Oil and gas pipeline risk evaluation method based on analytic hierarchy process-cooperative game comprehensive weighting method and improved fuzzy comprehensive evaluation method

Abstract

The invention discloses an oil and gas pipeline risk evaluation method based on an analytic hierarchy process-cooperative game comprehensive weighting method and an improved fuzzy comprehensive evaluation method, and the method comprises the following steps: 1, setting six levels of risk index evaluation levels, and carrying out the evaluation of all risk indexes according to the evaluation results of experts on all risk indexes; obtaining a risk index evaluation matrix and converting the risk index evaluation matrix into a Vague value matrix; 2, performing fuzzy comprehensive evaluation; step 3, carrying out analytic hierarchy process-cooperative game comprehensive empowerment; 4, introducing a matter-element extension theory to improve a risk evaluation result judgment principle so as to obtain an accurate three-level risk index evaluation result; and 5, performing instance analysis, applying the instance analysis to the risk evaluation problem of the oil and gas pipeline, and comparing with different risk evaluation models. According to the method, the oil and gas pipeline risk priority can be accurately analyzed, and the problems of uncertainty of risk factor evaluation by experts, inaccurate evaluation results and the like in the oil and gas pipeline risk evaluation process are solved.

Description

Technical Field

[0001] The invention belongs to the field of oil and gas chemical industry risk assessment, and in particular relates to a risk assessment method based on a hierarchical analysis-cooperative game comprehensive weighting method and an improved fuzzy comprehensive evaluation method. Background Art

[0002] Pipeline transportation is the main mode of transportation for oil and natural gas. Regular risk assessment of oil and gas pipeline systems and the implementation of risk management are of great significance to the safe operation of pipelines. Previous evaluation methods were mostly based on the identification of pipeline damage factors and the use of data-driven quantitative risk assessment. However, when conducting risk assessment on oil and gas pipelines, many risk factors that cause accidents in oil and gas pipelines are highly uncertain, which will lead to the lack of relevant data and make it impossible to conduct accurate risk assessment. Therefore, a scientific and efficient risk assessment model is needed to accurately evaluate them. Summary of the invention

[0003] In view of the above problems, the purpose of the present invention is to solve the problems in the prior art that risk assessment does not fit the on-site process, many risk factors of oil and gas pipeline accidents are highly uncertain, which will lead to the loss of relevant data and make it impossible to conduct accurate risk assessment, based on a risk assessment method based on hierarchical analysis-cooperative game comprehensive weighting method and improved fuzzy comprehensive evaluation method.

[0004] In order to achieve the purpose of the present invention, the present invention is implemented by the following technical solutions:

[0005] The oil and gas pipeline risk assessment method based on hierarchical analysis-cooperative game comprehensive weighting method and improved fuzzy comprehensive evaluation method includes the following steps:

[0006] Step 1: Set 6 levels of risk indicator evaluation and convert the expert language variables into Vague fuzzy values ​​that are compatible with agreement and opposition information. According to the expert's evaluation results of all risk indicators in the risk index, the risk indicator evaluation matrix is ​​obtained and converted into a Vague value matrix;

[0007] Step 2: Perform fuzzy comprehensive evaluation and calculate the expert weight W(E j ), the initial single weight of the third-level risk indicator Secondary risk indicator membership matrix And the three-level risk indicator membership matrix

[0008] Step 3: Perform comprehensive weighting using the AHP-CG method, using the weights calculated using the AHP, entropy weight method, and grayscale correlation method. As a member of the game, the cooperative game method is used to determine the weight W2 of the secondary risk indicator. According to the idea of ​​the comprehensive weight method, the weight of the secondary risk indicator calculated by the cooperative game method is used as the intermediate event weight in the comprehensive weight method. The initial single weight of the third-level risk indicator calculated by the analytic hierarchy process is used as the single event weight in the comprehensive weight method. The intermediate event weight is multiplied by the single event weight to obtain the final comprehensive weight W3 of the third-level risk indicator.

[0009] Step 4: Based on the three-level risk indicator weights obtained in step 3 and the risk levels set in step 1, the classical domain, section domain and matter-element to be evaluated of the three-level risk indicator are determined using the matter-element extension theory, and the comprehensive correlation between the three-level risk indicator and each risk level is calculated; the weights of the three-level risk indicators and their membership matrix are fused to obtain the weighted three-level risk indicator membership matrix, and the weighted three-level risk indicator membership matrix is ​​combined with the comprehensive correlation between the risk indicator and the risk level to obtain the three-level risk indicator evaluation result judgment matrix, and the comprehensive correlation calculated using the matter-element extension theory is normalized to obtain the relative importance of each risk level, and then the relative importance of each risk level is used to improve the judgment principle of the evaluation result to obtain the final three-level risk indicator evaluation result;

[0010] Step 5: Conduct a case analysis, relying on the evaluation data of an oil and gas pipeline built in 2002 in a certain oil field, using the risk assessment methods of steps 1 to 4, and relying on the risk indicator system established based on the analysis of historical accident cases of oil and gas pipelines. According to the evaluation results, make effective suggestions for the safety supervision of oil and gas pipelines to verify the accuracy of this method and its strong pertinence to risk factors.

