Leakage risk assessment method and device for buried natural gas pipeline

Abstract

According to the leakage risk assessment method and device for the buried natural gas pipeline provided by the invention, the interaction relationship among the indexes is analyzed by analyzing the correlation degree among the indexes, and the weight of each index is obtained, so that the method and device are applied to the process of subsequently calculating the digital characteristics of the indexes of each level. According to the method, the leakage risk levels are divided, the digital characteristics of the indexes of all levels are weighted by combining the divided weights, the digital characteristics of the leakage risk of the buried natural gas pipeline are finally obtained, and the leakage risk assessment of the buried natural gas pipeline can be realized by comparing the digital characteristics with the standard digital characteristics. According to the method and device provided by the invention, the reliability and accuracy of evaluating the leakage risk of the natural gas buried pipeline can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas storage and transportation safety and maintenance, and in particular to a leakage risk assessment method and device for a buried natural gas pipeline. Background Art

[0002] Natural gas buried pipelines are exposed to a complex and ever-changing external environment for a long time, so they are prone to natural gas leakage due to pipeline damage. Given the characteristics of natural gas being light and flammable, once a buried natural gas pipeline leaks in a densely populated area, the potential safety risk will be extremely high. Therefore, it is particularly urgent to ensure the safety of natural gas pipelines in densely populated areas.

[0003] At present, the leakage risk of buried natural gas pipelines can be evaluated based on qualitative methods, semi-quantitative evaluation methods and quantitative evaluation methods to prevent leakage of buried natural gas pipelines. However, these methods require a large amount of historical pipeline leakage data support, and on the other hand, it is difficult to quantify pipeline leakage that may be caused by human factors, which reduces the accuracy of the leakage risk assessment of buried natural gas pipelines. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method and device for assessing the leakage risk of a buried natural gas pipeline, which can improve the accuracy of assessing the leakage risk of a buried natural gas pipeline.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] In a first aspect, the present invention provides a method for assessing the leakage risk of a buried natural gas pipeline, the method comprising determining a risk assessment index. The risk assessment index comprises a primary index, at least one secondary index, and a plurality of tertiary indexes. Any secondary index corresponds to at least one tertiary index among the plurality of tertiary indexes. The primary index is used to characterize the leakage risk of the pipeline, the secondary index is used to characterize the category of the influencing factors causing the pipeline leakage, and the tertiary index is used to characterize the influencing factors causing the pipeline leakage. The weight of any secondary index in at least one secondary index is determined, and the weight of any tertiary index in the plurality of tertiary indexes is determined. Based on at least one risk score corresponding to any tertiary index, a digital feature of at least one risk score corresponding to any tertiary index is determined. The digital features comprise expectation, entropy and super entropy. Based on the digital features of each target tertiary index and the weight of each target tertiary index in the plurality of tertiary indexes, a digital feature of any secondary index is determined. The target tertiary index is a tertiary index corresponding to any secondary index. Based on the digital features of each secondary index and the weight of each secondary index in at least one secondary index, a digital feature of the primary index is determined. Based on the gap between the digital features of the primary index and the standard digital features, the leakage risk level of the pipeline is determined.

[0007] Based on the above technical solution, the present invention can also be improved as follows.

[0008] Further, based on the degree of association between any secondary indicator and other secondary indicators except any secondary indicator, the centrality of any secondary indicator is determined. The centrality of any secondary indicator is used to characterize the importance of any secondary indicator in at least one secondary indicator. Based on the centrality of any secondary indicator, the weight of any secondary indicator in at least one secondary indicator is determined.

[0009] Furthermore, based on the degree of association between any third-level indicator and other third-level indicators except any third-level indicator, the centrality of any third-level indicator is determined. The centrality of any third-level indicator is used to characterize the importance of any third-level indicator among multiple third-level indicators. Based on the centrality of any third-level indicator, the weight of any third-level indicator among multiple third-level indicators is determined.

[0010] Furthermore, the degree of association includes influence and influence. The influence of any secondary indicator represents the influence of any secondary indicator on other secondary indicators. The influence of any secondary indicator represents the influence of other secondary indicators on any secondary indicator. The influence of any tertiary indicator represents the influence of any tertiary indicator on other tertiary indicators. The influence of any tertiary indicator represents the influence of other tertiary indicators on any tertiary indicator. Based on the sum of the influence and influence of any secondary indicator, the centrality of any secondary indicator is determined. Based on the sum of the influence and influence of any tertiary indicator, the centrality of any tertiary indicator is determined.

