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

Abstract

The invention relates to a ship shore end control system risk assessment method based on an interval type-2 fuzzy number, and the method comprises the following steps: obtaining the related data of a plurality of sub-modules / parts through the function analysis of a system, evaluating the fault probability and the loss severity of each sub-module / part, and obtaining the risk assessment result of each sub-module / part. Wherein the fault probability and the loss severity are interval type-2 fuzzy numbers; according to the fault probability and the loss severity of each submodule / component, the fuzzy risk of the system or the component is estimated by adopting an arithmetic operation rule of an improved interval type-2 fuzzy number, and the interval type-2 fuzzy number is composed of elements and a high; obtaining an improved arithmetic operation rule of the interval type-2 fuzzy number by unifying the high arithmetic operation rule of the interval type-2 fuzzy number; and comparing and analyzing the verification result by comparing the deviation degree and the similarity of the result under different operation rules.

Description

Technical Field

[0001] The invention relates to the technical field of fuzzy risk assessment, and in particular to a risk assessment method for a ship shore control system based on interval type-2 fuzzy numbers. Background Art

[0002] As an emerging thing, smart ships will promote the upgrading and transformation of the shipbuilding industry. At present, the International Maritime Organization defines smart ships as maritime autonomous surface ships (MASS), and divides them into four categories according to the level of automation, among which remote-controlled ships with people on board belong to the second category, and remote-controlled ships without people on board belong to the third category. Both types of ships require support from the shore-based control center for navigation decisions and emergency takeover. However, there is still a vague understanding of the shore-based control center of remote-controlled ships. For example, how to design its system, how to widely use the existing system, etc. Therefore, there is a certain uncertainty in the risk assessment of the shore-based control system of remote-controlled ships. In addition, insufficient data is also common in the risk assessment of emerging things. Such deficiencies can be compensated by experts' fuzzy judgment, that is, fuzzy risk assessment is carried out when objective data is insufficient or incomplete, providing certain risk information as a reference for system improvement or navigation operations.

[0003] Fuzzy set theory was proposed by Zadeh. It is widely used to simulate the ambiguity in human judgment in the form of Type-1 Fuzzy Sets (T1FS). The membership function of T1FS is an accurate value in the range [0,1]. However, in many cases, the value of the membership function is still uncertain. To this end, Zadeh proposed the concept of Type-2 Fuzzy Sets (T2FS) as an extension of T1FS. The characteristic of T2FSs is that the membership function is also variable, which provides additional degrees of freedom and makes it possible to directly model and deal with uncertainty. However, due to the complex calculations involved, T2FS has not been widely used so far. In order to reduce the required amount of calculations, the upper and lower bounds of the membership functions of T2FS are fixed to form Interval Type-2 Fuzzy Sets (IT2FSs). As a special case of T2FS, IT2FS can not only better represent uncertainty than T1FS, but also simplify the calculation compared with T2FS. Therefore, IT2FS has been widely used in many fields, such as multi-criteria decision-making, fuzzy risk analysis and risk matrix design. However, it can be seen from existing studies that the parameters of IT2FS have the following deficiencies in arithmetic operation rules such as addition, subtraction, multiplication, division, multiplication and exponential operation:

[0004] (1) The rules for the elements in IT2FS are relatively unified, and there is a lack of unified rules with high correlation with the elements.

[0005] (2) The high mutual influence of elements in IT2FS is mathematically reflected by using shapes such as planes and irregular planes, but there is no reasonable explanation for the relationship between these shapes and actual conditions.

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

[0007] The above shortcomings also lead to problems such as insufficient flexibility and incomplete risk assessment in fuzzy risk analysis to a certain extent. Summary of the invention

[0008] The purpose of the present invention is to provide a risk assessment method for a ship shore control system based on interval type-2 fuzzy numbers.

[0009] The purpose of the present invention can be achieved by the following technical solutions:

[0010] A risk assessment method for a ship shore control system based on interval type-2 fuzzy numbers comprises the following steps:

[0011] Through the functional analysis of the intelligent ship shore control system, the relevant data of multiple submodules / components are obtained, and the failure probability and loss severity of each submodule / component are evaluated, where the failure probability and loss severity are interval type-2 fuzzy numbers;

[0012] According to the failure probability and loss severity of each submodule / component, the fuzzy risk of the system or component is estimated by using the improved arithmetic operation rules of interval type-2 fuzzy numbers, wherein the interval type-2 fuzzy number is composed of elements and height, and the improved arithmetic operation rules of interval type-2 fuzzy numbers are obtained by unifying the arithmetic operation rules of the height of interval type-2 fuzzy numbers.

