Risk evaluation method for FPSO single point mooring anchor pile installation

Abstract

The invention relates to the technical field of petroleum ocean engineering risk prediction, in particular to a risk evaluation method for FPSO single point mooring anchor pile installation, which comprises the following steps: identifying a failure mode, and establishing a secondary risk index hierarchical structure; dividing risk levels, constructing a spherical fuzzy linguistic variable table, and obtaining an evaluation matrix of each failure mode under each secondary risk index; calculating a score value of the evaluation matrix of each failure mode under each secondary risk index; determining the comprehensive weight of the second-level risk index; calculating the risk sorting score of each failure mode, and determining the risk sorting of the failure modes according to the risk sorting scores; and risk evaluation is carried out on FPSO single point mooring anchor pile installation. According to the method provided by the invention, the risk assessment result is more reliable, the flexibility of the decision process is improved, the information expression space of the traditional fuzzy set is expanded, and the limitation of the traditional fuzzy set is improved.

Description

Technical Field

[0001] The invention relates to the technical field of risk prediction of petroleum marine engineering, and in particular to a risk assessment method for installation of FPSO single-point mooring anchor piles. Background Art

[0002] As the development of offshore oil and gas resources develops towards the deep sea, floating production storage and offloading (FPSO) has been widely used. As the core component connecting FPSO and underwater production system, the single-point mooring system undertakes core tasks such as well fluid transmission, power and communication transmission. It is an important facility used to resist harsh environment and achieve offshore positioning. In recent years, maritime safety accidents have occurred frequently. As an auxiliary component of the FPSO single-point mooring system, offshore anchor piles are large in size and weight, and the installation process involves complex steps, which has a high operational risk. Most of the existing studies list the known risks and analyze the causes of the offshore installation process of anchor piles, lacking qualitative analysis and ranking evaluation of potential risks. Research on the installation risk of single-point mooring anchor piles focuses on the frequency and consequences of failures during the transportation, lifting and offshore installation of anchor piles, and lacks a systematic risk assessment method for quantitative analysis and evaluation.

[0003] Failure Mode and Effect Analysis (FMEA) is a risk assessment tool that can efficiently predict the occurrence and impact of potential risks. By analyzing the occurrence (O), severity (S) and detection (D) of each failure mode, the product is obtained to obtain the Risk Priority Number (RPN), and then the risk priority ranking is obtained. The FMEA method can be applied to the risk analysis of FPSO single-point mooring anchor installation, comprehensively considering the frequency of occurrence, degree of impact, etc., to obtain a rational risk analysis result. Although traditional FMEA has been applied to many fields, it still has defects such as not considering the uncertainty of evaluation thinking, the relationship between evaluations, and the weight of risk indicators. It is necessary to find a suitable method to solve the existing deficiencies. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a risk assessment method for the installation of FPSO single-point mooring anchor piles. Based on the spherical fuzzy theory, the failure modes and impacts of the installation of FPSO single-point mooring anchor piles are analyzed, which can make the risk assessment results more reliable, improve the flexibility of the decision-making process, expand the information expression space of traditional fuzzy sets, and improve the limitations of traditional fuzzy sets.

[0005] The present invention is achieved through the following technical solutions: A risk assessment method for installation of FPSO single point mooring anchor piles comprises the following steps: S1: Identify the failure modes of the FPSO single-point mooring anchor installation process and establish a secondary risk indicator hierarchy based on the impact of installation risks; S2: Risk indicators in the secondary risk indicator hierarchy are divided into risk levels, and a spherical fuzzy linguistic variable table is constructed according to the risk level, so that each risk level corresponds to a set of spherical fuzzy numbers. Multiple experts evaluate the risk level of each failure mode under each secondary risk indicator, and convert the evaluation into a spherical fuzzy set through linguistic variables to obtain the evaluation matrix of each failure mode under each secondary risk indicator for each expert; S3: Determine the expert weight according to the expert information, aggregate the evaluation matrix of each failure mode under each secondary risk indicator by the spherical fuzzy weighted average operator, and calculate the score value of the evaluation matrix of each failure mode under each secondary risk indicator by using the spherical fuzzy score function; S4: Each expert evaluates the risk level of each first-level risk indicator, uses the indicator importance evaluation method based on spherical fuzzy to determine the relative weight of the first-level risk indicator, and then uses the spherical fuzzy entropy weight method to determine the relative weights of all second-level risk indicators under the first-level risk indicator according to the score value of the evaluation matrix of each failure mode under each second-level risk indicator, and finally obtains the comprehensive weight of all second-level risk indicators; S5: Use the cumulative prospect theory to deal with the irrational decision-making of experts based on the comprehensive weights of all secondary risk indicators, use the combined compromise ideal solution method to calculate the risk ranking score of each failure mode, and determine the risk ranking of the failure mode according to the high and low risk ranking scores; S6: Conduct risk assessment on the installation of FPSO single point mooring anchor piles based on the risk ranking of failure modes.

