Gas safety monitoring system based on cloud internet of things analysis

Abstract

The application relates to the technical field of data processing, in particular to a gas safety monitoring system based on cloud end Internet of Things analysis, which comprises the following steps: collecting natural gas concentration data, wind speed data and wind direction data of each natural gas concentration sensor of each monitoring point at each moment; obtaining the gas diffusion intensity of each monitoring point at each moment according to the difference of different natural gas concentration data; obtaining the wind force influence degree of each monitoring point at each moment according to the relationship between the wind speed and the leakage direction, thereby obtaining the gas leakage level of each monitoring point; combining the extreme difference of the natural gas concentration data to obtain the leakage degree value of each monitoring point, and further obtaining the gas risk monitoring index of the pipeline to divide the gas safety level. The application can analyze the natural gas leakage of the transmission pipeline in the pipe bridge transportation process to realize real-time safety monitoring on the safety performance of the transmission pipeline and accurately divide the safety level.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and more specifically to a gas safety monitoring system based on cloud-based Internet of Things (IoT) analysis. Background Technology

[0002] Natural gas is a widely used fuel that is typically transported from gas stations to users via underground pipeline systems. To mitigate transportation risks in challenging terrain, pipeline bridges are constructed to support and protect the pipelines. However, this method exposes the pipelines directly to the air, and due to the unpredictable nature of weather conditions, defects such as cracks or gaps can easily appear on the pipeline surface, leading to leaks during natural gas transportation.

[0003] Because natural gas is compressed into high-pressure gas during transportation, the large pressure difference with the outside environment increases the gap at pipeline leaks, resulting in complex changes in natural gas concentration data. Existing methods often involve setting up multiple monitoring points at various locations along the target pipeline and using gas concentration sensors to obtain the natural gas concentration at each monitoring point. This data is then used to reflect the safety status of the target pipeline. However, since the concentration change relationship between different distances at the monitoring points is not considered, it is impossible to accurately classify the safety level of natural gas transmission pipelines, thus reducing the effectiveness of safety monitoring. Summary of the Invention

[0004] This invention provides a gas safety monitoring system based on cloud-based Internet of Things (IoT) analytics to address existing problems.

[0005] The gas safety monitoring system based on cloud-based Internet of Things (IoT) analysis of the present invention adopts the following technical solution:

[0006] One embodiment of the present invention provides a gas safety monitoring system based on cloud-based Internet of Things (IoT) analysis, the system comprising the following modules:

[0007] Data acquisition module: used to collect natural gas concentration data, wind speed data, and wind direction data of each natural gas concentration sensor at each monitoring point at each time moment;

[0008] Data processing module: This module is used to obtain the gas diffusion intensity at each monitoring point at each time moment based on the differences in natural gas concentration data from different natural gas concentration sensors at each monitoring point; to obtain the wind force influence at each monitoring point at each time moment based on the relationship between wind speed and leakage direction of each natural gas concentration sensor at each monitoring point; and to obtain the gas leakage level at each monitoring point by combining the wind force influence and gas diffusion intensity at each monitoring point at each time moment; to obtain the leakage degree value at each monitoring point by combining the extreme value differences of natural gas concentration data from all natural gas concentration sensors at each monitoring point with the gas leakage level at each monitoring point; and to obtain the gas risk monitoring index for the pipeline based on the leakage degree values ​​of all monitoring points.

[0009] Risk level monitoring module: used to classify the gas safety level of the target natural gas transmission pipeline according to the gas risk monitoring indicators, and to carry out pipeline maintenance according to the classified gas safety level.

[0010] Furthermore, obtaining the gas diffusion intensity at each monitoring point at each time moment includes:

[0011]

[0012] In the formula, α k,t M represents the gas diffusion intensity at the k-th monitoring point at time t; k P represents the number of natural gas concentration sensors at the k-th monitoring point; k,x,t P represents the natural gas concentration data of the x-th natural gas concentration sensor at the k-th monitoring point at time t; k,x-1,t This represents the natural gas concentration data of the (x-1)th natural gas concentration sensor at the kth monitoring point at time t; exp() represents an exponential function with the natural constant as the base.

[0013] Furthermore, obtaining the degree of wind impact at each monitoring point at each moment includes:

[0014]

[0015] In the formula, u k,t V′ represents the degree of wind influence at the k-th monitoring point at the t-th data collection time. k,x,t M represents the wind speed component at time t of the x-th natural gas concentration sensor at the k-th monitoring point; k V′ represents the number of natural gas concentration sensors at the k-th monitoring point; t,min V′ represents the minimum wind speed component at time t for all sensor locations at all monitoring points. t,maxThis represents the maximum value of the wind speed component at time t for all sensor locations at all monitoring points.