[0013] Furthermore, the expression of the interval type-2 fuzzy number is:

[0014]

[0015] In the formula, is the i-th interval type-2 fuzzy number, is the upper bound membership function, is the lower bound membership function, The value of the upper bound membership function is defined as Each element of high, The value of the lower bound membership function is defined as Each element of high,

[0016] Furthermore, the arithmetic operation rules of the improved interval type-2 fuzzy numbers include addition, subtraction, multiplication, division, number multiplication and exponentiation.

[0017] Furthermore, the operation rule of addition is:

[0018]

[0019] In the formula, and They represent two interval type-2 fuzzy numbers, k∈{0.5,1,2} is the preference coefficient, which indicates the tolerance to risk. The value of the upper bound membership function is defined as Each element of high, The value of the lower bound membership function is defined as Each element of high, is the upper bound membership function, is the lower bound membership function.

[0020] Furthermore, the operation rule of subtraction is:

[0021]

[0022] In the formula, and They represent two interval type-2 fuzzy numbers, k∈{0.5,1,2} is the preference coefficient, which indicates the tolerance to risk. The value of the upper bound membership function is defined as Each element of high, The value of the lower bound membership function is defined as Each element of high, is the upper bound membership function, is the lower bound membership function.

[0023] Furthermore, the multiplication operation rule is:

[0024]

[0025] in:

[0026]

[0027] In the formula, and They represent two interval type-2 fuzzy numbers, k∈{0.5,1,2} is the preference coefficient, which indicates the tolerance to risk. The value of the upper bound membership function is defined as Each element of high, The value of the lower bound membership function is defined as Each element of high, for or The elements in is the upper bound membership function, is the lower bound membership function.

[0028] Furthermore, the division operation rule is:

[0029]

[0030] In the formula, and They represent two interval type-2 fuzzy numbers, k∈{0.5,1,2} is the preference coefficient, which indicates the tolerance to risk. The value of the upper bound membership function is defined as Each element of high, The value of the lower bound membership function is defined as Each element of high, is the upper bound membership function, is the lower bound membership function, and p∈{1,2},q∈{3,4}.

[0031] Furthermore, the operation rules of number multiplication are:

[0032]

[0033] In the formula, represents an interval type-2 fuzzy number, k∈{0.5,1,2} is the preference coefficient, which represents the tolerance to risk. The value of the upper bound membership function is defined as Each element of high, The value of the lower bound membership function is defined as Each element of high, for, is,λ∈[0,1], is the upper bound membership function, is the lower bound membership function.

[0034] Furthermore, the operation rule of the index is:

[0035]

[0036] In the formula, represents an interval type-2 fuzzy number, k∈{0.5,1,2} is the preference coefficient, which represents the tolerance to risk. The value of the upper bound membership function is defined as Each element of high, is the upper bound membership function, The value of the lower bound membership function is defined as Each element of high, is the lower bound membership function, for, is,λ∈[0,1].

[0037] Furthermore, the improved arithmetic operation rules of interval type-2 fuzzy numbers satisfy one or more of the following characteristics:

[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) Speciality: When k = 1, H = (x + y) / 2; in particular, when x = y, H = x = y;

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

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

[0043]

[0044]

[0045] The similarity is a similarity calculation method 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 high arithmetic operation rules of interval type-2 fuzzy numbers, and better reflects the high symmetry, uniqueness and specificity of the interval type-2 fuzzy numbers involved in the operation. The preference coefficient is introduced into the operation rules to represent the differentiated risk preferences of decision makers, which further adjusts the flexibility of the operation rules, describes the different risk attitudes of decision makers, and more comprehensively reflects the uncertainty of people's risk cognition.

[0050] (2) On the one hand, the fuzzy evaluation method of the present invention completes the risk cognition of the system under the condition of incomplete data. The fuzzy risk evaluation calculation process better reflects the fuzzy judgment of people, and provides 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 cognition of risks, but also for comparing products of different manufacturers when data is insufficient. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0053] Figure 3 The high operation rule of interval type-2 fuzzy number of the present invention, wherein (a) the graph preference coefficient k is 0.5, (b) the graph preference coefficient k is 1 (c) the graph preference coefficient k is 2;

[0054] Figure 4The result comparison diagram 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;

[0055] Figure 5 This is a result comparison chart of another case 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 DESCRIPTION

[0056] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

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

[0058] S1. Analyze the composition of system functional modules and evaluate the failure probability and loss severity of each submodule, where the failure probability and loss severity are expressed as interval type-2 fuzzy numbers.