[0006] Optimized, the secondary risk indicator hierarchy established in step S1 includes 5 primary risk indicators and 12 secondary risk indicators, the 5 primary risk indicators are occurrence, severity, detection, maintenance and prevention, among which the occurrence is subdivided into 2 secondary risk indicators, namely, occurrence frequency and repetition, the severity is subdivided into 4 secondary risk indicators, namely, personnel injury, equipment damage, material reliability, and schedule delay, the detection is subdivided into 2 secondary risk indicators, namely, detection difficulty and collaboration difficulty, the maintenance is subdivided into 2 secondary risk indicators, namely, technical difficulty and maintenance cost, and the prevention is subdivided into 2 secondary risk indicators, namely, backup measures and inspection and prevention costs.

[0007] Optimized, there are 8 failure modes in the FPSO single-point mooring anchor pile installation process identified in step S1, namely, anchor pile collision damage, abnormal ship shaking, pile hammer collision with the hull, sling breakage, operation failure, catenary wire entanglement interference, anchor pile underwater turning failure, hammer rejection or pile slippage.

[0008] Optimally, in step S2, the risk indicators in the secondary risk indicator hierarchy are divided into 9 risk levels, namely, extremely high, very high, high, relatively high, medium, relatively low, low, very low, and extremely low.

[0009] Furthermore, in step S2, the evaluation matrix of each expert for each failure mode under each secondary risk indicator is formula (1): (1); in: Indicates The evaluation matrix of each failure mode under each secondary risk indicator by an expert, Indicates Experts in Under the second-level risk indicator The membership degree of a failure mode, Indicates Experts in Under the second-level risk indicator The non-membership degree of a failure mode, Indicates Experts in Under the second-level risk indicator The hesitation of each failure mode, represents the total number of failure modes, Represents the total number of secondary risk indicators.

[0010] Furthermore, in step S3, the evaluation matrix of each failure mode under each secondary risk indicator by the experts is formula (2): (2); in: Indicates Under the second-level risk indicator The failure mode evaluation matrix is Indicates Under the second-level risk indicator The membership degree of a failure mode, Indicates Under the second-level risk indicator The non-membership degree of a failure mode, Indicates Under the second-level risk indicator The hesitation of each failure mode, represents the total number of experts, represents one of the experts among all experts, represents the weight of one of the experts among all experts, Indicates that all experts are excluded Another expert, Indicates one of the experts among all experts Parameters that affect the situation, Indicates that all experts are excluded another expert other than the one who influences the parameters of the situation, Indicates that all experts are excluded The weight of another expert other than Indicates that one of the experts among all experts Under the second-level risk indicator The membership degree of a failure mode, Indicates that all experts are excluded Another expert Under the second-level risk indicator The membership degree of a failure mode, Indicates that one of the experts among all experts Under the second-level risk indicator The non-membership degree of a failure mode, Indicates that all experts are excluded Another expert Under the second-level risk indicator The non-membership degree of a failure mode, Indicates that one of the experts among all experts Under the second-level risk indicator The hesitation of each failure mode, Indicates that all experts are excluded Another expert Under the second-level risk indicator The hesitation of each failure mode, Represents the parameters that affect the overall relationship of the aggregation process.

[0011] Furthermore, the spherical fuzzy score function in step S3 is formula (3): (3); in: represents the spherical fuzzy score function.

[0012] Further, in step S4, the relative weight of the primary risk indicator is determined as follows: S401: Each expert uses the risk level table to evaluate all first-level risk indicators, and then converts them into corresponding spherical fuzzy numbers to construct the first-level risk indicator evaluation matrix as formula (4): (4); in: represents the first-level risk indicator evaluation matrix, Indicates An expert on The membership degree of the first-level risk indicator is Indicates An expert on The non-membership degree of the first-level risk indicator is Indicates An expert on The hesitation of the first-level risk indicator, Indicates the total number of first-level risk indicators; S402: Calculate the score value in the first-level risk indicator evaluation matrix using a spherical fuzzy score function: S403: Calculate the normalized index evaluation matrix according to formula (5), and calculate the expert weighted index evaluation matrix according to formula (6): (5); (6); in: represents the normalized indicator evaluation matrix, Indicates Experts on The normalized indicator evaluation matrix formed by the evaluation of the first-level risk indicators is: Indicates Experts on The first-level risk indicator evaluation matrix is ​​formed by evaluating the first-level risk indicators. represents the expert weighted indicator evaluation matrix, Indicates Experts on The expert weighted indicator evaluation matrix formed by the evaluation of the first-level risk indicators is Indicates The weight of each expert; S404: According to Experts on The expert weighted indicator evaluation matrix formed by the evaluation of the first-level risk indicators is used to calculate the expert weighted indicator evaluation maximum value matrix and the expert weighted indicator evaluation minimum value matrix respectively, and the expert weighted indicator difference matrix is ​​calculated based on the expert weighted indicator evaluation maximum value matrix and the expert weighted indicator evaluation minimum value matrix; S405: Calculate the first-level risk indicator weight matrix based on the expert weighted indicator difference matrix according to formula (7): (7); in: represents the primary risk indicator weight matrix, represents the expert weighted indicator difference matrix; S406: Calculate the reliability index based on the first-level risk index weight matrix according to formula (8). If the calculated reliability index is less than or equal to 0.1, determine the first-level risk index weight matrix and proceed to the next step. If the calculated reliability index is greater than 0.1, repeat steps S401 to S405 to re-perform index weighting processing until the reliability index is less than or equal to 0.1, then determine the first-level risk index weight matrix and proceed to the next step. (8); in: Represents the reliability index, Represents the second round indicator weight matrix.

