An unmanned aerial vehicle based gas pipeline safety detection system

Abstract

The present application relates to gas pipeline safety detection technical field, especially in kind based on unmanned aerial vehicle's gas pipeline safety detection system, system includes data acquisition port, data processing port and data feedback port, data acquisition port, for arranging detection point above gas pipeline and using unmanned aerial vehicle to collect gas pipeline image, methane concentration data and detection time data, data processing port, for processing the gas pipeline image, methane concentration data and detection time data collected by unmanned aerial vehicle, and then calculating the threat value of gas pipeline, data feedback port, for feedback and early warning to the threat value of gas pipeline.

Description

Technical Field

[0001] This invention relates to the field of gas pipeline safety inspection technology, and in particular to a gas pipeline safety inspection system based on unmanned aerial vehicles (UAVs). Background Technology

[0002] Gas pipeline networks are a crucial component of urban energy supply, making their safety monitoring paramount. Traditional gas pipeline safety inspection methods primarily rely on regular manual inspections, resulting in low efficiency, high costs, and blind spots. With the development of drone technology, drone-based gas pipeline safety inspection is gradually becoming a trend. Currently, much research in the field of gas pipeline safety inspection focuses on defect detection, such as leak detection, leak location, or leak propagation range. Few methods address the challenge of simultaneously detecting temperature and leaks in different areas of the gas pipeline by dividing it into zones, and quantifying defects into data that directly reflects their threat level.

[0003] For example, Chinese patent CN108930914B discloses a method and apparatus for tracing gas leaks. The method includes: if a gas leak is detected in a gas pipeline network, then based on the gas diffusion characteristics in adjacent underground spaces of the gas pipeline network, and according to the detected gas concentration values ​​and location information of the adjacent underground spaces, predicting the target gas pipeline in the gas pipeline network where the gas leak has occurred. The method and apparatus provided by this invention are highly efficient in tracing gas leaks because they only require detecting the gas concentration values ​​in adjacent underground spaces of the gas pipeline network to predict the target gas pipeline where the gas leak has occurred. At the same time, they have wide applicability, can be applied to old pipelines, and are low in cost.

[0004] The aforementioned patents highlight that while much research in the field of gas pipeline safety inspection technology focuses on defect detection in gas pipeline networks, such as leak detection, leak point location, or leak diffusion range, few methods address the challenge of simultaneously detecting temperature and leaks in different areas of the gas pipeline by dividing it into zones, and quantifying defects into data that directly reflects their threat level. This invention proposes a UAV-based gas pipeline safety inspection system. By dividing the gas pipeline into zones and deploying detection points above these zones, the UAV collects infrared thermal images and methane concentration data at the monitoring points, recording the detection time points. The infrared thermal image data is imported into a temperature threat calculation strategy to calculate the temperature threat value, and the methane concentration data is imported into a leak threat calculation strategy to calculate the leak threat value. Finally, the temperature and leak threat values ​​are imported into a total threat calculation strategy to calculate the total threat value, and different zones are categorized into warning levels. Therefore, this system offers good operability, high flexibility, and high efficiency, effectively improving the intuitiveness, accuracy, and orderliness of gas pipeline safety inspection results. It has significant practical implications for daily safety inspections and risk avoidance of gas pipelines. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention proposes a gas pipeline safety inspection system based on unmanned aerial vehicles (UAVs). This invention divides the gas pipeline into regions and deploys inspection points over these regions. UAVs are used to collect infrared thermal images and methane concentration data at the monitoring points, recording the inspection time points. The infrared thermal image data is imported into a temperature threat calculation strategy to calculate the temperature threat value, and the methane concentration data is imported into a leakage threat calculation strategy to calculate the leakage threat value. The temperature threat value and leakage threat value are then imported into a total threat calculation strategy to calculate the total threat value. Furthermore, different warning levels are assigned to different regions based on the total threat level. This effectively improves the intuitiveness, accuracy, and orderliness of the gas pipeline safety inspection results.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] A gas pipeline safety inspection system based on unmanned aerial vehicles (UAVs) includes a data acquisition port, a data processing port, and a data feedback port. The data acquisition port is used to deploy detection points above the gas pipeline and use the UAV to collect images of the gas pipeline, methane concentration data, and detection time data. The data processing port is used to process the gas pipeline images, methane concentration data, and detection time data collected by the UAV to calculate the threat value of the gas pipeline. The data feedback port is used to provide feedback on the threat value of the gas pipeline and issue early warnings.

