High-pressure gas leakage safety feedback system based on artificial intelligence

Abstract

The present invention discloses a high-pressure gas leakage safety feedback system based on artificial intelligence, and relates to the technical field of gas safety monitoring. The present invention includes a sensing module: including a natural gas concentration sensor arranged outside each interface position of the natural gas transportation pipeline, and a pressure sensor and a flow sensor arranged inside each interface position of the natural gas transportation pipeline; used to monitor the natural gas concentration of the external environment through the natural gas concentration sensor outside each pipeline interface. If any natural gas concentration sensor detects the natural gas concentration, the negative pressure wave inside the pipeline is monitored through the negative pressure sensor inside the pipeline interface where the natural gas concentration sensor is located and the negative pressure sensors at the two adjacent pipeline interfaces, and the monitoring results are output to the positioning module. The present invention improves the efficiency of the maintenance process by customizing an exclusive maintenance plan according to the pipeline leakage area, and calculates the probability of pipeline leakage ignition to form a risk report.

Description

Technical Field

[0001] The present invention belongs to the technical field of gas leakage monitoring, and in particular relates to a high-pressure gas leakage safety feedback system based on artificial intelligence. Background Art

[0002] With the rapid development of the industrial economy, the application of gas in the industrial field, daily life, and military field is common, and the use of hazardous chemicals is also increasing; hydrogen energy is favored as an ideal clean energy, and adding hydrogen to natural gas pipelines is an effective way to achieve large-scale transportation of hydrogen energy. Once a pipeline leaks during transportation, it will seriously affect the safe operation of the pipeline. Therefore, it is necessary to monitor the natural gas high-pressure transportation pipeline and promptly feedback the monitoring results to the management level.

[0003] At present, most gas leakage monitoring systems are based on gas sensors. According to the law of gas diffusion, the gas source is determined in combination with the positioning algorithm, and the location of the natural gas pipeline leakage is then fed back to the management level, which then performs subsequent remedial measures. Therefore, a large part of the positioning accuracy depends on the performance of the positioning algorithm. In addition, most gas leakage safety feedback systems have the problems of cumbersome wiring and lack of flexibility when realizing data transmission at each monitoring point. At the same time, more serious safety problems may occur when the natural gas pipeline leaks.

[0004] The existing natural gas high-pressure transportation pipeline leakage safety feedback system is rather cumbersome in the process of monitoring, locating and subsequently uploading feedback to the management level when the pipeline leaks. It cannot quickly and accurately locate the precise position of the leaking pipeline, nor can it accurately and quickly estimate the safety factor of the leakage point after the natural gas leak. For this reason, we propose a high-pressure gas leakage safety feedback system based on artificial intelligence. Summary of the invention

[0005] The purpose of the present invention is to provide a high-pressure gas leakage safety feedback system based on artificial intelligence to solve the problem that the existing natural gas high-pressure transportation pipeline leakage safety feedback system is relatively cumbersome in the feedback process of monitoring, locating and subsequently uploading the pipeline leakage to the management level, and cannot quickly and accurately locate the precise position of the leaking pipeline, and cannot accurately and quickly estimate the safety factor of the leakage point after the natural gas leak.

[0006] In order to solve the above technical problems, the present invention is achieved through the following technical solutions:

[0007] The present invention is an artificial intelligence-based high-pressure gas leakage safety feedback system, comprising a sensing module, a positioning module, a feedback module, a management application module and a risk estimation module;

[0008] Sensing module: including natural gas concentration sensors arranged outside each interface position of the natural gas transportation pipeline, and pressure sensors and flow sensors arranged inside each interface position of the natural gas transportation pipeline; used to monitor the natural gas concentration of the external environment through the natural gas concentration sensors outside each pipeline interface. If any natural gas concentration sensor detects the natural gas concentration, the negative pressure wave inside the pipeline is monitored through the negative pressure sensor inside the pipeline interface where the natural gas concentration sensor is located and the negative pressure sensors at the two adjacent pipeline interfaces, and the monitoring results are output to the positioning module;

