal.

[0018] Gas concentration sensors, including methane concentration sensors, are used to monitor and obtain the gas concentration at a monitoring location within the target space. For example, a high-precision, interference-resistant methane concentration sensor can be placed in areas where gas leaks may accumulate in the kitchen, such as above the stove or near the ceiling, to ensure timely detection of changes in the concentration of leaked gas.

[0019] S200: Obtain the airflow characteristic distribution in the target space, and compensate the gas concentration according to the airflow characteristic distribution to obtain the compensated gas concentration.

[0020] In this embodiment, the airflow characteristic distribution within the target space is obtained. Based on this airflow characteristic distribution, the gas concentration is compensated to obtain a compensated gas concentration. This solves the problem of false alarms caused by environmental ventilation interference in traditional gas monitoring. By constructing the airflow characteristic distribution of the target space and using a trained gas concentration change analyzer, the impact of current ventilation conditions on the gas concentration readings at the monitoring point is quantitatively evaluated. This compensates the original value obtained by the concentration sensor, resulting in a compensated gas concentration that more closely approximates the actual leakage situation and eliminates ventilation interference.

[0021] Specifically, step S200 includes the following sub-steps: An array of air flow sensors deployed within the target space is used to collect air flow characteristics from multiple air monitoring locations. These air flow characteristics include air velocity and air flow direction. An airflow feature distribution is generated based on multiple airflow characteristics and the spatial coordinates of multiple air detection locations. Based on the airflow characteristic distribution, the gas concentration is compensated to obtain a compensated gas concentration.

[0022] In this embodiment, an array of airflow sensors deployed within the target space collects airflow characteristics from multiple air monitoring locations. These airflow characteristics include airflow velocity and airflow direction. Airflow sensors, such as thermal anemometers and ultrasonic anemometers, can be used to detect airflow velocity and direction. For example, within the target space, an array of multiple miniature airflow sensor nodes can be deployed, each node integrating a sensor capable of measuring airflow velocity and direction.

[0023] An airflow feature distribution is generated based on multiple airflow characteristics and the spatial coordinates of multiple air detection locations. For example, a three-dimensional spatial coordinate system is constructed in a target space such as a kitchen, forming spatial coordinates. Using data from multiple sensors, an airflow feature distribution for all spatial coordinates within the target space is generated through interpolation. Interpolation is an important method for approximating discrete functions. It allows us to estimate the approximate value of a function at other points by considering the function's values ​​at a finite number of points. Interpolation can also be used to fill gaps between pixels during image transformation. For example, in a target space such as a kitchen, there are two air monitoring locations A and B. The midpoint between A and B, which is not monitored, is C. According to interpolation, the wind speed and direction at midpoint C are the average of the wind speeds and directions at monitoring locations A and B, hence C = (A + B) / 2. A single point's wind speed and direction cannot reflect the airflow situation of the entire space. By generating a distribution through interpolation, the airflow environment around any location can be inferred, thus more accurately compensating for concentration values.

[0024] Based on the airflow characteristic distribution, the gas concentration is compensated to obtain a compensated gas concentration. The data measured by the gas concentration sensor is a local concentration. Dilution or enrichment of the airflow may cause the local concentration to differ from the actual leakage concentration. By compensating for the gas concentration, the reading can be corrected to a value closer to the true leakage level.

[0025] The method of compensating the gas concentration based on the airflow characteristic distribution to obtain a compensated gas concentration includes: Within the airflow characteristic distribution, a set of neighboring airflow characteristics within a preset range near the monitoring location is indexed. The neighboring airflow characteristic set refers to the set of local airflow velocity and direction data near the gas concentration sensor location. Local airflow characteristics have a significant impact on sensor readings. For example, the wind speed and direction within a 1-meter radius of the sensor directly affect the gas diffusion around the sensor, thus affecting the sensor reading. For instance, a virtual sphere with a radius of 1 meter is defined, centered on the coordinates of the gas concentration sensor. Data from all interpolation points within this sphere are extracted from the airflow characteristic distribution to form a neighboring airflow characteristic set. Airflow is represented by wind speed and direction. Airflow direction is generally represented by a horizontal azimuth angle and a vertical elevation angle. A reference coordinate system is defined: the X-axis points due east; the Y-axis points due north; and the Z-axis points vertically upward. The horizontal azimuth angle is the angle between the projection of the wind speed vector onto the horizontal plane (XY plane) and the due north direction (Y-axis), ranging from 0° to 360°, rotating clockwise. The vertical elevation angle is the angle between the wind speed vector itself and the horizontal plane (XY plane), ranging from -90° to +90°, rotating clockwise. For example, the set of nearby airflow characteristics might include: {point 1:(1.7m / s,95°,0°), point 2:(1.9m / s,105°,-10°), point 3:(1.8m / s,100°,+15°),...}.