[0011] Further, the step 1 specifically includes the following steps:

[0012] Step 1.1: Conduct expert evaluation

[0013] The experts’ evaluation levels for risk indicators are set to 6 levels: extremely high impact, large impact, relatively large impact, medium impact, small impact, and extremely small impact. The corresponding Vague values ​​are: L1<0.9,1>, L2<0.8,0.9>, L3<0.6,0.8>, L4<0.4,0.6>, L5<0.2,0.3>, L6<0.1,0.2>. - When n experts have evaluated all the risk indicators in the risk index, the risk indicator evaluation matrix can be obtained;

[0014] Through the setting of the Vague values ​​of the above evaluation levels, according to the Vague fuzzy set uncertainty calculation formula: π A (u) = 1-t A (u)-f A(u) It can be obtained that the uncertainty of expert judgment is strictly controlled between 0.1 and 0.2;

[0015] Step 1.2: Get Vague fuzzy sets

[0016] After obtaining the expert evaluation data set for risk assessment indicators, the attribute language evaluation term data set is converted into a Vague value matrix A nm .

[0017] Further, the step 2 specifically includes the following steps:

[0018] Step 2.1: Calculate expert weights

[0019] The expert weights are calculated using the arithmetic mean method:

[0020]

[0021] Where: WS e and W(E j ) are the weight value and weight factor of expert e respectively;

[0022] Step 2.2: Use the analytic hierarchy process to calculate the initial single weight of the three-level risk indicators

[0023] According to the established risk indicator evaluation system with a three-level structure, a two-by-two three-level risk indicator judgment matrix under the secondary risk indicator is constructed as shown in formula R.

[0024]

[0025] Then, the three-level risk indicator judgment matrix is ​​subjected to hierarchical single sorting and consistency test. When the matrix satisfies the consistency test, the initial single weight of the three-level risk indicator can be calculated.

[0026] Step 2.3: Calculate the weighted assessment scores of the three-level risk indicators.

[0027] The evaluation scores of the three-level risk indicators are obtained according to the utility function of the Vague fuzzy set, and then combined with the expert weights to obtain the weighted evaluation scores of the three-level risk indicators. The formula is:

[0028]

[0029] in: Assess the scores for each of the three-level risk indicators;

[0030] Step 2.4: Calculate the membership matrix of the secondary risk index and the tertiary risk index

[0031] The trapezoidal membership function is selected as the membership function of the fuzzy number in this paper, and then the membership matrix of each third-level risk indicator is calculated according to the weighted evaluation score of each third-level risk indicator. The fuzzy synthesis result of the initial single weight of the third-level risk indicator and the third-level risk indicator membership matrix is ​​used as the second-level risk indicator membership matrix. The formula is:

[0032]

[0033] in: is the fuzzy composition operator, is the three-level risk indicator membership matrix, is the initial single weight vector of the three-level risk indicators.

[0034] Further, the step 3 specifically includes the following steps:

[0035] Step 3.1: Calculate the weight of secondary risk indicators using cooperative game method

[0036] The weights are calculated using three methods: hierarchical analysis method, entropy weight method, and gray correlation method. As a member of the game, the cooperative game method is used to determine the combined weight vector W2, the formula is:

[0037]

[0038] Where: w ij is the weight of each indicator in the combined weight vector W2; l (l = 1, ..., 3) is the number of game members, For Two other weights; “—” indicates average value; T i (i=1,2,…) is the number of third-level risk indicators under the second-level risk indicators; L l is the consistent correlation coefficient;

[0039] Step 3.2: Calculate the final comprehensive weight of the three-level risk indicators

[0040] According to the idea of ​​comprehensive weight method, the weight of secondary risk indicator calculated by cooperative game method is used as the intermediate event weight in comprehensive weight method, and the initial single weight of tertiary risk indicator calculated by analytic hierarchy process is used as the single event weight in comprehensive weight method. Finally, the two weights are multiplied to obtain the final comprehensive weight of tertiary risk indicator. The formula is:

[0041]

[0042] Further, the step 4 specifically includes the following steps:

[0043] Step 4.1: Calculate the comprehensive correlation between the three-level risk indicators and each risk level

[0044] On the basis of the weights of the three-level risk indicators and the set risk levels, the matter-element extension theory is used to determine the classic domain, section domain and matter-element to be evaluated of the three-level risk indicators, and then the correlation between the three-level risk indicators and each risk level is calculated. The specific steps are as follows:

[0045] Step 4.1.1, calculate the distance. In the matter-element extension theory, the distance refers to the relative position of a point in the interval. The formula is:

[0046]

[0047] Step 4.1.2: Calculate the single index correlation. The single index correlation refers to the correlation of the i-th feature of the object to be evaluated with respect to the classification level j. The formula is:

[0048] When ρ[y ij ,y iuj ]-ρ[y ij ,y imj ]≠0:

[0049]

[0050] When ρ[y ij ,y iuj ]-ρ[y ij ,y imj ]=0:

[0051] K m (c ij )=-ρ[y ij ,y imj ]-1

[0052] Step 4.1.3, calculate the comprehensive index correlation degree. The comprehensive index correlation degree refers to the degree to which the evaluation object belongs to each evaluation level. The formula is:

[0053] K s =ΣW3K m (c ij )

[0054] Where: s=1,2,…,i, W3 is feature c ij The weight coefficient of .