[0009] Positioning module: used to calculate the coordinates of the natural gas leakage point according to the negative pressure wave positioning method, and output the coordinates of the leakage point to the feedback module;

[0010] Feedback module: used to receive in real time through the cellular network the environmental pressure data of each transport pipeline interface position and the flow data of the pipeline interface position output by the sensing module at each fixed time period, and to enter the environmental pressure data and flow data into the pressure monitoring table and the flow monitoring table respectively, and to judge whether a natural gas leak occurs in a certain section of the transport pipeline according to the data fluctuations on the pressure monitoring table and the flow monitoring table, and then to feed back to the management application module;

[0011] Management application module: used to monitor the pipeline at the leaking location and dispatch resources to repair the leaking pipeline;

[0012] Risk estimation module: It is used to establish a quantitative risk calculation model based on the pipeline leakage data detected by the sensing module, calculate the pipeline leakage hole area and the probability of pipeline leakage ignition, and finally form a risk report to output to maintenance personnel.

[0013] Preferably, the sensing module monitors the negative pressure wave through the negative pressure sensor corresponding to the natural gas concentration sensor position that detects the natural gas concentration and the negative pressure sensors at the two adjacent pipeline interface positions. If a section of the pipeline leaks, the negative pressure sensor located in the middle of the three negative pressure sensors and another negative pressure sensor will definitely detect the negative pressure wave, and the time it takes for the negative pressure wave to be transmitted to the two upstream and downstream negative pressure sensors is recorded and substituted into the negative pressure wave positioning formula for calculation.

[0014] Preferably, the negative pressure wave positioning formula is as shown in formula (1):

[0015] (1) Among them, , It is the time it takes for the negative pressure wave to be transmitted to the two negative pressure sensors upstream and downstream; is the propagation speed of negative pressure wave in natural gas, is the time difference between the negative pressure sensors at the upstream and downstream ends receiving the negative pressure wave, then , L is the total length of the pipeline section, in meters; C is the distance from the leakage point to the upstream negative pressure sensor, in meters.

[0016] Preferably, the negative pressure wave positioning method captures the mutation point of the leaked negative pressure wave signal and removes the noise mixed in the negative pressure wave signal through wavelet analysis, and the continuous wavelet transform positioning is shown in formula (2):

[0017] (2) Among them, is the basic wavelet; is the scale factor, ; is a time shift; if the wavelet basis function is extended on the time axis compared to the basic wavelet times, the frequency resolution is reduced times, through the scale factor Adjust the window size by the displacement factor Adjust the window position to analyze signals at different locations at different scales.

[0018] Preferably, after the leaking pipeline is located, flow sensors at both ends of the leaking pipeline monitor the natural gas flow of the pipeline, and the peak leakage rate at the initial moment of pipeline leakage is obtained by calculating the difference between the natural gas flow flowing into the leaking pipeline at the head end and the flow flowing out of the leaking pipeline at the tail end during the initial leakage time period.

[0019] Preferably, according to the quantitative risk calculation model, the peak leakage rate at the initial moment of pipeline leakage is substituted into formula (3) to calculate the pipeline leakage hole area:

[0020] (3) Among them: is the peak leakage rate at the initial moment of pipeline leakage, kg / s; d is the inner diameter of the pipeline, m; z is the area of ​​the pipeline leakage hole; s is the cross-sectional area of ​​the pipeline; is the natural gas adiabatic coefficient; is the stagnation density of the gas under operating conditions, kg / m³; is the stagnation pressure of the gas under operating conditions, Pa;

[0021] Within a very short period of time after the leak occurred, the pipeline leakage rate dropped sharply, and the effective leakage rate As shown in formula (4):

[0022] (4) Wherein, c is the attenuation coefficient, and the attenuation coefficient calculation formula is shown in formula (5):

[0023] (5) Among them, is the effective leakage rate of the pipeline, kg / s; is the Fanning friction coefficient of the pipeline; C is the distance from the leakage point to the upstream negative pressure sensor, m.

