CN101221633A - Gas pipe risk estimation method based on Mueller model - Google Patents
Gas pipe risk estimation method based on Mueller model Download PDFInfo
- Publication number
- CN101221633A CN101221633A CNA2007100286653A CN200710028665A CN101221633A CN 101221633 A CN101221633 A CN 101221633A CN A2007100286653 A CNA2007100286653 A CN A2007100286653A CN 200710028665 A CN200710028665 A CN 200710028665A CN 101221633 A CN101221633 A CN 101221633A
- Authority
- CN
- China
- Prior art keywords
- pipeline
- risk
- gas pipeline
- parameter
- risk assessment
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000012502 risk assessment Methods 0.000 claims abstract description 25
- 239000002737 fuel gas Substances 0.000 claims abstract description 8
- 239000007789 gas Substances 0.000 claims description 43
- 230000011218 segmentation Effects 0.000 claims description 31
- 239000002689 soil Substances 0.000 claims description 13
- 238000013461 design Methods 0.000 claims description 11
- 239000011248 coating agent Substances 0.000 claims description 8
- 238000000576 coating method Methods 0.000 claims description 8
- 230000007797 corrosion Effects 0.000 claims description 7
- 238000005260 corrosion Methods 0.000 claims description 7
- 238000011156 evaluation Methods 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000010276 construction Methods 0.000 claims description 4
- 230000007547 defect Effects 0.000 claims description 4
- 238000009413 insulation Methods 0.000 claims description 4
- 239000000463 material Substances 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 230000001066 destructive effect Effects 0.000 claims description 2
- 238000007689 inspection Methods 0.000 claims description 2
- 239000002574 poison Substances 0.000 claims description 2
- 231100000614 poison Toxicity 0.000 claims description 2
- 238000004458 analytical method Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 4
- 238000011160 research Methods 0.000 description 4
- 230000018109 developmental process Effects 0.000 description 3
- 238000011835 investigation Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 2
- 230000005526 G1 to G0 transition Effects 0.000 description 1
- 230000002776 aggregation Effects 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 230000006378 damage Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000009837 dry grinding Methods 0.000 description 1
- 208000020442 loss of weight Diseases 0.000 description 1
- 230000035800 maturation Effects 0.000 description 1
- 239000003345 natural gas Substances 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Pipeline Systems (AREA)
Abstract
The invention provides a fuel gas pipeline risk assessment method based on Mu model. The method comprises the following steps: (1) dividing a fuel gas pipeline into sections; (2) classifying parameters causing pipeline risk and distributing weightings according to the degree of impact of each parameter on the pipeline risk; (3) through the parameters and weightings of the step (2), carrying out risk assessment of each factor for the pipeline and working out an exponential sum; (4) calculating the fuel gas pipeline leakage influence coefficient according to the risk and influence surface of a conveyance medium; (5) working out a relative risk value integrally according to the exponential sum of the step (3) and the leakage influence coefficient of the step (4);(6) analyzing the relative risk value obtained in the step (5) to obtain a risk conclusion. The invention adjusts the factor weightings of the Mu model risk assessment and is provided with the fuel gas leakage influence coefficient to be computed, thereby bringing about more adaptability and more accurate and reliable assessment results.
Description
Technical field
The invention belongs to the risk management technology field of gas pipeline, particularly relate to a kind of gas pipeline methods of risk assessment based on the Mu Shi model.
Technical background
Continuous laying along with oil, natural gas line, the risk management of pipe-line has caused global vast concern, the continuous development of computer software industry, the risk management of pipeline has been become a kind of trend with the form of computer software, abroad starting has early had a lot of powerful assessment software on pipe risk software; Domestic is the software development that this respect is just arranged in recent years, some products have also been arranged so far, but these products, except Mu Shi software, also do not have a kind ofly, have one in the Mu Shi software specially at the module of city gas pipeline at the city gas pipeline risk assessment.But because some singularity of Chinese gas pipeline design, construction and operational management have many factors not consider at Mu Shi software, in the factor of perhaps considering, the assignment of its weight needs to adjust.
Summary of the invention
The deficiency that the objective of the invention is to the existing gas pipeline risk assessment technology of customer service, proposes a kind of can according to implement the self-adjusting safety evaluation method of concrete condition.