[0013] Furthermore, in step S4, the following method is used to obtain the comprehensive weight of the secondary risk indicator: S407: Calculate the average score of the secondary risk indicator evaluation according to formula (9) : (9); in: Indicates The average score of the secondary risk indicator evaluation is Indicates the total number of failure modes; S408: Calculate the first The relative objective weights of each secondary risk indicator under each primary risk indicator: (10); in: Indicates First level risk indicator The relative objective weight of the secondary risk indicators, Indicates The number of secondary risk indicators under the primary risk indicator, Indicates The number of the first-level risk indicator under the first-level risk indicator; S409: Calculate the comprehensive weight of each secondary risk indicator according to formula (11): (11); in: Indicates The comprehensive weight of the secondary risk indicators.

[0014] Further, in step S5, the risk ranking score of each failure mode is calculated using the following method: S501: According to formula (12), construct the positive ideal solution and the negative ideal solution as reference points, and obtain the profit matrix and loss matrix according to formula (13): (12); (13); in: represents the positive ideal solution, represents the maximum value of the spherical fuzzy score function, represents a negative ideal solution, represents the maximum value of the spherical fuzzy score function, represents the payoff matrix, represents the loss matrix, The Euclidean distance representing the spherical fuzzy number; S502: According to formula (14), the value function of the positive ideal solution and the negative ideal solution is obtained: (14); in: represents the value function of the positive ideal solution, represents the value function of the negative ideal solution, represents the risk loss aversion coefficient of the decision maker, represents the concavity of the simulated value function, represents the convexity of the simulated value function; S503: According to formula (15), the weight function of the positive ideal solution and the negative ideal solution is obtained: (15); in: represents the weight function of the positive ideal solution, represents the weight function of the negative ideal solution, Indicates the degree of distortion in risk-return probability assessment, Indicates the degree of distortion in the risk loss probability assessment; S503: Calculate the foreground evaluation matrix according to formula (16), and calculate the normalized foreground evaluation matrix according to formula (17): (16); (17); in: represents the prospect evaluation matrix, Indicates Under the second-level risk indicator The prospect evaluation matrix of failure modes is represents the normalized prospect evaluation matrix, Indicates Under the second-level risk indicator The failure mode normalized prospect evaluation matrix, Indicates Under the second-level risk indicator The minimum value of the failure mode prospect evaluation matrix, Indicates the total number of secondary risk indicators; S504: Calculate the weighted sum of the similarity sequence of each failure mode according to formula (18), and calculate the power weighted sum of the similarity sequence according to formula (19): (18); (19); in: Indicates The weighted sum of similar failure mode sequences, Indicates The power-weighted sum of similar failure mode sequences, Indicates the total number of secondary risk indicators; S505: Calculate the first evaluation score of each failure mode according to formula (20), calculate the second evaluation score of each failure mode according to formula (21), and calculate the third evaluation score of each failure mode according to formula (22): (20); (twenty one); (twenty two); in: Indicates The first evaluation score of the failure mode, Indicates The second evaluation score of the failure mode, Indicates The third evaluation score of the failure mode, Indicates The maximum value of the weighted sum of similar failure mode sequences, Indicates The maximum value of the power-weighted sum of similar failure mode sequences, Indicates the control Parameters of the third evaluation score range of each failure mode; S506: Calculate the risk ranking score of each failure mode according to formula (23): (twenty three); in: Indicates The risk ranking score of each failure mode.

[0015] Beneficial effects of the invention: The present invention provides a risk assessment method for installation of FPSO single-point mooring anchor piles, which has the following advantages: (1) In view of the problem that the traditional FMEA method does not consider the risk indicator weight, the present invention constructs a new two-level risk indicator hierarchy for the target, and uses the spherical fuzzy CIMAS method and entropy weight method to determine the risk indicator weight, which can make the risk assessment result more reliable; (2) In view of the problem that existing research cannot accurately represent expert evaluation information, the present invention uses spherical fuzzy sets to represent expert linguistic evaluation, which can handle the randomness and uncertainty of expert evaluation, while considering the relationship between failure modes, improving the flexibility of the decision-making process, expanding the information expression space of traditional fuzzy sets, and improving the limitations of traditional fuzzy sets; (3) The present invention combines the cumulative prospect theory and the combined compromise ideal solution method and integrates them into the FMEA method. It realizes the risk priority ranking of failure modes through multi-criteria decision-making, improves the traditional RPN calculation method, and makes the evaluation results more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the process of the present invention.

[0017] Figure 2 It is a schematic diagram of the hierarchical structure of the secondary risk indicators of the present invention. DETAILED DESCRIPTION

[0018] A risk assessment method for the installation of FPSO single-point mooring anchor piles, the flow diagram of which is as follows: Figure 1 As shown, the specific steps include: S1: Identify the failure modes of the FPSO single-point mooring anchor installation process and establish a secondary risk indicator hierarchy based on the impact of installation risks; Specifically, eight failure modes were identified during the installation of FPSO single-point mooring anchor piles, namely, anchor pile collision damage ( )、Abnormal ship shaking( ), collision between pile hammer and ship hull ( ), sling rope breakage ( )、Operational Failure( )、catenary winding interference( ), anchor pile failure due to underwater turning over ( ), refusing to hammer or slipping piles ( ).

