A method for evaluating manufacturing risks of a nuclear power equipment value chain

Abstract

The application discloses a kind of nuclear power equipment value chain manufacturing risk assessment methods.The present application determines risk group and risk factor in nuclear power equipment manufacturing from risk database;Adopt hesitating fuzzy language term set method, define a set of risk grade evaluation language set, define semantic function as conversion standard;According to risk group, risk factor and risk grade evaluation language set, obtain the evaluation information of each nuclear power equipment value chain manufacturing risk factor from risk database;The evaluation information is converted into triangular fuzzy number and aggregated, the risk importance and risk grade of each risk factor and risk group are determined, and the overall risk importance and risk grade are determined.The present application effectively and accurately processes fuzzy information in risk assessment, comprehensively analyzes the risk importance of each risk factor and risk group, and provides a systematic method for developing nuclear power equipment value chain manufacturing risk assessment.

Description

Technical Field

[0001] This invention relates to a value chain manufacturing risk assessment method, specifically a value chain manufacturing risk assessment method for nuclear power equipment. Background Technology

[0002] Global environmental problems are becoming increasingly severe, and the energy structure is shifting towards cleaner and lower-carbon energy. Nuclear power is a clean and efficient energy source, and its development has become an important national strategy for my country. In promoting the innovative, safe, and scientific development of nuclear power, the nuclear power equipment manufacturing industry is facing diversified value chain manufacturing risks. There is a lack of relevant risk assessment research, a lack of theoretical guidance on value chain manufacturing risk assessment, and an immature value chain manufacturing risk management system for nuclear power equipment. Furthermore, there are few domestic and international case studies available for reference.

[0003] Fuzzy language methods offer a solution for risk assessment, but in traditional linguistic decision-making frameworks, the representation of linguistic information is very limited because information must be expressed in predefined terms. The hesitant fuzzy linguistic term set (HFLTS) method allows the simultaneous use of multiple consecutive terms, aligning with the uncertainty and hesitation inherent in human language assessment, and thus improving the accuracy of risk assessment.

[0004] Fuzzy comprehensive evaluation is a method based on fuzzy mathematics and utilizing the principle of fuzzy relation synthesis to quantify factors with unclear boundaries and non-quantitative characteristics, and to comprehensively evaluate the membership degree of the evaluated object from multiple factors. Membership degree theory can transform qualitative evaluation into quantitative evaluation, has strong practicality for various uncertain problems, and has the advantages of clear results and strong systematicity. It can solve fuzzy and difficult-to-quantify problems.

[0005] In conclusion, it is of great significance to study a method that can effectively assess the importance of manufacturing risks in the nuclear power equipment value chain. Summary of the Invention

[0006] To address the aforementioned problems, this invention provides a method for risk assessment in the nuclear power equipment value chain manufacturing process. This invention identifies risk groups and risk factors in nuclear power equipment manufacturing from a risk database; defines a risk level assessment language set based on a hesitant fuzzy language terminology set, and defines a semantic function as a transformation standard; based on the risk groups, risk factors, and risk level assessment language set, it obtains assessment information for each nuclear power equipment value chain manufacturing risk factor from the risk database; it transforms the assessment information into triangular fuzzy numbers and aggregates them to determine the risk importance and risk level of each risk factor and risk group, and ultimately determines the overall risk importance and risk level.

[0007] The specific technical solution of the present invention is as follows:

[0008] 1) Based on the risk database of nuclear power equipment, determine the three-dimensional assessment data corresponding to each risk factor in the manufacturing of nuclear power equipment. The three-dimensional assessment data include the probability of occurrence assessment data, the degree of impact assessment data, and the degree of uncontrollability assessment data. Each risk factor forms a different risk group, and a risk list is constructed from the risk groups and risk factors.