[0026] The adjacent airflow feature set is input into the gas concentration change analyzer, which outputs the gas concentration change coefficient. The gas concentration change analyzer is a computational module trained based on a machine learning model, used to predict the degree of influence of airflow on gas concentration. The gas concentration change coefficient represents the concentration deviation caused by airflow. Training the gas concentration change analyzer using machine learning methods can continuously optimize the compensation coefficient and anomaly detection model, adapting to different environments, such as open kitchens, enclosed workshops, and outdoor work areas.

[0027] The training steps for the gas concentration change analyzer include: Based on the test data of gas concentration changes, multiple sets of adjacent air flow feature sets of samples are collected, and the magnitude of gas concentration change under each set of adjacent air flow feature sets of samples is obtained. The set of sample gas concentration change coefficients is labeled and obtained. Based on machine learning, a gas concentration change analyzer is constructed and trained until convergence using the multiple sets of adjacent air flow feature sets of samples and the set of sample gas concentration change coefficients.

[0028] Different environments have different airflow characteristics, necessitating a data-driven approach applicable to various scenarios, such as kitchens or workshops. For instance, three different environments can be simulated in a laboratory: low wind speed (0.1 m / s), medium wind speed (0.5 m / s), and high wind speed (2.0 m / s). Sensors can collect corresponding methane data for each environment, and the actual diffusion concentration can be recorded. The difference between these values ​​can be calculated, resulting in a set of sample gas concentration change coefficients, such as +0.05, +0.2, and +0.4. These sample data can then be used to train a neural network model, ultimately leading to a gas concentration change analyzer that can generalize to real-world scenarios.

[0029] The gas concentration variation coefficient is used to compensate for the gas concentration, resulting in a compensated gas concentration. The corrected compensated concentration is closer to the actual gas leak concentration, thus avoiding false alarms or missed alarms caused by airflow interference. For example, if a gas concentration sensor in a kitchen detects a methane concentration of 0.18%, and the airflow characteristics show a wind speed of 0.5 m / s with the airflow direction rapidly carrying away the gas, the calculated concentration variation coefficient is -0.2, indicating that the concentration has been affected and reduced by 20%. Since the detected concentration is lower, reverse compensation is needed. Therefore, the methane concentration is corrected to 0.18% × (1 + 0.2) = 0.216%, which is closer to the actual concentration under windless conditions.

[0030] S300: Verify gas anomalies by analyzing the gas flow sequence and the compensated gas concentration to obtain the gas anomaly rate. Based on the air flow characteristic distribution, perform gas anomaly impact analysis and correction to obtain the corrected gas anomaly rate.

[0031] In this embodiment, by introducing airflow characteristic distribution, the concentration data of the sensor is compensated, eliminating errors caused by factors such as wind speed, wind direction, and ventilation conditions, making the gas leak detection results closer to the actual situation and avoiding false alarms or missed alarms.

[0032] Specifically, step S300 includes the following sub-steps: Based on the gas flow sequence, the predicted gas concentration at the monitoring location is calculated; The similarity between the compensated gas concentration and the predicted gas concentration is calculated as the gas anomaly rate; Based on the airflow characteristic distribution, an analysis of the impact of gas anomalies is conducted to obtain the gas anomaly impact coefficient. The gas anomaly influence coefficient is used to correct the gas anomaly rate, thereby obtaining the corrected gas anomaly rate.

[0033] In this embodiment, the predicted gas concentration at the monitoring location is calculated based on the gas flow sequence. The gas flow sequence refers to the flow rate of gas in the pipeline over time. The predicted gas concentration is a theoretical concentration value calculated based on pipeline flow rate, gas diffusion models, etc. For example, in a kitchen, the gas flow sensor detects a gas flow rate of 1 L / min, and the target space volume is 20 m³. 3 According to the diffusion model, the indoor gas concentration is expected to be (5×1) / 20=0.25% within 5 minutes. This value is the predicted gas concentration.

[0034] The similarity between the compensated gas concentration and the predicted gas concentration is calculated as the gas anomaly rate. Similarity is typically calculated using difference or correlation. Assuming the predicted gas concentration in the target kitchen space is 0.25% and the compensated gas concentration is 0.23%, the similarity is 1 - (|0.25 - 0.23|) / 0.25 = 0.92, meaning the gas anomaly rate is 92%, indicating an extremely high probability of gas leakage.

