Gas leakage rate temperature compensation method and system based on internet-of-things gas appliance

Abstract

The invention discloses a gas leakage rate temperature compensation method and system based on an internet-of-things gas appliance, and belongs to the technical field of gas metering. The method specifically comprises the steps that a monitoring period is set, and monitoring data of an internet-of-things ultrasonic gas meter in the monitoring period is obtained in real time; clustering the monitoring data by taking the gas temperature and the pipeline pressure as feature vectors to generate a plurality of category clusters; selecting any category cluster, acquiring external temperature data and gas flow data corresponding to all feature points in the category cluster, constructing a point location diagram by taking the external temperature data as a horizontal coordinate and the gas flow data as a vertical coordinate, and fitting the point location diagram to obtain a fitting function; monitoring data of the current Internet of Things ultrasonic gas meter are obtained, the gas flow data are corrected according to the fitting function, and final gas flow data are obtained; according to the invention, the gas metering precision is improved, and the possibility of misjudgment and missed judgment of gas leakage is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of gas metering, and in particular to a gas leakage temperature compensation method and system based on an Internet of Things gas appliance. Background Art

[0002] In modern gas transmission systems, accurate monitoring and measurement of gas flow is a key link to ensure the stability of energy supply, optimize resource allocation, and ensure safe operation of the system. With the acceleration of urbanization and the increase in industrial activities, the demand for gas as a clean and efficient energy source is growing. Therefore, accurate measurement of gas flow has become an indispensable part of gas supply chain management.

[0003] The IoT ultrasonic gas meter is an advanced gas metering device that combines IoT technology and ultrasonic measurement principles. Its principle is to use the relationship between the propagation speed of ultrasound in gas and the gas flow rate, and to accurately calculate the gas flow by measuring the propagation time of the ultrasonic signal. Its unique non-invasive characteristics enable it to measure without interfering with the flow of the fluid, which is essential for maintaining the continuous flow of gas in the pipeline. Its high-precision measurement capability means that even tiny flow changes can be accurately captured, which is of great significance for accurate billing of gas usage and fine adjustment of supply and demand balance. In addition, the fast response characteristics of the IoT ultrasonic gas meter enable it to monitor flow changes in real time, providing real-time data support for the dynamic management of the gas system. In the flow monitoring of gas pipelines, the application of the IoT ultrasonic gas meter not only improves the accuracy of measurement, but also enhances the reliability and safety of the system. Through real-time analysis of gas flow data, abnormal conditions such as leaks or blockages can be discovered in a timely manner, so that corresponding measures can be taken to prevent accidents.

[0004] However, in actual operation, temperature changes will directly affect the physical properties of gas, especially volume and density. For example, when the temperature rises, the gas expands, the volume increases, and the density decreases; when the temperature drops, the gas compresses, the volume decreases, and the density increases. The working principle of the IoT ultrasonic gas meter usually depends on the propagation speed of ultrasound in the medium. The propagation speed of ultrasound is closely related to the physical properties of the medium (such as temperature, density, viscosity, gas or liquid composition, etc.). Changes in medium temperature will directly affect its density, viscosity and other thermodynamic properties, thereby changing the propagation speed of ultrasound. At the same time, changes in external temperature will also cause the optimal frequency of the IoT ultrasonic gas meter to change, causing deviations in gas metering and leading to abnormal leakage identification. Summary of the invention

[0005] The purpose of the present invention is to provide a gas leakage temperature compensation method and system based on IoT gas appliances to solve the following technical problems:

[0006] However, in actual operation, temperature changes will directly affect the physical properties of gas, especially volume and density. For example, when the temperature rises, the gas expands, the volume increases, and the density decreases; when the temperature drops, the gas compresses, the volume decreases, and the density increases. The working principle of the IoT ultrasonic gas meter usually depends on the propagation speed of ultrasound in the medium. The propagation speed of ultrasound is closely related to the physical properties of the medium (such as temperature, density, viscosity, gas or liquid composition, etc.). Changes in medium temperature will directly affect its density, viscosity and other thermodynamic properties, thereby changing the propagation speed of ultrasound. At the same time, changes in external temperature will also cause the optimal frequency of the IoT ultrasonic gas meter to change, causing deviations in gas metering and abnormal gas leak identification.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A gas leakage temperature compensation method based on an IoT gas appliance is characterized by comprising the following steps:

[0009] S1, set a monitoring period, and obtain the monitoring data of the IoT ultrasonic gas meter in real time during the monitoring period; the monitoring data includes gas flow data, external temperature data, gas temperature data and pipeline pressure data;

[0010] S2, using gas temperature as the horizontal coordinate and pipeline pressure as the vertical coordinate, construct a two-dimensional coordinate, generate feature points corresponding to all monitoring data, select any feature point as the center, set the clustering control radius R, obtain the feature point density P within the control radius R of each feature point, calculate the feature point density mean VagP, if there is any feature point with a feature point density greater than VagP within the radius R, then take the feature point as the core point and generate a category cluster, and generate several category clusters;

[0011] S3, select any category cluster, obtain the outside temperature data and gas flow data corresponding to all feature points in the category cluster, construct a point map with the outside temperature data as the horizontal coordinate and the gas flow data as the vertical coordinate, fit the point map to obtain a fitting function F(T), and the fitting function F(T) is used to fit the relationship between the gas flow data and the ambient temperature;

[0012] S4, obtaining the monitoring data of the current IoT ultrasonic gas meter, and correcting the gas flow data according to the fitting function to obtain the final gas flow data.

[0013] As a further solution of the present invention, it is characterized in that, in S2, the specific process of setting the cluster control radius R is:

[0014] Taking any feature point as the center, calculate the Euclidean distance d between the feature point and any other feature point, sum each Euclidean distance to get u, and get the control radius R based on the value u. The calculation formula is as follows:

[0015]

[0016] Among them, n is the number of all feature points, u is the sum of the Euclidean distances between any feature point and all other feature points, and d is the Euclidean distance between any feature points.

[0017] As a further solution of the present invention: it is characterized in that, in S2, the specific calculation process of the feature point density mean VagP is:

[0018] P = a / (πR 2 );

[0019]

[0020] Among them, a is the number of feature points within the control radius R, and n is the number of all feature points.

[0021] As a further solution of the present invention: it is characterized in that, in said S2, if in any category cluster, there exists a non-core point whose density P within the control radius R is also greater than VagP, then the category cluster generated by the non-core point is merged with the original category cluster to generate several category clusters.

[0022] As a further solution of the present invention: it is characterized in that, said S3 further includes:

[0023] Taking any characteristic point in the category cluster as the center and R as the radius, generate several subclusters, calculate the degree of aggregation of each subcluster and mark it as DP; select the subcluster with the highest DP value as the representative cluster, calculate the mean of each axis coordinate of the characteristic point corresponding to the representative cluster, calibrate the mean of the horizontal axis coordinate to the standard gas temperature data, and calibrate the mean of the vertical axis coordinate to the standard pipeline pressure data, and obtain the standard gas temperature data and standard pipeline pressure data of all category clusters;

[0024] The calculation formula of DP is:

[0025]

[0026] Among them, z is the number of feature points in any subcluster, z 0 is the central feature point, v 0 are other feature points in the sub-cluster.

[0027] As a further solution of the present invention: it is characterized in that, in S3, the specific expression of the fitting function is:

[0028] The point map is fitted by the least squares method to obtain the fitting straight line equation: F=k×T+b, where k represents the slope of the fitting straight line, T is the gas temperature, b represents the intercept of the fitting straight line, and k and b are constants.

[0029] As a further solution of the present invention: it is characterized in that it also includes:

[0030] Determine the reference outside temperature as H0, obtain the feature point whose outside temperature data corresponds to the feature point in any category cluster as H0 and mark it as a pending feature point, calculate the product of the standard gas temperature data and the standard pipeline pressure data to obtain the target parameter, perform linear fitting on the relationship between the gas flow data at the same outside temperature and the target parameter, and obtain the fitting function F(G).