[0011] Furthermore, the higher the risk score corresponding to any third-level indicator, the greater the impact of any third-level indicator on pipeline leakage.

[0012] Furthermore, the expectation corresponding to any third-level indicator is: Among them, Ex i is the expectation corresponding to any three-level indicator, r is the number of risk scores corresponding to any three-level indicator, x i is the i-th risk score corresponding to any three-level indicator. The entropy corresponding to any three-level indicator is Among them, En i is the entropy corresponding to any three-level indicator. The super entropy corresponding to any three-level indicator is Among them, He i is the super entropy corresponding to any third-level indicator.

[0013] Furthermore, for any target level 3 indicator, based on the weight of any target level 3 indicator in multiple level 3 indicators, the digital features of any target level 3 indicator are weighted to obtain the weighted digital features corresponding to any target level 3 indicator. Based on the weighted digital features corresponding to each target level 3 indicator, the digital features of any level 2 indicator are determined.

[0014] Furthermore, for any secondary indicator, based on the weight of any secondary indicator in at least one secondary indicator, the digital features of any secondary indicator are weighted to obtain the weighted digital features corresponding to any secondary indicator. Based on the weighted digital features corresponding to each secondary indicator, the digital features of the primary indicator are determined.

[0015] Further, at least one standard digital feature is obtained. One standard digital feature corresponds to one leakage risk level. Based on the gap between the digital feature of the primary indicator and each standard digital feature, a target standard digital feature is determined. The target standard digital feature is the standard digital feature with the smallest gap with the digital feature of the primary indicator among the standard digital features. The leakage risk level corresponding to the target standard digital feature is determined as the leakage risk level of the pipeline.

[0016] In a second aspect, the present invention provides a leakage risk assessment device for a buried natural gas pipeline, comprising: an influencing factor identification module, used to determine a risk assessment index. The risk assessment index includes a primary index, at least one secondary index, and a plurality of tertiary indexes. Any secondary index corresponds to at least one tertiary index among the plurality of tertiary indexes. The primary index is used to characterize the leakage risk of the pipeline, the secondary index is used to characterize the category of the influencing factors causing the pipeline leakage, and the tertiary index is used to characterize the influencing factors causing the pipeline leakage. A weight acquisition module is used to determine the weight of any secondary index in at least one secondary index, and to determine the weight of any tertiary index in the plurality of tertiary indexes. A risk assessment module is used to: determine the digital features of at least one risk score corresponding to any tertiary index based on at least one risk score corresponding to any tertiary index. The digital features include expectation, entropy, and super entropy. The digital features of any secondary index are determined based on the digital features of each target tertiary index and the weight of each target tertiary index in the plurality of tertiary indexes. The target tertiary index is the tertiary index corresponding to any secondary index. The digital features of the primary index are determined based on the digital features of each secondary index and the weight of each secondary index in at least one secondary index. The leakage risk level of the pipeline is determined based on the gap between the digital characteristics of the primary indicators and the standard digital characteristics.

[0017] The beneficial effects of the present invention are as follows: the method and device for assessing the leakage risk of a buried natural gas pipeline provided by the present invention analyzes the interaction relationship between the various indicators by analyzing the degree of correlation between the indicators at various levels, and obtains the weight of each indicator, which is applied to the subsequent process of calculating the digital characteristics of the indicators at various levels. By dividing the leakage risk levels and weighting the digital characteristics of the indicators at various levels in combination with the divided weights, the digital characteristics of the leakage risk of the buried natural gas pipeline are finally obtained, and the risk assessment is achieved by comparing with the standard digital characteristics. The method and device provided by the present invention can better deal with the uncertainty and ambiguity in the risk assessment of buried natural gas pipeline leakage, and improve the reliability and accuracy of the assessment results.

[0018] In a third aspect, the present invention provides an electronic device comprising: a memory, one or more processors; the memory and the processor are coupled; wherein the memory stores computer program code, the computer program code comprises computer instructions, and when the computer instructions are executed by the processor, the electronic device executes the leakage risk assessment method for a buried natural gas pipeline as described in any one of the first aspects above.

[0019] In a fourth aspect, a computer-readable storage medium is provided, comprising computer instructions. When the computer instructions are executed on an electronic device, the electronic device executes the leakage risk assessment method for a buried natural gas pipeline as described in any one of the first aspects above.

[0020] In a fifth aspect, a computer program product is provided. When the computer program product is run on a computer, the computer executes the leakage risk assessment method for a buried natural gas pipeline as described in any one of the first aspects above.