[0035] Based on the aforementioned airflow characteristic distribution, an analysis of the impact of gas anomalies is performed to obtain the gas anomaly impact coefficient. For example, in a highly ventilated environment, rapid airflow dilutes the gas, reducing the probability of hazardous events. In this case, the gas anomaly impact coefficient needs to be used to correct the data; otherwise, false alarms may occur.

[0036] Among them, based on the air flow characteristic distribution, an analysis of the impact of abnormal gas flow is performed to obtain the gas flow abnormality impact coefficient, including: Based on the airflow characteristics, the average airflow velocity is calculated. Air flows unevenly and irregularly within a space; the value from a single sensor cannot represent the overall airflow situation. Calculating the average airflow velocity can roughly reflect the overall level of airflow in the entire space. For example, if four airflow sensors are installed at different locations in a kitchen, and the detection results are 0.8 m / s, 0.6 m / s, 0.2 m / s, and 0.4 m / s respectively, then the average airflow velocity is (0.8 + 0.6 + 0.2 + 0.4) / 4 = 0.5 m / s.

[0037] Based on historical gas monitoring data, the ratio of gas anomalies at the given average air velocity to the historical average gas anomaly warning rate is used as the gas anomaly impact coefficient. Using historical monitoring data, the proportion of gas anomalies at a specific average wind speed is statistically analyzed and compared with the overall anomaly rate to obtain a correction coefficient. For example, when the average air velocity is less than 0.2 m / s, the gas anomaly trigger rate is 20%; when the average air velocity is 2.0 m / s, the gas anomaly trigger rate is 5%; the historical average anomaly rate is 10%. If the average air velocity in the target space is 2.0 m / s, then the gas anomaly impact coefficient is 5% / 10% = 0.5.

[0038] The gas anomaly influence coefficient is used to correct the gas anomaly rate, resulting in a corrected gas anomaly rate. For example, if the gas anomaly rate in the target space is 92% and the gas anomaly influence coefficient is 0.5, then the corrected gas anomaly rate is: 92% × 0.5 = 46%. The corrected gas anomaly rate better reflects the actual risk level and can avoid false alarms or missed alarms caused by air flow.

[0039] S400: Based on the corrected gas anomaly rate, perform gas anomaly early warning judgment and obtain gas monitoring results.

[0040] In this embodiment of the application, gas anomaly warning is determined based on the corrected gas anomaly rate to obtain gas monitoring results, which can reduce false alarms and improve safety. For example, if gas diffuses momentarily in a target space, such as a kitchen, during gas use, but the airflow is fast and the risk is not significant, the gas anomaly rate will be below the threshold after correction, and no alarm will be triggered; if gas leakage continues and the corrected anomaly rate exceeds the threshold, an alarm will be triggered immediately.

[0041] Specifically, step S400 includes the following sub-steps: Determine whether the corrected gas anomaly rate is greater than or equal to the gas anomaly rate threshold; If yes, a gas anomaly result is obtained, and a gas anomaly warning is issued; if no, a normal gas result is obtained, and no gas anomaly warning is issued, which is taken as the gas monitoring result.

[0042] In this embodiment, it is determined whether the corrected gas anomaly rate is greater than or equal to a gas anomaly rate threshold. If so, a gas anomaly result is obtained, and a gas anomaly warning is issued. If not, a normal gas result is obtained, and no gas anomaly warning is issued, which is taken as the gas monitoring result. The gas anomaly rate threshold is set based on experiments, historical data statistics, or industry safety standards. For example, if the gas anomaly rate threshold is 70%, then when the corrected gas anomaly rate in the space is greater than or equal to 70%, a gas leak can be basically determined, and an alarm is immediately triggered. When the corrected gas anomaly rate is less than 70%, the gas is considered normal, and no alarm is triggered; only data is recorded. Recording long-term flow and concentration change data, combined with airflow characteristics, forms a historical database, enabling leak risk prediction. Through multi-dimensional detection and anomaly correction mechanisms, accident risks can be effectively reduced, and the accuracy and intelligence of gas monitoring can be improved.

