Ship shore control system risk assessment method based on interval type-2 fuzzy numbers

Abstract

The present application relates to a kind of ship shore end control system risk assessment method based on interval two fuzzy numbers, comprising the following steps: by the function analysis of system, obtain the relevant data including multiple sub-modules / components, assess the failure probability and loss severity of each sub-module / component, wherein the failure probability and loss severity are interval two fuzzy numbers;According to the failure probability and loss severity of each sub-module / component, the fuzzy risk of system or component is estimated using the improved arithmetic operation rule of interval two fuzzy numbers, wherein interval two fuzzy numbers are composed of elements and high, and the arithmetic operation rule of improved interval two fuzzy numbers is obtained by the arithmetic operation rule of the high of unified interval two fuzzy numbers;The deviation degree and similarity of the results under different operation rules are compared and analyzed to verify the results.

Description

Technical Field

[0001] This invention relates to the field of fuzzy risk assessment technology, and in particular to a risk assessment method for ship shore control systems based on interval type II fuzzy numbers. Background Technology

[0002] As an emerging technology, intelligent ships will drive the upgrading and transformation of the shipbuilding industry. Currently, the International Maritime Organization defines intelligent ships as Maritime Autonomous Surface Ships (MASS), and classifies them into four categories based on their level of automation. Remotely controlled vessels (MASS) with manned vessels belong to Category 2, while unmanned MASS vessels belong to Category 3. Both types of vessels require support from shore-based control centers for navigation decision-making and emergency takeover. However, there is still a lack of clear understanding of shore-based control centers for MASS vessels. For example, how should their systems be designed, and how can existing systems be widely applied? Therefore, there is a certain degree of uncertainty when conducting risk assessments of shore-based control systems for MASS vessels. Furthermore, insufficient data is also common in risk assessments of emerging technologies. Expert assessments of fuzzy data can compensate for this deficiency; that is, conducting fuzzy risk assessments when objective data is insufficient or incomplete can provide some risk information for system improvement or navigation operations.

[0003] Fuzzy set theory, proposed by Zadeh, is widely used to simulate fuzziness in human judgment in the form of Type-1 Fuzzy Sets (T1FS). The membership function of T1FS is an exact value within the range [0,1]. However, in many cases, the value of the membership function remains uncertain. To address this, Zadeh proposed the concept of Type-2 Fuzzy Sets (T2FS) as an extension of T1FS. T2FSs are characterized by variable membership functions, providing additional degrees of freedom and enabling direct modeling and handling of uncertainty. However, due to the complex computation involved, T2FS has not yet been widely adopted. To reduce the required computation, the upper and lower bounds of the membership function of T2FS are fixed to form Interval Type-2 Fuzzy Sets (IT2FSs). As a special case of T2FS, IT2FS not only represents uncertainty better than T1FS but also simplifies the computation compared to T2FS. Therefore, IT2FS has been widely applied in various fields, such as multi-criteria decision making, fuzzy risk analysis, and risk matrix design. However, existing research shows that the parameters of IT2FS have the following shortcomings in terms of arithmetic operation rules such as addition, subtraction, multiplication, division, scalar multiplication, and exponentiation:

[0004] (1) The rules for elements in IT2FS are relatively uniform, but there is a lack of uniform rules that are highly correlated with elements.

[0005] (2) The interaction of the heights of the elements in IT2FS is mathematically reflected by the use of shapes such as planes and irregular planes, but there is no reasonable explanation for the relationship between these shapes and the actual situation.

[0006] (3) The operation rules fail to directly reflect the differentiated risk preferences of decision-makers.

[0007] The aforementioned shortcomings also contribute to problems such as insufficient flexibility and incomplete risk assessment in fuzzy risk analysis. Summary of the Invention

[0008] The purpose of this invention is to provide a risk assessment method for ship shore control systems based on interval type-II fuzzy numbers.

