Method for optimizing probe setting parameters of gas pipeline fire prevention system

Abstract

The application provides a kind of gas pipeline fire prevention system probe setting parameter optimization method, the steps of the optimization method include: simulating gas delivery pipeline segment actual working condition builds pipeline test section;Along the direction of being perpendicular and being far away from pipeline test section, sequentially, several heat flux sensors are arranged and the heat flux measurement range of heat flux sensor is estimated to be set;Explosion is implemented to pipeline test section;The heat flux data in a measurement time range at the moment of explosion is collected by the several heat flux sensors;According to the distance of the several heat flux sensors from pipeline test section and the distribution characteristics of the collected heat flux data in Cartesian coordinate system, a probe parameter model is established;The fitting parameters in the probe parameter model are calculated by using the least square method;The probe setting parameters in the gas pipeline fire prevention system are optimized by the probe parameter model and the fitting parameters. In this way, the accuracy of the probe parameter setting can be improved, and the reliability of the gas pipeline fire prevention system can be improved.

Description

Technical Field

[0001] This invention relates to the field of oil and gas pipeline safety transportation technology, and in particular to a method for optimizing the setting parameters of probes in a gas pipeline fire prevention system. Background Technology

[0002] Accidents involving gas pipelines (including natural gas and coal gas) are frequent, and these incidents can have extremely adverse negative impacts on the surrounding environment, facilities, and personal safety. The hazards of gas pipeline accidents typically arise after a pipeline malfunction, caused by a rapid change in heat flux resulting from the combustion or explosion of flammable gas within the pipeline. Within seconds of the leaking gas into the surrounding atmosphere, a sudden event such as a spark from friction can ignite the flammable gas-air mixture, leading to severe thermal effects on the surrounding area and causing serious damage to the environment, facilities, and personal property. This is especially true in sparsely populated remote areas, where pipeline accidents can spread rapidly due to the lack of timely detection and intervention. While various monitoring devices, such as pressure sensors, can generally detect pipeline malfunctions in these areas, they cannot detect potential fires outside the malfunctioning pipeline. Fire protection systems comprised of various sensors, such as heat flux sensors, are crucial for monitoring gas pipeline accidents, particularly fire hazards occurring outside faulty pipelines. However, the accuracy of these systems in detecting external fire hazards is often compromised due to difficulties in precisely configuring parameters such as the heat flux measurement range for heat flux sensors during deployment.

[0003] Therefore, how to accurately set the parameter data of the heat flux sensor in the fire protection system has become one of the technical problems that urgently need to be solved in this field. Summary of the Invention

[0004] The technical problem this solution aims to solve is how to more accurately set the parameters of the heat flux sensors in the fire protection system when deploying the fire protection system for gas pipelines, so as to improve the accuracy of the fire protection system in monitoring fire hazards outside the pipeline.

[0005] To address the aforementioned technical problems, this technical solution provides a method for optimizing the setting parameters of detectors in a gas pipeline fire prevention system. The optimization method includes the following steps:

[0006] Construction of a pipeline test section: To simulate the actual working conditions of a gas transmission pipeline section with a planned gas pipeline fire protection system, a pipeline test section is constructed.

[0007] Deploying heat flux sensors: Starting from a point on the central axis of the pipeline test section, several heat flux sensors are deployed at intervals on the same horizontal plane along a direction perpendicular to and away from the pipeline test section. The detection direction of these heat flux sensors is all towards the pipeline test section. During deployment, the heat flux measurement range of each heat flux sensor is estimated and set as the distance between each heat flux sensor and the pipeline test section increases.

[0008] Detonating the pipeline test section: The pipeline test section is used to simulate a failure in a gas transmission pipeline section, and the pipeline test section is then detonated.

[0009] Heat flux data acquisition: These heat flux sensors acquire heat flux data within a measurement time range at the moment of blasting according to a set data acquisition frequency;

[0010] Heat flux measurement range model establishment: Based on the distance of each heat flux sensor from the starting point and the characteristic that the distribution of heat flux data collected by each heat flux sensor in the Cartesian coordinate system exhibits a typical negative exponential equation, a heat flux measurement range model is established: HF = C 1× e (-d / t1) +C 2× e (-d / t2) Where HF represents the heat flux measurement range, and its unit is kW / m³. 2 d represents the distance from the heat flux sensor to the starting point, in meters (m). C1, C2, t1, and t2 are all fitting parameters, with C1 and C2 in kW / m³. 2 The units for t1 and t2 are meters (m).