[0024] Preferably, calculating the pipeline leakage ignition probability according to the quantitative risk calculation model includes the following contents: The ignition probability formula is shown in formula (6):

[0025] (6) Among them, is the probability of ignition due to pipeline rupture; P is the internal pressure of the pipeline at the rupture point, MPa; d is the diameter of the pipeline, m.

[0026] Preferably, the system further comprises a data storage module for storing the coordinate position data of the leaking pipeline, the leakage area of ​​the leaking pipeline and the risk report data of the leaking pipeline, so that management personnel can review the past data at any time.

[0027] The present invention has the following beneficial effects:

[0028] The present invention sets a sensing module. In hardware facilities, at each pipeline interface, a pressure sensor and a flow sensor are set inside the pipeline interface, and a natural gas concentration sensor is set outside the pipeline interface. Since the pipeline interface is a place where the pipeline is prone to leakage, setting the sensing device there can monitor natural gas leakage faster and more effectively; and by sensing natural gas leakage through three sensing elements, namely, the natural gas concentration sensor, the pressure sensor and the flow sensor, natural gas leakage can be monitored more accurately to avoid false alarms.

[0029] The present invention sets a positioning module and calculates the coordinates of the natural gas leakage point by the negative pressure wave positioning method, so as to accurately and quickly locate the leaking pipeline, and continuously feeds back the flow data and pressure detection data to the management application module through the feedback module. Therefore, the management application module can judge whether a natural gas leakage occurs in a certain section of the transportation pipeline according to the data fluctuations on the pressure detection meter and the flow detection meter.

[0030] The present invention sets a risk estimation module. First, flow sensors at both ends of the leaking pipeline monitor the natural gas flow of the pipeline. The peak leakage rate at the initial moment of pipeline leakage is calculated by calculating the difference between the natural gas flow flowing into the leaking pipeline at the head end and the flow flowing out of the leaking pipeline at the tail end during the initial leakage period. The pipeline leakage area is then calculated by the calculated peak leakage rate at the initial moment of pipeline leakage. Therefore, before the staff goes to the maintenance treatment room before the pipeline leaks, an exclusive maintenance plan can be customized according to the pipeline leakage area, thereby improving the efficiency of the maintenance process. In addition, the ignition probability of pipeline leakage is calculated to form a risk report, so that the maintenance personnel can understand the risk factor of the pipeline leakage at the first time and make protective preparations. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0032] Figure 1 This is a schematic diagram of the overall structure of the artificial intelligence-based high-pressure gas leakage safety feedback system provided by the present invention. DETAILED DESCRIPTION

[0033] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0034] In the description of the present invention, it should be noted that the terms "upper", "lower", "inner", "outer", "front end", "rear end", "two ends", "one end", "the other end" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance.

[0035] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "provided with", "connected", etc. should be understood in a broad sense. For example, "connected" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0036] Example 1: See Figure 1 , the present invention is a high-pressure gas leakage safety feedback system based on artificial intelligence, including a sensing module, a positioning module, a feedback module, a management application module and a risk estimation module;

[0037] Sensing module: including natural gas concentration sensors arranged outside each interface position of the natural gas transportation pipeline, and pressure sensors and flow sensors arranged inside each interface position of the natural gas transportation pipeline; used to monitor the natural gas concentration of the external environment through the natural gas concentration sensors outside each pipeline interface. If any natural gas concentration sensor detects the natural gas concentration, the negative pressure wave inside the pipeline is monitored through the negative pressure sensor inside the pipeline interface where the natural gas concentration sensor is located and the negative pressure sensors at the two adjacent pipeline interfaces, and the monitoring results are output to the positioning module;

[0038] Positioning module: used to calculate the coordinates of the natural gas leakage point according to the negative pressure wave positioning method, and output the coordinates of the leakage point to the feedback module;

[0039] Feedback module: used to receive in real time through the cellular network the environmental pressure data of each transport pipeline interface position and the flow data of the pipeline interface position output by the sensing module at each fixed time period, and to enter the environmental pressure data and flow data into the pressure monitoring table and the flow monitoring table respectively, and to judge whether a natural gas leak occurs in a certain section of the transport pipeline according to the data fluctuations on the pressure monitoring table and the flow monitoring table, and then to feed back to the management application module;

[0040] Management application module: used to monitor the pipeline at the leaking location and dispatch resources to repair the leaking pipeline;

[0041] Risk estimation module: It is used to establish a quantitative risk calculation model based on the pipeline leakage data detected by the sensing module, calculate the pipeline leakage hole area and the probability of pipeline leakage ignition, and finally form a risk report to output to maintenance personnel.