[0031] As a further solution of the present invention, it is characterized in that, in S4, the specific process of correcting the gas flow data is as follows:

[0032] S11, extracting gas temperature data T and pipeline pressure data N from the monitoring data of the current IoT ultrasonic gas meter, obtaining standard gas temperature data T'i and standard pipeline pressure data N'i corresponding to any category cluster, and calculating the similarity Xi between the monitoring data of the current IoT ultrasonic gas meter and any category cluster according to the calculation formula Xi = α(T-T'i)+β(N-N'i);

[0033] S12, select the category cluster corresponding to the minimum similarity MINHi and mark it as the target category cluster, obtain the fitting function F(T) corresponding to the target category cluster, obtain the current outside temperature data H1 and the reference outside temperature H0, calculate the difference △H between H1 and H0, substitute △H into the fitting function F(T), calculate the first correction score, sum the current gas flow data and the first correction score, and obtain the first gas flow data;

[0034] S13, determine the reference gas temperature data and the reference pipeline pressure data, calculate the product of the reference gas temperature data and the reference pipeline pressure data to obtain the reference target parameter, extract the gas temperature data and the pipeline pressure data from the monitoring data, calculate the product of the gas temperature data and the pipeline pressure data to obtain the current target parameter, calculate the difference between the current target parameter and the reference target parameter, substitute the difference into the fitting function F(G), calculate the second correction score, sum the first gas flow data and the second correction score, and obtain the final gas flow data.

[0035] The gas leakage temperature compensation system based on the Internet of Things gas appliance is characterized by comprising:

[0036] The data acquisition module is used to set the monitoring period and obtain the monitoring data of the IoT ultrasonic gas meter in real time during the monitoring period; the monitoring data includes gas flow data, external temperature data, gas temperature data and pipeline pressure data;

[0037] The data analysis module is used to construct a two-dimensional coordinate system with the gas temperature as the horizontal coordinate and the pipeline pressure as the vertical coordinate, generate feature points corresponding to all monitoring data, select any feature point as the center, set the clustering control radius R, obtain the feature point density P within the control radius R of each feature point, and calculate the feature point density mean VagP. If there is any feature point with a feature point density P greater than VagP within the radius R of the feature point, the feature point is taken as the core point and a category cluster is generated to generate several category clusters.

[0038] Select any category cluster, obtain the outside temperature data and gas flow data corresponding to all feature points in the category cluster, construct a point map with the outside temperature data as the horizontal coordinate and the gas flow data as the vertical coordinate, fit the point map to obtain the fitting function F(T), and the fitting function F(T) is used to fit the relationship between the gas flow data and the ambient temperature;

[0039] The result generation module is used to obtain the monitoring data of the current IoT ultrasonic gas meter, and to correct the gas flow data according to the fitting function to obtain the final gas flow data.

[0040] Beneficial effects of the present invention:

[0041] The present invention first determines the monitoring period and obtains the monitoring data of the Internet of Things ultrasonic gas meter within the monitoring period, which is the basis for subsequent analysis. By taking the gas temperature as the horizontal coordinate and the pipeline pressure as the vertical coordinate, a two-dimensional coordinate is constructed to generate feature points corresponding to all monitoring data, and the monitoring data is clustered to obtain several category clusters. It can be understood that the metering data of the Internet of Things ultrasonic gas meter is affected by the external temperature data, the gas temperature data and the pipeline pressure data at the same time. The data can be grouped by clustering. First, the monitoring data is grouped using the gas temperature data and the pipeline pressure data as characteristic values, so as to obtain the gas flow data corresponding to different external temperatures under similar gas temperature data and pipeline pressure data, and the external temperature data is used as the horizontal coordinate. The gas flow data is taken as the vertical coordinate, a point map is constructed, the point map is fitted to obtain a fitting function, and the relationship between the gas flow data and the ambient temperature is obtained, which is the basis for the subsequent correction of the gas flow data. Similarly, the reference external temperature is determined to be H0, and the characteristic point corresponding to the external temperature data of the characteristic point in any category cluster is obtained as the characteristic point of H0 and marked as a pending characteristic point. The product of the standard gas temperature data and the standard pipeline pressure data is calculated to obtain the target parameter, and the relationship between the gas flow data and the target parameter at the same external temperature is linearly fitted to obtain the fitting function, and the reference target parameter and the reference external temperature are determined. According to the fitting function, the currently detected gas flow data is compensated, thereby realizing accurate gas flow measurement. The present invention considers the influence of external temperature, pipeline pressure and gas temperature factors on gas flow data, and uses cluster analysis and fitting functions to compensate the metering data, thereby improving the metering accuracy and reducing the possibility of misjudgment and omission of gas leakage. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The present invention will be further described below in conjunction with the accompanying drawings.