In order to realize the foregoing invention purpose, the technical scheme of employing is as follows:
A kind of gas pipeline methods of risk assessment based on the Mu Shi model comprises the steps:
(1) gas pipeline is carried out segmentation;
(2) parameter that causes the pipeline risk is classified, and assign weight according to the degree of influence of each parameter to the gas pipeline risk;
(3) utilize the parameter and the weight thereof of step (2), pipeline is carried out the risk assessment of each factor, and calculate exponential sum;
(4) calculate the fuel gas pipeline leakage influence coefficient according to the danger of pumped (conveying) medium and the size of influence surface;
(5) go out the relative risk value according to the exponential sum of step (3) and the leakage contribution coefficient COMPREHENSIVE CALCULATING of step (4);
(6) the relative risk value that step (5) is obtained is analyzed, and draws the risk conclusion.
In the technique scheme, the principle of described step (1) duct segments is will have the pipeline part of identical external environment condition, material, laying time, structural design, conveying medium as an evaluation object, and the segmentation foundation comprises the Years Of Service of corrosivity, pipeline anticorrosion coating insulation resistivity and the pipeline of the pipeline laying place density of population, tube circumference soil.When pipeline is carried out risk management, because each bar pipeline, even same residing external environment condition of each section of pipeline, the material of pipeline itself, the difference of laying time etc., concrete grammar all will carry out suitable fine setting when carrying out risk management, so, will be before risk assessment with the pipeline segmentation, be divided into and have the identical internal and external environment and the pipeline section of parameter as far as possible, and in four factors of segmentation institute foundation, the tube circumference density of population is a factor that influences duct segments situation maximum, next is soil corrosion, the anticorrosive coat situation is only pipeline tenure of use, at last so when carrying out duct segments, at first pipeline is carried out segmentation at the difference of the density of population, on the basis of this segmentation, carry out segmentation again at the difference of soil corrosivity more then, and the like, after four segmentations, obtain the final segmentation result of pipeline at last.
The described parameter of described step (2) comprises that detected parameters, the parameter of pipe network generalized information system, inquiry agency get parameter; Described detected parameters comprises the corrosivity of soil, insulation resistance, anticorrosive coat defect inspection, stray current and the pipeline cathode protection current potential of pipeline anticorrosion coating; The parameter of described official website generalized information system comprises the design pressure of thickness, pipeline of caliber, the tube wall of gas pipeline and actual motion pressure etc.; Described inquiry agency gets parameter and comprises and obtain the parameter relevant with humanity, geography, the construction conditions in place that pipeline is buried underground by inquiry.
Because the risk assessment of gas pipeline belongs to a kind of more special safety assessment, in the parameter of required consideration, it is relevant with conditions such as the humanity in place that pipeline is buried underground, geography, buildings that a lot of parameters are arranged, so in the process of data aggregation, the investigation that must come to is on the spot obtained.And, there have in these parameters to be more special, the investigation result that obtains at last can not provide out with the form of data, can only as high, medium and low or the like represent that the tabulation and the parameter assignment form of concrete parameter are as shown in the table with the explanation of very abstract descriptive matter in which there:
The parameter of step of the present invention (2) is categorized as third party's destructive factor, corrosion failure factor, design defect factor, improper operation factors according to the influence to gas pipeline accident possibility occurrence.
In order to adjust the correlation parameter of risk evaluating system according to concrete enforcement environment, make risk assessment more accurate, step of the present invention (2) also comprises carries out the weight adjustment to parameter.
The mode that the macroscopic view adjustment that the mathematics probability statistics are adopted in described weight adjustment combines in conjunction with the microcosmic adjustment of expert opinion.
The specific implementation method of described weight adjustment is at first to calculate the risk probability that influences all kinds of factors of gas pipeline, and then with these risk probability normalization, and the total score value of weight of all factors was redistributed according to normalized success ratio originally.
Because in influencing all Fundamentals of risk assessment, be not that the generation to all incidents all plays same function.When therefore weight was marked, its corresponding score value should be corresponding with its risk probability, thus, the method that weight is adjusted, at first obtain the risk probability that influences all kinds of factors of top event, then with these risk probability normalization, and total score value of will marking is redistributed according to normalized success ratio.From existing statistical data as can be known, tub curve takes place generally to satisfy in the accident of pipeline, and stage pipeline work and unstable is closed in initial stage place's dry grinding, and the later stage, accident took place also more frequent near designed life.
The risk probability computing method of described gas pipeline are as follows:
If the stationary phase (about 10～20 years) in the middle of the consideration, then its failure rate can be considered constant, and the time of casualty of gas pipeline is obeyed the exponential distribution that parameter is V at interval, and the number of times that accident takes place is obeyed the Poison distribution:
Wherein: P
rBe probability function; X is the number of times that fault takes place; R is a failure rate, and t is the time, and in conjunction with top formula as can be known, the pipeline number of times that breaks down is that 0 probability is:
P
r(x＝0；rt)＝e
-rt
Thereby as can be known, the probability that breaks down of pipeline is P
f(t)=1-P
r(x=0; Rt)=1-e
-rt
China pipeline accident analysis shows: in the gathering of culprit, statistical figure do not have the obvious development direction, therefore, adopt certain period the average data of (more than 10 years) when referring to risk speed (r); When calculation risk speed (r), should get the pipeline of certain-length in addition, make that the variation of pipeline annual casualty data under this length is very little.Under this condition, the aforementioned calculation method is feasible and suitable.