[0009] 2) The hesitant fuzzy language term set method is used to fuzz the occurrence probability assessment data, impact assessment data, and uncontrollability assessment data corresponding to each risk factor, respectively, to obtain the occurrence probability, impact assessment value, and uncontrollability assessment value corresponding to each risk factor. Then, the geometric mean of the occurrence probability assessment value, impact assessment value, and uncontrollability assessment value corresponding to each risk factor is calculated to obtain the risk importance assessment value of each risk factor. Finally, the risk importance assessment value of each risk factor is converted into a risk level assessment language value according to the semantic function and used as the final assessment value of each risk factor.

[0010] 3) Based on the probability of occurrence assessment value, impact assessment value and uncontrollability assessment value corresponding to each risk factor, calculate and obtain the probability of occurrence assessment value, impact assessment value and uncontrollability assessment value corresponding to each risk group. Then calculate the geometric mean of the probability of occurrence assessment value, impact assessment value and uncontrollability assessment value corresponding to each risk group to obtain the risk importance assessment value of each risk group, thereby determining the final assessment value of each risk group.

[0011] 4) Based on the probability of occurrence assessment value, impact assessment value, and uncontrollability assessment value corresponding to each risk group, calculate and obtain the probability of occurrence assessment value, impact assessment value, and uncontrollability assessment value corresponding to the overall risk. Then calculate the geometric mean of the probability of occurrence assessment value, impact assessment value, and uncontrollability assessment value corresponding to the overall risk to obtain the risk importance assessment value of the overall risk. Thus, determine the final assessment value of the overall risk. The final assessment values ​​of each risk factor, the final assessment values ​​of each risk group, and the final assessment value of the overall risk are used together as the risk assessment result.

[0012] In step 2), the three-dimensional assessment data corresponding to each risk factor are transformed into fuzzy numbers to obtain the three-dimensional triangular fuzzy numbers corresponding to each risk factor. Each dimension assessment data corresponding to each risk factor contains one or more risk level assessment linguistic values. When there is only one risk level assessment linguistic value in each dimension assessment data, the assessment value corresponding to the current dimension is the triangular fuzzy number corresponding to the current dimension. When there are multiple risk level assessment linguistic values ​​in each dimension assessment data, the triangular fuzzy number obtained by weighted summation of the triangular fuzzy numbers corresponding to the current dimension is used as the assessment value corresponding to the current dimension.

[0013] In step 3), the weight of the risk factor corresponding to each risk group is calculated based on the risk list. The calculation formula is as follows:

[0014]

[0015]

[0016]

[0017] Among them, W PO-i Let be the weight of the probability of occurrence of the i-th risk factor in the corresponding risk group; i is the risk factor number; k is the minimum risk factor number in the current group; l is the maximum risk factor number in the current group; W MI-i W represents the weight of the influence of the i-th risk factor in the risk group. UL-i T represents the weight of the uncontrollability of the i-th risk factor in the risk group; PO-i T represents the probability assessment value of the occurrence of the i-th risk factor; MI-i T represents the assessment value of the impact of the i-th risk factor; UL-i This is the assessment value for the uncontrollability of the i-th risk factor;

[0018] Based on the weights of the risk factors corresponding to each risk group and the assessment values ​​of the probability of occurrence, the degree of impact, and the degree of uncontrollability for each risk factor, the assessment values ​​of the probability of occurrence, the degree of impact, and the degree of uncontrollability for each risk group are calculated using the following formulas:

[0019]

[0020]

[0021]

[0022] Among them, T' PO-j T' is the probability assessment value for the occurrence of the j-th risk group; j is the risk group number; T' MI-j T' represents the impact assessment value for the j-th risk group; UL-j This is the assessment value for the uncontrollability of the j-th risk group.

[0023] In step 4), the weight of each risk group in the overall risk is calculated based on the probability of occurrence assessment value, the degree of impact assessment value, and the degree of uncontrollability assessment value corresponding to each risk group. The calculation formula is as follows:

[0024]

[0025]

[0026]

[0027] Among them, W' PO-j Let be the weight of the occurrence probability of the j-th risk group in the overall risk; r is the minimum number of the risk group; s is the maximum number of the risk group; W' MI-j W' represents the weight of the impact of the j-th risk group in the overall risk; UL-j Let be the weight of the uncontrollability of the j-th risk group in the overall risk;

[0028] Based on the weight of each risk group in the overall risk, the probability of occurrence, the degree of impact, and the degree of uncontrollability of the overall risk are calculated using the following formulas:

[0029]

[0030]

[0031]

[0032] Among them, T' PO-all T' is the overall probability assessment value for the occurrence of risk; MI-all This is the overall risk impact assessment value; T' UL-all This is an assessment value for the degree of uncontrollability of the overall risk.