[0043] Figure 1 It is a schematic flow chart of a method for temperature compensation of gas leakage based on an IoT gas appliance of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] See also Figure 1 As shown, the present invention is a gas leakage temperature compensation method based on an IoT gas appliance, which is characterized by comprising the following steps:

[0046] S1, set a monitoring period, and obtain the monitoring data of the IoT ultrasonic gas meter in real time during the monitoring period; the monitoring data includes gas flow data, external temperature data, gas temperature data and pipeline pressure data;

[0047] S2, using gas temperature as the horizontal coordinate and pipeline pressure as the vertical coordinate, construct a two-dimensional coordinate, generate feature points corresponding to all monitoring data, select any feature point as the center, set the clustering control radius R, obtain the feature point density P within the control radius R of each feature point, calculate the feature point density mean VagP, if there is any feature point with a feature point density greater than VagP within the radius R, then take the feature point as the core point and generate a category cluster, and generate several category clusters;

[0048] S3, select any category cluster, obtain the outside temperature data and gas flow data corresponding to all feature points in the category cluster, construct a point map with the outside temperature data as the horizontal coordinate and the gas flow data as the vertical coordinate, fit the point map to obtain a fitting function F(T), and the fitting function F(T) is used to fit the relationship between the gas flow data and the ambient temperature;

[0049] S4, obtaining the monitoring data of the current IoT ultrasonic gas meter, and correcting the gas flow data according to the fitting function to obtain the final gas flow data.

[0050] The present invention first determines the monitoring period and obtains the monitoring data of the Internet of Things ultrasonic gas meter within the monitoring period, which is the basis for subsequent analysis. By taking the gas temperature as the horizontal coordinate and the pipeline pressure as the vertical coordinate, a two-dimensional coordinate is constructed to generate feature points corresponding to all monitoring data, and the monitoring data is clustered to obtain several category clusters. It can be understood that the metering data of the Internet of Things ultrasonic gas meter is affected by the external temperature data, the gas temperature data and the pipeline pressure data at the same time. The data can be grouped by clustering. First, the monitoring data is grouped using the gas temperature data and the pipeline pressure data as characteristic values, so as to obtain the gas flow data corresponding to different external temperatures under similar gas temperature data and pipeline pressure data, and the external temperature data is used as the horizontal coordinate. The gas flow data is taken as the vertical coordinate, a point map is constructed, the point map is fitted to obtain a fitting function, and the relationship between the gas flow data and the ambient temperature is obtained, which is the basis for the subsequent correction of the gas flow data. Similarly, the reference external temperature is determined to be H0, and the characteristic point corresponding to the external temperature data of the characteristic point in any category cluster is obtained as the characteristic point of H0 and marked as a pending characteristic point. The product of the standard gas temperature data and the standard pipeline pressure data is calculated to obtain the target parameter, and the relationship between the gas flow data and the target parameter at the same external temperature is linearly fitted to obtain the fitting function, and the reference target parameter and the reference external temperature are determined. According to the fitting function, the currently detected gas flow data is compensated, thereby realizing accurate gas flow measurement. The present invention considers the influence of external temperature, pipeline pressure and gas temperature factors on gas flow data, and uses cluster analysis and fitting functions to compensate the metering data, thereby improving the metering accuracy and efficiency, and reducing the possibility of misjudgment and omission of gas leakage.

[0051] It is worth noting that in the gas pipeline transportation system, after the gas is extracted from the natural gas well or gas production facility, it is transmitted to the target location through the pipeline system. During the transportation process, the gas temperature in the pipeline is different due to different external temperatures. However, the amount of gas input into each pipeline is the same. Therefore, by obtaining gas monitoring data before each pipeline network enters the pressure regulating valve for the first time, the real-time data of the same time period at the output end of different pipelines can be directly monitored. Therefore, there is no need to rely on a large amount of test data, and the gas transportation information under actual operating conditions can be accurately captured, providing support for efficient management and safe operation of the gas pipeline network.

[0052] In the prior art, a gas detector is also used to identify whether a gas leak occurs. The gas detector determines whether there is a risk of gas leakage by detecting the concentration of combustible gas in the air. When the gas concentration reaches the preset alarm value, the alarm controller will send out an alarm signal and instruction, and may combine with the solenoid valve or fan to drive exhaust, cut off the power and close the valve. However, the gas detector will be affected by environmental factors such as temperature and humidity during use. Extreme environmental conditions may cause unstable equipment performance, or even false alarms or missed alarms, affecting the accuracy of the detection results to ensure that gas leaks can be accurately and reliably detected in various environments. Therefore, the present invention can be combined with a gas detector to perform more comprehensive gas leak detection.