[0009] The objective of this invention can be achieved through the following technical solutions:

[0010] A risk assessment method for ship shore-based control systems based on interval type-II fuzzy numbers includes the following steps:

[0011] By analyzing the functions of the intelligent ship shore control system, relevant data including multiple sub-modules / components are obtained, and the failure probability and loss severity of each sub-module / component are evaluated. The failure probability and loss severity are interval type II fuzzy numbers.

[0012] Based on the failure probability and severity of loss of each submodule / component, the fuzzy risk of the system or component is estimated by using the improved arithmetic operation rules of interval type II fuzzy numbers. The interval type II fuzzy numbers are composed of elements and heights. The improved arithmetic operation rules of interval type II fuzzy numbers are obtained by unifying the arithmetic operation rules of the heights of interval type II fuzzy numbers.

[0013] Furthermore, the expression for the interval type-II fuzzy number is:

[0014]

[0015] In the formula, Let i be the type II fuzzy number for the i-th interval. Let the membership function be the upper bound. Let the lower bound membership function be used. The upper bound of the membership function is defined as follows: Each element of high, The value of the lower bound membership function is defined as follows: Each element of high,

[0016] Furthermore, the improved arithmetic operation rules for interval type II fuzzy numbers include addition, subtraction, multiplication, division, scalar multiplication, and exponentiation.

[0017] Furthermore, the rules for addition are as follows:

[0018]

[0019] In the formula, and Let represent two interval type II fuzzy numbers, and k∈{0.5,1,2} be the preference coefficient, representing the tolerance for risk. The upper bound of the membership function is defined as follows: Each element of high, The value of the lower bound membership function is defined as follows: Each element of high, Let the membership function be the upper bound. This is the lower bound membership function.

[0020] Furthermore, the rules for subtraction are as follows:

[0021]

[0022] In the formula, and Let represent two interval type II fuzzy numbers, and k∈{0.5,1,2} be the preference coefficient, representing the tolerance for risk. The upper bound of the membership function is defined as follows: Each element of high, The value of the lower bound membership function is defined as follows: Each element of high, Let the membership function be the upper bound. This is the lower bound membership function.

[0023] Furthermore, the rules for multiplication are as follows:

[0024]

[0025] in:

[0026]

[0027] In the formula, and Let represent two interval type II fuzzy numbers, and k∈{0.5,1,2} be the preference coefficient, representing the tolerance for risk. The upper bound of the membership function is defined as follows: Each element of high, The value of the lower bound membership function is defined as follows: Each element of high, for or The elements in Let the membership function be the upper bound. This is the lower bound membership function.

[0028] Furthermore, the rules for division are as follows:

[0029]

[0030] In the formula, and Let represent two interval type II fuzzy numbers, and k∈{0.5,1,2} be the preference coefficient, representing the tolerance for risk. The upper bound of the membership function is defined as follows: Each element of high, The value of the lower bound membership function is defined as follows: Each element of high, Let the membership function be the upper bound. Let the lower bound membership function be used. and p∈{1,2},q∈{3,4}.

[0031] Furthermore, the rules for scalar multiplication are as follows:

[0032]

[0033] In the formula, Let k ∈ {0.5, 1, 2} be the interval type II fuzzy number, and k ∈ {0.5, 1, 2} be the preference coefficient, representing the tolerance for risk. The upper bound of the membership function is defined as follows: Each element of high, The value of the lower bound membership function is defined as follows: Each element of high, for, For, λ∈[0,1], Let the membership function be the upper bound. This is the lower bound membership function.

[0034] Furthermore, the calculation rules for the exponent are as follows:

[0035]

[0036] In the formula, Let k ∈ {0.5, 1, 2} be the interval type II fuzzy number, and k ∈ {0.5, 1, 2} be the preference coefficient, representing the tolerance for risk. The upper bound of the membership function is defined as follows: Each element of high, Let the membership function be the upper bound. The value of the lower bound membership function is defined as follows: Each element of high, Let the lower bound membership function be used. for, For, λ∈[0,1].