[0033] When there are two risk level assessment linguistic values ​​in each dimension's assessment data, the weights of the triangular fuzzy numbers corresponding to the current dimension are set to 0.5 and 0.5 according to the order of the risk level assessment linguistic values; when there are three risk level assessment linguistic values ​​in each dimension's assessment data, the weights of the triangular fuzzy numbers corresponding to the current dimension are set to 0.25, 0.5, and 0.25 according to the order of the risk level assessment linguistic values.

[0034] The beneficial effects of this invention are as follows:

[0035] This invention effectively and accurately handles fuzzy information in risk assessment, comprehensively analyzes the risk importance of each risk factor and risk group, and provides a systematic method for conducting manufacturing risk assessment of nuclear power equipment value chain. Attached Figure Description

[0036] Figure 1 This is a flowchart of the method of the present invention.

[0037] Figure 2 This is a semantic function graph. Detailed Implementation

[0038] The present invention will be further described below. Obviously, the following embodiments are only used to more clearly illustrate the technical solution of the present invention and cannot be used to limit the scope of protection of the present invention.

[0039] This embodiment uses the manufacturing process of a certain nuclear power equipment as an example for case study.

[0040] like Figure 1 As shown, the present invention includes the following steps:

[0041] 1) Based on the risk database of nuclear power equipment, determine the three-dimensional assessment data corresponding to each risk factor in the manufacturing of nuclear power equipment. The three-dimensional assessment data include probability of occurrence (PO) assessment data, magnitude of impact (MI) assessment data, and uncontrollable level (UL) assessment data. Each risk factor forms a different risk group, and a risk list is constructed from the risk groups and risk factors.

[0042] In this embodiment, the manufacturing risk list of the nuclear power equipment value chain includes 5 risk groups, namely, technical risks, equipment risks, process risks, safety risks, and other risks;

[0043] Among them, technical risks include equipment design change rate, nuclear power information transmission efficiency, and large forging manufacturing qualification rate; equipment risks include workshop equipment load rate, equipment reliability index, and equipment commissioning index; process risks include nuclear power production line layout index, process evaluation and testing index, and new process breakthrough and application index; safety risks include industry major accident incidence rate, nuclear safety planning index, and emergency response index; and other risks include nuclear power equipment manufacturing resource planning index, nuclear power equipment manufacturing environment index, and regional energy supply index, totaling 15 risk factors.

[0044] The hesitant fuzzy language terminology set method is adopted, defining a set of language arrays as the risk level assessment language set for risk factors. In this embodiment, the assessment set S = {very low, low, slightly low, moderate, slightly high, high, very high} is defined, and a semantic function is defined as the conversion standard. The semantic function is as follows: Figure 2 As shown;

[0045] The conversion rules are as follows:

[0046] Very low = (0, 0, 0.17), Low = (0, 0.17, 0.33), Slightly low = (0.17, 0.33, 0.5), Medium = (0.33, 0.5, 0.67), Slightly high = (0.5, 0.67, 0.83), High = (0.67, 0.83, 1), Very high = (0.83, 1, 1);

[0047] 2) The hesitant fuzzy language term set method is used to fuzz the occurrence probability assessment data, impact assessment data, and uncontrollability assessment data corresponding to each risk factor, respectively, to obtain the occurrence probability, impact assessment value, and uncontrollability assessment value corresponding to each risk factor. Then, the geometric mean of the occurrence probability assessment value, impact assessment value, and uncontrollability assessment value corresponding to each risk factor is calculated to obtain the risk criticality (RC) assessment value of each risk factor. Finally, the risk criticality assessment value of each risk factor is converted into a risk level assessment language value according to the semantic function and used as the final assessment value of each risk factor.