[0059] Table 1 Fuzzy risk analysis input

[0060]

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

[0062] In this embodiment, the remote control ship shore control system consists of three modules, namely, a remote control module (submodule 1), a communication module (submodule 2), and a system security module (submodule 3). Figure 2 To conduct fuzzy risk assessment on the shore control system of remote-controlled ships, it is assumed that The risk of failure of the shore-based control system of the remote-controlled ship is represented by It is submodule A i The probability of failure, It is submodule A i The severity of the loss, including and is an interval type-2 fuzzy number, see Table 1.

[0063] S2. According to the failure probability and loss severity of each submodule, the fuzzy risk of each submodule is estimated by using the improved arithmetic operation rules of interval type II fuzzy numbers, wherein the interval type II fuzzy number is composed of elements and height, and the arithmetic operation rules of interval type II fuzzy numbers proposed by the present invention are used for calculation. The present invention formulates a unified arithmetic operation rule of interval type II fuzzy numbers and reflects the risk attitude of the decision maker. The arithmetic operation rules of interval type II fuzzy numbers are as follows: Figure 3 As shown, (a) shows the high when the preference coefficient k is 0.5, (b) shows the high when the preference coefficient k is 1, and (c) shows the high when the preference coefficient k is 0.5.

[0064] Interval Type-2 Fuzzy Numbers It is composed of elements and high, and can be expressed by formula (1).

[0065]

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

[0067] The present invention proposes arithmetic operation rules for interval type-2 fuzzy numbers, namely, the operation rules of addition, subtraction, multiplication, division, number 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 operation (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 Multiplication operation (λ∈[0,1],k∈{0.5,1,2})

[0080]

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

[0082]

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

[0084] 1. The preference coefficient k is introduced into the operation rules to represent the preference of the decision maker. If k = 0.5, the high of the interval type II fuzzy number is a concave surface, indicating that the decision maker is more cautious; if k = 1, the high of the interval type II fuzzy number is a flat surface, indicating that the decision maker is more neutral; if k = 2, the high of the interval type II fuzzy number is a convex surface, indicating that the decision maker is more adventurous. These three situations cover three risk attitudes, namely risk aversion, risk neutrality and risk preference, expressing the different risk tolerance of decision makers.

[0085] 2. The new operation rules increase the computational burden of height to a certain extent, but considering the mutual influence between heights (H), 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] ·Speciality: When k=1, H=(x+y) / 2, and in particular, 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 loss consequences of each sub-module. The proposed operation rules are used in the calculation process.

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

[0091]

[0092] The rules used in formula (8) include addition, multiplication and division rules. According to the corresponding operation rules proposed above, if k = 0.5, the results of each item in formula (8) are shown in Table 2, and Figure 4 Similarly, if we set k = 1 and k = 2, we can also get The upper bound membership function and the lower bound membership function Results Figure 4 Figure (b) and Figure 4 Figure (c) in .

[0093] Table 2 Calculation results of formula (8) using the proposed operation rules and k = 0.5

[0094]

[0095] Depend on Figure 4 It can be seen that the height of the upper bound membership function of the fuzzy risk of the remote control ship shore control system is the same, but the height of the lower bound membership function is different. And as the preference coefficient k increases, the height of the lower bound membership function becomes smaller. Therefore, the larger the area enclosed by the upper bound membership function and the lower bound membership function, the greater the uncertainty contained, that is, the more aggressive the risk attitude, the greater the risk.

[0096] S4. Compare the deviation and similarity analysis results of the results under different operation rules. By comparing the first paper "Fuzzy risk analysis based on measures of similarity between interval-valued fuzzy numbers" proposed by Shi-Jay Chen and Shyi-Ming Chen in 2008, and the corresponding operation rules proposed in the embodiment of the present invention 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 and The result is the same, because and All heights of the upper bound membership function are equal to 1. However, the two heights of the lower bound membership function are equal to 1. and The results are different. The result when k = 1 is close to the result in the first paper, the result when k = 2 is the lowest, and the result when k = 0.5 is the highest. The corresponding operation rules obtained according to the embodiment of the present invention are The deviation and similarity between them are calculated according to the deviation 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 calculation 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 corresponding operation rules proposed in the present invention are used to obtain the deviation when k=1. Compared with the first paper The most similar is k = 0.981, followed by k = 0.5, with a similarity of 0.957, and the smallest similarity is k = 2, with a similarity of 0.955. When k = 0.5 and k = 2, the difference in similarity between the two is small, but the difference in deviation is large. On the other hand, it shows that the calculation results of the first document express that the decision maker tends to be risk neutral.