[0021] It can be understood that the beneficial effects that can be achieved by the leakage risk assessment device for the buried natural gas pipeline of the second aspect provided above, the electronic device described in the third aspect, the computer-readable storage medium described in the fourth aspect, and the computer program product described in the fifth aspect can refer to the beneficial effects in the first aspect and any possible design method thereof, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic flow chart of a leakage risk assessment method for a buried natural gas pipeline provided by the present invention;

[0023] Figure 2 A schematic diagram of the structure of a risk assessment index system provided by the present invention;

[0024] Figure 3 A schematic structural diagram of a leakage risk assessment device for a buried natural gas pipeline provided by the present invention. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application. Among them, in the description of the present application, unless otherwise specified, " / " indicates that the objects associated before and after are in an "or" relationship, for example, A / B can represent A or B; "and / or" in the present application is only a description of the association relationship of the associated objects, indicating that there can be three relationships, for example, A and / or B, which can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. And, in the description of the present application, unless otherwise specified, "multiple" refers to two or more than two. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple. In addition, in order to facilitate the clear description of the technical solutions of the embodiments of the present application, in the embodiments of the present application, the words "first", "second" and the like are used to distinguish between the same items or similar items with substantially the same functions and effects. Those skilled in the art will understand that the words "first", "second" and the like do not limit the quantity and execution order, and the words "first", "second" and the like do not necessarily limit the differences. At the same time, in the embodiments of the present application, the words "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or design.

[0026] Because natural gas buried pipelines are in a complex and changeable external environment for a long time, they are easily affected by a variety of factors that may cause pipeline damage, such as corrosion, pipeline quality defects, operational wear, illegal operations, and human damage. Therefore, natural gas leaks are prone to occur due to pipeline damage.

[0027] Given the characteristics of natural gas, such as being light and flammable, once a buried natural gas pipeline leaks in a densely populated area, the potential safety risk will be extremely high. Therefore, it is particularly urgent to ensure the safety of natural gas pipelines in densely populated areas. In order to assess the risk of leakage of natural gas pipelines in densely populated areas, it is necessary to conduct in-depth analysis and assessment of the risks in each link of the accident development process. This approach will help to formulate effective prevention and control strategies for key risks in the development of accidents, thereby minimizing potential safety hazards.

[0028] At present, the leakage risk of buried natural gas pipelines can be evaluated based on qualitative methods, semi-quantitative evaluation methods and quantitative evaluation methods to prevent leakage of buried natural gas pipelines. Among them, qualitative evaluation methods can include event trees, HAZOP (hazard and operability analysis), and FMEA (failure mode and effect analysis). However, qualitative evaluation methods usually lack sufficient quantitative data support, and it is difficult to provide specific numerical results or quantifiable indicators, making the evaluation results relatively vague. Semi-quantitative evaluation methods can include AHP (hierarchical analysis method), fuzzy comprehensive evaluation method, etc. However, semi-quantitative evaluation methods usually divide weights through subjective methods such as expert scoring methods, and combine expert scoring results and weight calculations to obtain scores to reflect the effectiveness of safety barriers, with low accuracy. Quantitative evaluation methods can include Bayesian networks and LOPA (layer of protection analysis). However, quantitative evaluation methods are usually based on the failure probability of various types of equipment in the existing failure accident database or the probability of a certain type of accident.

[0029] That is to say, on the one hand, the above method requires a large amount of historical pipeline leakage data support, and on the other hand, it is difficult to quantify pipeline leakage that may be caused by human factors, thereby reducing the accuracy of the assessment of leakage risks of buried natural gas pipelines.

[0030] In response to the above problems, the present invention provides a method and device for assessing the leakage risk of buried natural gas pipelines, which can combine historical natural gas pipeline failure accident cases, analyze and determine the various influencing factors that may cause leakage of buried natural gas pipelines in densely populated areas, construct risk evaluation indicators, and analyze the degree of correlation between the indicators, construct an influence-dependency matrix, and then divide the weights of each indicator. Combined with the cloud model, the digital characteristics such as expectation, entropy, and super entropy of each third-level indicator are calculated, and the digital characteristics of each level of indicators are obtained by weighting with the obtained weights, and then the leakage risk of the pipeline is evaluated. The present invention can better deal with the uncertainty, ambiguity, and subjectivity in the risk assessment of leakage of buried natural gas pipelines in densely populated areas, and can improve the reliability and accuracy of the assessment results.

[0031] See also Figure 1 , is a flow chart of a leakage risk assessment method for a buried natural gas pipeline provided by the present invention, comprising steps S110-S160:

[0032] S110: Determine risk assessment indicators.