[0042] Among them, the sensing module monitors the negative pressure wave through the negative pressure sensor corresponding to the natural gas concentration sensor position that detects the natural gas concentration and the negative pressure sensors at the two adjacent pipeline interface positions. If a section of the pipeline leaks, the negative pressure sensor in the middle of the three negative pressure sensors and another negative pressure sensor will definitely detect the negative pressure wave, and the time it takes for the negative pressure wave to be transmitted to the two upstream and downstream negative pressure sensors will be recorded and substituted into the negative pressure wave positioning formula for calculation.

[0043] Among them, the negative pressure wave positioning formula is shown in formula (1):

[0044] (1) Among them, , It is the time it takes for the negative pressure wave to be transmitted to the two negative pressure sensors upstream and downstream; is the propagation speed of negative pressure wave in natural gas, is the time difference between the negative pressure sensors at the upstream and downstream ends receiving the negative pressure wave, then , L is the total length of the pipeline section, in meters; C is the distance from the leakage point to the upstream negative pressure sensor, in meters.

[0045] Among them, the negative pressure wave positioning method uses wavelet analysis to capture the mutation point of the leaking negative pressure wave signal and remove the noise mixed in the negative pressure wave signal. The continuous wavelet transform positioning is shown in formula (2):

[0046] (2) Among them, is the basic wavelet; is the scale factor, ; is a time shift; if the wavelet basis function is extended on the time axis compared to the basic wavelet times, the frequency resolution is reduced times, through the scale factor Adjust the window size by the displacement factor Adjust the window position to analyze signals at different locations at different scales.

[0047] Among them, after the leaking pipeline is located, the flow sensors at both ends of the leaking pipeline monitor the natural gas flow of the pipeline. By calculating the difference between the natural gas flow flowing into the leaking pipeline at the head end and the flow flowing out of the leaking pipeline at the tail end during the initial leakage period, the peak leakage rate at the initial moment of the pipeline leakage is obtained.

[0048] Among them, according to the quantitative risk calculation model, the peak leakage rate at the initial moment of pipeline leakage is substituted into formula (3) to calculate the pipeline leakage hole area:

[0049] (3) Among them: is the peak leakage rate at the initial moment of pipeline leakage, kg / s; d is the inner diameter of the pipeline, m; z is the area of ​​the pipeline leakage hole; s is the cross-sectional area of ​​the pipeline; is the natural gas adiabatic coefficient; is the stagnation density of the gas under operating conditions, kg / m³; is the stagnation pressure of the gas under operating conditions, Pa;

[0050] Within a very short period of time after the leak occurred, the pipeline leakage rate dropped sharply, and the effective leakage rate As shown in formula (4):

[0051] (4) Where c is the attenuation coefficient. The calculation formula of the attenuation coefficient is shown in formula (5):

[0052] (5) Among them, is the effective leakage rate of the pipeline, kg / s; is the Fanning friction coefficient of the pipeline; C is the distance from the leakage point to the upstream negative pressure sensor, m.

[0053] Among them, the calculation of pipeline leakage ignition probability according to the quantitative risk calculation model includes the following contents: The ignition probability formula is shown in formula (6):

[0054] (6) Among them, is the probability of ignition due to pipeline rupture; P is the internal pressure of the pipeline at the rupture point, MPa; d is the diameter of the pipeline, m.

[0055] The system also includes a data storage module: used to store the coordinate location data of the leaking pipeline, the leakage area of ​​the leaking pipeline and the leaking pipeline risk report data, so that management personnel can review past data at any time.