[0097] Table 3 Differences and similarities between the results of this example and the first document

[0098]

[0099] Note: The first article 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 explanation. Assume that there are n manufacturers C1, C2, ..., Cn, and each component Ai is composed of p subcomponents Ai1, Ai2, ..., Aip produced by manufacturer Ci, where i∈{1,2, ...,D}. Figure 2The battery is composed of solar panels provided by a manufacturer Ci, that is, solar panels 1 (A i1 )、Solar Panel 2(A i2 )、Solar Panel 3(A i3 ). To conduct fuzzy risk assessment on the battery composed of solar panels produced by manufacturer Ci, assume that the solar panel A produced by manufacturer Ci i The failure probability A ik The failure probability of a subcomponent, A ik The severity of the loss. 11 , A 12 and A 13 ) component A1, and As shown in Table 4, referring to formula (8), The calculation is as follows:

[0101]

[0102] Table 4 Input of formula (9) (data source: fourth paper)

[0103]

[0104] Note: For the convenience of comparison, the data comes from the fourth paper, which is "Chen SM, Chen JH. Fuzzy risk analysis based on similarity measures between interval-valued fuzzy numbers and interval-valued fuzzy number arithmetic operators. Expert Systems with Applications. 2009; 36: 6309–17."

[0105] Depend on Figure 5 It can be seen that the fuzzy risk of the battery composed of solar panels produced by manufacturer Ci increases with the increase of preference coefficient k, and the height of the upper and lower bounds of the membership function decreases. And the larger the area enclosed by the upper and lower bounds of the membership function, the greater the uncertainty contained, that is, the more aggressive the risk attitude, the greater the risk. In addition, this method can be used to evaluate the risks of batteries composed of solar panels produced by multiple manufacturers, and then conduct comparative analysis, and the results can be used to make decisions on suppliers.

[0106] By comparing the fourth article and the corresponding operation rules obtained using the embodiment of the present invention Figure 5 show, The elements and height values ​​in are completely different because the arithmetic operation rules defined for them are different. The range of elements in the lower bound membership function obtained by the corresponding operation rules proposed in the embodiment of the present invention 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) in Figure ) and k = 1 ( Figure 5 When k = 2 ( Figure 5 (c) in the figure), and The value of is also larger than that in the fourth paper. On the contrary, when k = 2, and The value of is smaller than that in the fourth paper.

[0107] In addition, this embodiment also uses the addition and multiplication rules in the second document and the division rules in the first document to calculate Figure 5 The dotted line represents The value is the same as that in the fourth paper represented by the pure dashed line. The values ​​and the solid line represent the corresponding operation rules obtained using the embodiment of the present invention. The dotted line is compared with the The height of is greater than the other two lines, except that when k = 0.5, their height in the lower bound membership function is less than the solid line. The height of is smaller than the other two lines, but in the case of k = 2, their height in the lower bound membership function is greater than the solid line. The values ​​of the elements are the same because the arithmetic operations defined on them are the same.

[0108] This embodiment also uses the deviation in the second document and the similarity in the third document to quantify the three lines represent The deviation and similarity between the two are shown in Table 5. The results show that the deviation between the solid line and the dotted line is smaller than the deviation between the solid line and the pure dashed line. In terms of similarity, when k=2, the corresponding operation rules proposed in the embodiment of the present invention are used to obtain The fourth article The high similarity indicates that the calculation results in the fourth document express the risk preference of the decision maker; when k = 1, the corresponding calculation rules proposed in the embodiment of the present invention are used to obtain Compared with the addition and multiplication rules proposed in the second paper and the division rules proposed in the first paper, The high similarity indicates that the calculation results using the relevant rules in the first and second papers express the decision maker's tendency to be risk neutral.