[0019] The established secondary risk indicator hierarchy includes 5 primary risk indicators and 12 secondary risk indicators. The 5 primary risk indicators are occurrence, severity, detection, maintenance and prevention. The occurrence is subdivided into 2 secondary risk indicators, namely, frequency of occurrence (RF1) and repetition (RF2). The severity is subdivided into 4 secondary risk indicators, namely, personal injury (RF3), equipment damage (RF4), material reliability (RF5) and schedule delay (RF6). The detection is subdivided into 2 secondary risk indicators, namely, detection difficulty (RF7) and collaboration difficulty (RF8). The maintenance is subdivided into 2 secondary risk indicators, namely, technical difficulty (RF9) and maintenance cost (RF10). The prevention is subdivided into 2 secondary risk indicators, namely, backup measures (RF11) and inspection and prevention costs (RF12).

[0020] The established secondary risk indicator hierarchy diagram is as follows: Figure 2 shown.

[0021] S2: Risk indicators in the secondary risk indicator hierarchy are divided into risk levels, and a spherical fuzzy linguistic variable table is constructed according to the risk level, so that each risk level corresponds to a set of spherical fuzzy numbers. Multiple experts evaluate the risk level of each failure mode under each secondary risk indicator, and convert the evaluation into a spherical fuzzy set through linguistic variables to obtain the evaluation matrix of each failure mode under each secondary risk indicator for each expert; Specifically, there are 9 risk levels for the risk indicators in the secondary risk indicator hierarchy, namely extremely high (EH), very high (VH), high (H), relatively high (RH), medium (M), relatively low (RL), low (L), very low (VL), and extremely low (EL).

[0022] Experts can select 5 experts to be invited, namely , , , , .

[0023] Furthermore, in step S2, the evaluation matrix of each expert for each failure mode under each secondary risk indicator is formula (1): (1); in: Indicates The evaluation matrix of each failure mode under each secondary risk indicator by an expert, Indicates Experts in Under the second-level risk indicator The membership degree of a failure mode, Indicates Experts in Under the second-level risk indicator The non-membership degree of a failure mode, Indicates Experts in Under the second-level risk indicator The hesitation of each failure mode, represents the total number of failure modes, Represents the total number of secondary risk indicators.

[0024] The constructed spherical fuzzy linguistic variable table is shown in Table 1: Table 1

[0025] The expert risk level evaluation table for each failure mode under each secondary risk indicator is shown in Table 2: Table 2 S3: Determine the expert weight according to the expert information, aggregate the evaluation matrix of each failure mode under each secondary risk indicator by the spherical fuzzy weighted average operator, and calculate the score value of the evaluation matrix of each failure mode under each secondary risk indicator by using the spherical fuzzy score function; The evaluation matrix of each failure mode under each secondary risk indicator is as follows: (2); in: Indicates Under the second-level risk indicator The failure mode evaluation matrix is Indicates Under the second-level risk indicator The membership degree of a failure mode, Indicates Under the second-level risk indicator The non-membership degree of a failure mode, Indicates Under the second-level risk indicator The hesitation of each failure mode, represents the total number of experts, represents one of the experts among all experts, represents the weight of one of the experts among all experts, Indicates that all experts are excluded Another expert, Indicates one of the experts among all experts Parameters that affect the situation, Indicates that all experts are excluded another expert other than the one who influences the parameters of the situation, Indicates that all experts are excluded The weight of another expert other than Indicates that one of the experts among all experts Under the second-level risk indicator The membership degree of a failure mode, Indicates that all experts are excluded Another expert Under the second-level risk indicator The membership degree of a failure mode, Indicates that one of the experts among all experts Under the second-level risk indicator The non-membership degree of a failure mode, Indicates that all experts are excluded Another expert Under the second-level risk indicator The non-membership degree of a failure mode, Indicates that one of the experts among all experts Under the second-level risk indicator The hesitation of each failure mode, Indicates that all experts are excluded Another expert Under the second-level risk indicator The hesitation of each failure mode, Represents the parameters that affect the overall relationship of the aggregation process.

[0026] Here , , are greater than or equal to 1, and .

[0027] Furthermore, the spherical fuzzy score function in step S3 is formula (3): (3); in: represents the spherical fuzzy score function.

[0028] The comprehensive evaluation of each failure mode by experts under each secondary risk index is shown in Table 3. The score of the evaluation matrix of each failure mode under each secondary risk index is shown in Table 4. The parameters here are , , The value of is 2.