[0048] In step 2), the three-dimensional assessment data corresponding to each risk factor are transformed into fuzzy numbers to obtain the three-dimensional triangular fuzzy numbers (i.e., the probability of occurrence triangular fuzzy number, the degree of influence triangular fuzzy number, and the degree of uncontrollability triangular fuzzy number) for each risk factor. Each dimension's assessment data for each risk factor contains one or more risk level assessment linguistic values. When each dimension's assessment data contains only one risk level assessment linguistic value, the assessment value for that dimension is the triangular fuzzy number corresponding to that dimension. When each dimension's assessment data contains multiple risk level assessment linguistic values, the weighted sum of the triangular fuzzy numbers corresponding to that dimension is used as the assessment value for that dimension. Specifically:

[0049] A risk level assessment linguistic value, once converted into a triangular fuzzy number, does not require aggregation.

[0050] After converting the two risk level assessment linguistic values ​​into triangular fuzzy numbers, the weights of the two triangular fuzzy numbers are each 0.5, as shown in the following formula:

[0051] T PO-in =0.5T PO-in-a +0.5T PO-in-b

[0052] T ML-in =0.5T ML-in-a +0.5T ML-in-b

[0053] T UL-in =0.5T UL-in-a +0.5T UL-in-b

[0054] Among them, T PO-in Let T be the triangular fuzzy number representing the probability of the i-th risk factor occurring in the n-th data set; i is the risk factor number; n is the number of data sets; T PO-in-a T is the triangular fuzzy number representing the transformation of the first risk level assessment linguistic value of the probability of occurrence of the i-th risk factor in the n-th data set;PO-in-b T is the triangular fuzzy number derived from the second risk level assessment linguistic value of the probability of occurrence of the i-th risk factor in the n-th data set; MI-in T is the triangular fuzzy number representing the degree of influence of the i-th risk factor in the n-th data set; ML-in-a T is the triangular fuzzy number representing the transformation of the first risk level assessment linguistic value of the influence degree of the i-th risk factor in the n-th data set; ML-in-b T is the triangular fuzzy number derived from the linguistic value of the second risk level assessment of the influence of the i-th risk factor in the n-th data set; UL-in T is the triangular fuzzy number representing the degree of uncontrollability of the i-th risk factor in the n-th data set; UL-in-a T is the triangular fuzzy number representing the transformation of the first risk level assessment linguistic value of the uncontrollability of the i-th risk factor in the n-th data set; UL-in-b The triangular fuzzy number is the linguistic value of the second risk level assessment of the uncontrollability of the i-th risk factor in the n-th data set.

[0055] When converting the three risk level assessment linguistic values ​​into triangular fuzzy numbers, the weights are 0.25, 0.5, and 0.25 respectively, as shown in the following formula:

[0056] T PO-in =0.25T PO-in-a +0.5T PO-in-b +0.25T PO-in-c

[0057] T ML-in =0.25T ML-in-a +0.5T ML-in-b +0.25T ML-in-c

[0058] T UL-in =0.25T UL-in-a +0.5T UL-in-b +0.25T UL-in-c

[0059] Among them, T PO-in-c T is the triangular fuzzy number derived from the third risk level assessment linguistic value of the probability of occurrence of the i-th risk factor in the n-th data set; ML-in-c T is the triangular fuzzy number representing the transformation of the third risk level assessment linguistic value of the influence degree of the i-th risk factor in the n-th data set; UL-in-c The triangular fuzzy number is the linguistic value of the third risk level assessment of the uncontrollability of the i-th risk factor in the n-th data set.

[0060] In this embodiment, 105 sets of assessment information data for various risk factors were obtained from the risk database, and the assessment scope was limited to three risk level assessment language values.