[0037] Furthermore, the improved arithmetic rules for the height of the interval type II fuzzy number satisfy one or more of the following properties:

[0038] 1) Symmetry: H is symmetric about x = y;

[0039] 2) Uniqueness: H, x, y ∈ [0, 1]; H = 0 if and only if x = y = 0; H = 1 if and only if x = y = 1;

[0040] 3) Special case: When k = 1, H = (x + y) / 2; in particular, when x = y, H = x = y;

[0041] Where H represents height, and x and y represent the heights of the two interval type-2 fuzzy numbers in the operation, respectively.

[0042] Finally, the results were verified by comparing the deviation and similarity of the results under different computational rules. The deviation was calculated according to the method proposed by Hu et al. in 2013, "Multi-criteria decision making method based on possibility degree of interval type-2 fuzzy number," as follows:

[0043]

[0044]

[0045] The similarity calculation method was proposed by Sen et al. in 2016 in the paper "Fuzzy risk analysis in familial breast cancer using a similarity measure of interval-valued fuzzy numbers", as follows:

[0046]

[0047] in,

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] (1) The present invention unifies the arithmetic operation rules for the height of interval type II fuzzy numbers, which better reflects the symmetry, uniqueness and particularity of the height of the interval type II fuzzy numbers involved in the operation. The introduction of a preference coefficient into the operation rules to represent the differentiated risk preferences of decision-makers further adjusts the flexibility of the operation rules, characterizes the different risk attitudes of decision-makers, and more comprehensively reflects the uncertainty of people's perception of risk.

[0050] (2) The fuzzy evaluation method of this invention, on the one hand, completes the risk perception of the system even when data is incomplete. The fuzzy risk evaluation calculation process better reflects human fuzzy judgment, providing a flexible and reliable implementation process for fuzzy risk assessment. On the other hand, the results are verified by comparing the deviation and similarity of the calculation results under different rules. The calculation results can not only be used for fuzzy perception of risk, but also for comparing products from different manufacturers when data is insufficient. Attached Figure Description

[0051] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0052] Figure 2 This is a schematic diagram of a remote-controlled ship shore-based control system.

[0053] Figure 3 The calculation rules for the high-order interval type II fuzzy number of the present invention are as follows: (a) the graph preference coefficient k is 0.5, (b) the graph preference coefficient k is 1, and (c) the graph preference coefficient k is 2.

[0054] Figure 4The results comparison charts of the present invention are shown in the following figures: (a) the graph preference coefficient k is 0.5, (b) the graph preference coefficient k is 1, and (c) the graph preference coefficient k is 2.

[0055] Figure 5 The following is a comparison chart of results from another example of the present invention, wherein (a) the graph preference coefficient k is 0.5, (b) the graph preference coefficient k is 1, and (c) the graph preference coefficient k is 2. Detailed Implementation

[0056] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0057] This embodiment provides a risk assessment method for ship shore-based control systems based on interval type-II fuzzy numbers, such as... Figure 1 As shown, the method includes the following steps:

[0058] S1. Analyze the composition of the system's functional modules and evaluate the failure probability and severity of loss of each sub-module, where the failure probability and severity of loss are represented by interval type II fuzzy numbers.

[0059] Table 1 Inputs for Fuzzy Risk Analysis

[0060]

[0061] Note: For ease of comparison, the data in this table comes from the 2008 article "Fuzzy risk analysis based on measures of similarity between interval-valued fuzzy numbers" by Shi-Jay Chen and Shyi-Ming Chen. Computers and Mathematics with Applications, 55(2008)1670–1685.