[0053] In a preferred case of this embodiment, in S2, the specific process of setting the cluster control radius R is:

[0054] Taking any feature point as the center, calculate the Euclidean distance d between the feature point and any other feature point, sum each Euclidean distance to get u, and get the control radius R based on the value u. The calculation formula is as follows:

[0055]

[0056] Among them, n is the number of all feature points, u is the sum of the Euclidean distances between any feature point and all other feature points, and d is the Euclidean distance between any feature points.

[0057] In another preferred embodiment of the present invention, in S2, the specific calculation process of the feature point density mean VagP is:

[0058] P = a / (πR 2 );

[0059]

[0060] Among them, a is the number of feature points within the control radius R, and n is the number of all feature points.

[0061] In another preferred situation of the present embodiment, S2 also includes if in any category cluster, there is a non-core point whose density P within the control radius R is also greater than VagP, then the category cluster generated by the non-core point is merged with the original category cluster to generate several category clusters.

[0062] In another preferred situation of this embodiment, the S3 further includes:

[0063] Taking any characteristic point in the category cluster as the center and R as the radius, generate several subclusters, calculate the degree of aggregation of each subcluster and mark it as DP; select the subcluster with the highest DP value as the representative cluster, calculate the mean of each axis coordinate of the characteristic point corresponding to the representative cluster, calibrate the mean of the horizontal axis coordinate to the standard gas temperature data, and calibrate the mean of the vertical axis coordinate to the standard pipeline pressure data, and obtain the standard gas temperature data and standard pipeline pressure data of all category clusters;

[0064] The calculation formula of DP is:

[0065]

[0066] Among them, z is the number of feature points in any subcluster, z 0 is the central feature point, v 0 are other feature points in the sub-cluster.

[0067] The DP value represents the average distance from other feature set points to the center point in each cluster. The smaller the DP value, the higher the compactness of the subcluster and the better the clustering effect. By selecting the cluster with the smallest DP value in each category cluster, the most representative subcluster in the category cluster can be obtained. By analyzing each representative cluster, it is not necessary to analyze each feature set in the category cluster, thereby reducing the workload of actual data analysis and improving work efficiency. At the same time, the representative cluster is calculated by calculating the mean of any axis coordinates of the feature points corresponding to the representative cluster, and the mean of the horizontal axis coordinates is calibrated as the standard gas temperature data, and the mean of the vertical axis coordinates is calibrated as the standard pipeline pressure data. The optimal standard reference conditions for each category cluster can be determined, thereby providing a basis for subsequent analysis.

[0068] In another preferred embodiment of the present invention, in S3, the specific expression of the fitting function is:

[0069] The point map is fitted by the least squares method to obtain the fitting straight line equation: F=k×T+b, where k represents the slope of the fitting straight line, T is the gas temperature, b represents the intercept of the fitting straight line, and k and b are constants.

[0070] In another preferred aspect of this embodiment, the method further includes:

[0071] Determine the reference outside temperature as H0, obtain the feature point of the outside temperature data corresponding to the feature point in any category cluster as H0 and mark it as a pending feature point, calculate the product of the standard gas temperature data and the standard pipeline pressure data to obtain the target parameter, and perform linear fitting on the relationship between the gas flow data and the target parameter at the same outside temperature to obtain the fitting function F(G).

[0072] In another preferred embodiment of the present invention, in S4, the specific process of correcting the gas flow data is as follows:

[0073] S11, extracting gas temperature data T and pipeline pressure data N from the monitoring data of the current IoT ultrasonic gas meter, obtaining standard gas temperature data T'i and standard pipeline pressure data N'i corresponding to any category cluster, and calculating the similarity Xi between the monitoring data of the current IoT ultrasonic gas meter and any category cluster according to the calculation formula Xi = α(T-T'i)+β(N-N'i);

[0074] S12, select the category cluster corresponding to the minimum similarity MINHi and mark it as the target category cluster, obtain the fitting function F(T) corresponding to the target category cluster, obtain the current outside temperature data H1 and the reference outside temperature H0, calculate the difference △H between H1 and H0, substitute △H into the fitting function F(T), calculate the first correction score, sum the current gas flow data and the first correction score, and obtain the first gas flow data;