The present invention is in conjunction with design, construction and the operational management its own particularity of Chinese gas pipeline, factor weight to the risk assessment of Mu Shi model is adjusted, increased calculating fuel gas pipeline leakage influence coefficient again, make methods of risk assessment of the present invention have more applicability, and assessment result more accurately and reliably.
Embodiment
Being embodied as example by Gas Co Ltd of Guangzhou City in the Guangzhou with the present invention below is described further.
Implement following steps:
(1) gas pipeline is carried out segmentation;
(2) parameter that causes the pipeline risk is classified, and assign weight according to the degree of influence of each parameter to the gas pipeline risk;
(3) utilize the parameter and the weight thereof of step (2), pipeline is carried out the risk assessment of each factor, and calculate exponential sum;
(4) calculate the fuel gas pipeline leakage influence coefficient according to the danger of pumped (conveying) medium and the size of influence surface;
(5) go out the relative risk value according to the exponential sum of step (3) and the leakage contribution coefficient COMPREHENSIVE CALCULATING of step (4);
(6) the relative risk value that step (5) is obtained is analyzed, and draws the risk conclusion.
Wherein in the middle of four factors of the duct segments of step (1), soil corrosion and pipeline anticorrosion coating situation are by measuring, obtain the tenure of use of pipeline from the operation history data of Guangzhou gas company gas pipeline, and the tube circumference density of population then obtains by inquiry.Segmentation is carried out according to the following steps:
(11) at first, selected starting point is carried out every kilometer segmentation to pipeline, and then, each section is carried out the survey of population at this.By survey, obtain along 1000 meters in pipeline longly, be the center with the pipeline, each 200 meters of both sides are the size of population in the totally 400 wide areal extents.
(12) in the process of investigation, may meet some more special situations, such as, in 1000 meters that are investigated scope, a sub-district is arranged, and the length of sub-district is greater than 200 meters, at this time, need do as a whole taking into account to this sub-district, last COMPREHENSIVE CALCULATING.Be divided into three sections as pipeline, wherein one section is passed through a sub-district, one section is passed through a semiworks, and the length of sub-district and factory all surpasses 400 meters, so will whole sub-district and factory all takes into account rather than only just those 400 meters of the pipeline both sides when considering this situation.Carry out the calculating of the density of population at last, i.e. the density of population=population/total area (comprising the off-limits that part of area in sub-district)
(13) obtain after the density of population of each section, if the variation of the adjacent two sections density of population in 10%, will these two sections be combined into one section, the rest may be inferred.Segmentation situation after merging is the result that density is per capita carried out segmentation.If suitably reduce the hop count of telling by the density of population, the variation range of the adjacent two sections density of population can be expanded to 20% even bigger, this method to soil corrosivity segmentation also use.
Carry out on the basis of segmentation in density per capita, the corrosivity of pipeline soil along the line is detected.And in the above on the basis of segmentation result, by 1 km segmentation, per 1 km detects once.Detected soil corrosivity is analyzed, if the difference of adjacent two sections soil corrosivities of seeing in 30%, just is combined into one section to these two sections.The segmentation situation of soil corrosivity that obtained a new consideration at last.
The same with top method, on the basis of former segmentation result, also will be at pipeline anticorrosion coating, pipeline carries out segmentation tenure of use.
If the result after the segmentation is dissatisfied, think that perhaps hop count is too many, worry that the compiling costs of assessment data is too high, need carry out aftertreatment to the result of segmentation.
Such as, 60 kilometers pipelines in city have been carried out segmentation.The step of segmentation is by top method, has divided 15 sections by the density of population, has increased by 8 sections according to soil corrosion, has increased by 14 sections again according to the pipeline anticorrosion coating situation, has increased by 6 sections again tenure of use by pipeline at last, and total hop count of so last segmentation is 43 sections.If feel with these 60 kilometers pipelines be divided into 43 sections too many, want to reduce this hop count.That just deletes by the different importance of these four factors, subtracts unessentially earlier, and loss of weight is wanted again.So, at first should consider to reduce those 6 sections by pipeline generation tenure of use, secondly be 14 sections that pipeline anticorrosion coating produces, the rest may be inferred.If think that also such segmentation hop count is too many, can increase by 1 km spacing of density of population segmentation on demand, till last segmentation result makes you satisfied.