[0061] Process the assessment information, convert the risk level assessment linguistic values ​​into triangular fuzzy numbers and aggregate them;

[0062] Once a risk level assessment linguistic value is converted into a numerical value, aggregation is not required. For example, if a risk factor is assessed as high, then the triangular fuzzy number for that risk factor is (0.67, 0.83, 1).

[0063] After converting the two risk level assessment linguistic values ​​into triangular fuzzy numbers, the weights of the two triangular fuzzy numbers are each 0.5, as shown in the following formula:

[0064] T PO-in =0.5T PO-in-a +0.5T PO-in-b

[0065] T ML-in =0.5T ML-in-a +0.5T ML-in-b

[0066] T UL-in =0.5T UL-in-a +0.5T UL-in-b

[0067] For example, if a risk factor is assessed as being between low and slightly low, then the triangular fuzzy number for that factor is calculated as follows:

[0068] T PO-in ={(0,0.17,0.33),(0.17,0.33,0.5)}

[0069] = (0.5×0+0.5×0.17,0.5×0.17+0.5×0.33,0.5×0.33+0.5×0.5)

[0070] =(0.085,0.25,0.415)

[0071] When converting the three risk level assessment linguistic values ​​into triangular fuzzy numbers, the weights are 0.25, 0.5, and 0.25 respectively, as shown in the following formula:

[0072] T PO-in =0.25T PO-in-a +0.5T PO-in-b +0.25T PO-in-c

[0073] T ML-in =0.25T ML-in-a +0.5T ML-in-b +0.25T ML-in-c

[0074] T UL-in =0.25T UL-in-a +0.5TUL-in-b +0.25T UL-in-c

[0075] For example, if a risk factor is assessed as being between slightly high and very high, then the triangular fuzzy number for that factor is calculated as follows:

[0076] T PO-in ={(0.5,0.67,0.83),(0.67,0.83,1),(0.83,1,1)}

[0077] = (0.25×0.5+0.5×0.67+0.25×0.83,0.25×0.67+0.5×0.83+0.25×1,0.25×0.83+0.5×1+0.25×1)

[0078] =(0.668,0.833,0.958)

[0079] In practice, each risk factor has multiple sets of assessment data, and each set of data has an equal weight in assessing the same risk factor. The arithmetic mean of all aggregated triangular fuzzy numbers corresponding to the three dimensions of each risk factor is calculated to obtain the assessment value corresponding to the three dimensions of the risk factor. The formula is as follows:

[0080]

[0081]

[0082]

[0083] Among them, T PO-i Let m be the probability assessment value of the i-th risk factor; m be the number of data groups; T MI-i T represents the assessment value of the impact of the i-th risk factor; UL-i This is the assessment value for the uncontrollability of the i-th risk factor.

[0084] Table 1: Assessment Values ​​of Risk Factor Occurrence Probability, Impact, and Uncontrollability

[0085]

[0086]

[0087] The formula for calculating the risk importance assessment value of each risk factor is as follows:

[0088]

[0089] Among them, T RC-i This represents the risk importance assessment value of the i-th risk factor.

[0090] Based on the semantic function, the risk importance assessment value is transformed into a risk level assessment language value to obtain the final assessment of each risk factor.

[0091] Table 2: Risk Factor Risk Level Assessment

[0092]

[0093] 3) Based on the probability of occurrence assessment value, impact assessment value, and uncontrollability assessment value corresponding to each risk factor, calculate and obtain the probability of occurrence assessment value, impact assessment value, and uncontrollability assessment value corresponding to each risk group. Then, calculate the geometric mean of the probability of occurrence assessment value, impact assessment value, and uncontrollability assessment value corresponding to each risk group to obtain the risk importance assessment value of each risk group. Finally, convert the risk importance assessment value of each risk group into a risk level assessment language value according to the semantic function, thereby determining the final assessment value of each risk group.