[0011] Calculation of fitting parameters for the heat flux measurement range model: based on the heat flux data measured by these heat flux sensors and the distance from the starting point (HF). i d i (i = 1, 2, 3...k), the least squares method is used to calculate the heat flux measurement range model HF. i =C 1× e (-di / t1) +C 2× e (-di / t2) The values ​​of fitting parameters C1, C2, t1, and t2 are given, where HF i This refers to the heat flux data measured by the i-th heat flux sensor deployed sequentially from the starting point, with units of kW / m³. 2 d i is the distance of the i-th heat flux sensor from the starting point, in meters, and k is the number of heat flux sensors deployed.

[0012] Optimization of heat flux sensor measurement range: Application of the heat flux measurement range model HF=C 1× e(-d / t1) +C 2× e (-d / t2) The values ​​of fitting parameters C1, C2, t1, and t2 are used to set the heat flux measurement range of the heat flux sensor in the planned gas pipeline fire protection system according to its vertical distance from the gas transmission pipeline section.

[0013] Therefore, by simulating the actual operating conditions of a gas transmission pipeline section and collecting data from explosion experiments on the pipeline test section, a heat flux measurement range model can be established using the distribution characteristics of the collected data. The fitting parameters in this model can then be determined through mathematical calculations. This heat flux measurement range model and its fitting parameters can represent the relationship and variation between the heat flux measured by the heat flux sensor and the distance to the pipeline when a fault occurs and a fire hazard arises in the actual operating conditions of the gas transmission pipeline section. Thus, this experimental data model and its fitting parameters can be used to precisely set the parameters (i.e., the heat flux measurement range) of the heat flux sensor in the fire protection system of the gas transmission pipeline section, thereby improving the accuracy of the fire protection system in monitoring fire hazards outside the pipeline.

[0014] As another implementation of this technical solution, the optimization method may further include the following steps:

[0015] Heat flux measurement distance model establishment: Based on the distance of each heat flux sensor from the starting point and the characteristic that the distribution of heat flux data collected by each heat flux sensor in the Cartesian coordinate system exhibits a typical negative exponential equation, a heat flux measurement distance model is established: d = C 3× e (-HF / t3) +C 4× e (-HF / t4) Where C3, C4, t3, and t4 are fitting parameters, and the units of C3 and C4 are kW / m³. 2 The units for t3 and t4 are meters (m).

[0016] Calculation of fitting parameters for the heat flux measurement distance model: based on the heat flux data measured by these heat flux sensors and the distance from the starting point (HF). i d i (i = 1, 2, 3...k), calculate the heat flux measurement distance model d using the least squares method. i =C 3× e (- HF i / t3) +C 4× e (-HF i / t4) The values ​​of the fitting parameters C3, C4, t3, and t4;

[0017] Optimization of heat flux sensor measurement distance: Application of the heat flux measurement distance model d=C 3× e (-HF / t3) +C 4× e (-HF / t4) The values ​​of fitting parameters C3, C4, t3, and t4 are used to set the measurement distance of the heat flux sensor in the planned gas pipeline fire protection system according to its set heat flux measurement range.

[0018] Therefore, a heat flux measurement distance model can be established by fitting the distribution characteristics of the collected data, and the fitting parameters in the heat flux measurement distance model can be determined through mathematical calculations. This heat flux measurement distance model and its fitting parameters can represent the relationship and variation between the location of the heat flux sensor and the heat flux at that location when a fault occurs and a fire hazard arises in the actual operating conditions of the gas transmission pipeline section. Thus, the location of the heat flux sensor can be precisely set using this heat flux measurement distance model and its fitting parameters, based on the threshold values ​​of each stage of heat flux damage, thereby enabling the fire protection system to more accurately monitor fire hazards outside the pipeline.