[0075] S13, determine the reference gas temperature data and the reference pipeline pressure data, calculate the product of the reference gas temperature data and the reference pipeline pressure data to obtain the reference target parameter, extract the gas temperature data and the pipeline pressure data from the monitoring data, calculate the product of the gas temperature data and the pipeline pressure data to obtain the current target parameter, calculate the difference between the current target parameter and the reference target parameter, substitute the difference into the fitting function F(G), calculate the second correction score, sum the first gas flow data and the second correction score, and obtain the final gas flow data.

[0076] The gas leakage temperature compensation system based on IoT gas appliances includes:

[0077] The data acquisition module is used to set the monitoring period and obtain the monitoring data of the IoT ultrasonic gas meter in real time during the monitoring period; the monitoring data includes gas flow data, external temperature data, gas temperature data and pipeline pressure data;

[0078] The data analysis module is used to construct a two-dimensional coordinate system with the gas temperature as the horizontal coordinate and the pipeline pressure as the vertical coordinate, generate feature points corresponding to all monitoring data, select any feature point as the center, set the clustering control radius R, obtain the feature point density P within the control radius R of each feature point, and calculate the feature point density mean VagP. If there is any feature point with a feature point density P greater than VagP within the radius R of the feature point, the feature point is taken as the core point and a category cluster is generated to generate several category clusters.

[0079] Select any category cluster, obtain the outside temperature data and gas flow data corresponding to all feature points in the category cluster, construct a point map with the outside temperature data as the horizontal coordinate and the gas flow data as the vertical coordinate, fit the point map to obtain the fitting function F(T), and the fitting function F(T) is used to fit the relationship between the gas flow data and the ambient temperature;

[0080] The result generation module is used to obtain the monitoring data of the current IoT ultrasonic gas meter, and to correct the gas flow data according to the fitting function to obtain the final gas flow data.

[0081] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A gas leakage temperature compensation method based on IoT gas appliances, characterized in that: The following steps are involved: S1, set a monitoring period, and obtain the monitoring data of the IoT ultrasonic gas meter in real time during the monitoring period; the monitoring data includes gas flow data, external temperature data, gas temperature data and pipeline pressure data; S2, using gas temperature as the horizontal coordinate and pipeline pressure as the vertical coordinate, construct a two-dimensional coordinate, generate feature points corresponding to all monitoring data, select any feature point as the center, set the clustering control radius R, obtain the feature point density P within the control radius R of each feature point, calculate the feature point density mean VagP, if there is any feature point with a feature point density greater than VagP within the radius R, then take the feature point as the core point and generate a category cluster, and generate several category clusters; S3, select any category cluster, obtain the outside temperature data and gas flow data corresponding to all feature points in the category cluster, construct a point map with the outside temperature data as the horizontal coordinate and the gas flow data as the vertical coordinate, fit the point map to obtain a fitting function F(T), and the fitting function F(T) is used to fit the relationship between the gas flow data and the ambient temperature; S4, obtaining the monitoring data of the current IoT ultrasonic gas meter, and correcting the gas flow data according to the fitting function to obtain the final gas flow data.

2. The gas leakage temperature compensation method based on IoT gas appliances according to claim 1 is characterized in that: In S2, the specific process of setting the cluster control radius R is: Taking any feature point as the center, calculate the Euclidean distance d between the feature point and any other feature point, sum each Euclidean distance to get u, and get the control radius R based on the value u. The calculation formula is as follows: Among them, n is the number of all feature points, u is the sum of the Euclidean distances between any feature point and all other feature points, and d is the Euclidean distance between any feature points.

3. The gas leakage temperature compensation method based on IoT gas appliances according to claim 1 is characterized in that: In S2, the specific calculation process of the feature point density mean VagP is: P=a / (πR 2 ); Among them, a is the number of feature points within the control radius R, and n is the number of all feature points.

4. The gas leakage temperature compensation method based on IoT gas appliances according to claim 1, characterized in that: In the above S2, if in any category cluster, there is a non-core point whose density P within the control radius R is also greater than VagP, then the category cluster generated by the non-core point is merged with the original category cluster to generate several category clusters.