[0055] Step 4.2: Introduce matter-element extension theory to improve the judgment principle of comprehensive evaluation results of fuzzy comprehensive evaluation method

[0056] The comprehensive correlation between each risk indicator and the risk level is calculated using the matter-element extension theory as the relative importance of each risk level, and then the membership of each risk indicator is calculated to make a comprehensive evaluation result judgment. The specific steps are as follows:

[0057] Step 4.2.1, merge the weights of the three-level risk indicators with their membership matrix to obtain the weighted three-level risk indicator membership matrix;

[0058] Step 4.2.2, combining the weighted three-level risk indicator membership matrix with the comprehensive correlation between risk indicators and risk levels to obtain the three-level risk indicator evaluation result judgment matrix;

[0059] Step 4.2.3: Use the improved evaluation result judgment principle to obtain the final three-level risk indicator evaluation result. The calculation formula is as follows:

[0060]

[0061] RO=max[Ro]

[0062] Among them: Ro is the three-level risk indicator evaluation result judgment matrix, RO is the three-level risk indicator priority evaluation result.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] 1. When dealing with uncertain expert judgment problems, the Vague fuzzy set theory is introduced to convert expert language variables into Vague fuzzy values ​​that are compatible with agreement and disagreement information, which can effectively overcome the impact of uncertainty in expert judgment caused by insufficient data on the judgment results.

[0065] 2. Use the idea of ​​comprehensive weight method to construct a comprehensive weighting system of hierarchical analysis-cooperative game to obtain more accurate weights of the three-level risk indicators.

[0066] 3. Apply the relative importance of risk levels calculated using the matter-element extension theory to the fuzzy comprehensive evaluation method as a new evaluation principle for the comprehensive evaluation results of risk factors to obtain more accurate risk assessment results. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a Vague value matrix diagram for risk index evaluation of the present invention;

[0068] Figure 2 This is a diagram of the oil and gas pipeline risk indicator system structure of the present invention;

[0069] Figure 3 It is a change diagram of the secondary weight comparison curve of the present invention;

[0070] Figure 4 This is a diagram showing the parameter adjustment of the present invention;

[0071] Figure 5 This is a graph showing the results of the parameter sensitivity study of the present invention;

[0072] Figure 6 This is a risk priority result diagram of each model of the present invention;

[0073] Figure 7 This is a comparison chart of risk priorities of various models of the present invention;

[0074] Figure 8 It is a membership matrix diagram of each risk indicator of the present invention. DETAILED DESCRIPTION

[0075] The following is combined with Figure 1-8 The present invention is further illustrated by the following embodiments.

[0076] In an embodiment of the present invention, the oil and gas pipeline risk assessment method based on the hierarchical analysis-cooperative game comprehensive weighting method and the improved fuzzy comprehensive evaluation method specifically includes the following steps:

[0077] Step 1:

[0078] Step 1.1: Conduct expert evaluation

[0079] The experts’ evaluation level for risk indicators is set to 6 levels: extremely high impact, large impact, relatively large impact, medium impact, small impact, and extremely small impact. The corresponding Vague values ​​are: L1<0.9,1>, L2<0.8,0.9>, L3<0.6,0.8>, L4<0.4,0.6>, L5<0.2,0.3>, L6<0.1,0.2>, such as Figure 1 When n experts judge all risk indicators in the risk index, the risk indicator evaluation matrix can be obtained.

[0080] Through the setting of the Vague values ​​of the above evaluation levels, according to the Vague fuzzy set uncertainty calculation formula: π A (u) = 1-t A (u)-f A (u) It can be obtained that the uncertainty of expert judgment is strictly controlled between 0.1 and 0.2, which reasonably solves the uncertainty caused by the ambiguous expression of risk indicators due to insufficient data and excessive reliance on personal knowledge, experience and intuition when judging risk indicators, which affects the accuracy of risk assessment results.

[0081] Step 1.2: Get Vague fuzzy sets

[0082] After obtaining the expert evaluation data set for risk assessment indicators, the attribute language evaluation term data set is converted into a Vague value matrix A nm .

[0083] Step 2: Conduct fuzzy comprehensive evaluation.

[0084] Step 2.1: Calculate expert weights

[0085] Due to different working time and work experience, different experts will make different judgments on different risk indicators. Therefore, this paper uses the arithmetic mean method to calculate the expert weight to overcome the influence of this factor. The formula is as follows:

[0086]

[0087] Where: WS e and W(E j ) are the weight value and weight factor of expert e respectively.

[0088] Step 2.2: Use the analytic hierarchy process to calculate the initial single weight of the three-level risk indicators

[0089] According to the established risk indicator evaluation system with a three-level structure, a two-by-two three-level risk indicator judgment matrix under the secondary risk indicator is constructed as shown in formula R. The formula is as follows:

[0090]

[0091] Then, the three-level risk indicator judgment matrix is ​​sorted and checked for consistency. When the matrix satisfies the consistency check, the initial single weight of the three-level risk indicator can be calculated.

[0092] Step 2.3: Calculate the weighted assessment scores of the three-level risk indicators.