The mathematics probabilistic method is mainly used in the adjustment of described parameter weight, and again in conjunction with expert opinion, the basic step of adjusting weight is as follows:
(21) weight of four big classes of adjustment
Because in original Mu Shi assessment models, four shared weights of big class third party's destruction, corrosion factor, design factor and improper operation factors all are 100 minutes, but after the pipeline accident reason being carried out actual analysis, the reason of finding these four aspects is not wait to the contribution of pipeline accident, and difference is bigger, therefore, must at first adjust the weight of these four big classes.
By analysis and statistics, obtain the frequency distribution table of gas pipeline culprit, shown in table 2-2 to Guangzhou past 15 years gas pipeline interruption of service history.
Table 2-2 accident frequency distribution table
Calculate the risk probability of four big classes, and after probability carried out normalized, 400 minutes total points is distributed, obtain the shared weight of each big class, shown in table 2-3.
Table 2-3 accident frequency distribution table
(22) adjust the weight of each group
Obtained after the weight of each big class, because each big class all comprises a lot of groups.So on the basis of resulting weight, group is carried out the weight secondary distribution, the weight of every big class is redistributed in each group goes.Owing to the original record to the pipeline accident reason is confined to four big classes, do not refine in each group and go, so, according to each group originally in big class proportion distribute the weight of group.Distribute the result who obtains at last shown in table 2-4,2-5,2-6,2-7.
Table 2-4: third party's factor weight distributes
Table 2-5: corrosion factor weight allocation
Table 2-6: design factor weight allocation
Table 2-7: design factor weight allocation
(23) fine setting of expert opinion
The pipework venture analysis is at the early-stage in China, does not also set up the database of relevant reliability data, adds some subjective and objective factors of aspect existence such as management, is difficult to collect more complete, accurate data.And that the mathematics probabilistic method depends on the complete sum of historical casualty data to a great extent is accurate, and need some experienced experts and managerial personnel that the weight based on data statistics is adjusted, in the hope of expressing more accurately, embody the truth of China's pipeline better.
This weight adjustment based on the mathematics probability statistics, Ying Xianyou expert and experienced managerial personnel carry out observation analysis to the ratio of various culprit after systematization of calculating with said method, consensus of opinion then, judge that can this ratio (being weight) reflect the situation of institute's assessment pipeline more objectively:, then need not adjust if can reflect; Otherwise then need adjust.
The present invention uses for reference advanced foreign technology, in conjunction with this city truth, sets up complete underground gas pipe network safety evaluating system, to realize the target of " follow the tracks of and detect---safety assessment---planned reparation ".
The present invention is a systems engineering, stride multidisciplinary, research contents belongs to the current research trend in international gas pipeline integrity techniques field, integrate the engineering science principle, pipe network hidden danger position System in Small Sample Situation sampling theory that proposes based on quantitative risk and application, pipe network failure probability dynamic evaluation technology etc. are at home along belonging to initiative.Towards the research of gas ductwork dynamic risk evaluation integrated system, be in the forward position of this area research both at home and abroad.
Implement by the pilot that takes the lead in the Guangzhou, will produce positive social benefit, also can bring direct economic benefit for the Guangzhou.After the risk evaluating system maturation, can promote achievement and software, can make the safety assessment system of underground pipe network for various places amount body degree in conjunction with the market condition in fraternal city to other gas companies or pipeline company.
Claims (8)
1. the gas pipeline methods of risk assessment based on the Mu Shi model is characterized in that comprising the steps:
(1) gas pipeline is carried out segmentation;
(2) parameter that causes the pipeline risk is classified, and assign weight according to the degree of influence of each parameter to the gas pipeline risk;
(3) utilize the parameter and the weight thereof of step (2), pipeline is carried out the risk assessment of each factor, and calculate exponential sum;
(4) calculate the fuel gas pipeline leakage influence coefficient according to the danger of pumped (conveying) medium and the size of influence surface;
(5) go out the relative risk value according to the exponential sum of step (3) and the leakage contribution coefficient COMPREHENSIVE CALCULATING of step (4);
(6) the relative risk value that step (5) is obtained is analyzed, and draws the risk conclusion.
2. the gas pipeline methods of risk assessment based on the Mu Shi model according to claim 1, the principle that it is characterized in that step (1) duct segments is will have the pipeline part of identical external environment condition, material, laying time, structural design, conveying medium as an evaluation object, and the segmentation foundation comprises the Years Of Service of corrosivity, pipeline anticorrosion coating insulation resistivity and the pipeline of the pipeline laying place density of population, tube circumference soil.