[0019] As another implementation of this technical solution, in the above steps of calculating the fitting parameters of the heat flux measurement range model and / or the heat flux measurement distance model, the lsqcurvefit function in MATLAB software is used to calculate the fitting parameters, so that the values ​​of the fitting parameters in the above models can be obtained quickly and accurately.

[0020] As another implementation of this technical solution, in the above-mentioned steps of deploying the heat flux sensors: the heat flux sensors are fixed on several bases on the ground, and there are no obstructions between each base and the pipeline test section. This strengthens the stability of the heat flux sensor deployment and ensures the validity of the measurement data.

[0021] As another implementation of this technical solution, in the above steps of deploying heat flux sensors: the heat flux sensors are deployed at equal or different intervals. This flexible deployment facilitates operation by construction personnel, and all of the above deployment methods can collect effective heat flux data.

[0022] As another implementation of this technical solution, in the above steps of deploying heat flux sensors, the detection direction of these heat flux sensors is all 300 meters directly above the starting point. Since, under normal geographical and weather conditions, the flames in a pipeline tend to rise from the start of a fire until it is extinguished, calculations based on multiple experimental data show that the heat flux data measured 300 meters directly above the fire point in the pipeline is optimal.

[0023] As another implementation of this technical solution, in the above steps of deploying heat flux sensors: the estimated setting of the heat flux measurement range of each heat flux sensor is reduced as the distance between the heat flux sensor and the pipeline test section increases. Attached Figure Description

[0024] Figure 1 A flowchart illustrating the steps of the method for optimizing the parameters of the probe in the gas pipeline fire prevention system of the present invention;

[0025] Figure 2 A schematic diagram for installing heat flux sensors in a pipeline test section;

[0026] Figure 3 This is a schematic diagram of the distance between the heat flux sensor and the pipe, and the distribution of the optimal heat flux measured in the coordinate system.

[0027] Figure 4 This is another schematic diagram of the distribution of the optimal heat flux measured by the heat flux sensor at the distance from the pipe in the coordinate system.

[0028] Explanation of symbols in the attached diagram:

[0029] 1, 2, 3, 4, 5, 6, 7, 8 heat flux sensors; 10 pipes. Detailed Implementation

[0030] The detailed description and technical content of the present invention are explained below with reference to the accompanying drawings. However, the accompanying drawings are provided for reference and illustration only and are not intended to limit the present invention.

[0031] This invention provides an optimization method for the setting parameters of probes in a gas pipeline fire prevention system (hereinafter referred to as the optimization method). This optimization method establishes a probe parameter model by simulating the actual working conditions of a gas transmission pipeline section and collecting data from explosion experiments on a pipeline test section. In this invention, the probe is limited to a heat flux sensor. The fitting parameters in the probe parameter model are determined through mathematical calculations, and the setting parameters of the probe in the gas transmission pipeline fire prevention system are precisely set using this model and its fitting parameters. This improves the accuracy of the fire prevention system in monitoring external fire hazards.

[0032] Specifically, such as Figure 1 As shown, the main steps of this optimization method include:

[0033] Constructing a pipeline test section: A pipeline test section is constructed to simulate the actual operating conditions of a gas transmission pipeline section with a planned gas pipeline fire protection system. In actual implementation, due to differences in geographical environment such as temperature, humidity, altitude, wind speed, oxygen content, and day / night duration, as well as differences in pipeline parameters such as diameter, wall thickness, and steel type, the installation of fire protection systems (mainly referring to the installation of various measuring probes) for different regions and pipeline sections is personalized and differentiated. In the pipeline test section construction step of this invention, X80 grade steel pipes with an outer diameter of 48 inches and wall thicknesses ranging from 18.4 mm to 22.0 mm are used. The pressurized gas inside the pipeline is mainly methane, containing a certain amount of hydrogen, carbon dioxide, and nitrogen, to simulate the actual operating conditions of a full-size gas transmission pipeline section.