[0093] The evaluation scores of each level three risk index are obtained based on the utility function of the Vague fuzzy set, and then combined with the expert weights to obtain the weighted evaluation scores of each level three risk index. The formula is as follows:

[0094]

[0095] Where: Assess scores for each of the three-level risk indicators.

[0096] Step 2.4: Calculate the membership matrix of the secondary risk index and the tertiary risk index

[0097] The trapezoidal membership function is selected as the membership function of the fuzzy number in this paper, and then the membership matrix of each three-level risk indicator is calculated according to the weighted evaluation score of each three-level risk indicator. The fuzzy synthesis result of the initial single weight of the three-level risk indicator and the three-level risk indicator membership matrix is ​​used as the secondary risk indicator membership matrix. The representation is as follows:

[0098]

[0099] Where: is the fuzzy composition operator, is the three-level risk indicator membership matrix, is the initial single weight vector of the three-level risk indicators.

[0100] Step 3: Conduct analytic hierarchy process-cooperative game (AHP-CG) comprehensive weighting.

[0101] Step 3.1: Calculate the weight of secondary risk indicators using cooperative game method

[0102] The weights are calculated using three methods: hierarchical analysis method, entropy weight method, and gray correlation method. As a member of the game, the cooperative game method is used to determine the combined weight vector W2, the formula is as follows:

[0103]

[0104] Where: w ij is the weight of each indicator in the combined weight vector W2; l (l = 1, ..., 3) is the number of game members, For Two other weights; “—” indicates average value; T i (i=1,2,…) is the number of third-level risk indicators under the second-level risk indicators; L l is the consistent correlation coefficient.

[0105] Step 3.2: Calculate the final comprehensive weight of the three-level risk indicators

[0106] According to the idea of ​​comprehensive weight method, the weight of secondary risk indicator calculated by cooperative game method is used as the intermediate event weight in comprehensive weight method, and the initial single weight of tertiary risk indicator calculated by analytic hierarchy process is used as the single event weight in comprehensive weight method. Finally, the two weights are multiplied to get the final comprehensive weight of tertiary risk indicator. The representation is as follows:

[0107]

[0108] Step 4:

[0109] Step 4.1: Calculate the comprehensive correlation between the three-level risk indicators and each risk level

[0110] On the basis of the weights of the three-level risk indicators and the set risk levels, after the classical domain, section domain and matter-element to be evaluated of the three-level risk indicators are determined using the matter-element extension theory, the correlation between the three-level risk indicators and each risk level is calculated. The formula is as follows:

[0111] Calculate the distance. In the matter-element extension theory, the distance refers to the relative position of a point in the interval. The formula is as follows:

[0112]

[0113] Calculate the single index correlation. The single index correlation refers to the correlation of the i-th feature of the object to be evaluated with respect to the classification level j. The formula is as follows:

[0114] When ρ[y ij ,y iuj ]-ρ[y ij ,y imj ]≠0:

[0115]

[0116] When ρ[y ij ,y iuj ]-ρ[y ij ,y imj ]=0:

[0117] K m (c ij )=-ρ[y ij ,y imj ]-1

[0118] Calculate the comprehensive index correlation. The comprehensive index correlation refers to the degree to which the evaluation object belongs to each evaluation level. The formula is as follows:

[0119] K s =ΣW3K m (c ij )

[0120] Where: s = 1, 2, ..., i, W3 is the characteristic c ij The weight coefficient of .

[0121] Step 4.2: Introduce matter-element extension theory to improve the judgment principle of comprehensive evaluation results of fuzzy comprehensive evaluation method

[0122] There are two main principles for judging the evaluation results of the fuzzy comprehensive evaluation method. The first is the maximum membership principle. The core idea of ​​this principle is that for a specific evaluation indicator, if its membership in a certain fuzzy subset is the largest among all fuzzy subsets, then this indicator is considered to be relatively affiliated with this fuzzy subset. When the membership of the indicator in multiple fuzzy subsets is the largest, it is impossible to judge the evaluation result of the indicator, and this principle becomes invalid; the second is the priority weighted average principle, which calculates the membership of each indicator in the fuzzy subset by considering the weight of each evaluation indicator and then obtains the final evaluation result. When judging the evaluation results of multiple indicators, the membership of multiple indicators will be equal, and the risk priority of each indicator cannot be effectively distinguished. In response to these two problems, the present invention is based on the ideas of the above two principles for judging the comprehensive evaluation results, and uses the material element extension theory to calculate the comprehensive correlation between each risk indicator and the risk level as the relative importance of each risk level, and then calculates the membership of each risk indicator for comprehensive evaluation result judgment.

[0123] First, the weights of the three-level risk indicators and their membership matrix are fused to obtain the weighted three-level risk indicator membership matrix; then, the weighted three-level risk indicator membership matrix is ​​combined with the comprehensive correlation between risk indicators and risk levels to obtain the three-level risk indicator evaluation result judgment matrix; finally, the improved evaluation result judgment principle is used to obtain the final three-level risk indicator evaluation result, and the calculation formula is as follows:

[0124]

[0125] RO=max[Ro]

[0126] Where: Ro is the judgment matrix of the three-level risk indicator evaluation results, and RO is the priority evaluation result of the three-level risk indicator.