3. the gas pipeline methods of risk assessment based on the Mu Shi model according to claim 1 is characterized in that the described parameter of step (2) comprises that detected parameters, the parameter of pipe network generalized information system, inquiry agency get parameter;
Described detected parameters comprises the corrosivity of soil, insulation resistance, anticorrosive coat defect inspection, stray current and the pipeline cathode protection current potential of pipeline anticorrosion coating;
The parameter of described official website generalized information system comprises the design pressure of thickness, pipeline of caliber, the tube wall of gas pipeline and actual motion pressure etc.;
Described inquiry agency gets parameter and comprises and obtain the parameter relevant with humanity, geography, the construction conditions in place that pipeline is buried underground by inquiry.
4. the gas pipeline methods of risk assessment based on the Mu Shi model according to claim 1 is characterized in that the described parameter of step (2) is categorized as third party's destructive factor, corrosion failure factor, design defect factor, improper operation factors according to the influence to gas pipeline accident possibility occurrence.
5. according to claim 3 or 4 described gas pipeline methods of risk assessments based on the Mu Shi model, it is characterized in that described step (2) also comprises carries out the weight adjustment to parameter.
6. the gas pipeline methods of risk assessment based on the Mu Shi model according to claim 5 is characterized in that the mode that macroscopic view adjustment that described weight adjustment adopts the mathematics probability statistics combines in conjunction with the microcosmic adjustment of expert opinion.
7. the gas pipeline methods of risk assessment based on the Mu Shi model according to claim 6, the specific implementation method that it is characterized in that described weight adjustment is at first calculating the risk probability that influences all kinds of factors of gas pipeline, redistributed according to normalized success ratio then with these risk probability normalization, and with the total score value of weight of original all factors.
8. the gas pipeline methods of risk assessment based on the Mu Shi model according to claim 7 is characterized in that the risk probability computing method of described gas pipeline are as follows:
The time of casualty of gas pipeline is obeyed the exponential distribution that parameter is V at interval, and the number of times that accident takes place is obeyed the Poison distribution:
Wherein: P
rBe probability function; X is the number of times that fault takes place; R is a failure rate, and t is the time, and in conjunction with top formula as can be known, the pipeline number of times that breaks down is that 0 probability is:
P
r(x＝0；rt)＝e
-rt
Thereby as can be known, the probability that breaks down of pipeline is P
f(t)=1-P
r(x=0; Rt)=1-e
-rt
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNA2007100286653A CN101221633A (en) | 2007-06-19 | 2007-06-19 | Gas pipe risk estimation method based on Mueller model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNA2007100286653A CN101221633A (en) | 2007-06-19 | 2007-06-19 | Gas pipe risk estimation method based on Mueller model |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN101221633A true CN101221633A (en) | 2008-07-16 |
Family
ID=39631455
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CNA2007100286653A Pending CN101221633A (en) | 2007-06-19 | 2007-06-19 | Gas pipe risk estimation method based on Mueller model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN101221633A (en) |
Cited By (27)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101894209A (en) * | 2010-06-21 | 2010-11-24 | 合肥通用机械研究院 | Determination method and application of equipment damage factor under the coexistence environment of multiple failure mechanisms |
| CN102292625A (en) * | 2009-05-18 | 2011-12-21 | 富兰克林加油系统公司 | Method and apparatus for detecting a leak in a fuel delivery system |
| CN102495935A (en) * | 2011-12-15 | 2012-06-13 | 中国石油天然气集团公司 | Determination method for risk of storage medium leakage of underground natural gas storage reservoir |