[0062] In this embodiment, the remote-controlled ship shore-based control system consists of three modules: a remote control module (sub-module 1), a communication module (sub-module 2), and a system security module (sub-module 3). Figure 2 As shown. To conduct a fuzzy risk assessment of the shore-based control system for remotely controlled vessels, the following assumptions are made. This indicates the failure risk of the remote-controlled ship shore-based control system. It is submodule A i The probability of failure, It is submodule A i The severity of the loss, among which and It is an interval type II fuzzy number, as shown in Table 1.

[0063] S2. Based on the failure probability and severity of loss of each submodule, the fuzzy risk of each submodule is estimated using the improved arithmetic operation rules of interval type-2 fuzzy numbers. The interval type-2 fuzzy number consists of elements and a height, and is calculated using the arithmetic operation rules for the height of interval type-2 fuzzy numbers proposed in this invention. This invention establishes a unified arithmetic operation rule for the height of interval type-2 fuzzy numbers and reflects the decision-maker's risk attitude. The operation rules for the height of interval type-2 fuzzy numbers are as follows: Figure 3 As shown, (a) represents the high when the preference coefficient k is 0.5, (b) represents the high when the preference coefficient k is 1, and (c) represents the high when the preference coefficient k is 0.5.

[0064] Interval Type II Fuzzy Number Composed of elements and height, it can be represented by equation (1).

[0065]

[0066] The membership function consists of an upper bound membership function and a lower bound membership function. The upper bound of the membership function is defined as follows: Each element of high, The value of the lower bound membership function is defined as follows: Each element of high;

[0067] This invention proposes arithmetic operation rules for interval type II fuzzy numbers, namely, the operation rules for addition, subtraction, multiplication, division, scalar multiplication, and exponentiation are defined as follows:

[0068] Definition 2.1 Addition operation (k∈{0.5,1,2})

[0069]

[0070] Definition 2.2 Subtraction operation (k∈{0.5,1,2})

[0071]

[0072] Definition 2.3 Multiplication (k∈{0.5,1,2})

[0073]

[0074] in, and

[0075]

[0076] Definition 2.4 Division operation (k∈{0.5,1,2})

[0077]

[0078] in, and

[0079] Definition 2.5 scalar multiplication (λ∈[0,1],k∈{0.5,1,2})

[0080]

[0081] Definition 2.6 Exponentiation (λ∈[0,1],k∈{0.5,1,2})

[0082]

[0083] The above operational rules have the following characteristics:

[0084] 1. The calculation rules introduce a preference coefficient k, representing the decision-maker's preference. If k = 0.5, the height of the interval type II fuzzy number is concave, indicating that the decision-maker is relatively cautious; if k = 1, the height of the interval type II fuzzy number is planar, indicating that the decision-maker is relatively neutral; if k = 2, the height of the interval type II fuzzy number is convex, indicating that the decision-maker is relatively risk-taking. These three cases cover three risk attitudes: risk aversion, risk neutrality, and risk preference, expressing the decision-maker's different risk tolerance levels.

[0085] 2. The new operation rules increase the computational burden of height to some extent, but considering the mutual influence between heights (H), and taking x and y as the heights of the two interval type-II fuzzy numbers in the operation, the new operation rules have the following three characteristics:

[0086] • Symmetry: H is symmetric about x = y;

[0087] • Uniqueness: H, x, y ∈ [0, 1]; H = 0 if and only if x = y = 0; H = 1 if and only if x = y = 1;

[0088] Special case: When k = 1, H = (x + y) / 2, and especially when x = y, H = x = y.

[0089] S3. The fuzzy risk of the system is the sum of the risks of each module divided by the sum of the losses of each sub-module. The proposed calculation rules are applied during the calculation process.

[0090] The algorithm for fuzzy risk in the remote-controlled ship shore-based control system in this implementation case is as follows:

[0091]

[0092] The rules used in formula (8) include addition, multiplication, and division rules. Operations are performed according to the corresponding operational rules mentioned above. If k = 0.5, the results of each term in formula (8) are shown in Table 2, and see... Figure 4 Figure (a) in the diagram. Similarly, if we let k = 1 and k = 2, we can also obtain... upper bound membership function and lower bound membership function See results Figure 4 Figure (b) in the middle and Figure 4 Figure (c) in the middle.