[0127] Step 5: Conduct case analysis to verify the accuracy and strong pertinence of the oil and gas pipeline risk assessment method based on hierarchical analysis-cooperative game comprehensive weighting method and improved fuzzy comprehensive evaluation method. The evaluation data of an oil and gas pipeline built in 2002 in a certain oil field is used as the basis. The analysis is based on the risk indicator system established based on the case analysis of historical oil and gas pipeline accidents. Based on the evaluation results, effective suggestions are put forward for the safety supervision of oil and gas pipelines.

[0128] In the embodiment of the present invention, Figures 1 to 8As shown, in order to more intuitively demonstrate and verify the accuracy of the evaluation results of the oil and gas pipeline risk evaluation method based on hierarchical analysis-cooperative game comprehensive weighting method and improved fuzzy comprehensive evaluation method proposed in the present invention, the present invention adopts Vague fuzzy sets to transform language variables, calculates the risk priority of risk factors affecting the safety of oil and gas pipelines, and puts forward effective suggestions for the safety management of oil and gas pipelines according to the specific evaluation results, and conducts simulation verification.

[0129] Practical application simulation examples:

[0130] Based on the evaluation data of an oil and gas pipeline built in 2002 in a certain oil field, the analysis is carried out based on the risk indicator system established according to the historical accident case analysis of the oil and gas pipeline, and the risk factors affecting the normal operation of the oil and gas pipeline are analyzed according to the evaluation results. A risk indicator system is established, and the risk indicator system is divided into three levels based on the historical accident case analysis of the oil and gas pipeline. According to the application engineering direction, n experts are invited to conduct language evaluation on the three-level risk indicators based on the risk indicator system. After n experts have judged all the risk indicators in the risk indicator, the Vague value matrix of the risk indicator evaluation can be obtained as follows: Figure 1 shown.

[0131] Based on the analysis of historical oil and gas pipeline accident cases, the present invention divides the risk indicator system into three levels. Figure 2 As shown in the figure, oil and gas pipeline safety is taken as the first-level risk indicator F; the second-level risk indicators are corrosion factors S1, structural defects S2, third-party damage S3, natural disasters S4, and free span S5; the third-level risk indicators are internal corrosion T1, external corrosion T2, corrosion prevention T3, design factors T4, construction factors T5, laying and operation factors T6, maintenance factors T7, structural degradation T8, third-party activities T9, and third-party protection measures T10. 10 、Pipeline route T 11 , patrol frequency T 12 , Public Education 13 、Safety Regulations 14 、Earthquake T 15 、Landslide 16 , Typhoon T 17 , burial depth T 18 , Soil type T 19 , air flow T 20 , Overhang control measures T 21 .

[0132] The oil and gas pipeline risk assessment method based on the analytic hierarchy process-cooperative game comprehensive weighting method and the improved fuzzy comprehensive evaluation method, that is, constructing the AHP-CG-ImFCE model. The main weighting method is the AHP-CG comprehensive weighting method. The core idea is to combine the weights of the secondary risk indicators calculated by the cooperative game method with the initial single weights of the tertiary risk indicators calculated by the analytic hierarchy process to obtain the final comprehensive weights of the tertiary risk indicators. Because the cooperative game method can make up for the shortcomings of the single weighting method, when comparing the weighting methods, it is only necessary to compare the weights of the secondary risk indicators calculated by each risk assessment method. The weighting methods commonly used for risk assessment are the analytic hierarchy process, the entropy weight method, and the grey correlation method. By importing the instance data into these methods, a weight comparison chart can be obtained, as shown below: Figure 3 shown.

[0133] Depend on Figure 3 It can be seen that the indicator weights calculated by the analytic hierarchy process are extremely variable. The maximum weighted value of the secondary risk indicator corrosion factor S1 is 0.5128, and the minimum weighted value of the secondary risk indicator free span S5 is 0.0333, which is 0.4795 different from the maximum weighted value. The large difference in the weighting of risk indicators makes the weighting of risk indicators unbalanced, indicating that this method is susceptible to subjective evaluation. As an objective weighting method, the entropy weight method has strong indicator weighting reliability, but in the evaluation situation where the difference in indicators is not large, the accuracy of the weight obtained is not high. In the weighting of third-party damage S3 and natural disaster S4, the difference between the two is only 0.001. Compared with other weighting methods, the risk level of the two indicators cannot be highlighted, which has a greater impact on the integrated calculation of data. The weighted values ​​of the four indicators of the gray correlation method are 0.1995, 0.2188, 0.1818, 0.1905, and 0.2091 respectively. The difference in weighted values ​​is too small to reflect expert experience, which has a great impact on the risk assessment results. In addition, the weighted values ​​of third-party damage S3 and natural disaster S4 are greatly deviated from the weighted values ​​obtained by the other three methods, and the weighted results are of low accuracy. The weighted values ​​of the AHP-CG-ImFCE model using the cooperative game method are close to the middle weighted values ​​of the three weighting methods, avoiding extreme situations caused by subjective preferences and fuzzy risk assessment results caused by small differences in indicator weights, thus improving the accuracy of weighting.