[0029] Table 3 Table 4

[0030] S4: Each expert evaluates the risk level of each first-level risk indicator, uses the indicator importance evaluation method based on spherical fuzzy to determine the relative weight of the first-level risk indicator, and then uses the spherical fuzzy entropy weight method to determine the relative weights of all second-level risk indicators under the first-level risk indicator according to the score value of the evaluation matrix of each failure mode under each second-level risk indicator, and finally obtains the comprehensive weight of all second-level risk indicators; Specifically, the following method can be used to determine the relative weight of each primary risk indicator: S401: Each expert uses the risk level table to evaluate all first-level risk indicators, and then converts them into corresponding spherical fuzzy numbers to construct the first-level risk indicator evaluation matrix as formula (4): (4); in: represents the first-level risk indicator evaluation matrix, Indicates An expert on The membership degree of the first-level risk indicator is Indicates An expert on The non-membership degree of the first-level risk indicator is Indicates An expert on The hesitation of the first-level risk indicator, Indicates the total number of first-level risk indicators; Specifically, the evaluation results of each expert on all first-level risk indicators are shown in Table 5: Table 5

[0031] S402: Calculate the score value in the primary risk indicator evaluation matrix using the spherical fuzzy score function: The calculation here is the same as formula (3).

[0032] S403: Calculate the normalized index evaluation matrix according to formula (5), and calculate the expert weighted index evaluation matrix according to formula (6): (5); (6); in: represents the normalized indicator evaluation matrix, Indicates Experts on The normalized indicator evaluation matrix formed by the evaluation of the first-level risk indicators is: Indicates Experts on The first-level risk indicator evaluation matrix is ​​formed by evaluating the first-level risk indicators. represents the expert weighted indicator evaluation matrix, Indicates Experts on The expert weighted indicator evaluation matrix formed by the evaluation of the first-level risk indicators is Indicates The weight of each expert; Specifically, the expert weighted indicator evaluation matrix is ​​shown in Table 6: Table 6

[0033] S404: According to Experts on The expert weighted indicator evaluation matrix formed by the evaluation of the first-level risk indicators is used to calculate the expert weighted indicator evaluation maximum value matrix and the expert weighted indicator evaluation minimum value matrix respectively, and the expert weighted indicator difference matrix is ​​calculated based on the expert weighted indicator evaluation maximum value matrix and the expert weighted indicator evaluation minimum value matrix; The indicator difference moment here can be calculated by the following formula: ; in: represents the expert weighted indicator difference matrix, represents the maximum value matrix of expert weighted index evaluation, represents the minimum value matrix of expert weighted index evaluation; S405: Calculate the first-level risk indicator weight matrix based on the expert weighted indicator difference matrix according to formula (7): (7); in: represents the primary risk indicator weight matrix, represents the expert weighted indicator difference matrix; The specific first-level risk indicator weight matrix is ​​shown in Table 7: Table 7

[0034] S406: Calculate the reliability index based on the first-level risk index weight matrix according to formula (8). If the calculated reliability index is less than or equal to 0.1, determine the first-level risk index weight matrix and proceed to the next step. If the calculated reliability index is greater than 0.1, repeat steps S401 to S405 to re-perform index weighting processing until the reliability index is less than or equal to 0.1, then determine the first-level risk index weight matrix and proceed to the next step. (8); in: Represents the reliability index, Represents the second round indicator weight matrix.

[0035] After calculating the reliability index here, a consistency analysis is required; all experts are required to re-evaluate the relative weights of each first-level risk index using a percentage ratio, and the cumulative sum is 1; the second round of index weighting is performed based on the evaluation, and the average value is used to calculate and obtain the second round of index weight matrix; Specifically, the secondary evaluation of the first-level risk indicator experts is shown in Table 8: Table 8

[0036] Furthermore, in step S4, the spherical fuzzy entropy weight method is used to obtain the comprehensive weight of the secondary risk index. The specific method is as follows: S407: Calculate the average score of the secondary risk indicator evaluation according to formula (9) : (9); in: Indicates The average score of the secondary risk indicator evaluation is Indicates the total number of failure modes; Specifically, the average scores of the secondary risk indicator evaluation are shown in Table 9: Table 9

[0037] S408: Calculate the first The relative objective weights of each secondary risk indicator under each primary risk indicator: (10); in: Indicates First level risk indicator The relative objective weight of the secondary risk indicators, Indicates The number of secondary risk indicators under the primary risk indicator, Indicates The number of the starting secondary risk indicator under the first-level risk indicator; taking the second first-level risk indicator as an example, since the number of secondary risk indicators under the first first-level risk indicator is 2, the starting secondary risk indicator number under the second first-level risk indicator is 3.

[0038] S409: Calculate the comprehensive weight of each secondary risk indicator according to formula (11): (11); in: Indicates The comprehensive weight of the secondary risk indicators.

[0039] Specifically, the comprehensive weights of the secondary risk indicators are shown in Table 10: Table 10

[0040] S5: The cumulative prospect theory is used to deal with the irrational decision-making of experts according to the comprehensive weight of all secondary risk indicators, and the combined compromise ideal solution method (CoCoFISo) is used to calculate the risk ranking score of each failure mode, and the risk ranking of the failure mode is determined according to the high and low risk ranking scores; Further, in step S5, the risk ranking score of each failure mode is calculated using the following method: S501: According to formula (12), construct the positive ideal solution and the negative ideal solution as reference points, and obtain the profit matrix and loss matrix according to formula (13): (12); (13); in: represents the positive ideal solution, represents the maximum value of the spherical fuzzy score function, represents a negative ideal solution, represents the maximum value of the spherical fuzzy score function, represents the payoff matrix, represents the loss matrix, The Euclidean distance representing the spherical fuzzy number; Specifically, the constructed positive ideal solution and negative ideal solution are shown in Table 11: Table 11

[0041] S502: According to formula (14), the value function of the positive ideal solution and the negative ideal solution is obtained: (14); in: represents the value function of the positive ideal solution, represents the value function of the negative ideal solution, represents the risk loss aversion coefficient of the decision maker, represents the concavity of the simulated value function, represents the convexity of the simulated value function; and , , .