[0033] Among them, the risk assessment indicators include a primary indicator, at least one secondary indicator, and multiple tertiary indicators. Any secondary indicator corresponds to at least one tertiary indicator among multiple tertiary indicators. The primary indicator is used to characterize the leakage risk of the pipeline, the secondary indicator is used to characterize the category of the influencing factors causing pipeline leakage, and the tertiary indicator is used to characterize the influencing factors causing pipeline leakage.

[0034] For example, see Figure 2 , the second-level indicators can be corrosion damage, personnel misoperation, third-party damage, design and construction defects, natural disasters, etc. Among them, the third-level indicators corresponding to corrosion damage can be internal corrosion, buried corrosion, anti-corrosion coating defects, cathodic protection defects, etc. The third-level indicators corresponding to personnel misoperation can be insufficient personnel training, negligence in operation management, poor system / equipment reliability, poor pipeline maintenance effect, etc. The third-level indicators corresponding to third-party damage can be third-party construction damage, illegal occupation, pipeline burial depth not meeting standards, lack of public education, pipeline site selection, intentional damage by personnel, insufficient line inspection, etc. The third-level indicators corresponding to design and construction defects can be material defects, structural defects, illegal construction, unqualified construction quality, etc. The third-level indicators corresponding to natural disasters can be landslides, earthquakes, mudslides, ground subsidence, and extremely severe weather.

[0035] In some embodiments, natural gas pipeline accident cases from previous years can be obtained, and through natural language processing, identification and analysis can be performed to obtain influencing factors that may cause leakage of buried natural gas pipelines in densely populated areas, and a leakage risk assessment index system for buried natural gas pipelines in densely populated areas can be constructed, including primary indicators, secondary indicators, and tertiary indicators.

[0036] S120: Determine the weight of any secondary indicator in at least one secondary indicator, and determine the weight of any tertiary indicator in multiple tertiary indicators.

[0037] In some embodiments, the centrality of any secondary indicator can be determined based on the degree of association between any secondary indicator and other secondary indicators other than any secondary indicator. The centrality of any secondary indicator is used to characterize the importance of any secondary indicator in at least one secondary indicator. Based on the centrality of any secondary indicator, the weight of any secondary indicator in at least one secondary indicator is determined.

[0038] In some embodiments, the centrality of any third-level indicator can be determined based on the degree of association between any third-level indicator and other third-level indicators except any third-level indicator. The centrality of any third-level indicator is used to characterize the importance of any third-level indicator among multiple third-level indicators. Based on the centrality of any third-level indicator, the weight of any third-level indicator among multiple third-level indicators is determined.

[0039] In some embodiments, the degree of association includes influence and influence. The influence of any secondary indicator represents the influence of any secondary indicator on other secondary indicators. The influence of any secondary indicator represents the influence of other secondary indicators on any secondary indicator. The influence of any tertiary indicator represents the influence of any tertiary indicator on other tertiary indicators. The influence of any tertiary indicator represents the influence of other tertiary indicators on any tertiary indicator. Based on the sum of the influence and influence of any secondary indicator, the centrality of any secondary indicator is determined. Based on the sum of the influence and influence of any tertiary indicator, the centrality of any tertiary indicator is determined.

[0040] For example, the correlation between the secondary indicators and the correlation between the tertiary indicators can be analyzed based on the established buried natural gas pipeline leakage risk assessment index system in densely populated areas.

[0041] According to the degree of correlation between the secondary indicators, a numerical value between 0 and 5 can be used for scaling (0 means there is no correlation between the two indicators, and 5 means the correlation between the two indicators is relatively strong), and an influence-dependence matrix for each secondary indicator is constructed, which is homogenized to finally obtain the direct influence matrix A.

[0042]

[0043] In the matrix A, ij is the direct impact of secondary indicator i on secondary indicator j, expressed in scale value form, with a scale value range of 0-5. When i=j, a ij =0.

[0044] Similarly, according to the degree of correlation between the three-level indicators, a numerical value between 0 and 5 can be used for scaling (0 means there is no correlation between the two indicators, and 5 means the correlation between the two indicators is relatively strong), and an influence-dependence matrix for each three-level indicator is constructed, which is homogenized to finally obtain the direct influence matrix B.

[0045]

[0046] In the matrix Bb ij b is the direct impact of the third-level indicator i on the third-level indicator j, expressed in the form of a scale value, with a scale value range of 0-5. When i=j, b ij =0.