[0134] By adjusting the relative importance of each risk level, the risk priority ranking is affected. Select values ​​in the interval [0,1] and repeat the above evaluation process. Other conditions remain unchanged. The parameter adjustment is as follows: Figure 4 The risk priority rankings for different risk levels are shown in Figure 5As shown. Case 1, Case 2 and Case 3 are obtained by increasing, decreasing and approximating the relative importance of the risk level calculated after the introduction of the matter-element extension theory; Case 4 and Case 5 are obtained randomly without introducing the matter-element extension theory and without considering other factors. Figure 5 It can be clearly seen that the risk priority calculation results of case 5 are significantly different from those of the other four scenarios. This is because the relative importance of each risk level was not correctly calculated before the risk priority calculation. The results also show that the relative importance of risk levels has a practical impact on risk priority. On the other hand, although the risk priority calculation results of case 4 are roughly the same as those of the first three cases, this is because there are no risk indicators belonging to the fifth risk level U5 in the three-level risk indicators studied in this paper, and there are relatively few risk indicators belonging to the first U1 and the second risk level U2. The third U3 and the fourth risk level U4 still maintain the correct relative importance. However, there are repeated risk priorities in this case, indicating that the relative importance of the overall risk level should be considered. Therefore, considering the relative importance between risk levels and establishing an improved fuzzy comprehensive evaluation model is beneficial to risk assessment.

[0135] The risk priority of each third-level risk indicator is obtained through step 5, and the same data is brought into a variety of different risk assessment models to compare and verify the evaluation results. The selected models include a risk assessment model based on the hierarchical analysis weighting method and the traditional fuzzy comprehensive evaluation method, a risk assessment model based on the entropy weighting method and the improved fuzzy comprehensive evaluation method, a risk assessment model based on the grey correlation weighting method and the improved fuzzy comprehensive evaluation method, and a risk assessment model based on the AHP-CG comprehensive weighting method and the traditional fuzzy comprehensive evaluation method. These models are selected because they are widely used to solve risk assessment problems and can reasonably compare the advantages of the improved fuzzy comprehensive evaluation method of the present invention. The actual data is brought into these models to obtain the respective risk priority calculation results as shown Figure 6 shown. Figure 7 It is a comparison chart of the risk priorities of various risk indicators calculated by various risk assessment models.

[0136] from Figure 6 and Figure 7 It can be seen that in the above four risk assessment models, the risk priorities of internal corrosion T1 and external corrosion T2 are relatively high, while maintenance factor T7 and third-party protection measures T 10 The risk priority of is relatively low, which is consistent with the results calculated by the AHP-CG-ImFCE model, and in most risk indicators such as line inspection frequency T 12 and safety regulations 14The results are highly similar to those of other models, which indicates that the calculation results of the risk priority of the risk indicator of the AHP-CG-ImFCE model have high credibility. In addition, the models using the improved fuzzy comprehensive evaluation method have a lower repetition rate of the risk priority calculation results. The risk evaluation results with a lower repetition rate can provide more valuable guidance for the risk prevention measures of oil and gas pipelines. As can be seen from the table, the AHP-CG-ImFCE model has a high reliability in the risk priority calculation results of the earthquake T 15 and airflow T 20 The risk priority calculation results of the AHP-CG-ImFCE model are significantly different from those of the other four models, but the results given by the AHP-CG-ImFCE model meet the Figure 8 The weighted membership matrix of each risk indicator shown in the figure gives the preliminary risk priority information of each risk indicator, with earthquake T 15 For example, earthquake T 15 The membership degree of the fourth risk level is 0.0549, ranking third after internal corrosion T1 and external corrosion T2. 20 Similarly, this is consistent with the risk priority calculation results of the AHP-CG-ImFCE model, which shows that the AHP-CG-ImFCE model has unique advantages in dealing with outliers or special cases and can more accurately identify the true priority of each risk indicator.