[0008] A further improvement of this invention is that the data acquisition port includes a detection point layout module, an image acquisition module, a methane concentration acquisition module, and a detection time acquisition module. The detection point layout module is used to divide the gas pipeline into areas and arrange UAV detection points at equal intervals along the wind direction above different areas. The image acquisition module is used to acquire infrared thermal images of the gas pipeline at the detection points using UAVs. The methane concentration acquisition module is used to acquire methane concentration data at the detection points. The detection time acquisition module is used to acquire detection time points and time intervals.

[0009] A further improvement of this invention is that the data processing port includes a temperature threat calculation module, a leakage threat calculation module, and a total threat calculation module. The temperature threat calculation module is used to import the collected infrared thermal image data of the gas pipeline into the temperature threat calculation strategy to calculate the temperature threat value. The leakage threat calculation module is used to import the collected methane concentration data at the detection point into the leakage threat calculation strategy to calculate the leakage threat value. The total threat calculation module is used to import the temperature threat value and the leakage threat value into the total threat calculation strategy to calculate the total threat value.

[0010] A further improvement of the present invention is that the data feedback port includes a threat feedback module and an early warning module. The threat feedback module is used to provide the calculated threat values ​​of different areas of the gas pipeline to the management personnel. The early warning module is used to import the threat values ​​of different areas of the gas pipeline into the threat threshold comparison strategy to classify the early warning level.

[0011] A further improvement of this invention is that the temperature threat calculation module runs a temperature threat calculation strategy, which includes the following specific steps:

[0012] S11: Divide the gas pipeline into k areas evenly, collect wind direction data, and arrange k drone monitoring points at equal intervals along the wind direction above each area.

[0013] S12: Use a drone to collect infrared thermal images of the gas pipeline at any detection point at times t1 and t2. Based on the defined regions of the gas pipeline, divide the two collected infrared thermal images into regions, then divide each region into pixels. Extract the temperature values ​​of the pixels to form a region temperature value sequence. Represent the temperature value sequence of a single region at times t1 and t2 as follows: T i 1 T represents the temperature value of the i-th pixel in a single region of the infrared thermal image acquired at time t1. i 2 This represents the temperature value of the i-th pixel in a single region of the infrared thermal image acquired at time t2;

[0014] S13: Calculate the temperature difference ratio k1 between two acquisitions of each region in the infrared thermal image. The formula for calculating the temperature difference ratio of a single region is:

[0015] S14: Calculate the temperature threat value for each region. The formula for calculating the temperature threat value for a single region is β1=(t2-t1)k1.

[0016] A further improvement of this invention is that the leakage threat calculation module runs a leakage threat calculation strategy, which includes a methane diffusion loss value calculation sub-strategy, and the methane diffusion loss value calculation sub-strategy includes the following specific steps:

[0017] S21: Collect the methane concentration values ​​at each detection point at times t1 and t2. Let be the methane concentration value collected at the j-th detection point at time t1. The methane concentration value at the j-th detection point collected at time t2;

[0018] S22: Extract methane concentration values ​​from k detection points at time t1. The methane diffusion loss value k2 is calculated using the following formula:

[0019] A further improvement of this invention is that the leakage threat calculation module runs a leakage threat calculation strategy, which further includes a methane leakage value calculation sub-strategy, the methane leakage value calculation sub-strategy including the following specific steps:

[0020] S31: Extract the methane concentration values ​​at each detection point at times t1 and t2.

[0021] S32: Calculate the methane leakage value k3 using the following formula:

[0022] A further improvement of the present invention is that the formula for calculating the leakage threat value is: β2=(1+α)(k3-k2), where α is the leakage threat value correction coefficient.

[0023] A further improvement of this invention is that the total threat calculation module runs a total threat calculation strategy, the calculation formula of which is as follows:

[0024] Q = α1β1 + α2β2;

[0025] Where Q represents the total threat value, α1 is the temperature threat value ratio coefficient, α2 is the leakage threat value ratio coefficient, and α1+α2=1.

[0026] A further improvement of the present invention is that the early warning module runs a threat threshold comparison strategy, which includes the following specific steps:

[0027] S41: Set four different levels of total threat thresholds, ordered from highest to lowest.