| CN102609778A (en) * | 2012-02-17 | 2012-07-25 | 广东省电力调度中心 | Method and device for assessing risk of electric power communication network |
| CN102841952A (en) * | 2011-06-23 | 2012-12-26 | 中国石油天然气集团公司 | Petrochemical enterprise plane layout security design method |
| CN104281921A (en) * | 2014-10-10 | 2015-01-14 | 讯腾数码科技（北京）有限公司 | Method for obtaining dynamic risk evaluation data of city underground pipe network |
| CN104318117A (en) * | 2014-10-31 | 2015-01-28 | 中国石油天然气股份有限公司 | A Quantitative Evaluation Method for Corrosion Leakage Risk of Oil Tank Bottom Plate |
| CN104376437A (en) * | 2014-12-05 | 2015-02-25 | 深圳市中燃科技有限公司 | Production and operation information management system based on GIS (geographic information system) |
| CN104657550A (en) * | 2015-02-10 | 2015-05-27 | 吉林建筑大学 | Method for assessing reconstruction sequence of old city water supply pipelines |
| CN106932334A (en) * | 2015-12-30 | 2017-07-07 | 中国石油天然气股份有限公司 | Prediction method of process pipeline corrosion degree in oil and gas station |
| CN107194599A (en) * | 2017-05-31 | 2017-09-22 | 北京市燃气集团有限责任公司 | A kind of sort method of the doubtful leakage point leakage probability of gas ductwork |
| CN108345705A (en) * | 2017-01-24 | 2018-07-31 | 中国石油化工股份有限公司 | A kind of method and device of assessment pipe leakage consequence influence area |
| CN108346111A (en) * | 2017-01-24 | 2018-07-31 | 中国石油化工股份有限公司 | Collection transmission pipe network risk of leakage appraisal procedure and device |
| CN109751986A (en) * | 2019-01-25 | 2019-05-14 | 重庆予胜远升网络科技有限公司 | A kind of processing system and method generating AR image according to pipe network attribute data |
| CN110163454A (en) * | 2018-02-07 | 2019-08-23 | 中石油昆仑燃气有限公司 | A kind of city gas pipeline network leak risk evaluating method and device |
| CN110390452A (en) * | 2018-04-18 | 2019-10-29 | 中国石油天然气股份有限公司 | Risk Assessment Method for Gas Pipeline Network |
| CN110516933A (en) * | 2019-08-13 | 2019-11-29 | 常州大学 | A Calculation Method for the Probability of External Interference Accidents of Gas Pipeline Based on Evolution Tree |
| CN110889576A (en) * | 2018-09-11 | 2020-03-17 | 中国石油天然气股份有限公司 | Safety assessment method and system for gas field water transmission pipeline |
| CN111582737A (en) * | 2020-05-12 | 2020-08-25 | 华能国际电力股份有限公司玉环电厂 | A risk assessment method for high temperature and high pressure steam pipeline based on metal inspection data |
| CN111612336A (en) * | 2020-05-20 | 2020-09-01 | 中国安全生产科学研究院 | A correction method for oil and gas pipeline failure factors based on big data |
| CN112183912A (en) * | 2019-07-05 | 2021-01-05 | 中国石油化工股份有限公司 | Oil transportation station pipeline failure probability assessment method and device |
| CN112529265A (en) * | 2020-11-27 | 2021-03-19 | 安徽泽众安全科技有限公司 | Comprehensive risk assessment and prediction method and system for gas pipeline |
| CN116698285A (en) * | 2023-07-31 | 2023-09-05 | 合肥航谱时代科技有限公司 | Dangerous chemical gas pipeline leakage monitoring system based on infrared imager |
| CN117670037A (en) * | 2023-11-20 | 2024-03-08 | 北京数字政通科技股份有限公司 | A gas pipeline risk information assessment method and its system |
| CN117805145A (en) * | 2024-02-28 | 2024-04-02 | 西安汉华建筑实业有限公司 | Aluminum template surface defect detection method and system |
| CN117821984A (en) * | 2024-03-04 | 2024-04-05 | 成都秦川物联网科技股份有限公司 | Intelligent gas pipeline cathode protection intelligent detection method and Internet of things system |
| CN118607889A (en) * | 2024-08-08 | 2024-09-06 | 山东济钢众电智能科技有限公司 | Power equipment operation management method and system based on big data |
-
2007
- 2007-06-19 CN CNA2007100286653A patent/CN101221633A/en active Pending
Cited By (43)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102292625A (en) * | 2009-05-18 | 2011-12-21 | 富兰克林加油系统公司 | Method and apparatus for detecting a leak in a fuel delivery system |
| US8677805B2 (en) | 2009-05-18 | 2014-03-25 | Franklin Fueling Systems, Inc. | Method and apparatus for detecting a leak in a fuel delivery system |
| CN102292625B (en) * | 2009-05-18 | 2015-03-25 | 富兰克林加油系统公司 | Method and apparatus for detecting a leak in a fuel delivery system |
| CN104951659B (en) * | 2010-06-21 | 2017-04-05 | 合肥通用机械研究院 | Method for determining equipment damage factor in naphthenic acid and sulfur coexistence environment |