[0094] In step 3), the weight of the risk factor corresponding to each risk group is calculated based on the risk list. The calculation formula is as follows:

[0095]

[0096]

[0097]

[0098] Among them, W PO-i Let be the weight of the probability of occurrence of the i-th risk factor in the corresponding risk group; k is the minimum number of the risk factor in the current group; l is the maximum number of the risk factor in the current group; W MI-i W represents the weight of the influence of the i-th risk factor in the risk group. UL-i T represents the weight of the uncontrollability of the i-th risk factor in the risk group; PO-i T represents the probability assessment value of the occurrence of the i-th risk factor; MI-i T represents the assessment value of the impact of the i-th risk factor; UL-i This is the assessment value for the uncontrollability of the i-th risk factor;

[0099] For example, the weight of the probability of occurrence of equipment design change rate risk factors in the technical risk group is calculated as follows:

[0100]

[0101] Table 3: Weights of the probability of occurrence, degree of impact, and degree of uncontrollability of risk factors

[0102]

[0103]

[0104] Based on the weights of the risk factors corresponding to each risk group and the assessment values ​​of the probability of occurrence, the degree of impact, and the degree of uncontrollability for each risk factor, the assessment values ​​of the probability of occurrence, the degree of impact, and the degree of uncontrollability for each risk group are calculated using the following formulas:

[0105]

[0106]

[0107]

[0108] Among them, T' PO-j T' is the probability assessment value for the occurrence of the j-th risk group; j is the risk group number; T' MI-j T' represents the impact assessment value for the j-th risk group; UL-j This represents the uncontrollability assessment value for the j-th risk group;

[0109] For example, the probability assessment value of the occurrence of technical risks in a risk group is calculated as follows:

[0110]

[0111] Table 4: Assessment values ​​of the probability of occurrence, impact, and uncontrollability of risk groups

[0112]

[0113] Calculate the geometric mean of the probability of occurrence assessment, the degree of impact assessment, and the degree of uncontrollability assessment for each risk group to obtain the risk importance assessment value for that risk group, as shown in the following formula:

[0114]

[0115] Where T' RC-j This represents the risk importance assessment value for the j-th risk group; Let T' be the triangular fuzzy number representing the risk importance of the j-th risk group after calculation. RC-j The specific numerical values ​​are obtained; based on the semantic function, the risk importance assessment value is converted into a risk level assessment language value to obtain the final assessment for each risk group.

[0116] Table 5: Risk Group Risk Level Assessment

[0117]

[0118] 4) Based on the probability of occurrence assessment value, impact assessment value, and uncontrollability assessment value corresponding to each risk group, calculate and obtain the probability of occurrence assessment value, impact assessment value, and uncontrollability assessment value corresponding to the overall risk. Then, calculate the geometric mean of the probability of occurrence assessment value, impact assessment value, and uncontrollability assessment value corresponding to the overall risk to obtain the risk importance assessment value of the overall risk. Then, convert the risk importance assessment value of the overall risk into a risk level assessment language value according to the semantic function, thereby determining the final assessment value of the overall risk. The final assessment values ​​of each risk factor, the final assessment values ​​of each risk group, and the final assessment value of the overall risk are used together as the risk assessment result.

[0119] In step 4), based on the probability of occurrence assessment value, impact assessment value, and uncontrollability assessment value corresponding to each risk group, the weight of each risk group in the overall risk is calculated. The calculation formula is as follows:

[0120]

[0121]

[0122]

[0123] Among them, W' PO-j Let be the weight of the occurrence probability of the j-th risk group in the overall risk; r is the minimum number of the risk group; s is the maximum number of the risk group; W' MI-j W' represents the weight of the impact of the j-th risk group in the overall risk; UL-j Let be the weight of the uncontrollability of the j-th risk group in the overall risk;

[0124] For example, the weight of the probability of technological risk occurring in the overall risk is calculated as follows:

[0125]

[0126] Table 6: Weights of Risk Group Occurrence Probability, Impact, and Uncontrollability

[0127]

[0128]

[0129] Based on the weight of each risk group in the overall risk, the probability of occurrence, the degree of impact, and the degree of uncontrollability of the overall risk are calculated using the following formulas:

[0130]

[0131]

[0132]

[0133] Among them, T' PO-all This is the overall probability assessment value for the occurrence of risk; The triangular fuzzy number representing the probability of occurrence of the overall risk is calculated, i.e., T'. PO-all The specific value of T' MI-all This is the assessment value for the overall risk impact. The triangular fuzzy number for calculating the impact of the overall risk, i.e., T' MI-all The specific value of T' UL-all This is an assessment value for the overall degree of uncontrollability of risk; The triangular fuzzy number for calculating the impact of the overall risk, i.e., T' UL-all The specific value.