[0016] Furthermore, the specific methods for obtaining the wind speed component include:

[0017] The wind speed data of the x-th natural gas concentration sensor at the k-th monitoring point at time t is vector decomposed along the leakage direction and denoted as the wind speed component of the x-th natural gas concentration sensor at the k-th monitoring point at time t.

[0018] Furthermore, the specific method for obtaining the leakage direction includes:

[0019] The vector direction of the tangent perpendicular to the edge of the pipe section is denoted as the leakage direction.

[0020] Furthermore, obtaining the gas leakage level at each monitoring point includes:

[0021]

[0022] β k The gas leakage level at the k-th monitoring point is represented by T; T represents the quantity at all times; α k,t represents the gas diffusion intensity at the k-th monitoring point at the t-th collection time; exp[] represents an exponential function with the natural exponent as the base.

[0023] Furthermore, obtaining the leakage level value for each monitoring point includes:

[0024] Retrieve all extreme values ​​from natural gas concentration data;

[0025] The leakage level value of the kth monitoring point is calculated as follows:

[0026] β k ′ =γ k ×β k

[0027] In the formula, β k ′ β represents the leakage level at the k-th monitoring point; k γ represents the gas leakage level at the k-th monitoring point; k This represents the leakage control coefficient at the k-th monitoring point.

[0028] Furthermore, the specific method for obtaining the leakage adjustment coefficient includes:

[0029]

[0030] M kN represents the number of natural gas concentration sensors at the k-th monitoring point; k,x P represents the number of all extreme values ​​in the natural gas concentration data of the x-th natural gas concentration sensor at the k-th monitoring point; k,x,y This represents the y-th extreme value in the natural gas concentration data of the x-th natural gas concentration sensor at the k-th monitoring point; γ represents the mean of all natural gas concentration data from the x-th natural gas concentration sensor at the k-th monitoring point; k This represents the leakage control coefficient at the k-th monitoring point.

[0031] Furthermore, the obtained gas risk monitoring indicators for the pipeline include:

[0032]

[0033] In the formula, σ represents the gas risk monitoring index of the pipeline; R represents the number of monitoring points; β k ′ This represents the leakage level value at the k-th monitoring point; sigmoid() represents the sigmoid function.

[0034] Furthermore, the maintenance of pipelines according to the classified gas safety level includes:

[0035] The safety level of the target natural gas transmission pipeline is determined by classifying the gas safety level according to the gas risk monitoring indicators. When managing and maintaining the natural gas transmission pipeline, the pipeline is ranked in descending order of its gas safety level and then used as the priority sequence for maintenance.

[0036] The beneficial effects of the technical solution of this invention are as follows: Natural gas concentration data, wind speed data, and wind direction data are collected from each natural gas concentration sensor at each monitoring point at each moment; based on the differences in different natural gas concentration data, the gas diffusion intensity at each monitoring point at each moment is obtained to represent the magnitude of the leakage intensity; based on the relationship between wind speed and leakage direction, the degree of wind influence at each monitoring point at each moment is obtained, thereby obtaining the gas leakage level at each monitoring point, illustrating the influence of natural wind on leakage monitoring; combined with the extreme differences in natural gas concentration data, the leakage degree value at each monitoring point is obtained, thus obtaining the gas risk monitoring index for the pipeline and accurately classifying the gas safety level. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a structural block diagram of the gas safety monitoring system based on cloud-based Internet of Things analysis according to the present invention. Detailed Implementation

[0039] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the gas safety monitoring system based on cloud-based Internet of Things analysis proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0041] The specific solution of the gas safety monitoring system based on cloud-based Internet of Things analysis provided by the present invention will be described in detail below with reference to the accompanying drawings.

[0042] Please see Figure 1 The diagram illustrates a structural block diagram of a gas safety monitoring system based on cloud-based Internet of Things (IoT) analysis, according to an embodiment of the present invention. The system includes the following modules:

[0043] Data acquisition module 101: Collects natural gas concentration data, wind speed data, and wind direction data from each natural gas concentration sensor at each monitoring point at each time.

[0044] The purpose of this embodiment is to conduct real-time safety monitoring of the pipeline's safety performance by analyzing natural gas leakage during pipeline transportation. Therefore, multiple monitoring points need to be set up on the target pipeline in the target pipeline transportation area. Natural gas concentration sensors are installed perpendicular to the pipeline surface outside each monitoring point to collect the natural gas concentration data of each natural gas concentration sensor at each monitoring point at each time. At the same time, wind speed and direction sensors are installed to synchronously collect the wind speed and direction data of each natural gas concentration sensor location at each time. In this embodiment, there are 5 natural gas concentration sensors outside each monitoring point, with each sensor spaced 25 cm apart. The data collection time is 8 hours, the collection frequency is once per second, and the distance between each monitoring point is 10 meters. This is described using this example; other values ​​can be set in other embodiments, and this embodiment does not impose specific limitations.