| CN101894209B (en) * | 2010-06-21 | 2015-07-01 | 合肥通用机械研究院 | Method for determining equipment damage factor in environment with multiple failure mechanisms coexisting and application thereof |
| CN104951659A (en) * | 2010-06-21 | 2015-09-30 | 合肥通用机械研究院 | Method for determining equipment damage factor in naphthenic acid and sulfur coexistence environment |
| CN101894209A (en) * | 2010-06-21 | 2010-11-24 | 合肥通用机械研究院 | Determination method and application of equipment damage factor under the coexistence environment of multiple failure mechanisms |
| CN102841952A (en) * | 2011-06-23 | 2012-12-26 | 中国石油天然气集团公司 | Petrochemical enterprise plane layout security design method |
| CN102495935A (en) * | 2011-12-15 | 2012-06-13 | 中国石油天然气集团公司 | Determination method for risk of storage medium leakage of underground natural gas storage reservoir |
| CN102495935B (en) * | 2011-12-15 | 2014-04-02 | 中国石油天然气集团公司 | Determination method for risk of storage medium leakage of underground natural gas storage reservoir |
| CN102609778A (en) * | 2012-02-17 | 2012-07-25 | 广东省电力调度中心 | Method and device for assessing risk of electric power communication network |
| CN102609778B (en) * | 2012-02-17 | 2015-02-11 | 广东省电力调度中心 | Method and device for assessing risk of electric power communication network |
| CN104281921A (en) * | 2014-10-10 | 2015-01-14 | 讯腾数码科技（北京）有限公司 | Method for obtaining dynamic risk evaluation data of city underground pipe network |
| CN104318117A (en) * | 2014-10-31 | 2015-01-28 | 中国石油天然气股份有限公司 | A Quantitative Evaluation Method for Corrosion Leakage Risk of Oil Tank Bottom Plate |
| CN104318117B (en) * | 2014-10-31 | 2017-04-05 | 中国石油天然气股份有限公司 | A Quantitative Evaluation Method for Corrosion Leakage Risk of Oil Tank Bottom Plate |
| CN104376437A (en) * | 2014-12-05 | 2015-02-25 | 深圳市中燃科技有限公司 | Production and operation information management system based on GIS (geographic information system) |
| CN104657550A (en) * | 2015-02-10 | 2015-05-27 | 吉林建筑大学 | Method for assessing reconstruction sequence of old city water supply pipelines |
| CN106932334A (en) * | 2015-12-30 | 2017-07-07 | 中国石油天然气股份有限公司 | Prediction method of process pipeline corrosion degree in oil and gas station |
| CN108346111A (en) * | 2017-01-24 | 2018-07-31 | 中国石油化工股份有限公司 | Collection transmission pipe network risk of leakage appraisal procedure and device |
| CN108345705A (en) * | 2017-01-24 | 2018-07-31 | 中国石油化工股份有限公司 | A kind of method and device of assessment pipe leakage consequence influence area |
| CN108346111B (en) * | 2017-01-24 | 2022-02-08 | 中国石油化工股份有限公司 | Method and device for evaluating leakage risk of gathering and transportation pipe network |
| CN107194599A (en) * | 2017-05-31 | 2017-09-22 | 北京市燃气集团有限责任公司 | A kind of sort method of the doubtful leakage point leakage probability of gas ductwork |
| CN110163454A (en) * | 2018-02-07 | 2019-08-23 | 中石油昆仑燃气有限公司 | A kind of city gas pipeline network leak risk evaluating method and device |
| CN110390452A (en) * | 2018-04-18 | 2019-10-29 | 中国石油天然气股份有限公司 | Risk Assessment Method for Gas Pipeline Network |
| CN110889576A (en) * | 2018-09-11 | 2020-03-17 | 中国石油天然气股份有限公司 | Safety assessment method and system for gas field water transmission pipeline |
| CN109751986A (en) * | 2019-01-25 | 2019-05-14 | 重庆予胜远升网络科技有限公司 | A kind of processing system and method generating AR image according to pipe network attribute data |
| CN112183912A (en) * | 2019-07-05 | 2021-01-05 | 中国石油化工股份有限公司 | Oil transportation station pipeline failure probability assessment method and device |
| CN112183912B (en) * | 2019-07-05 | 2024-06-04 | 中国石油化工股份有限公司 | Method and device for evaluating failure probability of pipeline of oil delivery station |
| CN110516933B (en) * | 2019-08-13 | 2023-03-24 | 常州大学 | Gas pipeline external interference accident probability calculation method based on evolution tree |
| CN110516933A (en) * | 2019-08-13 | 2019-11-29 | 常州大学 | A Calculation Method for the Probability of External Interference Accidents of Gas Pipeline Based on Evolution Tree |
| CN111582737A (en) * | 2020-05-12 | 2020-08-25 | 华能国际电力股份有限公司玉环电厂 | A risk assessment method for high temperature and high pressure steam pipeline based on metal inspection data |
| CN111612336A (en) * | 2020-05-20 | 2020-09-01 | 中国安全生产科学研究院 | A correction method for oil and gas pipeline failure factors based on big data |
