A method for screening ship aging management objects

Abstract

The present invention discloses a method for screening ship aging management targets, comprising the following steps: 1) obtaining a ship component element diagram for a ship product to be aging managed; 2) constructing a comprehensive assessment model for aging management requirements; 3) obtaining the assessment level of each assessment indicator of the assessment target; 4) constructing an evaluation decision matrix to perform aging management priority assessment; 5) determining the highest priority solution for the elements in the assessment matrix; 6) calculating cloud model similarity; 7) calculating a correlation coefficient Ri based on the cloud model similarity; 8) evaluating the aging management priority of m assessment targets in the ship component element diagram based on the correlation coefficient Ri based on the cloud model similarity; and 9) obtaining the order of aging management priority of the base elements within the entire ship. The present invention establishes a ship aging management target screening method based on cloud model theory, reducing the ambiguity and randomness generated in the subjective assessment process.

Description

Technical Field

[0001] The present invention relates to ship maintenance technology, and in particular to a method for screening ship aging management objects. Background Art

[0002] As a crucial component of lifecycle management in the nuclear power industry, aging management has accumulated extensive experience in theory, technology, and practice, becoming a crucial tool for ensuring the safe operation of nuclear power plants. Within the shipbuilding industry, particularly for specialized vessels equipped with certain high-tech equipment, aging management is urgently needed to reduce the incidence of aging-related failures. While ensuring safe navigation, it can also reduce downtime for maintenance due to aging, improve ship availability, and maximize cost-effectiveness.

[0003] Aging management is gaining increasing importance in ship product assurance. Given the vast and complex variety of equipment and components onboard, scientifically and efficiently selecting the right candidates for aging management is crucial. However, there is a lack of experience in the research and practice of aging management for ship products, and relevant research data and statistical data on aging mechanisms are difficult to obtain. Therefore, a method for selecting candidates for ship aging management is urgently needed. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method for screening ship aging management objects in view of the defects in the prior art.

[0005] The technical solution adopted by the present invention to solve the technical problem is: a method for screening ship aging management objects, comprising the following steps:

[0006] 1) For the ship products to be managed for aging, the ship is divided into the components of each level from top to bottom according to the levels of system, subsystem, equipment, and components to obtain the ship component element diagram;

[0007] 2) Construct a comprehensive evaluation model for aging management needs;

[0008] Set the target layer to aging management priority A,

[0009] The middle criterion layer is: importance of the evaluation object, maintenance cost of the evaluation object, and aging trend of the evaluation object;

[0010] The evaluation indicators of the indicator layer are: the impact of the evaluation object on the navigation safety of the ship B1, the impact of the evaluation object on the completion of the ship's mission B2; the maintenance cycle of the evaluation object B3, the maintenance cost of the evaluation object B4; the aging cycle of the evaluation object B5; the study of the aging mechanism of the evaluation object B6;

[0011] On this basis, determine the factor set B = [B1, B2, B3, B4, B5, B6];

[0012] 3) Obtain the evaluation level of each evaluation indicator of the evaluation object;

[0013] Obtain the evaluation grade value of each evaluation indicator of the evaluation object according to the actual situation. The evaluation grade value is in the range of [0,1];

[0014] 4) Construct an evaluation decision matrix to assess the priority of aging management;

[0015] There are m sub-elements in the constituent element diagram. The i-th evaluation object is evaluated against the index B in the factor set. j The evaluation results of the cloud model C are constructed ij , where i = 1, 2, ..., m; j = 1, 2, 3, 4, 5, 6; C ij Construct an evaluation matrix of m rows and 6 columns for each element;

[0016] 5) Determine the solution with the highest priority for the elements in the evaluation matrix;

[0017] The larger the value of the element indicator result in the evaluation matrix, the higher the aging management priority represented; the calculation method is as follows:

[0018] In C ij In the evaluation matrix of m rows and 6 columns constructed for the element, for each column of j = 1, 2, ..., 6, the cloud model with the highest priority of aging management in each column is selected, which is recorded as C maxj , C with j=1,2,……,6 maxj Combine them into vectors in order and record them as the highest priority solution D max In the process of comparing the sizes of cloud models, the expected value of the cloud model feature number is used as the measure, that is, the cloud model with the largest expected value is considered to be the largest cloud model;

[0019] C maxj =max[C 1j ,C 2j ,……,C mj ]=(Ex maxj ,En maxj ,He maxj ),(j=1,2,3,4,5,6)

[0020] D max =max[C max1 ,C max2 ,C max3 ,C max4 ,C max5 ,C max6 ];

[0021] Among them, (Ex maxj ,En maxj ,He maxj ) are the characteristic numbers of the cloud model, namely expectation Ex, entropy En and super entropy He, and the subscript maxj represents the matrix position corresponding to the maximum value of the j-th column;

[0022] 6) Calculate cloud model similarity;

[0023] Calculate the cloud model similarity, and use the cloud model similarity to measure the comparison between the evaluation result vector of each evaluation object and the solution with the highest priority;

[0024] The cloud model similarity is calculated as follows:

[0025] Assume that the feature numbers of the two cloud models are (Ex1, En1, He1) and (Ex2, En2, He2), and the similarity S between them is calculated as follows.

[0026]

[0027] S=S1×S2

[0028] 7) Calculate the similarity of the 6 indicator evaluation results between each evaluation object and the highest priority solution, and calculate the correlation coefficient R based on the cloud model similarity i ;

[0029] First, based on the evaluation objects in steps 4 and 5, the index B in the factor set j Evaluation results of C ij And the highest priority solution D max Medium Index B j Evaluation results of C maxj , calculate the similarity S of the cloud model ij ; Then, the correlation coefficient R is calculated based on the cloud model similarity i ;

[0030] From step 4, we can see that the i-th evaluation object is for the indicator B in the factor set j The evaluation result is cloud model C ij (Ex ij ,En ij ,He ij ), (i=1,2,……,m;j=1,2,3,4,5,6); From step 5, we can determine the solution with the highest priority D max Medium Index B j The evaluation result is cloud model C maxj =(Ex maxj ,En maxj ,He maxj ), computing cloud model C ij with C maxjSimilarity S ij , the formula is as follows:

[0031] S ij =S ij1 ×S ij2

[0032]

[0033] Calculate the correlation coefficient R based on the cloud model similarity i , the formula is as follows:

[0034] P=max[max(S ij )], (i=1,2,...,m; j=1,2,3,4,5,6)

[0035] Q=min[min(S ij )], (i=1,2,...,m; j=1,2,3,4,5,6)

[0036]

[0037] Among them, ξ∈(0,1), here we take ξ=0.5, P is S ij The maximum value in row m and column j, Q is S ij The minimum value in the mth row and jth column, ξ is a preset constant, R i is the correlation coefficient of the i-th evaluation object.

[0038] 8) According to the correlation coefficient R based on the cloud model similarity i , sort the aging management priorities of m evaluation objects, R i The larger the value, the higher the aging management priority of the corresponding evaluation object.

[0039] 9) Based on the methods of steps 4) to 8), the aging management priority of other parts in the ship's component element diagram is evaluated in turn. After all evaluations are completed, the weight of the aging management priority of the sub-elements is calculated. For each base element in the bottom layer, its own weight is multiplied by the weight value of each level above it from bottom to top, until the top layer, and the aging management priority weight value of each element (component) in the bottom layer of the ship element diagram is obtained within the entire ship, which is recorded as T x (x is the base element number of the lower layer). x Sort from large to small to obtain the order of aging management priority of basic elements within the entire ship based on the splitting of the ship component element diagram in step 1.