[0137] Oil and gas pipeline risk assessment technology is an important way to ensure that oil and gas pipelines can work safely. Aiming at the problem that the experts in the oil and gas pipeline risk assessment process have uncertainty in judging risk indicators due to data loss, the present invention proposes an oil and gas pipeline risk assessment method (AHP-CG-ImFCE) based on the AHP-CG comprehensive weighting method and the improved fuzzy comprehensive evaluation method, which combines the comprehensive weight idea and the matter-element extension theory to improve the accuracy of oil and gas pipeline risk assessment. The AHP-CG-ImFCE model uses Vague fuzzy sets to effectively overcome the influence of expert judgment uncertainty. Based on the Vague fuzzy set utility function and the trapezoidal membership function, the expert weights are fused to calculate the secondary risk index and the tertiary risk index membership matrix for the subsequent use of the fuzzy comprehensive evaluation method. An AHP-CG comprehensive weighting system combining the hierarchical analysis weighting method and the cooperative game weighting method is constructed to obtain more accurate three-level risk index weights, and combined with the three-level risk index membership matrix to obtain the three-level risk index weighted membership matrix. The matter-element extension theory is introduced to calculate the correlation between each three-level risk index and each risk level, and then the relative importance of each risk level is obtained. It is applied to the fuzzy comprehensive evaluation method to improve the judgment principle of the evaluation result, so that the evaluation result is more accurate. Finally, the AHP-CG-ImFCE model is applied to the oil and gas pipeline risk assessment example and compared with four risk assessment models. The evaluation result of the AHP-CG-ImFCE model is more accurate. In addition, the AHP-CG-ImFCE model combines the advantages of Vague fuzzy sets and matter-element extension theory, which can not only avoid the uncertainty of experts' judgment of risk indicators due to data loss in model calculation, but also clearly reflect the relationship between risk indicators and risk levels, which fully demonstrates that the AHP-CG-ImFCE model has strong practicality and feasibility. In the risk assessment of oil and gas pipelines, the AHP-CG-ImFCE model based on the AHP-CG comprehensive weighting method and the improved fuzzy comprehensive evaluation method can effectively deal with the uncertainty of experts' risk index judgment and the inaccurate evaluation results in the process of oil and gas pipeline risk assessment. However, the established risk index system relies on the analysis of previous oil and gas pipeline accident examples, which may increase the subjectivity of the model. The AHP-CG-ImFCE model can be combined with other risk assessment examples with complex processes and multiple risk factors. For different fields, the purpose of evaluation can be achieved by changing the risk index system.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Other modifications or equivalent substitutions made to the technical solution of the present invention by ordinary technicians in the field should be included in the scope of the claims of the present invention as long as they do not depart from the spirit and scope of the technical solution of the present invention.

Claims

1. An oil and gas pipeline risk assessment method based on hierarchical analysis-cooperative game comprehensive weighting method and improved fuzzy comprehensive evaluation method, characterized by: The following steps are involved: Step 1: Set 6 levels of risk indicator evaluation and convert the expert language variables into Vague fuzzy values ​​that are compatible with agreement and opposition information. According to the expert's evaluation results of all risk indicators in the risk index, the risk indicator evaluation matrix is ​​obtained and converted into a Vague value matrix; Step 2: Perform fuzzy comprehensive evaluation and calculate the expert weight W(E j ), the initial single weight of the third-level risk indicator Secondary risk indicator membership matrix And the three-level risk indicator membership matrix Step 3: Perform comprehensive weighting using the AHP-CG method, using the weights calculated using the AHP, entropy weight method, and grayscale correlation method. As a member of the game, the cooperative game method is used to determine the weight W2 of the secondary risk indicator. According to the idea of ​​the comprehensive weight method, the weight of the secondary risk indicator calculated by the cooperative game method is used as the intermediate event weight in the comprehensive weight method. The initial single weight of the third-level risk indicator calculated by the analytic hierarchy process is used as the single event weight in the comprehensive weight method. The intermediate event weight is multiplied by the single event weight to obtain the final comprehensive weight W3 of the third-level risk indicator. Step 4: Based on the weights of the three-level risk indicators obtained in step 3 and the risk levels set in step 1, the classical domain, section domain and matter-element to be evaluated of the three-level risk indicators are determined using the matter-element extension theory, and the comprehensive correlation between the three-level risk indicators and each risk level is calculated; The weights of the three-level risk indicators and their membership matrix are merged to obtain the weighted three-level risk indicator membership matrix, and the weighted three-level risk indicator membership matrix is ​​combined with the comprehensive correlation between risk indicators and risk levels to obtain the three-level risk indicator evaluation result judgment matrix. The comprehensive correlation calculated using the matter-element extension theory is normalized to obtain the relative importance of each risk level, and then the relative importance of each risk level is used to improve the judgment principle of the evaluation result to obtain the final three-level risk indicator evaluation result; Step 5: Conduct a case analysis, relying on the evaluation data of an oil and gas pipeline built in 2002 in a certain oil field, using the risk assessment methods of steps 1 to 4, and relying on the risk indicator system established based on the analysis of historical accident cases of oil and gas pipelines. According to the evaluation results, make effective suggestions for the safety supervision of oil and gas pipelines to verify the accuracy of this method and its strong pertinence to risk factors.

2. The oil and gas pipeline risk assessment method based on hierarchical analysis-cooperative game comprehensive weighting method and improved fuzzy comprehensive evaluation method according to claim 1 is characterized by: The step 1 specifically comprises the following steps: Step 1.1: Conduct expert evaluation The experts’ evaluation levels for risk indicators are set to 6 levels: extremely high impact, large impact, relatively large impact, medium impact, small impact, and extremely small impact. The corresponding Vague values ​​are: L1<0.9,1>, L2<0.8,0.9>, L3<0.6,0.8>, L4<0.4,0.6>, L5<0.2,0.3>, and L6<0.1,0.2>. When n experts evaluate all risk indicators in the risk index, the risk indicator evaluation matrix can be obtained; Through the setting of the Vague values ​​of the above evaluation levels, according to the Vague fuzzy set uncertainty calculation formula: π A (u) = 1-t A (u)-f A (u) It can be obtained that the uncertainty of expert judgment is strictly controlled between 0.1 and 0.2; Step 1.2: Get Vague fuzzy sets After obtaining the expert evaluation data set for risk assessment indicators, the attribute language evaluation term data set is converted into a Vague value matrix A nm .