[0047] Afterwards, the direct influence matrix A and the direct influence matrix B may be normalized respectively to obtain the canonical influence matrix N1 corresponding to the direct influence matrix A and the canonical influence matrix N2 corresponding to the direct influence matrix B.

[0048] Based on the standard impact matrix N1, the comprehensive impact matrix M1 corresponding to the direct impact matrix A can be obtained. Based on the standard impact matrix N2, the comprehensive impact matrix M2 corresponding to the direct impact matrix B can be obtained.

[0049] If I is set as the unit matrix, we can get:

[0050] M1=N1+(N1)2+L+(N1)j≈N1(I-N1)-1;

[0051] M2=N2+(N2)2+L+(N2)j≈N2(I-N2)-1.

[0053] It can be based on the element M1 in the matrix M1 ij Calculate the influence and influence between each secondary indicator, based on the element M2 in the matrix M2 ij Calculate the influence and influence between each third-level indicator. Based on the influence and influence between each second-level indicator, the centrality of each second-level indicator can be determined. Based on the influence and influence between each third-level indicator, the centrality of each third-level indicator can be determined. The calculation formula is as follows:

[0054] C1 i =d1 i +D1 i ;

[0055] C2 i =d2 i +D2 i ;

[0056] Among them, C1 i is the centrality of the secondary index i, d1 i With D1 i They are the influence and influence of the secondary index i respectively. i It is obtained by adding up the elements of each row in the comprehensive influence matrix M1, indicating the degree of influence of this factor on other factors; D1 i It is obtained by adding up the elements in each column of the comprehensive influence matrix M1, indicating the degree to which this factor is affected by other factors. i is the centrality of the third-level index i, d2 i With D2 i They are the influence and influence of the third-level indicator i. i It is obtained by adding up the elements of each row in the comprehensive influence matrix M2, indicating the degree of influence of this factor on other factors; D2 i It is obtained by adding up the elements in each column of the comprehensive influence matrix M2, indicating the degree to which the factor is affected by other factors.

[0057] Since the centrality represents the importance of the corresponding indicator in the entire indicator evaluation system, in the present invention, the indicator centrality is normalized to obtain the indicator weight of the corresponding indicator in the corresponding level.

[0058] The weight calculation formula of the secondary index is:

[0059]

[0060] Among them, w j is the weight of the secondary index j, C j is the centrality of secondary index j, and n is the total number of secondary indicators.

[0061] The weight calculation formula of the three-level indicators is:

[0062]

[0063] Among them, w i is the weight of the secondary index i, C i is the centrality of the third-level indicator i, and m is the total number of third-level indicators contained under the second-level indicator i.

[0064] S130: Based on at least one risk score corresponding to any third-level indicator, determine a digital feature of at least one risk score corresponding to any third-level indicator.

[0065] Among them, digital features include expectation, entropy and super entropy.

[0066] In some embodiments, the three-level indicators included in the risk assessment system for buried natural gas pipelines in densely populated areas can be scored based on the actual situation of buried natural gas pipelines in densely populated areas, according to the established risk assessment system for buried natural gas pipelines in densely populated areas and the scoring intervals corresponding to different risk levels. The scoring set for a certain indicator is in the form of {X1 X2…Xi…Xr}, where Xi represents the score of the i-th expert for a certain three-level indicator, and r represents the total number of experts who perform the scoring. The higher the risk score corresponding to the three-level indicator, the greater the impact of the three-level indicator on pipeline leakage.

[0067] In some embodiments, the scoring intervals corresponding to different risk levels may be set as shown in Table 1 below.

[0068] Table 1

[0069] Level classification Rating range Standard digital features Level Ⅰ risk [1，3) (1.5，0.166 7，0.05) Level II risk [3，5) (3.5，0.166 7，0.05) Level III risk [5，7) (5.5，0.166 7，0.05) Level IV risk [7，9) (7.5，0.166 7，0.05) Level V risk [9，10] (9.5，0.166 7，0.05)

[0070] In some embodiments, based on at least one risk score corresponding to each third-level indicator, the expectation, entropy, and super entropy corresponding to each third-level indicator may be calculated respectively.

[0071] Among them, the expected Ex corresponding to any three-level indicator i for:

[0072]

[0073] r is the number of risk scores corresponding to any three-level indicator, x i is the i-th risk score corresponding to any third-level indicator.

[0074] The entropy En corresponding to any three-level indicator i for:

[0075]

[0076] The super entropy En corresponding to any three-level indicator i for:

[0077]

[0078] S140: Determine the numerical characteristics of any secondary indicator based on the numerical characteristics of each target third-level indicator and the weight of each target third-level indicator among multiple third-level indicators.