[0040] The beneficial effects produced by the present invention are:

[0041] The present invention establishes a method for screening ship aging management objects based on cloud model theory, which reduces the ambiguity and randomness generated in the subjective evaluation process. At the same time, this method relies less on data and is suitable for the current situation where there is a lack of research and practical experience in aging management of ship products and relevant statistical data is difficult to obtain. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0043] Figure 1 is a flow chart of a method according to an embodiment of the present invention;

[0044] Figure 2 This is a diagram of the components of a ship according to an embodiment of the present invention;

[0045] Figure 3 is an evaluation index diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0047] like Figure 1 As shown, a method for screening ship aging management objects includes the following steps:

[0048] 1) For the ship products to be managed for aging, the ship is divided into the components of each level from top to bottom according to the levels of system, subsystem, equipment, and components to obtain the ship component element diagram;

[0049] Said systems include hull structure, mechanical system and electrical system;

[0050] Subsystem division;

[0051] The hull structure is divided into: bottom structure, side structure, deck structure and bulkhead structure;

[0052] The mechanical system is divided into: power system, transmission system, execution system and control system;

[0053] The electrical system is divided into: power system, electric propulsion system, lighting system and communication and navigation system;

[0054] Then get the related equipment and its parts in turn; Figure 2 As shown;

[0055] 2) Construct a comprehensive evaluation model for aging management needs; the evaluation index diagram is as follows: Figure 3 As shown;

[0056] Set the target layer to aging management priority A,

[0057] The middle criterion layer is: importance of the evaluation object, maintenance cost of the evaluation object, and aging trend of the evaluation object;

[0058] The evaluation indicators of the indicator layer are: the impact of the evaluation object on the navigation safety of the ship B1, the impact of the evaluation object on the completion of the ship's mission B2; the maintenance cycle of the evaluation object B3, the maintenance cost of the evaluation object B4; the aging cycle of the evaluation object B5; the study of the aging mechanism of the evaluation object B5;

[0059] On this basis, determine the factor set B = [B1, B2, B3, B4, B5, B5];

[0060] 3) Obtain the evaluation level of each evaluation indicator of the evaluation object;

[0061] Obtain the evaluation grade value of each evaluation indicator of the evaluation object according to the actual situation. The evaluation grade value is in the range of [0,1];

[0062] 4) Construct an evaluation decision matrix to assess the priority of aging management;

[0063] There are m sub-elements in the constituent element diagram. The i-th evaluation object is evaluated against the index B in the factor set. j The evaluation results are used to construct the cloud model C through the reverse cloud generator ij , where i = 1, 2, ..., m; j = 1, 2, 3, 4, 5, 6; C ij Construct an evaluation matrix of m rows and 6 columns for each element;

[0064] 5) Determine the solution with the highest priority;

[0065] Determine whether each evaluation indicator in the factor set is a positive indicator or a negative indicator. The larger the value of the positive indicator result, the higher the aging management priority it represents; the smaller the value of the negative indicator result, the higher the aging management priority it represents;

[0066] In C ij In the evaluation matrix of m rows and 6 columns constructed for the element, for each column of j = 1, 2, ..., 6, the cloud model with the highest priority of aging management in each column is selected, which is recorded as C maxj , C with j=1,2,……,6 maxj Combine them into vectors in order and record them as the highest priority solution D max In the process of comparing the sizes of cloud models, the expected value of the cloud model feature number is used as the measure, that is, the cloud model with the largest expected value is considered to be the largest cloud model;

[0067] C maxj =max[C1j ,C 2j ,……,C mj ]=(Ex maxj ,En maxj ,He maxj ),(j=1,2,3,4,5,6)

[0068] D max =max[C max1 ,C max2 ,C max3 ,C max4 ,C max5 ,C max6 ];

[0069] Among them, (Ex maxj ,En maxj ,He maxj ) are the characteristic numbers of the cloud model, namely expectation Ex, entropy En and super entropy He, and the subscript maxj represents the matrix position corresponding to the maximum value of the j-th column;

[0070] 6) Calculate cloud model similarity;