[0109] Table 5 Differences and similarities between the calculated results and other literature

[0110]

[0111] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0112] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A risk assessment method for ship shore control system based on interval type-2 fuzzy numbers, characterized in that: The following steps are involved: Through the functional analysis of the intelligent ship shore control system, the relevant data of multiple submodules / components are obtained, and the failure probability and loss severity of each submodule / component are evaluated, where the failure probability and loss severity are interval type-2 fuzzy numbers; According to the failure probability and loss severity of each submodule / component, the fuzzy risk of the system or component is estimated by using the improved arithmetic operation rules of interval type-2 fuzzy numbers, wherein the interval type-2 fuzzy number is composed of elements and height, and the improved arithmetic operation rules of interval type-2 fuzzy numbers are obtained by unifying the arithmetic operation rules of the height of interval type-2 fuzzy numbers.

2. The risk assessment method for ship shore control system based on interval type-2 fuzzy numbers according to claim 1 is characterized in that: The expression of the interval type-2 fuzzy number is: In the formula, is the i-th interval type-2 fuzzy number, is the upper bound membership function, is the lower bound membership function, The value of the upper bound membership function is defined as Each element of high, The value of the lower bound membership function is defined as Each element of high, 3. The risk assessment method for ship shore control system based on interval type-2 fuzzy numbers according to claim 1 is characterized in that: The arithmetic operation rules of the improved interval type-2 fuzzy numbers include addition, subtraction, multiplication, division, number multiplication and exponentiation.

4. The risk assessment method for ship shore control system based on interval type-2 fuzzy numbers according to claim 3 is characterized in that: The operation rules for addition are: In the formula, and They represent two interval type-2 fuzzy numbers, k∈{0.5,1,2} is the preference coefficient, which indicates the tolerance to risk. The value of the upper bound membership function is defined as Each element of high, The value of the lower bound membership function is defined as Each element of high, is the upper bound membership function, is the lower bound membership function.

5. The risk assessment method for ship shore control system based on interval type-2 fuzzy numbers according to claim 3 is characterized in that: The operation rules for subtraction are: In the formula, and They represent two interval type-2 fuzzy numbers, k∈{0.5,1,2} is the preference coefficient, which indicates the tolerance to risk. The value of the upper bound membership function is defined as Each element of high, The value of the lower bound membership function is defined as Each element of high, is the upper bound membership function, is the lower bound membership function.

6. The risk assessment method for ship shore control system based on interval type-2 fuzzy numbers according to claim 3 is characterized in that: The multiplication operation rules are: in: In the formula, and They represent two interval type-2 fuzzy numbers, k∈{0.5,1,2} is the preference coefficient, which indicates the tolerance to risk. The value of the upper bound membership function is defined as Each element of high, The value of the lower bound membership function is defined as Each element of high, for or The elements in is the upper bound membership function, is the lower bound membership function.

7. The risk assessment method for ship shore control system based on interval type-2 fuzzy numbers according to claim 3 is characterized in that: The operation rules for division are: In the formula, and They represent two interval type-2 fuzzy numbers, k∈{0.5,1,2} is the preference coefficient, which indicates the tolerance to risk. The value of the upper bound membership function is defined as Each element of high, The value of the lower bound membership function is defined as Each element of high, is the upper bound membership function, is the lower bound membership function, and 8. The risk assessment method for ship shore control system based on interval type-2 fuzzy numbers according to claim 3 is characterized in that: The operation rules for number multiplication are: In the formula, represents an interval type-2 fuzzy number, k∈{0.5,1,2} is the preference coefficient, which represents the tolerance to risk. The value of the upper bound membership function is defined as Each element of high, The value of the lower bound membership function is defined as Each element of high, for, is,λ∈[0,1], is the upper bound membership function, is the lower bound membership function.

9. The risk assessment method for ship shore control system based on interval type-2 fuzzy numbers according to claim 3 is characterized in that: The operation rules of the index are: In the formula, represents an interval type-2 fuzzy number, k∈{0.5,1,2} is the preference coefficient, which represents the tolerance to risk. The value of the upper bound membership function is defined as Each element of high, is the upper bound membership function, The value of the lower bound membership function is defined as Each element of high, is the lower bound membership function, for, is,λ∈[0,1].

10. The risk assessment method for ship shore control system based on interval type-2 fuzzy numbers according to claim 1 is characterized in that: The improved arithmetic operation rules of interval type-2 fuzzy numbers satisfy one or more of the following characteristics: 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) Speciality: When k = 1, H = (x + y) / 2; in particular, when x = y, H = x = y; Among them, H represents height, x and y represent the heights of two interval type-2 fuzzy numbers respectively.

Images