[0034] Deploying heat flux sensors: Starting from a point on the central axis of the pipeline test section, several heat flux sensors are deployed at intervals on the same horizontal plane along a direction perpendicular to and away from the pipeline test section. The detection direction of all these heat flux sensors is towards the pipeline test section. During deployment, the heat flux measurement range of each heat flux sensor is estimated and set as the distance between the sensor and the pipeline test section increases. For example... Figure 2 As shown, in the step of installing heat flux sensors in this invention, a total of 8 heat flux sensors 1, 2, 3, 4, 5, 6, 7, and 8 were installed, all of which are Hukseflux SBG01. Since the heat flux measured by the heat flux sensor decreases with the increase of the distance from the pipe 10, the distance and heat flux measurement range of the above 8 heat flux sensors 1, 2, 3, 4, 5, 6, 7, and 8 were set as shown in Table 1 below.

[0035]

[0036] Table 1

[0037] To enhance the robustness of the heat flux sensor installation and ensure the validity of the measurement data, the aforementioned heat flux sensors 1, 2, 3, 4, 5, 6, 7, and 8 are fixed to several bases (not shown in the figure) on the ground. Each base is free from obstructions between itself and the pipe 10, thus preventing abnormal movement of the heat flux sensors due to wind or other reasons. Furthermore, these heat flux sensors 1, 2, 3, 4, 5, 6, 7, and 8 can be installed at equal or varying intervals, allowing for flexible deployment to collect effective heat flux data and facilitating operation by construction personnel.

[0038] The pipeline test section is blasted: a fault occurs in a gas transmission pipeline section, and the pipeline test section is blasted. In this step of blasting the pipeline test section of the present invention, the working pressure of the pipeline test section at the time of blasting is approximately 12.0 MPa.

[0039] Heat flux data acquisition: These heat flux sensors collect heat flux data within a measurement time range at the moment of explosion, according to a set data acquisition frequency. Since the set data acquisition frequency is relatively high, in this invention, the data acquisition frequency can be set to 500Hz, or other frequencies, and the measurement time range is an extremely short period of time; therefore, the heat flux data acquisition process is completed almost instantaneously.

[0040] Under normal geographical and weather conditions, the flames in a pipeline tend to rise from the start of a fire until it is extinguished. Based on data from multiple tests, it was found that the heat flux measured 300 meters directly above the fire point is optimal. Therefore, the detection direction of these heat flux sensors is all directed towards a position 300 meters directly above the starting point.

[0041] Heat flux measurement range model establishment: Based on the distance of each heat flux sensor from the starting point and the characteristic that the distribution of heat flux data collected by each heat flux sensor in the Cartesian coordinate system exhibits a typical negative exponential equation, a heat flux measurement range model is established: HF = C 1× e (-d / t1) +C 2× e (-d / t2) Where HF represents the heat flux measurement range, and its unit is kW / m³. 2 d represents the distance from the heat flux sensor to the starting point, in meters (m). C1, C2, t1, and t2 are all fitting parameters, with C1 and C2 in kW / m³. 2 The units of t1 and t2 are meters. In the heat flux measurement range model establishment step of this invention, the heat flux data collected by the eight heat flux sensors set in the aforementioned steps are shown in Table 2 below. It should be noted that although Table 2 records a set of corresponding values ​​of distance and heat flux data, in actual operation, within the measurement time range of the heat flux sensors, the eight heat flux sensors measure a set of heat flux data and perform average calculations to obtain the optimal values ​​of distance and heat flux data in Table 2.

[0042]

[0043] Table 2

[0044] like Figure 3 As shown in Table 2, the distribution of the optimal heat flux measured by each heat flux sensor from the pipe exhibits a typical negative exponential equation in the Cartesian coordinate system. Based on this characteristic, a heat flux measurement range model HF=C was established. 1× e (-d / t1) +C 2× e (-d / t2)In order to obtain more free parameters in this model to achieve optimal data interpolation and thus improve the accuracy of the simulated data, the heat flux measurement range model HF=C 1× e (-d / t1) +C 2× e (-d / t2) C in 2× e (-d / t2) The part is the part added to obtain the best data interpolation. This mathematical method of obtaining more free parameters to obtain the best data interpolation is a common mathematical means in model fitting and establishment. Therefore, this common means will not be described in detail in this invention.

