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Pipeline Corrosion Leakage Fire Deduction System Based on Bayesian Network Reasoning Model

A Bayesian network and pipeline technology, applied in the field of pipeline corrosion leakage fire deduction system, can solve problems such as pipeline leakage accidents, and achieve the effect of strengthening pre-disaster self-inspection and reducing fire accidents

Active Publication Date: 2021-04-20
CHINA ACAD OF SAFETY SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] At present, since pipeline transportation has become the main transportation mode of energy transportation in my country, it has the advantages of low cost, large transportation volume, and high efficiency. There are many uncertain factors such as liquid corrosion, soil or metal oxidation, and there is a risk of leakage accidents in pipelines. Therefore, there is an urgent need for a more accurate deduction model for probabilistic risks, which can quickly generate decisions to deal with risks

Method used

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  • Pipeline Corrosion Leakage Fire Deduction System Based on Bayesian Network Reasoning Model
  • Pipeline Corrosion Leakage Fire Deduction System Based on Bayesian Network Reasoning Model
  • Pipeline Corrosion Leakage Fire Deduction System Based on Bayesian Network Reasoning Model

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Embodiment 1

[0069] as attached figure 1 As shown, the present invention is a pipeline corrosion leakage fire deduction system based on the Bayesian network reasoning model, mainly through the Labview system using the Bayesian network to realize the probability calculation of uncertain risks and generate emergency decisions, including:

[0070] A pipeline corrosion leakage fire deduction system based on a Bayesian network reasoning model, characterized in that it includes:

[0071] Pipeline detection module: used to monitor the pipeline in real time and obtain the real-time situation of the environment in the pipeline; for example: the staff installs distributed optical fiber sensors and video equipment in the pipeline, and the photoelectric detector detects an abnormality, and the Labview console opens the video equipment Monitoring the pipeline; the video recording equipment is a miniature night vision camera.

[0072] Pipeline risk preview module: used to predict the risk of the corros...

Embodiment 2

[0076] As an embodiment of the present invention, the pipeline detection module includes:

[0077] Data acquisition unit: used to install sensing equipment on the inner wall of the pipeline to obtain status data in the pipeline wall; among them,

[0078] The state data includes pressure data, temperature data, gas data and pH concentration data in the pipe wall; detect whether the pipe wall is damaged, the pH concentration of gas or liquid in the air, etc.; for example: the parameter difference of the pipe wall thickness becomes larger, It means that the pipe wall becomes thinner, and there may be a risk of leakage due to damage, or the acidity of the gas or liquid in the air is too low, or the alkalinity is too high, then the pipe wall may be corroded, and the anti-corrosion coating of the pipe wall should be checked; The state of the pipeline needs to be determined through the design equipment in the pipeline, because the pipeline is in a dark state for a long time, and micr...

Embodiment 3

[0084] As an embodiment of the present invention, the pipeline risk preview module includes:

[0085] Bayesian network module preview unit: it is used to preview the corrosion data by using the Bayesian conditional probability algorithm to determine the fire-causing factors on the inner wall of the pipeline. The present invention specifically uses the Bayesian conditional probability algorithm to calculate the probability of occurrence of leakage fire-causing factors; for example: the Bayesian conditional probability area deduces specific fire-causing factors to judge the impact of your pipeline corrosion on fire.

[0086] Bayesian network template clustering unit: It is used to judge the type of abnormal situation that has occurred, and send the judgment result to the fire deduction module for classification. The present invention specifically judges the types of abnormal events that have occurred, and the judgment results are sent to the Labview control system for classifica...

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Abstract

The invention provides a pipeline corrosion leakage deduction system based on a Bayesian network reasoning model. Including pipeline detection module: used for real-time monitoring of pipelines. Pipeline risk preview module: used to calculate all fire source factors and mine risk nodes. Pipeline fire deduction module: used to calculate fire accidents and calculate disaster-causing factors with probability. The invention can monitor the air pressure difference, temperature and acid-base concentration in the pipeline in real time, and then judge the corrosion situation in the pipeline and whether leakage occurs, thereby judging whether the leakage of the pipeline will cause fire-causing factors, and then judge through the fire-causing factors Whether there is a fire accident and other derivative accidents. The present invention is based on the reasoning preview of the Bayesian network reasoning model, which not only deduces the fire-causing factors, but also deduces the budget of the fire accident, judges the specific fire occurrence probability, and realizes the advance preview of the fire accident. Pre-disaster self-inspection and protection to further reduce the occurrence of fire accidents.

Description

technical field [0001] The invention relates to the technical field of pipeline fire early warning, in particular to a pipeline corrosion leakage fire deduction system based on a Bayesian network reasoning model. Background technique [0002] At present, since pipeline transportation has become the main transportation mode of energy transportation in my country, it has the advantages of low cost, large transportation volume, and high efficiency. There are many uncertain factors such as liquid corrosion, soil or metal oxidation, and there is a risk of leakage accidents in pipelines. Therefore, there is an urgent need for a more accurate deduction model for probabilistic risks that can quickly generate decisions to deal with risks. Contents of the invention [0003] As an embodiment of the present invention, the pipeline corrosion leakage fire system based on Bayesian network reasoning model includes: [0004] A pipeline corrosion leakage fire deduction system based on a Bay...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G01D21/02F17D5/04
CPCG01D21/02F17D5/04G06F18/24155
Inventor 左哲高进东马世海姚志强赵军徐帅朱渊岳刘志强孙猛杨文涛关威王胜荣张晓蕾吴真真
Owner CHINA ACAD OF SAFETY SCI & TECH