5. The gas leakage temperature compensation method based on IoT gas appliance according to claim 1, characterized in that: The S3 further includes: Taking any characteristic point in the category cluster as the center and R as the radius, generate several subclusters, calculate the degree of aggregation of each subcluster and mark it as DP; select the subcluster with the highest DP value as the representative cluster, calculate the mean of each axis coordinate of the characteristic point corresponding to the representative cluster, calibrate the mean of the horizontal axis coordinate to the standard gas temperature data, and calibrate the mean of the vertical axis coordinate to the standard pipeline pressure data, and obtain the standard gas temperature data and standard pipeline pressure data of all category clusters; The calculation formula of DP is: Among them, z is the number of feature points in any subcluster, z0 is the central feature point, and v0 is the other feature points in the subcluster.

6. The gas leakage temperature compensation method based on IoT gas appliance according to claim 1, characterized in that: In S3, the specific expression of the fitting function is: The point map is fitted by the least squares method to obtain the fitting straight line equation: F=k×T+b, where k represents the slope of the fitting straight line, T is the gas temperature, b represents the intercept of the fitting straight line, and k and b are constants.

7. The gas leakage temperature compensation method based on IoT gas appliances according to claim 5, characterized in that: Also includes: Determine the reference outside temperature as H0, obtain the feature point of the outside temperature data corresponding to the feature point in any category cluster as H0 and mark it as a pending feature point, calculate the product of the standard gas temperature data and the standard pipeline pressure data to obtain the target parameter, and perform linear fitting on the relationship between the gas flow data and the target parameter at the same outside temperature to obtain the fitting function F(G).

8. The gas leakage temperature compensation method based on IoT gas appliances according to claim 1, characterized in that: In S4, the specific process of correcting the gas flow data is as follows: S11, extracting gas temperature data T and pipeline pressure data N from the monitoring data of the current IoT ultrasonic gas meter, obtaining standard gas temperature data T'i and standard pipeline pressure data N'i corresponding to any category cluster, and calculating the similarity Xi between the monitoring data of the current IoT ultrasonic gas meter and any category cluster according to the calculation formula Xi = α(T-T'i)+β(N-N'i); S12, select the category cluster corresponding to the minimum similarity MINHi and mark it as the target category cluster, obtain the fitting function F(T) corresponding to the target category cluster, obtain the current outside temperature data H1 and the reference outside temperature H0, calculate the difference △H between H1 and H0, substitute △H into the fitting function F(T), calculate the first correction score, sum the current gas flow data and the first correction score, and obtain the first gas flow data; S13, determine the reference gas temperature data and the reference pipeline pressure data, calculate the product of the reference gas temperature data and the reference pipeline pressure data to obtain the reference target parameter, extract the gas temperature data and the pipeline pressure data from the monitoring data, calculate the product of the gas temperature data and the pipeline pressure data to obtain the current target parameter, calculate the difference between the current target parameter and the reference target parameter, substitute the difference into the fitting function F(G), calculate the second correction score, sum the first gas flow data and the second correction score, and obtain the final gas flow data.

9. A gas leakage temperature compensation system based on IoT gas appliances, characterized in that: include: The data acquisition module is used to set the monitoring period and obtain the monitoring data of the IoT ultrasonic gas meter in real time during the monitoring period; the monitoring data includes gas flow data, external temperature data, gas temperature data and pipeline pressure data; The data analysis module is used to construct a two-dimensional coordinate system with the gas temperature as the horizontal coordinate and the pipeline pressure as the vertical coordinate, generate feature points corresponding to all monitoring data, select any feature point as the center, set the clustering control radius R, obtain the feature point density P within the control radius R of each feature point, and calculate the feature point density mean VagP. If there is any feature point with a feature point density P greater than VagP within the radius R of the feature point, the feature point is taken as the core point and a category cluster is generated to generate several category clusters. Select any category cluster, obtain the outside temperature data and gas flow data corresponding to all feature points in the category cluster, construct a point map with the outside temperature data as the horizontal coordinate and the gas flow data as the vertical coordinate, fit the point map to obtain the fitting function F(T), and the fitting function F(T) is used to fit the relationship between the gas flow data and the ambient temperature; The result generation module is used to obtain the monitoring data of the current IoT ultrasonic gas meter, and to correct the gas flow data according to the fitting function to obtain the final gas flow data.

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