[0134] For example, the overall risk probability assessment value is calculated as follows:

[0135]

[0136] The overall risk impact and uncontrollability assessment values ​​are as follows:

[0137] T' MI-all =(0.495,0.665,0.791)

[0138] T' UL-all =(0.449,0.612,0.751)

[0139] The overall risk importance assessment value is obtained by calculating the geometric mean of the overall risk probability assessment value, the impact assessment value, and the uncontrollability assessment value, using the following formula:

[0140]

[0141] Where T' RC-all This is the overall risk importance assessment value; The risk importance triangular fuzzy number, T', is used to calculate the overall risk. RC-all The specific value;

[0142] The overall risk importance assessment values ​​for the nuclear power equipment value chain manufacturing are as follows:

[0143] T' RC-all =(0.476,0.641,0.773)

[0144] Based on semantic functions, the risk importance assessment value is converted into a risk level assessment linguistic value to obtain the final assessment of the overall risk, which is "medium to slightly high". The current manufacturing risk in the nuclear power equipment value chain is slightly high, requiring adjustments to the risk factors and risk groups within the current nuclear power equipment value chain based on the final assessment values ​​of each risk factor and risk group, thereby reducing manufacturing risk.

[0145] The embodiments described above are for the purpose of more clearly demonstrating the technical solution of the present invention, and the scope of protection of the present invention is not limited to the above embodiments. Those skilled in the art can make various modifications and combinations based on the present invention without departing from its essence, and these modifications and combinations should also be considered within the scope of protection of the present invention.

Claims

1. A method of nuclear power equipment value chain manufacturing risk assessment, characterized by, The method comprises the following steps: 1) According to the risk database of nuclear power equipment, three-dimensional evaluation data corresponding to each risk factor in the manufacturing of nuclear power equipment is determined, the three-dimensional evaluation data comprising occurrence probability evaluation data, influence degree evaluation data and uncontrollable degree evaluation data, each risk factor forming different risk groups, and a risk list being constructed by the risk groups and the risk factors; 2) The occurrence probability evaluation data, the influence degree evaluation data and the uncontrollable degree evaluation data corresponding to each risk factor are respectively subjected to fuzzy processing by adopting a hesitant fuzzy language term set method, and occurrence probability, influence degree evaluation values and uncontrollable degree evaluation values corresponding to each risk factor are respectively obtained, and then a geometric mean value of the occurrence probability evaluation value, the influence degree evaluation value and the uncontrollable degree evaluation value corresponding to each risk factor is calculated to obtain a risk importance evaluation value of each risk factor, and then the risk importance evaluation value of each risk factor is converted into a risk grade evaluation language value according to a semantic function and taken as a final evaluation value of each risk factor; 3) According to the occurrence probability evaluation value, the influence degree evaluation value and the uncontrollable degree evaluation value corresponding to each risk factor, occurrence probability evaluation values, influence degree evaluation values and uncontrollable degree evaluation values corresponding to each risk group are calculated and obtained, and then a geometric mean value of the occurrence probability evaluation value, the influence degree evaluation value and the uncontrollable degree evaluation value corresponding to each risk group is calculated to obtain a risk importance evaluation value of each risk group, so as to determine a final evaluation value of each risk group; 4) According to the occurrence probability evaluation value, the influence degree evaluation value and the uncontrollable degree evaluation value corresponding to each risk group, occurrence probability evaluation values, influence degree evaluation values and uncontrollable degree evaluation values corresponding to the overall risk are calculated and obtained, and then a geometric mean value of the occurrence probability evaluation value, the influence degree evaluation value and the uncontrollable degree evaluation value corresponding to the overall risk is calculated to obtain a risk importance evaluation value of the overall risk, so as to determine a final evaluation value of the overall risk, and the final evaluation value of each risk factor, the final evaluation value of each risk group and the final evaluation value of the overall risk are taken as a risk evaluation result together.