[0079] Among them, the target third-level indicator is the third-level indicator corresponding to any second-level indicator.

[0080] In some embodiments, for any target level 3 indicator, based on the weight of any target level 3 indicator in multiple level 3 indicators, the digital features of any target level 3 indicator are weighted to obtain the weighted digital features corresponding to any target level 3 indicator. Based on the weighted digital features corresponding to each target level 3 indicator, the digital features of any level 2 indicator are determined.

[0081] Among them, the expected Ex corresponding to any secondary indicator j for:

[0082]

[0083] Among them, m is any secondary index x j The total number of third-level indicators included in Ex i For any secondary index x j Included three-level indicators x i Expectations, w i is the third-level indicator x i The corresponding weight.

[0084] The entropy En corresponding to any secondary indicator j for:

[0085]

[0086] Among them, En iThe third-level indicator x included in any second-level indicator i The entropy of .

[0087] The super entropy He corresponding to any secondary index j for:

[0088]

[0089] Among them, He i To change any secondary indicator including the third-level indicator x i of super entropy.

[0090] S150: Determine the digital characteristics of the primary indicators based on the digital characteristics of each secondary indicator and the weight of each secondary indicator in at least one secondary indicator.

[0091] In some embodiments, for any secondary indicator, based on the weight of any secondary indicator in at least one secondary indicator, the digital feature of any secondary indicator is weighted to obtain the weighted digital feature corresponding to any secondary indicator. Based on the weighted digital features corresponding to each secondary indicator, the digital feature of the primary indicator is determined.

[0092] Among them, the expected Ex corresponding to any first-level indicator is:

[0093]

[0094] Among them, n is the total number of secondary indicators contained in any primary indicator x, Ex j The second-level index x included in any first-level index x j Expectations, w j is the secondary index x j The corresponding weight.

[0095] The entropy En corresponding to any first-level indicator is:

[0096]

[0097] Among them, En j The second-level indicator x included in any first-level indicator j The entropy of .

[0098] The super entropy He corresponding to any first-level index j for:

[0099]

[0100] Among them, He i The second-level indicator x included in any first-level indicator j of super entropy.

[0101] S160: Determine the leakage risk level of the pipeline based on the gap between the digital characteristics of the primary indicators and the standard digital characteristics.

[0102] In some embodiments, at least one standard digital feature is obtained. One standard digital feature corresponds to one leakage risk level. Based on the gap between the digital feature of the primary indicator and each standard digital feature, a target standard digital feature can be determined. The target standard digital feature is the standard digital feature with the smallest gap with the digital feature of the primary indicator among the standard digital features. The leakage risk level corresponding to the target standard digital feature is determined as the leakage risk level of the pipeline.

[0103] In some embodiments, continuing to refer to Table 1 above, combined with the buried natural gas pipeline leakage risk assessment index system in densely populated areas, the buried natural gas pipeline leakage risk level in densely populated areas can be divided into 5 levels, namely: Level I risk, Level II risk, Level III risk, Level IV risk, and Level V risk; the scoring range is 1 to 10 points, 10 points means extremely high risk, 1 point means low risk, and each level corresponds to a different scoring range and characteristic parameters, with the calculated digital features as the standard digital features.

[0104] Figure 3 This is a schematic diagram of the structure of a leakage risk assessment device for a buried natural gas pipeline provided by the present invention. Figure 3 As shown, the leakage risk assessment device for buried natural gas pipelines provided by the present invention includes an influencing factor identification module, a weight acquisition module and a risk assessment module.

[0105] Among them, the influencing factor identification module is used to determine the risk assessment index. The risk assessment index includes a primary index, at least one secondary index, and multiple tertiary indexes. Any secondary index corresponds to at least one tertiary index among multiple tertiary indicators. The primary index is used to characterize the leakage risk of the pipeline, the secondary index is used to characterize the category of the influencing factors causing pipeline leakage, and the tertiary index is used to characterize the influencing factors causing pipeline leakage.

[0106] The weight acquisition module is used to determine the weight of any secondary indicator in at least one secondary indicator, and to determine the weight of any tertiary indicator in multiple tertiary indicators.

[0107] The risk assessment module is used to determine the digital characteristics of at least one risk score corresponding to any third-level indicator based on at least one risk score corresponding to any third-level indicator. The digital characteristics include expectation, entropy and super entropy. The digital characteristics of any second-level indicator are determined based on the digital characteristics of each target third-level indicator and the weight of each target third-level indicator in multiple third-level indicators. The target third-level indicator is a third-level indicator corresponding to any second-level indicator. The digital characteristics of the first-level indicator are determined based on the digital characteristics of each second-level indicator and the weight of each second-level indicator in at least one second-level indicator. The leakage risk level of the pipeline is determined based on the gap between the digital characteristics of the first-level indicator and the standard digital characteristics.