[0045] Calculation of fitting parameters for the heat flux measurement range model: based on the heat flux data measured by these heat flux sensors and the distance from the starting point (HF). i d i (i = 1, 2, 3...k), the least squares method is used to calculate the heat flux measurement range model HF. i =C 1× e (-di / t1) +C 2× e (-di / t2) The values ​​of fitting parameters C1, C2, t1, and t2 are given, where HF i This refers to the heat flux data measured by the i-th heat flux sensor deployed sequentially from the starting point, with units of kW / m³. 2 d i Let be the distance of the i-th heat flux sensor from the starting point, in meters (m), and k be the number of heat flux sensors deployed. In this invention, based on the fitting parameter calculation steps of the heat flux measurement range model, the heat flux data and the distance from the starting point in Table 2 are calculated using the least squares method to obtain the fitting parameter values ​​in Table 3 below.

[0046] t1 35.446m <![CDATA[C2]]> <![CDATA[49.205kW / m 2 ]]> t2 193.593m

[0047] Table 3

[0048] Optimization of heat flux sensor measurement range: Application of the heat flux measurement range model HF=C 1× e (-d / t1) +C 2× e (-d / t2) Based on the values ​​of fitting parameters C1, C2, t1, and t2, the heat flux measurement range of the heat flux sensor in the planned gas pipeline fire prevention system is set according to its vertical distance from the gas transmission pipeline section. In this invention, based on the heat flux sensor measurement range optimization step, the heat flux measurement range model HF=C is applied. 1× e (-d / t1) +C 2× e (-d / t2)The calculated fitting parameters (Table 3) can be used to calculate the accurate heat flux measurement range of the heat flux sensors at different distances in the planned fire protection system. Table 4 below shows the heat flux measurement range of the heat flux sensors set at distances of 80m and 120m from the pipeline.

[0049]

[0050] Table 4

[0051] In the aforementioned optimization method, by simulating the actual operating conditions of the gas transmission pipeline section and collecting data from the explosion test of the pipeline test section, a heat flux measurement range model can be established by fitting the distribution characteristics of the collected data. The fitting parameters in the heat flux measurement range model are then determined through mathematical calculations. This heat flux measurement range model and its fitting parameters represent the relationship and variation law between the heat flux that the heat flux sensor can measure and the distance to the pipeline when a fault occurs and a fire hazard arises in the actual operating conditions of the gas transmission pipeline section. Therefore, the experimental data model and its fitting parameters can be used to accurately set the parameters of the heat flux sensor in the fire protection system of the gas transmission pipeline section, that is, to set the precise heat flux measurement range of the heat flux sensor according to the actual distance from the pipeline, thereby improving the accuracy of the fire protection system in monitoring fire hazards outside the pipeline.

[0052] In addition, the optimization method of the present invention may further include the following steps:

[0053] Heat flux measurement distance model establishment: Based on the distance of each heat flux sensor from the starting point and the characteristic that the distribution of heat flux data collected by each heat flux sensor in the Cartesian coordinate system exhibits a typical negative exponential equation, a heat flux measurement distance model is established: d = C 3× e (-HF / t3) +C 4× e (-HF / t4) Where C3, C4, t3, and t4 are fitting parameters, and the units of C3 and C4 are kW / m³. 2 The units for t3 and t4 are meters. In the heat flux measurement distance model establishment step of this invention, the distances of each heat flux sensor from the pipe in Table 2 and the distribution of the measured optimal heat flux in the Cartesian coordinate system exhibit typical characteristics of a negative exponential equation, such as... Figure 4 As shown, a distance model for measuring this heat flux is established: d = C 3× e (-HF / t3) +C 4× e (-HF / t4) Similar to the establishment of the heat flux measurement range model, this heat flux measurement distance model, in order to have more free parameters to obtain optimal data interpolation and thus improve the accuracy of the simulated data, uses the following parameters: d = C 3×e (-HF / t3) +C 4× e (-HF / t4) C in 4× e (-HF / t4) The part is the part added to obtain the best data interpolation. This mathematical method of obtaining more free parameters to obtain the best data interpolation is a common mathematical means in model fitting and establishment. Therefore, this common means will not be described in detail in this invention.