[0028] S42: Extract the temperature threat value, leakage threat value, and total threat value of each area to form a threat value sequence. in This represents the temperature threat value of the j-th region. Let Qj represent the leakage threat value of the j-th region, and let Qj represent the total threat value of the j-th region.

[0029] S43: Compare Qj with the total threat threshold, when... For safety reasons, when A Level A warning will be issued at any time; when A Level B warning will be issued at this time; A Level C warning will be issued, dividing the gas pipeline network into different warning levels.

[0030] S43: When there are m areas classified as the same warning level, further compare the leakage threat values ​​among the m areas, sort the leakage threat values ​​from high to low, divide the same warning level into orders of magnitude, and handle the threats in an orderly manner.

[0031] The technical effects of this invention are as follows:

[0032] This invention divides gas pipelines into zones and deploys detection points above these zones. Using drones, it collects infrared thermal images and methane concentrations at the monitoring points, recording the detection time points. The infrared thermal image data is imported into a temperature threat calculation strategy to calculate the temperature threat value, and the methane concentration data is imported into a leak threat calculation strategy to calculate the leak threat value. Finally, the temperature threat value and the leak threat value are imported into a total threat calculation strategy to calculate the total threat value. Furthermore, the total threat level for different zones is categorized into warning levels, effectively improving the intuitiveness, accuracy, and orderliness of gas pipeline safety detection results. Attached Figure Description

[0033] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0034] Figure 1 This is a schematic diagram of a gas pipeline safety inspection system based on an unmanned aerial vehicle (UAV) according to an embodiment of the present invention.

[0035] Figure 2This is a schematic diagram of the computational strategy framework of a gas pipeline safety detection system based on an unmanned aerial vehicle (UAV) according to an embodiment of the present invention. Detailed Implementation

[0036] Example

[0037] This embodiment proposes a gas pipeline safety inspection system based on unmanned aerial vehicles (UAVs). By dividing the gas pipeline into regions and deploying inspection points over these regions, the UAVs collect infrared thermal images and methane concentrations at the monitoring points and record the inspection time points. The infrared thermal image data is imported into a temperature threat calculation strategy to calculate the temperature threat value, and the methane concentration data is imported into a leakage threat calculation strategy to calculate the leakage threat value. The temperature threat value and leakage threat value are imported into a total threat calculation strategy to calculate the total threat value. The total threat of different regions is then divided into warning levels, effectively improving the intuitiveness, accuracy, and orderliness of the gas pipeline safety inspection results.

[0038] like Figures 1-2 As shown, a gas pipeline safety inspection system based on unmanned aerial vehicles (UAVs) includes a data acquisition port, a data processing port, and a data feedback port. The data acquisition port is used to deploy detection points above the gas pipeline and use the UAV to collect images of the gas pipeline, methane concentration data, and detection time data. The data processing port is used to process the gas pipeline images, methane concentration data, and detection time data collected by the UAV, and then calculate the threat value of the gas pipeline. The data feedback port is used to provide feedback on the threat value of the gas pipeline and issue early warnings.

[0039] In this embodiment, the data acquisition port includes a detection point layout module, an image acquisition module, a methane concentration acquisition module, and a detection time acquisition module. The detection point layout module is used to divide the gas pipeline into areas and arrange UAV detection points at equal intervals along the wind direction above different areas. The image acquisition module is used to acquire infrared thermal images of the gas pipeline at the detection points using UAVs. The methane concentration acquisition module is used to acquire methane concentration data at the detection points. The detection time acquisition module is used to acquire detection time points and time intervals.

[0040] In this embodiment, the data processing port includes a temperature threat calculation module, a leakage threat calculation module, and a total threat calculation module. The temperature threat calculation module is used to import the collected infrared thermal image data of the gas pipeline into the temperature threat calculation strategy to calculate the temperature threat value. The leakage threat calculation module is used to import the collected methane concentration data at the detection point into the leakage threat calculation strategy to calculate the leakage threat value. The total threat calculation module is used to import the temperature threat value and the leakage threat value into the total threat calculation strategy to calculate the total threat value.

[0041] In this embodiment, the data feedback port includes a threat feedback module and an early warning module. The threat feedback module is used to provide the calculated threat values ​​of different areas of the gas pipeline to the management personnel. The early warning module is used to import the threat values ​​of different areas of the gas pipeline into the threat threshold comparison strategy to classify the early warning level.