[0042] S503: According to formula (15), the weight function of the positive ideal solution and the negative ideal solution is obtained: (15); in: represents the weight function of the positive ideal solution, represents the weight function of the negative ideal solution, Indicates the degree of distortion in risk-return probability assessment, Indicates the degree of distortion in the risk loss probability assessment; And there is , .

[0043] S503: Calculate the foreground evaluation matrix according to formula (16), and calculate the normalized foreground evaluation matrix according to formula (17): (16); (17); in: represents the prospect evaluation matrix, Indicates Under the second-level risk indicator The prospect evaluation matrix of failure modes is represents the normalized prospect evaluation matrix, Indicates Under the second-level risk indicator The failure mode normalized prospect evaluation matrix, Indicates Under the second-level risk indicator The minimum value of the failure mode prospect evaluation matrix, Indicates the total number of secondary risk indicators; Specifically, the calculated prospect evaluation matrix is ​​shown in Table 12, with parameters The value is 0.88, parameter The value is 0.88, parameter The value is 2.25, parameter The value is 0.61, parameter The value is 0.72: Table 12 S504: Calculate the weighted sum of the similarity sequence of each failure mode according to formula (18), and calculate the power weighted sum of the similarity sequence according to formula (19): (18); (19); in: Indicates The weighted sum of similar failure mode sequences, Indicates The power-weighted sum of similar failure mode sequences, Indicates the total number of secondary risk indicators; The weighted sum of similarity sequences and the weighted sum of similarity powers for each failure mode are shown in Table 13: Table 13

[0044] S505: Calculate the first evaluation score of each failure mode according to formula (20), calculate the second evaluation score of each failure mode according to formula (21), and calculate the third evaluation score of each failure mode according to formula (22): (20); (twenty one); (twenty two); in: Indicates The first evaluation score of the failure mode, Indicates The second evaluation score of the failure mode, Indicates The third evaluation score of the failure mode, Indicates The maximum value of the weighted sum of similar failure mode sequences, Indicates The maximum value of the power-weighted sum of similar failure mode sequences, Indicates the control The parameter of the third evaluation score range of each failure mode, ; The three evaluation scores of each failure mode are shown in Table 14. The value is 0.5: Table 14

[0045] S506: Calculate the risk ranking score of each failure mode according to formula (23): (twenty three); in: Indicates The risk ranking score of each failure mode.

[0046] The risk ranking scores of each failure mode are shown in Table 15: Table 15

[0047] S6: Conduct risk assessment on the installation of FPSO single point mooring anchor piles based on the risk ranking of failure modes.

[0048] The larger the risk ranking score of each failure mode, the greater the potential risk of the failure mode.

[0049] The above evaluation results show that the final ranking of the 8 failure mode risks is: Therefore, for the risks in the installation process of FPSO single-point mooring anchor piles, operational failure, hammer refusal or pile slippage are the main failure modes, and they should be paid special attention to during the engineering installation process to prevent risks from occurring.

[0050] In summary, the present invention provides a risk assessment method for the installation of FPSO single-point mooring anchor piles. Based on the spherical fuzzy theory, the failure modes and impacts of the installation of FPSO single-point mooring anchor piles are analyzed, which can make the risk assessment results more reliable, improve the flexibility of the decision-making process, expand the information expression space of traditional fuzzy sets, and improve the limitations of traditional fuzzy sets.

[0051] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A risk assessment method for installation of FPSO single point mooring anchor piles, characterized in that: The steps include: S1: Identify the failure modes of the FPSO single-point mooring anchor installation process and establish a secondary risk indicator hierarchy based on the impact of installation risks; S2: Risk indicators in the secondary risk indicator hierarchy are divided into risk levels, and a spherical fuzzy linguistic variable table is constructed according to the risk level, so that each risk level corresponds to a set of spherical fuzzy numbers. Multiple experts evaluate the risk level of each failure mode under each secondary risk indicator, and convert the evaluation into a spherical fuzzy set through linguistic variables to obtain the evaluation matrix of each failure mode under each secondary risk indicator for each expert; S3: Determine the expert weight according to the expert information, aggregate the evaluation matrix of each failure mode under each secondary risk indicator by the spherical fuzzy weighted average operator, and calculate the score value of the evaluation matrix of each failure mode under each secondary risk indicator by using the spherical fuzzy score function; S4: Each expert evaluates the risk level of each first-level risk indicator, uses the indicator importance evaluation method based on spherical fuzzy to determine the relative weight of the first-level risk indicator, and then uses the spherical fuzzy entropy weight method to determine the relative weights of all second-level risk indicators under the first-level risk indicator according to the score value of the evaluation matrix of each failure mode under each second-level risk indicator, and finally obtains the comprehensive weight of all second-level risk indicators; S5: Use the cumulative prospect theory to deal with the irrational decision-making of experts based on the comprehensive weights of all secondary risk indicators, use the combined compromise ideal solution method to calculate the risk ranking score of each failure mode, and determine the risk ranking of the failure mode according to the high and low risk ranking scores; S6: Conduct risk assessment on the installation of FPSO single point mooring anchor piles based on the risk ranking of failure modes.