[0045] Data processing module 102:

[0046] (1) The gas diffusion intensity at each monitoring point at each time is obtained based on the difference in natural gas concentration data of each natural gas concentration sensor at each monitoring point.

[0047] It should be noted that when cracks or gaps appear on the surface of monitoring points in natural gas transmission pipelines, natural gas leakage will occur. If the leakage intensity is large, the natural gas concentration at different distances outside the monitoring point will be more uniformly diffused. That is, the smaller the difference in natural gas concentration data at different distances of the same monitoring point, the higher the probability that the natural gas leakage intensity at that monitoring point is large.

[0048] Specifically, the calculation method for the gas diffusion intensity at the k-th monitoring point at time t is as follows:

[0049]

[0050] In the formula, α k,t M represents the gas diffusion intensity at the k-th monitoring point at time t; k P represents the number of natural gas concentration sensors at the k-th monitoring point; k,x,t P represents the natural gas concentration data of the x-th natural gas concentration sensor at the k-th monitoring point at time t; k,x-1,t This represents the natural gas concentration data of the (x-1)th natural gas concentration sensor at the kth monitoring point at time t; exp() represents an exponential function with the natural constant as the base.

[0051] This represents the mean of the differences in natural gas concentration data at the k-th monitoring point at time t. The smaller the mean, the smaller the difference in natural gas concentration at different distances outside the pipeline at the k-th monitoring point at time t, and the higher the probability of a large natural gas leak.

[0052] (2) Obtain the wind force influence at each moment based on the wind speed of each natural gas concentration sensor at each monitoring point, and obtain the gas leakage level at each monitoring point through the wind force influence and gas diffusion intensity.

[0053] It should be noted that natural wind outside the pipeline has a certain impact on the detection of leaked natural gas concentration. Therefore, it is necessary to analyze the data acquisition process in conjunction with wind speed and direction characteristics. The wind speed data of the sensors at different distances at each monitoring point should be vector-decomposed along the direction perpendicular to the leak and along the direction parallel to the leak. When the wind speed component is larger when it is parallel to the leak and opposite to the leak direction, the natural gas concentration data collected by the natural gas concentration sensor will be larger. When the wind speed component is larger when it is parallel to the leak and in the same direction as the leak, the natural gas concentration difference collected by the natural gas concentration sensor will be smaller.

[0054] Therefore, the wind force influence at each moment is first obtained by measuring the wind speed of each natural gas concentration sensor at each monitoring point, and the gas leakage level at each monitoring point is obtained by measuring the wind force influence and the gas diffusion intensity.

[0055] Since natural gas leaks out under certain pressure, the vector direction of the cross-section perpendicular to the pipe is recorded as the leakage direction. The wind speed data of the x-th natural gas concentration sensor at the k-th monitoring point at time t is decomposed into vector components along the leakage direction.

[0056] The method for calculating the wind force impact at time t for the kth monitoring point is as follows:

[0057]

[0058] The gas leakage level at the kth monitoring point is calculated as follows:

[0059]

[0060] In the formula, u k,t V′ represents the degree of wind influence at the k-th monitoring point at the t-th data collection time. k,x,t M represents the wind speed component at time t of the x-th natural gas concentration sensor at the k-th monitoring point; k V′ represents the number of natural gas concentration sensors at the k-th monitoring point; t,minV′ represents the minimum wind speed component at time t for all sensor locations at all monitoring points. t,max This represents the maximum value of the wind speed component at time t for all sensor locations at all monitoring points;

[0061] β k The gas leakage level at the k-th monitoring point is represented by T; T represents the quantity at all times; α k,t represents the gas diffusion intensity at the k-th monitoring point at the t-th sampling time; exp[] represents an exponential function with the natural constant as the base.

[0062] when The larger the value, the larger the wind speed component at time t of the x-th natural gas concentration sensor location at the k-th monitoring point, which is parallel to and in the same direction as the leakage direction. This will cause the concentration difference collected by adjacent natural gas concentration sensors at the k-th monitoring point to be smaller; α k,t This indicates that the smaller the difference in natural gas concentration collected by multiple natural gas concentration sensors at time t at the k-th monitoring point, the greater the level of natural gas leakage at the k-th monitoring point.