3. The oil and gas pipeline risk assessment method based on hierarchical analysis-cooperative game comprehensive weighting method and improved fuzzy comprehensive evaluation method according to claim 1 is characterized by: The step 2 specifically includes the following steps: Step 2.1: Calculate expert weights The expert weights are calculated using the arithmetic mean method: Where: WS e and W(E j ) are the weight value and weight factor of expert e respectively; Step 2.2: Use the analytic hierarchy process to calculate the initial single weight of the three-level risk indicators According to the established risk indicator evaluation system with a three-level structure, a two-by-two three-level risk indicator judgment matrix under the secondary risk indicator is constructed as shown in formula R. Then, the three-level risk indicator judgment matrix is ​​subjected to hierarchical single sorting and consistency test. When the matrix satisfies the consistency test, the initial single weight of the three-level risk indicator can be calculated. Step 2.3: Calculate the weighted assessment scores of the three-level risk indicators. The evaluation scores of the three-level risk indicators are obtained according to the utility function of the Vague fuzzy set, and then combined with the expert weights to obtain the weighted evaluation scores of the three-level risk indicators. The formula is: in: Assess the scores for each of the three-level risk indicators; Step 2.4: Calculate the membership matrix of the secondary risk index and the tertiary risk index The trapezoidal membership function is selected as the membership function of the fuzzy number in this paper, and then the membership matrix of each third-level risk indicator is calculated according to the weighted evaluation score of each third-level risk indicator. The fuzzy synthesis result of the initial single weight of the third-level risk indicator and the third-level risk indicator membership matrix is ​​used as the second-level risk indicator membership matrix. The formula is: in: is the fuzzy composition operator, is the three-level risk indicator membership matrix, is the initial single weight vector of the three-level risk indicators.

4. The oil and gas pipeline risk assessment method based on hierarchical analysis-cooperative game comprehensive weighting method and improved fuzzy comprehensive evaluation method according to claim 1 is characterized by: The step 3 specifically includes the following steps: Step 3.1: Calculate the weight of secondary risk indicators using cooperative game method The weights are calculated using three methods: hierarchical analysis method, entropy weight method, and gray correlation method. As a member of the game, the cooperative game method is used to determine the combined weight vector W2, the formula is: Where: w ij is the weight of each indicator in the combined weight vector W2; l (l = 1, ..., 3) is the number of game members, For The other two weights; "—" indicates the average value; T i (i=1,2,...) is the number of third-level risk indicators under the second-level risk indicators; L l is the consistent correlation coefficient; Step 3.2: Calculate the final comprehensive weight of the three-level risk indicators According to the idea of ​​comprehensive weight method, the weight of secondary risk indicator calculated by cooperative game method is used as the intermediate event weight in comprehensive weight method, and the initial single weight of tertiary risk indicator calculated by analytic hierarchy process is used as the single event weight in comprehensive weight method. Finally, the two weights are multiplied to obtain the final comprehensive weight of tertiary risk indicator. The formula is:

5. The oil and gas pipeline risk assessment method based on hierarchical analysis-cooperative game comprehensive weighting method and improved fuzzy comprehensive evaluation method according to claim 1 is characterized by: The step 4 specifically comprises the following steps: Step 4.1: Calculate the comprehensive correlation between the three-level risk indicators and each risk level On the basis of the weights of the three-level risk indicators and the set risk levels, the matter-element extension theory is used to determine the classic domain, section domain and matter-element to be evaluated of the three-level risk indicators, and then the correlation between the three-level risk indicators and each risk level is calculated. The specific steps are as follows: Step 4.1.1, calculate the distance. In the matter-element extension theory, the distance refers to the relative position of a point in the interval. The formula is: Step 4.1.2: Calculate the single index correlation. The single index correlation refers to the correlation of the i-th feature of the object to be evaluated with respect to the classification level j. The formula is: When ρ[y ij ,y iuj -ρ[y ij ,y imj ≠0: When ρ[y ij ,y iuj -ρ[y ij ,y imj =0: K m (c ij )=-ρ[y ij ,y imj ]-1 Step 4.1.3, calculate the comprehensive index correlation degree. The comprehensive index correlation degree refers to the degree to which the evaluation object belongs to each evaluation level. The formula is: K s =∑W3K m (c ij ) Where: s=1,2,…,i, W3 is feature c ij The weight coefficient of . Step 4.2: Introduce matter-element extension theory to improve the judgment principle of comprehensive evaluation results of fuzzy comprehensive evaluation method The comprehensive correlation between each risk indicator and the risk level is calculated using the matter-element extension theory as the relative importance of each risk level, and then the membership of each risk indicator is calculated to make a comprehensive evaluation result judgment. The specific steps are as follows: Step 4.2.1, merge the weights of the three-level risk indicators with their membership matrix to obtain the weighted three-level risk indicator membership matrix; Step 4.2.2, combining the weighted three-level risk indicator membership matrix with the comprehensive correlation between risk indicators and risk levels to obtain the three-level risk indicator evaluation result judgment matrix; Step 4.2.3: Use the improved evaluation result judgment principle to obtain the final three-level risk indicator evaluation result. The calculation formula is as follows: RO=max[Ro] Among them: Ro is the three-level risk indicator evaluation result judgment matrix, RO is the three-level risk indicator priority evaluation result.

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