2. A risk assessment method for installation of FPSO single point mooring anchor piles according to claim 1, characterized in that: The secondary risk indicator hierarchy established in step S1 includes 5 primary risk indicators and 12 secondary risk indicators. The five first-level risk indicators are occurrence, severity, detection, maintenance and prevention. The occurrence is subdivided into two second-level risk indicators, namely occurrence frequency and repetition. The severity is subdivided into four second-level risk indicators, namely personnel injury, equipment damage, material reliability and schedule delay. The detection is subdivided into two second-level risk indicators, namely detection difficulty and collaboration difficulty. The maintenance is subdivided into two second-level risk indicators, namely technical difficulty and maintenance cost. The prevention is subdivided into two second-level risk indicators, namely backup measures and inspection and prevention costs.

3. A risk assessment method for installation of FPSO single point mooring anchor piles according to claim 1, characterized in that: There are 8 failure modes in the FPSO single-point mooring anchor pile installation process identified in step S1, namely, anchor pile collision damage, abnormal ship shaking, pile hammer collision with the hull, sling breakage, operation failure, catenary wire entanglement interference, anchor pile underwater turning failure, hammer refusal or pile slippage.

4. A risk assessment method for installation of FPSO single point mooring anchor piles according to claim 1, characterized in that: In step S2, the risk indicators in the secondary risk indicator hierarchy are divided into 9 risk levels, namely, extremely high, very high, high, relatively high, medium, relatively low, low, very low, and extremely low.

5. The risk assessment method for installation of FPSO single point mooring anchor piles according to claim 1, characterized in that: In step S2, the evaluation matrix of each expert for each failure mode under each secondary risk indicator is formula (1): （1）； in: Indicates The evaluation matrix of each failure mode under each secondary risk indicator by an expert, Indicates Experts in Under the second-level risk indicator The membership degree of a failure mode, Indicates Experts in Under the second-level risk indicator The non-membership degree of a failure mode, Indicates Experts in Under the second-level risk indicator The hesitation of each failure mode, represents the total number of failure modes, Represents the total number of secondary risk indicators.

6. A risk assessment method for installation of FPSO single point mooring anchor piles according to claim 1, characterized in that: In step S3, the evaluation matrix of each failure mode under each secondary risk indicator is as follows: （2）； in: Indicates Under the second-level risk indicator The failure mode evaluation matrix is Indicates Under the second-level risk indicator The membership degree of a failure mode, Indicates Under the second-level risk indicator The non-membership degree of a failure mode, Indicates Under the second-level risk indicator The hesitation of each failure mode, represents the total number of experts, represents one of the experts among all experts, represents the weight of one of the experts among all experts, Indicates that all experts are excluded Another expert, Indicates one of the experts among all experts Parameters that affect the situation, Indicates that all experts are excluded another expert other than the one who influences the parameters of the situation, Indicates that all experts are excluded The weight of another expert other than Indicates that one of the experts among all experts Under the second-level risk indicator The membership degree of a failure mode, Indicates that all experts are excluded Another expert Under the second-level risk indicator The membership degree of a failure mode, Indicates that one of the experts among all experts Under the second-level risk indicator The non-membership degree of a failure mode, Indicates that all experts are excluded Another expert Under the second-level risk indicator The non-membership degree of a failure mode, Indicates that one of the experts among all experts Under the second-level risk indicator The hesitation of each failure mode, Indicates that all experts are excluded Another expert Under the second-level risk indicator The hesitation of each failure mode, Represents the parameters that affect the overall relationship of the aggregation process.

7. A risk assessment method for installation of FPSO single point mooring anchor piles according to claim 6, characterized in that: The spherical fuzzy score function in step S3 is formula (3): （3）； in: represents the spherical fuzzy score function.