2. The method for manufacturing risk assessment of nuclear power equipment value chain according to claim 1, characterized in that, In the step 2), after the three-dimensional evaluation data corresponding to each risk factor is respectively subjected to fuzzy number conversion, three-dimensional triangular fuzzy numbers corresponding to each risk factor are obtained, wherein one or more risk grade evaluation language values exist in each dimension evaluation data corresponding to each risk factor, when only one risk grade evaluation language value exists in each dimension evaluation data, the evaluation value corresponding to the current dimension is a triangular fuzzy number corresponding to the current dimension, and when multiple risk grade evaluation language values exist in each dimension evaluation data, a triangular fuzzy number obtained by weighted summation of the triangular fuzzy number corresponding to the current dimension is taken as the evaluation value corresponding to the current dimension.

3. The method for manufacturing risk assessment of nuclear power equipment value chain according to claim 1, characterized in that, In the step 3), the weight of the risk factor corresponding to each risk group is calculated according to the risk list, and the calculation formula is as follows: wherein, W PO-i is the weight of the occurrence probability of the ith risk factor in the corresponding risk group; i is the risk factor number; k is the minimum number of risk factors in the current group; 1 is the maximum number of risk factors in the current group; W MI-i is the weight of the influence degree of the ith risk factor in the risk group; W UL-i is the weight of the uncontrollable degree of the ith risk factor in the risk group; T PO-i is the evaluation value of the occurrence probability of the ith risk factor; T MI-i is the evaluation value of the influence degree of the ith risk factor; T UL-i is the evaluation value of the uncontrollable degree of the ith risk factor; According to the weight of the risk factors corresponding to each risk group and the occurrence probability evaluation value, the influence degree evaluation value and the uncontrollable degree evaluation value corresponding to each risk factor, the occurrence probability evaluation value, the influence degree evaluation value and the uncontrollable degree evaluation value of each risk group are calculated, and the calculation formula is as follows: T' = T' + T' + T' PO-j is the occurrence probability evaluation value of the jth risk group; j is the risk group number; T' MI-j is the impact degree evaluation value of the jth risk group; T' UL-j is the uncontrollable degree evaluation value of the jth risk group.

4. The method for manufacturing risk assessment of nuclear power equipment value chain according to claim 1, characterized in that, In the step 4), according to the occurrence probability evaluation value, the influence degree evaluation value and the uncontrollable degree evaluation value corresponding to each risk group, the weight of each risk group in the overall risk is calculated, and the calculation formula is as follows: wherein W PO-j is the weight of the occurrence probability of the jth risk group in the overall risk; r is the minimum number of the risk group; s is the maximum number of the risk group; W MI-j is the weight of the influence degree of the jth risk group in the overall risk; W UL-j is the weight of the uncontrollability degree of the jth risk group in the overall risk; According to the weight of each risk group in the overall risk, the occurrence probability evaluation value, the influence degree evaluation value and the uncontrollable degree evaluation value of the overall risk are calculated, and the formula is as follows: wherein T PO-all is the occurrence probability evaluation value of the overall risk; T MI-all is the influence degree evaluation value of the overall risk; T UL-all is the uncontrollable degree evaluation value of the overall risk.

5. The method for manufacturing risk assessment of nuclear power equipment value chain according to claim 2, characterized in that, When there are two risk level evaluation language values in each dimension evaluation data, the weight of the triangular fuzzy number corresponding to the current dimension is set to 0.5 and 0.5 in the order of the risk level evaluation language values; when there are three risk level evaluation language values in each dimension evaluation data, the weight of the triangular fuzzy number corresponding to the current dimension is set to 0.25, 0.5 and 0.25 in the order of the risk level evaluation language values.

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