[0042] In this embodiment, the temperature threat calculation module runs a temperature threat calculation strategy, which includes the following specific steps:

[0043] S11: Divide the gas pipeline into k areas evenly, collect wind direction data, and arrange k drone monitoring points at equal intervals along the wind direction above each area.

[0044] S12: Use a drone to collect infrared thermal images of the gas pipeline at any detection point at times t1 and t2. Based on the defined regions of the gas pipeline, divide the two collected infrared thermal images into regions, then divide each region into pixels. Extract the temperature values ​​of the pixels to form a region temperature value sequence. Represent the temperature value sequence of a single region at times t1 and t2 as follows: T i 1 T represents the temperature value of the i-th pixel in a single region of the infrared thermal image acquired at time t1. i 2 This represents the temperature value of the i-th pixel in a single region of the infrared thermal image acquired at time t2;

[0045] S13: Calculate the temperature difference ratio k1 between two acquisitions of each region in the infrared thermal image. The formula for calculating the temperature difference ratio of a single region is:

[0046] S14: Calculate the temperature threat value for each region. The formula for calculating the temperature threat value for a single region is β1=(t2-t1)k1.

[0047] In this embodiment, the leakage threat calculation module runs a leakage threat calculation strategy, which includes a methane diffusion loss value calculation sub-strategy. The methane diffusion loss value calculation sub-strategy includes the following specific steps:

[0048] S21: Collect the methane concentration values ​​at each detection point at times t1 and t2. Let be the methane concentration value collected at the j-th detection point at time t1. The methane concentration value at the j-th detection point collected at time t2;

[0049] S22: Extract methane concentration values ​​from k detection points at time t1. The methane diffusion loss value k2 is calculated using the following formula:

[0050] In this embodiment, the leakage threat calculation module runs a leakage threat calculation strategy, which further includes a methane leakage value calculation sub-strategy. The methane leakage value calculation sub-strategy includes the following specific steps:

[0051] S31: Extract the methane concentration values ​​at each detection point at times t1 and t2.

[0052] S32: Calculate the methane leakage value k3 using the following formula:

[0053] In this embodiment, the formula for calculating the leakage threat value is: β2=(1+α)(k3-k2), where α is the leakage threat value correction coefficient.

[0054] In this embodiment, the total threat calculation module runs a total threat calculation strategy, and the calculation formula for the total threat calculation strategy is as follows:

[0055]

[0056] Where Q represents the total threat value, α1 is the temperature threat value ratio coefficient, α2 is the leakage threat value ratio coefficient, and α1+α2=1.

[0057] In this embodiment, the early warning module runs a threat threshold comparison strategy, which includes the following specific steps:

[0058] S41: Set four different levels of total threat thresholds, ordered from highest to lowest.

[0059] S42: Extract the temperature threat value, leakage threat value, and total threat value of each area to form a threat value sequence. in This represents the temperature threat value of the j-th region. Let Qj represent the leakage threat value of the j-th region, and let Qj represent the total threat value of the j-th region.

[0060] S43: Compare Qj with the total threat threshold, when... For safety reasons, when A Level A warning will be issued at any time; when A Level B warning will be issued at this time; A Level C warning will be issued, dividing the gas pipeline network into different warning levels.

[0061] S43: When there are m areas classified as the same warning level, further compare the leakage threat values ​​among the m areas, sort the leakage threat values ​​from high to low, divide the same warning level into orders of magnitude, and handle the threats in an orderly manner.

[0062] It should be noted that by dividing the gas pipeline into zones and setting up detection points over these zones, drones are used to collect infrared thermal images and methane concentrations of the gas pipelines at the monitoring points and record the detection time points. The infrared thermal image data is imported into the temperature threat calculation strategy to calculate the temperature threat value, the methane concentration data is imported into the leakage threat calculation strategy to calculate the leakage threat value, and the temperature threat value and leakage threat value are imported into the total threat calculation strategy to calculate the total threat value. The total threat of different zones is then divided into warning levels, which effectively improves the intuitiveness, accuracy, and orderliness of the gas pipeline safety detection results.