[0043] Example 2, as Figure 2 As shown, this application provides a gas intelligent monitoring system, the system comprising: The gas concentration monitoring module 11 is used to monitor and obtain the gas flow sequence of the gas pipeline through a gas flow sensor, and to monitor and obtain the gas concentration at the monitoring location through a gas concentration sensor, wherein the monitoring location is within the target space. The gas concentration compensation module 12 is used to acquire the air flow characteristic distribution in the target space, and compensate the gas concentration according to the air flow characteristic distribution to obtain a compensated gas concentration. The gas anomaly verification module 13 is used to verify the gas flow sequence and the compensation gas concentration to obtain the gas anomaly rate, and to perform gas anomaly impact analysis and correction based on the air flow characteristic distribution to obtain the corrected gas anomaly rate. The gas anomaly early warning module 14 is used to perform gas anomaly early warning judgment based on the corrected gas anomaly rate and obtain gas monitoring results.

[0044] In one embodiment, the gas concentration monitoring module 11 is further configured to: When gas flow occurs in the gas pipeline, the gas flow sequence of the gas pipeline is monitored and obtained through a gas flow sensor; The gas concentration at the monitoring location is monitored and obtained through a gas concentration sensor, which includes a methane concentration sensor, and the monitoring location is within the target space.

[0045] In one embodiment, the gas concentration compensation module 12 is further configured to: An array of air flow sensors deployed within the target space is used to collect air flow characteristics from multiple air monitoring locations. These air flow characteristics include air velocity and air flow direction. An airflow feature distribution is generated based on multiple airflow characteristics and the spatial coordinates of multiple air detection locations. Based on the airflow characteristic distribution, the gas concentration is compensated to obtain a compensated gas concentration.

[0046] The method of compensating the gas concentration based on the airflow characteristic distribution to obtain a compensated gas concentration includes: Within the airflow feature distribution, index the set of neighboring airflow features within a preset range near the monitoring location; The adjacent airflow feature set is input into the gas concentration change analyzer, and the gas concentration change coefficient is output. The gas concentration is compensated using the gas concentration change coefficient to obtain a compensated gas concentration.

[0047] The training steps for the gas concentration change analyzer include: Based on the test data of gas concentration changes, multiple sets of adjacent air flow feature sets of samples were collected, and the magnitude of gas concentration change under adjacent air flow feature sets of each set of samples was obtained. The set of sample gas concentration change coefficients was then labeled. Based on machine learning, a gas concentration change analyzer is constructed. The analyzer is trained until convergence using the set of adjacent air flow features of multiple samples and the set of sample gas concentration change coefficients.

[0048] In one embodiment, the gas anomaly verification module 13 is further configured to: Based on the gas flow sequence, the predicted gas concentration at the monitoring location is calculated; The similarity between the compensated gas concentration and the predicted gas concentration is calculated as the gas anomaly rate; Based on the airflow characteristic distribution, an analysis of the impact of gas anomalies is conducted to obtain the gas anomaly impact coefficient. The gas anomaly influence coefficient is used to correct the gas anomaly rate, thereby obtaining the corrected gas anomaly rate.

[0049] Among them, based on the air flow characteristic distribution, an analysis of the impact of abnormal gas flow is performed to obtain the gas flow abnormality impact coefficient, including: The average air velocity is calculated based on the airflow characteristic distribution. Based on historical gas monitoring data, the ratio of the proportion of gas anomalies under the average air velocity to the historical average proportion of gas anomaly warnings is obtained as the gas anomaly impact coefficient.

[0050] In one embodiment, the gas anomaly warning module 14 is further configured to: Determine whether the corrected gas anomaly rate is greater than or equal to the gas anomaly rate threshold; If yes, a gas anomaly result is obtained, and a gas anomaly warning is issued; if no, a normal gas result is obtained, and no gas anomaly warning is issued, which is taken as the gas monitoring result.

[0051] It should be noted that the order of the embodiments described above is for descriptive purposes only and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0052] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0053] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for intelligent gas monitoring, characterized in that, The method includes: The gas flow sequence of the gas pipeline is monitored and obtained through a gas flow sensor, and the gas concentration at the monitoring location is monitored and obtained through a gas concentration sensor, wherein the monitoring location is within the target space. Obtain the airflow characteristic distribution within the target space, and compensate the gas concentration based on the airflow characteristic distribution to obtain a compensated gas concentration; The gas flow sequence and the compensated gas concentration are used to verify gas anomalies and obtain the gas anomaly rate. Based on the air flow characteristic distribution, the gas anomaly impact analysis and correction are performed to obtain the corrected gas anomaly rate. Based on the corrected gas anomaly rate, gas anomaly early warning judgment is performed to obtain gas monitoring results.