[0108] It can be seen that the leakage risk assessment method and device for buried natural gas pipelines provided by the present invention analyzes the degree of correlation between indicators at various levels, thereby analyzing the interaction relationship between various indicators, and obtains the weight of each indicator, which is applied to the subsequent calculation of the digital characteristics of indicators at various levels. By dividing the leakage risk level and weighting the digital characteristics of indicators at various levels in combination with the divided weights, the digital characteristics of the leakage risk of buried natural gas pipelines are finally obtained. By comparing with the standard digital characteristics, the leakage risk assessment of buried natural gas pipelines can be achieved. The method and device provided by the present invention can improve the reliability and accuracy of the leakage risk assessment of buried natural gas pipelines.

[0109] In some schemes, multiple embodiments of the present application can be combined, and the combined scheme can be implemented. Optionally, some operations in the process of each method embodiment are optionally combined, and / or the order of some operations is optionally changed. In addition, the execution order between the steps of each process is only exemplary and does not constitute a restriction on the execution order between the steps. There can also be other execution orders between the steps. It is not intended to indicate that the execution order is the only order in which these operations can be performed. A person of ordinary skill in the art will think of a variety of ways to reorder the operations described herein. In addition, it should be noted that the process details involved in a certain embodiment of this article are also applicable to other embodiments in a similar manner, or different embodiments can be used in combination.

[0110] In addition, some steps in the method embodiment may be equivalently replaced by other possible steps. Alternatively, some steps in the method embodiment may be optional and may be deleted in certain usage scenarios. Alternatively, other possible steps may be added to the method embodiment. Furthermore, the method embodiments may be implemented separately or in combination.

[0111] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the system can be divided into different functional modules to complete all or part of the functions described above.

[0112] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or units, which can be electrical, mechanical or other forms.

[0113] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0114] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium, including several instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0115] The above contents are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application shall be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for assessing leakage risk of a buried natural gas pipeline, characterized in that: include: Determine risk assessment indicators; the risk assessment indicators include a primary indicator, at least one secondary indicator, and a plurality of tertiary indicators; Any secondary indicator corresponds to at least one of the plurality of tertiary indicators; the primary indicator is used to characterize the leakage risk of the pipeline; the secondary indicator is used to characterize the category of the influencing factors causing the leakage of the pipeline; the tertiary indicator is used to characterize the influencing factors causing the leakage of the pipeline; Determine the weight of any one of the secondary indicators in the at least one secondary indicator, and determine the weight of any one of the tertiary indicators in the plurality of tertiary indicators; Based on at least one risk score corresponding to any one of the three-level indicators, determine a digital feature of the at least one risk score corresponding to any one of the three-level indicators; the digital feature includes expectation, entropy and super entropy; Determine the numerical feature of any one of the second-level indicators based on the numerical feature of each target third-level indicator and the weight of each target third-level indicator among the plurality of third-level indicators; The target third-level indicator is a third-level indicator corresponding to any of the second-level indicators; Determining the numerical characteristics of the primary indicator based on the numerical characteristics of each of the secondary indicators and the weight of each of the secondary indicators in the at least one secondary indicator; Based on the gap between the digital characteristics of the primary indicator and the standard digital characteristics, the leakage risk level of the pipeline is determined.

2. The method according to claim 1, characterized in that Determining the weight of any one of the secondary indicators in the at least one secondary indicator includes: Determine the centrality of any secondary indicator based on the degree of association between any secondary indicator and other secondary indicators other than any secondary indicator; the centrality of any secondary indicator is used to characterize the importance of any secondary indicator in at least one secondary indicator; Based on the centrality of any of the secondary indicators, the weight of any of the secondary indicators in the at least one secondary indicator is determined.

3. The method according to claim 2, characterized in that Determining the weight of any third-level indicator among the multiple third-level indicators includes: Determine the centrality of any third-level indicator based on the degree of association between any third-level indicator and other third-level indicators except any third-level indicator; the centrality of any third-level indicator is used to characterize the importance of any third-level indicator among the multiple third-level indicators; Based on the centrality of any one of the three-level indicators, the weight of any one of the three-level indicators among the multiple three-level indicators is determined.