Claims

1. A drone-based gas pipeline safety detection system, characterized in that, The system includes a data acquisition port, a data processing port, and a data feedback port. The data acquisition port is used to set up detection points above the gas pipeline and use a drone to collect images of the gas pipeline, methane concentration data, and detection time data. The data processing port is used to process the images of the gas pipeline, methane concentration data, and detection time data collected by the drone, and then calculate the threat value of the gas pipeline; the data feedback port is used to provide feedback on the threat value of the gas pipeline and issue an early warning. S11: Divide the gas pipeline into k areas evenly, collect wind direction data, and arrange k drone monitoring points at equal intervals along the wind direction above each area. S12: Use a drone to collect data at any detection point. , Infrared thermal images of the gas pipeline are collected at all times. Based on the defined regions of the gas pipeline, the two acquired infrared thermal images are divided into regions. Each region of the infrared thermal image is then divided into pixels, and the temperature values ​​of these pixels are extracted to form a region temperature value sequence. The temperature value sequence of a single region is then... , The times are respectively represented as , , express The temperature value of the i-th pixel in a single region of an infrared thermal image acquired at any time. express The temperature value of the i-th pixel in a single region of an infrared thermal image acquired at any time; S13: Calculate the temperature difference ratio between two acquisitions of each region in the infrared thermal image. The formula for calculating the temperature difference ratio of a single region is: ; S14: Calculate the temperature threat value for each region. The formula for calculating the temperature threat value for a single region is as follows: ; S21: Collection , methane concentration values ​​at each detection point at any given time , , for The methane concentration value collected at the j-th detection point at time t. for The methane concentration value collected at the j-th detection point at time j; S22: Extract methane concentration values ​​at k detection points at time 1 Calculate the methane diffusion loss value The calculation formula is: ; S31: Extract , methane concentration values ​​at each detection point at any given time , ; S32: Calculate methane leakage value The calculation formula is: ; The formula for calculating the threat value of a leak is: ,in, k2 is a correction factor for the leakage threat value, and it is used to represent the methane diffusion loss value. The formula for calculating the total threat strategy is as follows: ； Where Q represents the total threat value, This is the coefficient representing the proportion of temperature threat value. This represents the percentage of leaked threat values, and , Used to indicate temperature threat values.

2. The gas pipeline safety inspection system based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The data acquisition port includes a detection point layout module, an image acquisition module, a methane concentration acquisition module, and a detection time acquisition module. The detection point layout module is used to divide the gas pipeline into areas and arrange UAV detection points at equal intervals along the wind direction above different areas. The image acquisition module is used to acquire infrared thermal images of the gas pipeline at the detection points using UAVs. The methane concentration acquisition module is used to acquire methane concentration data at the detection point; The detection time acquisition module is used to acquire detection time points and time intervals.

3. The gas pipeline safety inspection system based on unmanned aerial vehicles (UAVs) according to claim 2, characterized in that, The data processing port includes a temperature threat calculation module, a leakage threat calculation module, and a total threat calculation module. The temperature threat calculation module is used to import the collected infrared thermal image data of the gas pipeline into the temperature threat calculation strategy to calculate the temperature threat value. The leakage threat calculation module is used to import the collected methane concentration data at the detection points into the leakage threat calculation strategy to calculate the leakage threat value; The total threat calculation module is used to import temperature threat values ​​and leakage threat values ​​into the total threat calculation strategy to calculate the total threat value.

4. The gas pipeline safety inspection system based on unmanned aerial vehicles (UAVs) according to claim 3, characterized in that, The data feedback port includes a threat feedback module and an early warning module. The threat feedback module is used to provide management personnel with the calculated threat values ​​of different areas of the gas pipeline. The early warning module is used to import the threat values ​​of different areas of the gas pipeline into a threat threshold comparison strategy to classify early warning levels.

5. A gas pipeline safety inspection system based on unmanned aerial vehicles (UAVs) according to claim 4, characterized in that, The early warning module operates a threat threshold comparison strategy, which includes the following specific steps: S41: Set four different levels of total threat thresholds, ordered from highest to lowest. ; S42: Extract the temperature threat value, leakage threat value, and total threat value of each area to form a threat value sequence. ,in This represents the temperature threat value of the j-th region. This represents the leakage threat value of the j-th region. This represents the total threat value of the j-th region; S43: Comparison With respect to the size of the total threat threshold, when For safety reasons, when A Level A warning will be issued at any time; when A Level B warning will be issued at this time; A Level C warning will be issued, dividing the gas pipeline network into different warning levels. S43: When there are m areas classified as the same warning level, further compare the leakage threat values ​​among the m areas, sort the leakage threat values ​​from high to low, divide the same warning level into orders of magnitude, and handle the threats in an orderly manner.

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