[0071] Calculate the cloud model similarity, and use the cloud model similarity to measure the comparison between the evaluation result vector of each evaluation object and the solution with the highest priority;

[0072] Assume that the feature numbers of the two cloud models are (Ex1, En1, He1) and (Ex2, En2, He2), and the similarity S between them is calculated as follows:

[0073]

[0074] S=S1×S2

[0075] 7) Calculate the similarity of the 6 indicator evaluation results between each evaluation object and the highest priority solution, and calculate the correlation coefficient R based on the cloud model similarity i ;

[0076] First, based on the evaluation objects in steps 4 and 5, the index B in the factor set j Evaluation results of C ij And the highest priority solution D max Medium Index B j Evaluation results of C maxj , calculate the similarity S of the cloud model ij ; Then, the correlation coefficient R is calculated based on the cloud model similarity i .

[0077] From step 4, we can see that the i-th evaluation object is for the indicator B in the factor set j The evaluation result is cloud model C ij(Ex ij ,En ij ,He ij ), (i=1,2,……,m;j=1,2,3,4,5,6); From step 5, we can determine the solution with the highest priority D max Medium Index B j The evaluation result is cloud model C maxj =(Ex maxj ,En maxj ,He maxj ), computing cloud model C ij with C maxj Similarity S ij , the formula is as follows:

[0078] S ij =S ij1 ×S ij2

[0079]

[0080] Calculate the correlation coefficient R based on the cloud model similarity i , the formula is as follows.

[0081] P=max[max(S ij )], (i=1,2,...,m; j=1,2,3,4,5,6)

[0082] Q=min[min(S ij )], (i=1,2,...,m; j=1,2,3,4,5,6)

[0083]

[0084] Among them, ξ∈(0,1), here we take ξ=0.5.

[0085] 8) According to the correlation coefficient R based on the cloud model similarity i , sort the aging management priorities of m evaluation objects, R i The larger it is, the higher the priority of aging management of the corresponding assessment object;

[0086] 9) Based on the method described in steps 4) to 8), the aging management priority of other parts in the ship component element diagram is evaluated in turn. After all the evaluations are completed, the weight of the aging management priority of the sub-elements is calculated. For each base element in the bottom layer, its own weight is multiplied by the weight value of each level above it from bottom to top, until the top layer, and the aging management priority weight value of each element in the bottom layer of the ship element diagram within the entire ship is obtained, which is recorded as T x (x is the base element number of the lower layer). xSort from large to small, and according to the splitting of the ship's component element diagram, obtain the order of aging management priority of the basic elements within the entire ship.

[0087] It should be understood that those skilled in the art can make improvements or changes based on the above description, and all such improvements and changes should fall within the scope of protection of the appended claims of the present invention.

Claims

1. A method for screening ship aging management objects, characterized in that: The following steps are involved: 1) For the ship products to be managed for aging, the ship is divided into the components of each level from top to bottom according to the levels of system, subsystem, equipment, and components to obtain the ship component element diagram; 2) Construct a comprehensive evaluation model for aging management needs; Set the target layer to aging management priority A, The middle criterion layer is: importance of the evaluation object, maintenance cost of the evaluation object, and aging trend of the evaluation object; The evaluation indicators of the indicator layer are: the impact of the evaluation object on the navigation safety of the ship B1, the impact of the evaluation object on the completion of the ship's mission B2; the maintenance cycle of the evaluation object B3, the maintenance cost of the evaluation object B4; the aging cycle of the evaluation object B5; the study of the aging mechanism of the evaluation object B6; On this basis, determine the evaluation index factor set B = [B1, B2, B3, B4, B5, B6]; 3) Obtain the evaluation level of each evaluation indicator of the evaluation object; Obtain the evaluation grade value of each evaluation indicator of the evaluation object according to the actual situation. The evaluation grade value is in the range of [0,1]; 4) Construct an evaluation decision matrix to assess the priority of aging management; There are m sub-elements in the constituent element diagram. The i-th evaluation object is evaluated against the index B in the factor set. j The evaluation results of the cloud model C are constructed ij , where i = 1, 2, ..., m; j = 1, 2, 3, 4, 5, 6; C ij Construct an evaluation matrix of m rows and 6 columns for each element; 5) Determine the solution with the highest priority for the elements in the evaluation matrix; 6) Calculate the cloud model similarity and use the cloud model similarity to measure the comparison between the evaluation results of each evaluation object and the solution with the highest priority; 7) Calculate the similarity of the 6 indicator evaluation results between each evaluation object and the highest priority solution, and calculate the correlation coefficient R based on the cloud model similarity i ; 8) According to the correlation coefficient R based on the cloud model similarity i , evaluate the aging management priority of m assessment objects in the ship component element diagram, R i The larger it is, the higher the priority of aging management of the corresponding assessment object; 9) Based on the aging management priority of m assessment objects, according to the aging management priority weight value T of each element x in the bottom layer of the ship element diagram within the whole ship x , and obtain the order of aging management priority of each basic element in the entire ship.