[0056] Example 2: The difference between this example and Example 1 is that the leaking pipeline is located by using a natural gas concentration sensor. First, a gas turbulence model is established, and then the positioning algorithm is analyzed in combination with the model. Since it is difficult to know the diffusion rate of the gas leakage point in actual monitoring, this paper first uses the weighted centroid positioning algorithm for preliminary positioning based on the diffusion model, monitoring concentration and coordinates of each monitoring point, and estimates the leakage point parameters including the diffusion rate of the leakage point. This parameter is substituted into the objective function constructed by the least squares method, and then the genetic algorithm is used for fine positioning to obtain the coordinates of the gas leakage point, thereby avoiding the problem of unknown parameters.

[0057] Assume that the gas leakage source starts to diffuse uniformly into space with a certain diffusion coefficient, ignore obstacles in the monitoring environment, and the space does not contain the gas before the gas leaks. According to Fick's diffusion law, the diffusion flux in a unit cross section perpendicular to the diffusion direction is proportional to the concentration gradient of the cross section within a certain period of time, and the gas diffusion direction is exactly opposite to the concentration gradient vector, and the rate of change of concentration with time is equal to the negative value of the rate of change of the diffusion flux with distance at that location;

[0058] The gas leak location algorithm includes the following steps:

[0059] The working principle of the present invention is as follows: under normal working conditions, the natural gas concentration of the external environment is monitored by the natural gas concentration sensor outside each pipeline interface. When any natural gas concentration sensor detects the natural gas concentration, the negative pressure wave inside the pipeline is monitored by the negative pressure sensor inside the pipeline interface where the natural gas concentration sensor is located and the negative pressure sensors at the two adjacent pipeline interfaces, and the monitoring result is output to the positioning module; the coordinates of the natural gas leakage point are calculated according to the negative pressure wave positioning method, and the coordinates of the leakage point are output to the feedback module; the environmental pressure data of each transport pipeline interface position and the flow data of the pipeline interface position outputted by the sensing module in each fixed period are received in real time through the cellular network, and the environmental pressure data and the flow data are respectively entered into the pressure monitoring table and the flow monitoring table, and whether a natural gas leakage occurs in a certain section of the transport pipeline is judged according to the data fluctuations on the pressure monitoring table and the flow monitoring table, and then the management application module is fed back; the pipeline at the leakage position is randomly monitored, and resources are dispatched to repair the leaking pipeline; according to the pipeline leakage data detected by the sensing module, a quantitative risk calculation model is established to calculate the pipeline leakage hole area and the pipeline leakage ignition probability, and finally a risk report is formed and output to the maintenance personnel, and the maintenance personnel then analyze the leaking pipeline and repair it.