[0054] Calculation of fitting parameters for the heat flux measurement distance model: based on the heat flux data measured by these heat flux sensors and the distance from the starting point (HF). i d i (i = 1, 2, 3...k), calculate the heat flux measurement distance model d using the least squares method. i =C 3× e (-HF i / t3) +C 4× e (-HF i / t4) The values ​​of fitting parameters C3, C4, t3, and t4 are given. In this invention, based on the calculation steps of the fitting parameters of the heat flux measurement distance model, the heat flux data and the distance from the starting point in Table 2 are calculated using the least squares method to obtain the following...

[0055] Table 5 shows the values ​​of the fitting parameters.

[0056] t3 <![CDATA[3.585kW / m 2 ]]> <![CDATA[C4]]> 224.522m t4 <![CDATA[82.786kW / m 2 ]]>

[0057] Table 5

[0058] Optimization of heat flux sensor measurement distance: Application of the heat flux measurement distance model d=C 3× e (-HF / t3) +C 4× e (-HF / t4) The values ​​of fitting parameters C3, C4, t3, and t4 are used to set the measurement distance of the heat flux sensor in the planned gas pipeline fire prevention system according to its set heat flux measurement range. In this invention, based on the heat flux sensor measurement distance optimization step, the heat flux measurement distance model d = C... 3× e (-HF / t3) +C 4× e (-HF / t4) The calculated fitting parameters (Table 5) can be used to determine the optimal location of the heat flux sensors for the heat flux range to be measured in the planned fire protection system. Table 6 below shows the three planned heat flux measurements of 15kW / m³. 2 35kW / m 2 and 70kW / m 2The determined installation location of the heat flux sensor.

[0059]

[0060] Table 6

[0061] Therefore, by applying the above method and referring to the heat flux (thermal effect) damage threshold, the installation location of the heat flux sensor can be specifically set to assist in activating corresponding safety plans or countermeasures in the event of a pipeline fire. The installation location of the heat flux sensor in the fire protection system can be set according to Table 7 below.

[0062]

[0063] Table 7

[0064] Based on this, a heat flux measurement distance model is established by fitting the distribution characteristics of the collected data, and the fitting parameters in the heat flux measurement distance model are determined through mathematical calculations. This heat flux measurement distance model and its fitting parameters can represent the relationship and variation between the location of the heat flux sensor and the heat flux at that location when a fault occurs and a fire hazard arises in the actual operating conditions of the gas transmission pipeline section. Therefore, the location of the heat flux sensor can be precisely set using this heat flux measurement distance model and its fitting parameters, based on the threshold values ​​of each stage of heat flux damage, thereby enabling the fire protection system to more accurately monitor fire hazards outside the pipeline.

[0065] In order to quickly and accurately obtain the values ​​of the fitting parameters in the above-mentioned models, the lsqcurvefit function in MATLAB software can be used to calculate the fitting parameters in the calculation of the fitting parameters of the heat flux measurement range model and / or the heat flux measurement distance model. Since MATLAB software and its lsqcurvefit function are one of the fast and effective methods for solving nonlinear fitting problems, and this calculation method is not the focus of this invention, it will not be described in detail.

[0066] The above are merely preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Other equivalent variations made using the patent concept of the present invention should all fall within the patent protection scope of the present invention.