[0063] (3) Based on the fluctuation of natural gas concentration data of each natural gas concentration sensor at each monitoring point, the leakage degree value of each monitoring point is obtained, thereby calculating the gas risk monitoring index of the pipeline.

[0064] It should be noted that due to the irregularity of pipeline leaks and the high speed of natural gas transmission within the pipeline, eddies and turbulence may occur at the leak points. These eddies and turbulence make the gas flow within the leak area more complex and irregular, increasing the mechanical stress and dynamic pressure around the leak point, thereby increasing the likelihood of the leak expanding and the risk of natural gas leakage. Therefore, it is necessary to analyze the probability of eddies and turbulence at the monitoring points and adjust the gas leakage level at the monitoring points based on the probability of eddies and turbulence.

[0065] If the natural gas concentration data collected by the natural gas concentration sensor at the monitoring point shows more significant fluctuations in time series, it indicates a higher probability of eddies and turbulence at the monitoring point, and a higher risk level at the monitoring point. Therefore, it is necessary to adjust the gas leakage level at the monitoring point to be increased.

[0066] Specifically, obtain all extreme values ​​in the natural gas concentration data;

[0067] The leakage adjustment coefficient for the kth monitoring point is calculated as follows:

[0068]

[0069] The leakage level value of the kth monitoring point is calculated as follows:

[0070] β k ′ =γ k ×β k

[0071] In the formula, β k ′ β represents the leakage level at the k-th monitoring point; k M represents the gas leakage level at the k-th monitoring point; k N represents the number of natural gas concentration sensors at the k-th monitoring point; k,x P represents the number of all extreme values ​​in the natural gas concentration data of the x-th natural gas concentration sensor at the k-th monitoring point; k,x,y This represents the y-th extreme value in the natural gas concentration data of the x-th natural gas concentration sensor at the k-th monitoring point; γ represents the mean of all natural gas concentration data from the x-th natural gas concentration sensor at the k-th monitoring point; k This represents the leakage control coefficient at the k-th monitoring point.

[0072] The leakage regulation coefficient represents the level of gas leakage. The larger the value, the more unstable the natural gas concentration data at the k-th monitoring point is, the more obvious the fluctuation characteristics are, the higher the risk level of the k-th monitoring point is, and the higher the leakage level value is.

[0073] Thus, the leakage level value at each monitoring point is obtained. Based on the leakage level value at each monitoring point, the risk of the entire pipeline is analyzed. The calculation method for the gas risk monitoring index of the pipeline is as follows:

[0074]

[0075] In the formula, σ represents the gas risk monitoring index of the pipeline; R represents the number of monitoring points; β k ′ This represents the leakage level value at the k-th monitoring point; sigmoid() represents the sigmoid function.

[0076] A higher leakage level value indicates a greater risk of natural gas leakage at the corresponding monitoring point. This represents the average leakage level values ​​at all monitoring points in the pipeline. The higher the value, the higher the gas risk monitoring index of the pipeline, and the higher the risk of the pipeline.

[0077] Risk Level Monitoring Module 103: Classifies the gas safety level of the target natural gas transmission pipeline based on gas risk monitoring indicators.

[0078] The safety level of a target natural gas transmission pipeline is determined by classifying it according to gas risk monitoring indicators. A higher safety level indicates a more severe leakage situation, thus requiring more management and maintenance. As an example, the levels in this embodiment are as follows: Level 1 when the gas risk monitoring indicator is less than 0.2; Level 2 when the indicator is greater than or equal to 0.2 and less than 0.4; Level 3 when the indicator is greater than or equal to 0.4 and less than 0.6; Level 4 when the indicator is greater than or equal to 0.6 and less than 0.8; and Level 5 when the indicator is greater than 0.8. This is just one example; no further specific limitations are made in this embodiment. When managing and maintaining natural gas transmission pipelines, maintenance is prioritized based on the gas safety level, arranged in descending order.

[0079] It should be noted that the exp(-x) model used in this embodiment only indicates that the negative correlation and the result of the constraint model output are in the interval (0,1], where x is the input of this model. In specific implementation, it can be replaced by other models with the same purpose. This embodiment only uses the exp(-x) model as an example for description and does not impose any specific limitations.

[0080] This invention is now complete.