[0093] Table 2 shows the calculation results of formula (8) when the proposed operation rule is adopted and k = 0.5.

[0094]

[0095] Depend on Figure 4 It is evident that the upper bound membership function heights of the fuzzy risk in the remote-controlled ship shore-based control system are the same, but the lower bound membership function heights differ. Furthermore, as the preference coefficient k increases, the lower bound membership function height decreases. Therefore, the larger the area enclosed by the upper and lower bound membership functions, the greater the inherent uncertainty; that is, the more aggressive the risk attitude, the greater the risk.

[0096] S4. Verify the results by comparing the deviation and similarity analysis of the results under different calculation rules. This is done by comparing the results with those in the first paper proposed by Shi-Jay Chen and Shyi-Ming Chen in 2008, "Fuzzy risk analysis based on measures of similarity between interval-valued fuzzy numbers". The corresponding calculation rules proposed in the embodiments of the present invention are obtained Figure 4 show, The values ​​of the elements in the upper bound are the same because the arithmetic operation rules defined for these elements are the same. Under different arithmetic operation rules, the two high values ​​of the upper bound membership function are different. and The result is the same, because and All heights of the membership function for the upper bound are equal to 1. However, the two heights of the membership function for the lower bound are equal to 1. and The results differed; the result at k=1 was close to that in the first paper, the result at k=2 was the lowest, and the result at k=0.5 was the highest. To quantify the results in the first paper... The corresponding calculation rules proposed in the embodiments of the present invention are obtained The deviation and similarity between the two are calculated according to the deviation method proposed in the second paper "Multi-criteria decision making method based on possibility degree of interval type-2 fuzzy number" proposed by Hu et al. in 2013 and the similarity method proposed in the third paper "Fuzzy risk analysis in familial breast cancer using a similarity measure of interval-valued fuzzy numbers" proposed by Sen et al. in 2016. The results are shown in Table 3. In Table 3, the results obtained when k=1 using the corresponding operation rules proposed in this invention are shown. Compared with the first article The most similar value was 0.981 when k=0.5, followed by 0.957 when k=0.5, and the lowest similarity was 0.955 when k=2. While the difference in similarity between k=0.5 and k=2 was small, the difference in bias was significant. This suggests that the calculation results from the first paper indicate a preference for risk neutrality among decision-makers.

[0097] Table 3 shows the differences and similarities between the results of this embodiment and the first literature.

[0098]

[0099] Note: The first reference is "Chen SJ, Chen SM. Fuzzy risk analysis based on measures of similarity between interval-valued fuzzy numbers. Computers and Mathematics with Applications. 2008; 55:1670–85."

[0100] This embodiment also provides a case for comparison and illustration. Assume there are n manufacturers C1, C2, ..., Cn, and each component Ai is composed of p sub-components Ai1, Ai2, ..., Aip produced by manufacturer Ci, where i∈{1,2,...,D}. Figure 2The medium-capacity battery is composed of solar panels supplied by a certain manufacturer, Ci, namely solar panel 1 (A) i1 ), Solar panel 2 (A) i2 ), Solar panel 3 (A) i3 To conduct a fuzzy risk assessment of a battery composed of solar panels manufactured by Ci, it is assumed that solar panel A manufactured by Ci... i failure probability For A ik The failure probability of the sub-component For A ik The severity of the loss. For cases involving three sub-components (i.e., A... 11 A 12 and A 13 Component A1, and As shown in Table 4, refer to formula (8), The calculation is as follows:

[0101]

[0102] Table 4 shows the inputs for formula (9) (Data source: fourth reference).