Claims

1. A method for optimizing the setting parameters of a gas pipeline fire prevention system probe, characterized in that the steps include... include: Construction of a pipeline test section: To simulate the actual working conditions of a gas transmission pipeline section with a planned gas pipeline fire protection system, a pipeline test section is constructed. Deploying heat flux sensors: Starting from a point on the central axis of the pipeline test section, several heat flux sensors are deployed at intervals on the same horizontal plane along a direction perpendicular to and away from the pipeline test section. The detection direction of the several heat flux sensors is all towards the pipeline test section. During the deployment, the heat flux measurement range of each heat flux sensor is estimated and set as the distance between each heat flux sensor and the pipeline test section increases. Detonation of the pipeline test section: The pipeline test section is used to simulate a fault that occurs in the gas transmission pipeline section, and the pipeline test section is then detonated. Heat flux data acquisition: The plurality of heat flux sensors acquire heat flux data within a measurement time range at the moment of blasting according to a set data acquisition frequency; When it is necessary to optimize the measurement range of the heat flux sensors, a heat flux measurement range model is established: Based on the distance of each heat flux sensor from the starting point and the characteristic that the distribution of the heat flux data collected by each heat flux sensor in the Cartesian coordinate system exhibits a typical negative exponential equation, a heat flux measurement range model is established: HF=C1×e (-d / t1) +C2×e (-d / t2) Where HF represents the heat flux measurement range, and its unit is kW / m³. 2 d represents the distance from the heat flux sensor to the starting point, in meters (m). C1, C2, t1, and t2 are all fitting parameters, with C1 and C2 in kW / m³. 2 The units for t1 and t2 are meters (m). Calculation of fitting parameters for the heat flux measurement range model: based on the heat flux data measured by the several heat flux sensors and the distance from the starting point (HF). i d i (i=1,2,3……k), where i=1,2,3……k, the least squares method is used to calculate the heat flux measurement range model HF. i =C1×e (-di / t1) +C2×e (-di / t2) The values ​​of fitting parameters C1, C2, t1, and t2 are given, where HF i The heat flux data is measured by the i-th heat flux sensor deployed in sequence starting from the aforementioned starting point, and its unit is kW / m³. 2 d i The distance from the i-th heat flux sensor to the starting point is in meters (m), and k is the number of heat flux sensors deployed. Optimization of heat flux sensor measurement range: Application of the heat flux measurement range model HF=C1×e (-d / t1) +C2×e (-d / t2) The values ​​of fitting parameters C1, C2, t1, and t2 are used to set the heat flux measurement range of the heat flux sensor in the planned gas pipeline fire protection system according to its vertical distance from the gas transmission pipeline section. Alternatively, when it is necessary to optimize the measurement distance of the heat flux sensors, a heat flux measurement distance model is established: based on the distance of each heat flux sensor from the starting point and the characteristic that the distribution of the heat flux data collected by each heat flux sensor in the Cartesian coordinate system exhibits a typical negative exponential equation, a heat flux measurement distance model is established: d = C3 × e (-HF / t3) +C4×e (-HF / t4) Where C3, C4, t3, and t4 are fitting parameters, and the units of C3 and C4 are kW / m³. 2 The units for t3 and t4 are meters (m). Calculation of fitting parameters for the heat flux measurement distance model: based on the heat flux data measured by the several heat flux sensors and the distance from the starting point (HF). i d i (i=1,2,3……k), where i=1,2,3……k, the least squares method is used to calculate the heat flux measurement distance model d. i =C3×e (-HFi / t3) +C4×e (-HFi / t4) The values ​​of the fitting parameters C3, C4, t3, and t4; Optimization of heat flux sensor measurement distance: Applying the heat flux measurement distance model d=C3×e (-HF / t3) +C4×e (-HF / t4) The values ​​of fitting parameters C3, C4, t3, and t4 are used to set the measurement distance of the heat flux sensor in the planned gas pipeline fire protection system according to its set heat flux measurement range.

2. The method for optimizing the setting parameters of the gas pipeline fire prevention system probe according to claim 1, characterized in that, In the calculation of fitting parameters for the heat flux measurement range model and / or the heat flux measurement distance model, the lsqcurvefit function in MATLAB software is used to calculate the fitting parameters.

3. The method for optimizing the setting parameters of the gas pipeline fire prevention system probe according to claim 1, characterized in that, In the deployment of heat flux sensors: the plurality of heat flux sensors are fixed on several bases on the ground, and there are no obstructions between each base and the pipeline test section.

4. The method for optimizing the setting parameters of the gas pipeline fire prevention system probe according to claim 1, characterized in that, In the aforementioned deployment of heat flux sensors: the plurality of heat flux sensors are deployed at the same distance or at different distances.

5. The method for optimizing the setting parameters of the gas pipeline fire prevention system probe according to claim 1, characterized in that, In the deployment of heat flux sensors: the detection direction of each heat flux sensor is 300 meters directly above the starting point.

6. The method for optimizing the setting parameters of the gas pipeline fire prevention system probe according to claim 1, characterized in that, In the deployment of heat flux sensors: the estimated range of heat flux measurement for each heat flux sensor is reduced as the distance between the heat flux sensor and the pipeline test section increases.

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