[0060] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. High-pressure gas leakage safety feedback system based on artificial intelligence, characterized by: It includes a sensing module, a positioning module, a feedback module, a management application module and a risk estimation module; Sensing module: including natural gas concentration sensors arranged outside each interface position of the natural gas transportation pipeline, and pressure sensors and flow sensors arranged inside each interface position of the natural gas transportation pipeline; used to monitor the natural gas concentration of the external environment through the natural gas concentration sensors outside each pipeline interface. If any natural gas concentration sensor detects the natural gas concentration, the negative pressure wave inside the pipeline is monitored through the negative pressure sensor inside the pipeline interface where the natural gas concentration sensor is located and the negative pressure sensors at the two adjacent pipeline interfaces, and the monitoring results are output to the positioning module; The sensing module monitors the negative pressure wave through the negative pressure sensor corresponding to the position of the natural gas concentration sensor that detects the natural gas concentration and the negative pressure sensors at the two adjacent pipeline interface positions. If a section of the pipeline leaks, the negative pressure sensor in the middle of the three negative pressure sensors and another negative pressure sensor must detect the negative pressure wave, and the time for the negative pressure wave to be transmitted to the two upstream and downstream negative pressure sensors is recorded and brought into the negative pressure wave positioning formula for calculation; Positioning module: used to calculate the coordinates of the natural gas leakage point according to the negative pressure wave positioning method, and output the coordinates of the leakage point to the feedback module; Feedback module: used to receive in real time through the cellular network the environmental pressure data of each transport pipeline interface position and the flow data of the pipeline interface position output by the sensing module at each fixed time period, and to enter the environmental pressure data and flow data into the pressure monitoring table and the flow monitoring table respectively, and to judge whether a natural gas leak occurs in a certain section of the transport pipeline according to the data fluctuations on the pressure monitoring table and the flow monitoring table, and then to feed back to the management application module; Management application module: used to monitor the pipeline at the leaking location and dispatch resources to repair the leaking pipeline; Risk estimation module: used to establish a quantitative risk calculation model based on the pipeline leakage data detected by the sensing module, calculate the pipeline leakage hole area and the probability of pipeline leakage ignition, and finally form a risk report to output to maintenance personnel; According to the quantitative risk calculation model, the peak leakage rate at the initial moment of pipeline leakage is substituted into formula (3) to calculate the pipeline leakage hole area: (3); in which: is the peak leakage rate at the initial moment of pipeline leakage, kg / s; d is the inner diameter of the pipeline, m; z is the area of ​​the pipeline leakage hole; s is the cross-sectional area of ​​the pipeline; is the natural gas adiabatic coefficient; is the stagnation density of the gas under operating conditions, kg / m³; is the stagnation pressure of the gas under operating conditions, Pa; Within a very short period of time after the leak occurred, the pipeline leakage rate dropped sharply, and the effective leakage rate As shown in formula (4): (4); where c is the attenuation coefficient, and the attenuation coefficient calculation formula is shown in formula (5): (5); among which, is the effective leakage rate of the pipeline, kg / s; is the Fanning friction coefficient of the pipeline; C is the distance from the leakage point to the upstream negative pressure sensor, m; The calculation of pipeline leakage ignition probability according to the quantitative risk calculation model includes the following contents: The ignition probability formula is shown in formula (6): (6); among which, is the probability of ignition due to pipeline rupture; P is the internal pressure of the pipeline at the rupture point, MPa; d is the diameter of the pipeline, m.

2. The artificial intelligence-based high-pressure gas leakage safety feedback system according to claim 1 is characterized in that: The negative pressure wave positioning formula is shown in formula (1): (1); among which, , It is the time it takes for the negative pressure wave to be transmitted to the two negative pressure sensors upstream and downstream; is the propagation speed of negative pressure wave in natural gas, is the time difference between the negative pressure sensors at the upstream and downstream ends receiving the negative pressure wave, then , L is the total length of the pipeline section, in meters; C is the distance from the leakage point to the upstream negative pressure sensor, in meters.

3. The artificial intelligence-based high-pressure gas leakage safety feedback system according to claim 2 is characterized in that: The negative pressure wave positioning method captures the mutation point of the leaking negative pressure wave signal and removes the noise mixed in the negative pressure wave signal through wavelet analysis. The continuous wavelet transform positioning is shown in formula (2): (2); among which, is the basic wavelet; is the scale factor, ; is a time shift; if the wavelet basis function is extended on the time axis compared to the basic wavelet times, the frequency resolution is reduced times, through the scale factor Adjust the window size by the displacement factor Adjust the window position to analyze signals at different locations at different scales.

4. The artificial intelligence-based high-pressure gas leakage safety feedback system according to claim 3 is characterized in that: When the leaking pipeline is located, flow sensors at both ends of the leaking pipeline monitor the natural gas flow of the pipeline, and the peak leakage rate at the initial moment of pipeline leakage is obtained by calculating the difference between the natural gas flow entering the leaking pipeline at the head end and the flow flowing out of the leaking pipeline at the tail end during the initial leakage period.

5. The artificial intelligence-based high-pressure gas leakage safety feedback system according to claim 4 is characterized in that: The system also includes a data storage module: used to store the coordinate position data of the leaking pipeline, the leakage area of ​​the leaking pipeline and the leaking pipeline risk report data, so that the management personnel can check the past data at any time.

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