[0103]

[0104] Note: For ease of comparison, the data is from the fourth reference, "Chen SM, Chen JH. Fuzzyrisk analysis based on similarity measures between interval-valued fuzzynumbers and interval-valued fuzzy number arithmetic operators. Expert Systems with Applications. 2009; 36:6309–17."

[0105] Depend on Figure 5 It is evident that the fuzzy risk of batteries composed of solar panels manufactured by Ci increases with the increase of the preference coefficient k, while the upper and lower bound membership functions become smaller. Furthermore, the larger the area enclosed by the upper and lower bound membership functions, the greater the inherent uncertainty; that is, the more aggressive the risk attitude, the greater the risk. In addition, this method can be used to evaluate the risk of batteries composed of solar panels from multiple manufacturers, followed by comparative analysis. The results can then be used to select suppliers.

[0106] By comparing the fourth article And obtained using the corresponding calculation rules proposed in the embodiments of the present invention Figure 5 show, The elements and height values ​​are completely different because the arithmetic rules defining them are different. The range of elements in the lower bound membership function obtained by the corresponding operation rules proposed in this embodiment is narrower than that in the fourth document, but the range of elements in the upper bound membership function is larger. When k = 0.5 ( Figure 5 (a) and k=1 ( Figure 5 In Figure (b) of the paper, the height value is significantly greater than that in the fourth paper. And when k = 2 ( Figure 5 When (c) in the middle, and The value is also larger than that in the fourth paper. Conversely, when k=2, and The value is less than the value in the fourth paper.

[0107] Furthermore, this embodiment also utilizes the addition and multiplication rules from the second reference and the division rules from the first reference to calculate... Figure 5 The midpoint line is represented by the dashed line. The value and the fourth paper representing pure dashed lines The values ​​and solid lines represent the results obtained using the corresponding operational rules proposed in the embodiments of this invention. The values ​​were compared. (Dotted lines and dashed lines) The height value of the solid line is greater than that of the other two lines, but when k = 0.5, their height values ​​in the lower bound membership function are smaller than those of the solid line. In the pure dashed line... The height values ​​of the dashed lines are less than the other two lines, but when k=2, their height values ​​in the lower bound membership function are greater than those of the solid lines. However, in the case of pure dashed lines and solid lines... The element values ​​are the same because the arithmetic operation rules defined for these elements are the same.

[0108] This embodiment also uses the deviation from the second paper and the similarity from the third paper to quantify what these three lines represent. The deviation and similarity between the solid lines and dotted / dashed lines are shown in Table 5. The results indicate that the deviation between solid lines and dotted / dashed lines is smaller than the deviation between solid lines and pure dashed lines. Regarding similarity, when k=2, the results obtained using the corresponding calculation rules proposed in this embodiment of the invention... Compared with the fourth document The high similarity indicates that the calculation results in the fourth paper express the decision-maker's risk preference; when k=1, the results obtained using the corresponding calculation rules proposed in this embodiment of the invention are... The results obtained using the addition and multiplication rules proposed in the second paper and the division rule proposed in the first paper The high degree of similarity indicates that the calculation results using the relevant rules in the first and second documents express the decision-maker's tendency towards risk neutrality.

[0109] Table 5 shows the differences and similarities between the calculation results and other literature.

[0110]

[0111] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0112] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A risk assessment method for a ship shore control system based on interval-valued intuitionistic fuzzy numbers, characterized in that, The method comprises the following steps: Through the function analysis of the intelligent ship shore control system, the relevant data of a plurality of sub-modules / components are obtained, and the failure probability and loss severity of each sub-module / component are evaluated, wherein the failure probability and loss severity are interval type-2 fuzzy numbers; According to the failure probability and loss severity of each sub-module / component, the fuzzy risk of the system or component is estimated by using an improved arithmetic operation rule of interval type-2 fuzzy numbers, wherein the interval type-2 fuzzy number is composed of an element and a high, the arithmetic operation rule of the improved interval type-2 fuzzy number is obtained by unifying the arithmetic operation rule of the high of the interval type-2 fuzzy number, and the improved arithmetic operation rule of the interval type-2 fuzzy number includes addition, subtraction, multiplication, division, number multiplication and exponentiation, the operation rule of the addition is that where, and denote two interval-valued bipolar fuzzy numbers, k ∈ {0.5, 1, 2} is a preference coefficient, which represents the tolerance to risk, The value of the upper membership function is defined as The height of each element in The value of the lower membership function is defined as The height of each element in is the upper membership function, is the lower membership function.