[0081] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A gas safety monitoring system based on cloud-based Internet of Things (IoT) analysis, characterized in that, The system includes the following modules: Data acquisition module: used to collect natural gas concentration data, wind speed data, and wind direction data of each natural gas concentration sensor at each monitoring point at each time moment; Data processing module: This module is used to obtain the gas diffusion intensity at each monitoring point at each time moment based on the differences in natural gas concentration data from different natural gas concentration sensors at each monitoring point; to obtain the wind force influence at each monitoring point at each time moment based on the relationship between wind speed and leakage direction of each natural gas concentration sensor at each monitoring point; and to obtain the gas leakage level at each monitoring point by combining the wind force influence and gas diffusion intensity at each monitoring point at each time moment; to obtain the leakage degree value at each monitoring point by combining the extreme value differences of natural gas concentration data from all natural gas concentration sensors at each monitoring point with the gas leakage level at each monitoring point; and to obtain the gas risk monitoring index for the pipeline based on the leakage degree values ​​of all monitoring points in the pipeline. The process of obtaining the leakage level value at each monitoring point includes: Retrieve all extreme values ​​in natural gas concentration data; The leakage level value of the kth monitoring point is calculated as follows: In the formula, This represents the leakage level value at the k-th monitoring point; This indicates the gas leakage level at the k-th monitoring point; This represents the leakage control coefficient at the k-th monitoring point; The specific methods for obtaining the leakage adjustment coefficient include: This represents the number of natural gas concentration sensors at the k-th monitoring point; This represents the number of all extreme values ​​in the natural gas concentration data of the x-th natural gas concentration sensor at the k-th monitoring point; This represents the y-th extreme value in the natural gas concentration data of the x-th natural gas concentration sensor at the k-th monitoring point; This represents the mean of all natural gas concentration data from the x-th natural gas concentration sensor at the k-th monitoring point. This represents the leakage control coefficient at the k-th monitoring point; Risk level monitoring module: used to classify the gas safety level of the target natural gas transmission pipeline according to the gas risk monitoring indicators, and to carry out pipeline maintenance according to the classified gas safety level.

2. The gas safety monitoring system based on cloud-based IoT analysis according to claim 1, characterized in that, The method for obtaining the gas diffusion intensity at each monitoring point at each time moment includes: In the formula, This represents the gas diffusion intensity at the k-th monitoring point at time t; This represents the number of natural gas concentration sensors at the k-th monitoring point; This represents the natural gas concentration data of the x-th natural gas concentration sensor at the k-th monitoring point at time t; This represents the natural gas concentration data of the (x-1)th natural gas concentration sensor at the kth monitoring point at time t; This represents an exponential function with the natural constant as its base.

3. The gas safety monitoring system based on cloud-based IoT analysis according to claim 1, characterized in that, The process of obtaining the wind force impact level at each monitoring point at each moment includes: In the formula, This indicates the degree of wind impact at the k-th monitoring point at the t-th data collection time; This represents the wind speed component of the x-th natural gas concentration sensor at the k-th monitoring point at time t; This represents the number of natural gas concentration sensors at the k-th monitoring point; This represents the minimum wind speed component at time t for all sensor locations across all monitoring points. This represents the maximum value of the wind speed component at time t for all sensor locations at all monitoring points.

4. The gas safety monitoring system based on cloud-based IoT analysis according to claim 3, characterized in that, The specific methods for obtaining the wind speed components include: The wind speed data of the x-th natural gas concentration sensor at the k-th monitoring point at time t is vector decomposed along the leakage direction and denoted as the wind speed component of the x-th natural gas concentration sensor at the k-th monitoring point at time t.

5. The gas safety monitoring system based on cloud-based IoT analysis according to claim 4, characterized in that, The specific methods for obtaining the direction of leakage include: The vector direction of the tangent perpendicular to the edge of the pipe section is denoted as the leakage direction.

6. The gas safety monitoring system based on cloud-based IoT analysis according to claim 1, characterized in that, The process of obtaining the gas leakage level at each monitoring point includes: This indicates the gas leakage level at the k-th monitoring point; Indicates the number of all moments; This represents the gas diffusion intensity at the k-th monitoring point at the t-th data collection time; This represents an exponential function with the natural exponent as its base.

7. The gas safety monitoring system based on cloud-based IoT analysis according to claim 1, characterized in that, The obtained gas risk monitoring indicators for the pipeline include: In the formula, Indicates gas risk monitoring indicators for pipelines; Indicates the number of monitoring points; This represents the leakage level value at the k-th monitoring point; This represents the sigmoid function.

8. The gas safety monitoring system based on cloud-based IoT analysis according to claim 1, characterized in that, The maintenance of pipelines according to the classified gas safety level includes: The safety level of the target natural gas transmission pipeline is determined by classifying the gas safety level according to the gas risk monitoring indicators. When managing and maintaining the natural gas transmission pipeline, the pipeline is ranked in descending order of its gas safety level and then used as the priority sequence for maintenance.

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