2. The method for screening ship aging management objects according to claim 1, characterized in that: In step 5), the solution with the highest priority is determined as follows: In C ij In the evaluation matrix of m rows and 6 columns constructed for the element, for each column of j = 1, 2, ..., 6, the cloud model with the highest priority of aging management in each column is selected, which is recorded as C maxj , C with j=1,2,……,6 maxj Combine them into vectors in order and record them as the highest priority solution D max In the process of comparing the sizes of cloud models, the expected value of the cloud model feature number is used as the measure, that is, the cloud model with the largest expected value is considered to be the largest cloud model; C maxj =max[C 1j ,C 2j ,……,C mj ]=(Ex maxj ,En maxj ,He maxj 0,(j=1,2,3,4,5,6) D max =max[C max1 ,C max2 ,C max3 ,C max4 ,C max5 ,C max6 ]; Among them, (Ex maxj ,En maxj ,He maxj ) are the characteristic numbers of the cloud model, namely expectation Ex, entropy En and super entropy He, and the subscript maxj represents the matrix position corresponding to the maximum value of the j-th column.

3. The method for screening ship aging management objects according to claim 1, characterized in that: In step 6), the cloud model similarity is calculated as follows: Assume that the feature numbers of the two cloud models are (Ex1, En1, He1) and (Ex2, En2, He2), and the similarity S between them is calculated as follows: S=S1×S2.

4. The method for screening ship aging management objects according to claim 1, characterized in that: In step 7), based on the evaluation object and the factor set index B j Evaluation results of C ij And the highest priority solution D max Medium Index B j Evaluation results of C maxj , calculate the similarity S of the cloud model ij ; Then, the correlation coefficient R is calculated based on the cloud model similarity i .

5. The method for screening ship aging management objects according to claim 1, characterized in that: In step 7), the similarity S of the cloud model is calculated ij The formula used is as follows: The i-th evaluation object is based on the indicator B in the factor set. j The evaluation result is cloud model C ij (Ex ij ,En ij ,He ij ), i=1,2,……,m;j=1,2,3,4,5,6;the highest priority solution is D max Medium Index B j The evaluation result is cloud model C maxj =(Ex maxj ,En maxj ,He maxj ), computing cloud model C ij with C maxj Similarity S ij , S ij =S ij1 ×S ij2 6. The method for screening ship aging management objects according to claim 1, characterized in that: In step 7), the correlation coefficient R based on the cloud model similarity is calculated. i , the formula is as follows: P=max[max(S ij )],(i=1,2,……,m;j=1,2,3,4,5,6) Q=min[min(S ij )],(i=1,2,……,m;j=1,2,3,4,5,6) Where P is S ij The maximum value in row m and column j, Q is S ij The minimum value in the mth row and jth column, ξ is a preset constant, R i is the correlation coefficient of the i-th evaluation object.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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