2. The risk assessment method for a ship shore control system based on interval-valued intuitionistic fuzzy numbers according to claim 1, characterized in that, The expression of the interval type-2 fuzzy number is where, is the i-th interval type-2 fuzzy number, is the upper membership function, is the lower membership function, the value of the upper membership function is defined as the height of each element in , the value of the lower membership function is defined as the height of each element in , 3. The risk assessment method for a ship shore control system based on interval-valued intuitionistic fuzzy numbers according to claim 1, characterized in that, The operation rule of the subtraction is that where, and denote two interval-valued bipolar fuzzy numbers, k ∈ {0.5, 1, 2} is a preference coefficient, which represents the tolerance to risk, The value of the upper membership function is defined as The height of each element in The value of the lower membership function is defined as The height of each element in is the upper membership function, is the lower membership function.

4. The risk assessment method for a ship shore control system based on interval-valued intuitionistic fuzzy numbers according to claim 1, characterized in that, The operation rule of the multiplication is that wherein wherein, and denote two interval-valued bi-type fuzzy numbers, k ∈ {0.5, 1, 2} is a preference coefficient, which represents the tolerance to risk, the value of the upper membership function is defined as the height of each element in the value of the lower membership function is defined as the height of each element in is or an element in is the upper membership function, is the lower membership function.

5. The risk assessment method for a ship shore control system based on interval-valued intuitionistic fuzzy numbers according to claim 1, characterized in that, The operation rule of the division is that wherein, and denote two interval-valued bipolar fuzzy numbers, k e {0.5, 1, 2} is a preference coefficient, denotes the tolerance to risk, the value of the upper membership function is defined as the height of each element in the value of the lower membership function is defined as the height of each element in is the upper membership function, is the lower membership function, and 6. The risk assessment method for a ship shore control system based on interval-valued intuitionistic fuzzy numbers according to claim 1, characterized in that, The operation rule of the number multiplication is that wherein, denotes interval-valued bipolar fuzzy number, k ∈ {0.5, 1, 2} is a preference coefficient, denotes the tolerance to risk, The value of the upper membership function is defined as The height of each element in the set The value of the lower membership function is defined as The height of each element in the set is is, λ ∈ [0, 1], is the upper membership function, is the lower membership function.

7. The risk assessment method for a ship shore control system based on interval-valued intuitionistic fuzzy numbers according to claim 1, characterized in that, The operation rule of the exponentiation is that wherein, denotes interval-valued fuzzy number, k ∈ {0.5, 1, 2} is a preference coefficient, and denotes the tolerance to risk, The value of the upper membership function is defined as The height of each element in is The upper membership function is The value of the lower membership function is defined as The height of each element in is The lower membership function is is is, λ ∈ [0, 1].

8. The risk assessment method for a ship shore control system based on interval-valued intuitionistic fuzzy numbers according to claim 1, characterized in that, The arithmetic operation rule of the high of the improved interval type-2 fuzzy number satisfies one or more of the following properties: 1) symmetry: H is symmetric about x=y; 2) uniqueness: H, x, y∈[0, 1]; H=0 if and only if x=y=0; H=1 if and only if x=y=1; 3) particularity: when k=1, H=(x+y) / 2; in particular, when x=y, H=x=y; wherein H represents the high, and x and y respectively represent the highs of two interval type-2 fuzzy numbers subjected to operation.

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