8. A risk assessment method for installation of FPSO single point mooring anchor piles according to claim 7, characterized in that: In step S4, the relative weight of the primary risk indicator is determined as follows: S401: Each expert uses the risk level table to evaluate all first-level risk indicators, and then converts them into corresponding spherical fuzzy numbers to construct the first-level risk indicator evaluation matrix as formula (4): （4）； in: represents the first-level risk indicator evaluation matrix, Indicates Experts on The membership degree of the first-level risk indicator is Indicates Experts on The non-membership degree of the first-level risk indicator is Indicates Experts on The hesitation of the first-level risk indicator, Indicates the total number of first-level risk indicators; S402: Calculate the score value in the first-level risk indicator evaluation matrix using a spherical fuzzy score function: S403: Calculate the normalized index evaluation matrix according to formula (5), and calculate the expert weighted index evaluation matrix according to formula (6): （5）； （6）； in: represents the normalized indicator evaluation matrix, Indicates Experts on The normalized indicator evaluation matrix formed by the evaluation of the first-level risk indicators is: Indicates Experts on The first-level risk indicator evaluation matrix is ​​formed by evaluating the first-level risk indicators. represents the expert weighted indicator evaluation matrix, Indicates Experts on The expert weighted indicator evaluation matrix formed by the evaluation of the first-level risk indicators is Indicates The weight of each expert; S404: According to Experts on The expert weighted indicator evaluation matrix formed by the evaluation of the first-level risk indicators is used to calculate the expert weighted indicator evaluation maximum value matrix and the expert weighted indicator evaluation minimum value matrix respectively, and the expert weighted indicator difference matrix is ​​calculated based on the expert weighted indicator evaluation maximum value matrix and the expert weighted indicator evaluation minimum value matrix; S405: Calculate the first-level risk indicator weight matrix based on the expert weighted indicator difference matrix according to formula (7): （7）； in: represents the primary risk indicator weight matrix, represents the expert weighted indicator difference matrix; S406: Calculate the reliability index based on the first-level risk index weight matrix according to formula (8). If the calculated reliability index is less than or equal to 0.1, determine the first-level risk index weight matrix and proceed to the next step. If the calculated reliability index is greater than 0.1, repeat steps S401 to S405 to re-perform index weighting processing until the reliability index is less than or equal to 0.1, then determine the first-level risk index weight matrix and proceed to the next step. （8）； in: Represents the reliability index, Represents the second round indicator weight matrix.

9. A risk assessment method for installation of FPSO single point mooring anchor piles according to claim 8, characterized in that: In step S4, the comprehensive weight of the secondary risk indicator is obtained by the following method: S407: Calculate the average score of the secondary risk indicator evaluation according to formula (9) : （9）； in: Indicates The average score of the secondary risk indicator evaluation is Indicates the total number of failure modes; S408: Calculate the first The relative objective weights of each secondary risk indicator under each primary risk indicator: （10）； in: Indicates First level risk indicator The relative objective weight of the secondary risk indicators, Indicates The number of secondary risk indicators under the primary risk indicator, Indicates The number of the first-level risk indicator under the first-level risk indicator; S409: Calculate the comprehensive weight of each secondary risk indicator according to formula (11): （11）； in: Indicates The comprehensive weight of the secondary risk indicators.

10. A risk assessment method for installation of FPSO single point mooring anchor piles according to claim 9, characterized in that: In step S5, the risk ranking score of each failure mode is calculated using the following method: S501: According to formula (12), construct the positive ideal solution and the negative ideal solution as reference points, and obtain the profit matrix and loss matrix according to formula (13): （12）； （13）； in: represents the positive ideal solution, represents the maximum value of the spherical fuzzy score function, represents a negative ideal solution, represents the maximum value of the spherical fuzzy score function, represents the payoff matrix, represents the loss matrix, The Euclidean distance representing the spherical fuzzy number; S502: According to formula (14), the value function of the positive ideal solution and the negative ideal solution is obtained: （14）； in: represents the value function of the positive ideal solution, represents the value function of the negative ideal solution, represents the risk loss aversion coefficient of the decision maker, represents the concavity of the simulated value function, represents the convexity of the simulated value function; S503: According to formula (15), the weight function of the positive ideal solution and the negative ideal solution is obtained: （15）； in: represents the weight function of the positive ideal solution, represents the weight function of the negative ideal solution, Indicates the degree of distortion in risk-return probability assessment, Indicates the degree of distortion in the risk loss probability assessment; S503: Calculate the foreground evaluation matrix according to formula (16), and calculate the normalized foreground evaluation matrix according to formula (17): (16); （17）； in: represents the prospect evaluation matrix, Indicates Under the second-level risk indicator The prospect evaluation matrix of failure modes is represents the normalized prospect evaluation matrix, Indicates Under the second-level risk indicator The failure mode normalized prospect evaluation matrix, Indicates Under the second-level risk indicator The minimum value of the failure mode prospect evaluation matrix, Indicates the total number of secondary risk indicators; S504: Calculate the weighted sum of similarity sequences of each failure mode according to formula (18) ， Calculate the power-weighted sum of similar sequences according to formula (19): ： （18）； （19）； in: Indicates The weighted sum of similar failure mode sequences, Indicates The power-weighted sum of similar failure mode sequences, Indicates the total number of secondary risk indicators; S505: Calculate the first evaluation score of each failure mode according to formula (20), calculate the second evaluation score of each failure mode according to formula (21), and calculate the third evaluation score of each failure mode according to formula (22): （20）； （21）； (22)； in: Indicates The first evaluation score of the failure mode, Indicates The second evaluation score of the failure mode, Indicates The third evaluation score of the failure mode, Indicates The maximum value of the weighted sum of similar failure mode sequences, Indicates The maximum value of the power-weighted sum of similar failure mode sequences, Indicates the control Parameters of the third evaluation score range of each failure mode; S506: Calculate the risk ranking score of each failure mode according to formula (23): （23）； in: Indicates The risk ranking score of each failure mode.

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