| CN111612336B (en) * | 2020-05-20 | 2022-04-08 | 中国安全生产科学研究院 | A correction method for oil and gas pipeline failure factors based on big data |
| CN112529265A (en) * | 2020-11-27 | 2021-03-19 | 安徽泽众安全科技有限公司 | Comprehensive risk assessment and prediction method and system for gas pipeline |
| CN112529265B (en) * | 2020-11-27 | 2021-10-26 | 安徽泽众安全科技有限公司 | Comprehensive risk assessment and prediction method and system for gas pipeline |
| CN116698285A (en) * | 2023-07-31 | 2023-09-05 | 合肥航谱时代科技有限公司 | Dangerous chemical gas pipeline leakage monitoring system based on infrared imager |
| CN116698285B (en) * | 2023-07-31 | 2023-10-24 | 合肥航谱时代科技有限公司 | Dangerous chemical gas pipeline leakage monitoring system based on infrared imager |
| CN117670037A (en) * | 2023-11-20 | 2024-03-08 | 北京数字政通科技股份有限公司 | A gas pipeline risk information assessment method and its system |
| CN117805145A (en) * | 2024-02-28 | 2024-04-02 | 西安汉华建筑实业有限公司 | Aluminum template surface defect detection method and system |
| CN117805145B (en) * | 2024-02-28 | 2024-05-14 | 西安汉华建筑实业有限公司 | Aluminum template surface defect detection method and system |
| CN117821984A (en) * | 2024-03-04 | 2024-04-05 | 成都秦川物联网科技股份有限公司 | Intelligent gas pipeline cathode protection intelligent detection method and Internet of things system |
| CN117821984B (en) * | 2024-03-04 | 2024-05-24 | 成都秦川物联网科技股份有限公司 | Intelligent gas pipeline cathode protection intelligent detection method and Internet of things system |
| CN118607889A (en) * | 2024-08-08 | 2024-09-06 | 山东济钢众电智能科技有限公司 | Power equipment operation management method and system based on big data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101221633A (en) | Gas pipe risk estimation method based on Mueller model | |
| Clark et al. | Water distribution systems: A spatial and cost evaluation | |
| Weller et al. | Vehicle-based methane surveys for finding natural gas leaks and estimating their size: Validation and uncertainty | |
| US10082478B2 (en) | Methods for evaluation and estimation of external corrosion damage on buried pipelines | |
| WO2010002659A2 (en) | Rapid data-based data adequacy procedure for pipepline integrity assessment | |
| Singh | Pipeline integrity handbook: risk management and evaluation | |
| Schauberger et al. | Odour emissions from a waste treatment plant using an inverse dispersion technique | |
| Farley | Non-revenue water–international best practice for assessment, monitoring and control | |
| JP5423983B2 (en) | Computer system and risk diagnosis method | |
| Candelieri et al. | Improving leakage management in urban water distribution networks through data analytics and hydraulic simulation | |
| Barfuss | Water main break rates in the USA and Canada: A comprehensive study | |
| KR102556371B1 (en) | Method and apparatus for analyzing damage risk of underground pipes | |
| Huang et al. | Optimization of the atmospheric corrosivity mapping of Guangdong Province | |
| Zhang et al. | A new pre-assessment model for failure-probability-based-planning by neural network | |
| Clark et al. | Developing a data base on infrastructure needs | |
| CN107784148A (en) | A kind of gathering line crash rate appraisal procedure and device | |
| Granato | Estimating basin lagtime and hydrograph-timing indexes used to characterize stormflows for runoff-quality analysis | |
| US20250243975A1 (en) | Methods, systems, and storage media for monitoring pipeline deposits based on smart gas internet of things (iot) | |
| Farouk et al. | Cost-Benefit analysis of rehabilitation approaches for water distribution networks | |
| CN201237085Y (en) | Apparatus used for gas pipe risk management | |
| Gu et al. | Approach to assessment of corrosion growth in pipelines | |
| CN113255125A (en) | Water supply network failure updating strategy method based on multi-target prediction model | |
| CN104569340A (en) | Underground environment quality determination method and device | |
| CN109027700B (en) | Method for evaluating leakage detection effect of leakage point | |
| US7096128B2 (en) | Process for the assessment of gravity-type collection networks |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C02 | Deemed withdrawal of patent application after publication (patent law 2001) | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Open date: 20080716 |