2. The intelligent gas monitoring method according to claim 1, characterized in that, The gas flow rate sequence of the gas pipeline is monitored and obtained through a gas flow sensor, and the gas concentration at the monitoring location is monitored and obtained through a gas concentration sensor, including: When gas flow occurs in the gas pipeline, the gas flow sequence of the gas pipeline is monitored and obtained through a gas flow sensor; The gas concentration at the monitoring location is monitored and obtained through a gas concentration sensor, which includes a methane concentration sensor, and the monitoring location is within the target space.

3. The intelligent gas monitoring method according to claim 1, characterized in that, Obtaining the airflow characteristic distribution within the target space, and compensating the fuel gas concentration based on the airflow characteristic distribution to obtain a compensated fuel gas concentration, includes: An array of air flow sensors deployed within the target space is used to collect air flow characteristics from multiple air monitoring locations. These air flow characteristics include air velocity and air flow direction. An airflow feature distribution is generated based on multiple airflow characteristics and the spatial coordinates of multiple air detection locations. Based on the airflow characteristic distribution, the gas concentration is compensated to obtain a compensated gas concentration.

4. The intelligent gas monitoring method according to claim 3, characterized in that, Based on the airflow characteristic distribution, the gas concentration is compensated to obtain a compensated gas concentration, including: Within the airflow feature distribution, index the set of neighboring airflow features within a preset range near the monitoring location; The adjacent airflow feature set is input into the gas concentration change analyzer, and the gas concentration change coefficient is output. The gas concentration is compensated using the gas concentration change coefficient to obtain a compensated gas concentration.

5. The intelligent gas monitoring method according to claim 4, characterized in that, The training steps for the gas concentration change analyzer include: Based on the test data of gas concentration changes, multiple sets of adjacent air flow feature sets of samples were collected, and the magnitude of gas concentration change under adjacent air flow feature sets of each set of samples was obtained. The set of sample gas concentration change coefficients was then labeled. Based on machine learning, a gas concentration change analyzer is constructed. The analyzer is trained until convergence using the set of adjacent air flow features of multiple samples and the set of sample gas concentration change coefficients.

6. The intelligent gas monitoring method according to claim 1, characterized in that, The gas flow sequence and compensated gas concentration are used to verify gas anomalies and obtain the gas anomaly rate. Based on the airflow characteristic distribution, the impact analysis and correction of gas anomalies are performed to obtain the corrected gas anomaly rate, including: Based on the gas flow sequence, the predicted gas concentration at the monitoring location is calculated; The similarity between the compensated gas concentration and the predicted gas concentration is calculated as the gas anomaly rate; Based on the airflow characteristic distribution, an analysis of the impact of gas anomalies is conducted to obtain the gas anomaly impact coefficient. The gas anomaly influence coefficient is used to correct the gas anomaly rate, thereby obtaining the corrected gas anomaly rate.

7. The intelligent gas monitoring method according to claim 6, characterized in that, Based on the aforementioned airflow characteristic distribution, an analysis of the impact of abnormal gas flow is performed to obtain the gas flow abnormality impact coefficient, including: The average air velocity is calculated based on the airflow characteristic distribution. Based on historical gas monitoring data, the ratio of the proportion of gas anomalies under the average air velocity to the historical average proportion of gas anomaly warnings is obtained as the gas anomaly impact coefficient.

8. The intelligent gas monitoring method according to claim 1, characterized in that, Based on the corrected gas anomaly rate, gas anomaly early warning judgment is performed to obtain gas monitoring results, including: Determine whether the corrected gas anomaly rate is greater than or equal to the gas anomaly rate threshold; If yes, a gas anomaly result is obtained, and a gas anomaly warning is issued; if no, a normal gas result is obtained, and no gas anomaly warning is issued, which is taken as the gas monitoring result.

9. A gas intelligent monitoring system, characterized in that, The system for implementing the intelligent gas monitoring method according to any one of claims 1-8, the system comprising: The gas concentration monitoring module is used to monitor and obtain the gas flow sequence of the gas pipeline through a gas flow sensor, and to monitor and obtain the gas concentration at the monitoring location through a gas concentration sensor, wherein the monitoring location is within the target space; The gas concentration compensation module is used to acquire the air flow characteristic distribution in the target space, and compensate the gas concentration according to the air flow characteristic distribution to obtain the compensated gas concentration. The gas anomaly verification module is used to verify gas anomalies in the gas flow sequence and the compensation gas concentration to obtain the gas anomaly rate. Based on the air flow characteristic distribution, it performs gas anomaly impact analysis and correction to obtain the corrected gas anomaly rate. The gas anomaly early warning module is used to perform gas anomaly early warning judgment based on the corrected gas anomaly rate and obtain gas monitoring results.

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