4. The method according to claim 3, characterized in that The degree of association includes influence and influence; the influence of any secondary indicator represents the influence of any secondary indicator on other secondary indicators; the influence of any secondary indicator represents the influence of other secondary indicators on any secondary indicator; the influence of any tertiary indicator represents the influence of any tertiary indicator on other tertiary indicators; the influence of any tertiary indicator represents the influence of other tertiary indicators on any tertiary indicator; The determining the centrality of any secondary indicator based on the degree of association between any secondary indicator and other secondary indicators except any secondary indicator includes: Determine the centrality of any secondary indicator based on the sum of the influence and the influence of any secondary indicator; The determining the centrality of any third-level indicator based on the degree of association between any third-level indicator and other third-level indicators except any third-level indicator comprises: The centrality of any of the three-level indicators is determined based on the sum of the influence and the influence of any of the three-level indicators.

5. The method according to claim 4, characterized in that The higher the risk score corresponding to any of the third-level indicators, the greater the impact of any of the third-level indicators on the pipeline leakage.

6. The method according to claim 5, characterized in that The expectation corresponding to any of the three-level indicators is: Among them, Ex i is the expectation corresponding to any of the three-level indicators, r is the number of risk scores corresponding to any of the three-level indicators, x i is the i-th risk score corresponding to any of the three-level indicators; The entropy corresponding to any of the three-level indicators is: Among them, En i is the entropy corresponding to any of the three-level indicators; The super entropy corresponding to any of the three-level indicators is: Among them, He i It is the super entropy corresponding to any of the three-level indicators.

7. The method according to claim 6, characterized in that The determining of the digital features of any of the secondary indicators based on the digital features of each target third-level indicator and the weight of each target third-level indicator among the plurality of third-level indicators includes: For any target third-level indicator, based on the weight of any target third-level indicator among the multiple third-level indicators, weight the digital features of any target third-level indicator to obtain the weighted digital features corresponding to any target third-level indicator; Based on the weighted digital features corresponding to each of the target third-level indicators, the digital features of any of the second-level indicators are determined.

8. The method according to claim 7, characterized in that The determining the digital feature of the primary indicator based on the digital feature of each of the secondary indicators and the weight of each of the secondary indicators in the at least one secondary indicator includes: For any of the secondary indicators, based on the weight of any of the secondary indicators in the at least one secondary indicator, weight the digital features of any of the secondary indicators to obtain the weighted digital features corresponding to any of the secondary indicators; Based on the weighted digital features corresponding to each of the secondary indicators, the digital features of the primary indicators are determined.

9. The method according to claim 8, characterized in that Also includes: obtaining at least one standard digital feature; One of the standard digital features corresponds to one leakage risk level; The step of determining the leakage risk level of the pipeline based on the gap between the digital feature of the primary indicator and the standard digital feature includes: Determining target standard digital features based on the gap between the digital features of the primary indicators and the digital features of the standards; The target standard digital feature is the standard digital feature with the smallest difference from the digital feature of the primary indicator among the standard digital features; The leakage risk level corresponding to the target standard digital feature is determined as the leakage risk level of the pipeline.

10. A leakage risk assessment device for a buried natural gas pipeline, characterized in that: include: An influencing factor identification module is used to determine risk assessment indicators; the risk assessment indicators include a primary indicator, at least one secondary indicator, and a plurality of tertiary indicators; Any secondary indicator corresponds to at least one of the plurality of tertiary indicators; the primary indicator is used to characterize the leakage risk of the pipeline; the secondary indicator is used to characterize the category of the influencing factors causing the leakage of the pipeline; the tertiary indicator is used to characterize the influencing factors causing the leakage of the pipeline; A weight acquisition module, used to determine the weight of any secondary indicator in the at least one secondary indicator, and to determine the weight of any tertiary indicator in the plurality of tertiary indicators; Risk assessment module for: Based on at least one risk score corresponding to any one of the three-level indicators, determine a digital feature of the at least one risk score corresponding to any one of the three-level indicators; the digital feature includes expectation, entropy and super entropy; Determine the numerical feature of any one of the second-level indicators based on the numerical feature of each target third-level indicator and the weight of each target third-level indicator among the plurality of third-level indicators; The target third-level indicator is a third-level indicator corresponding to any of the second-level indicators; Determining the numerical characteristics of the primary indicator based on the numerical characteristics of each of the secondary indicators and the weight of each of the secondary indicators in the at least one secondary indicator; Based on the gap between the digital characteristics of the primary indicator and the standard digital characteristics, the leakage risk level of the pipeline is determined.

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