CN112766515A - Complicated equipment maintenance strategy selection method based on extension theory - Google Patents

Abstract

The invention relates to the technical field of complex equipment maintenance, in particular to a complex equipment maintenance strategy selection method based on an extension theory. Which comprises the following steps: step S1, establishing an evaluation index system; step S2, determining the weight matrix of all the first-level indexes based on the correlation matrix method; step S3, establishing a maintenance strategy set; step S4, performing dimensionless processing on all the original index data sets; and step S5, selecting an optimal maintenance strategy based on an extension theory. By the above, the maintenance strategy can be preferably evaluated.

Description

Complicated equipment maintenance strategy selection method based on extension theory

Technical Field

The invention relates to the technical field of complex equipment maintenance, in particular to a complex equipment maintenance strategy selection method based on an extension theory.

Background

The functions of different devices in the production and operation processes of enterprises are different, and the adopted maintenance modes are different. Excellent equipment maintenance comes from outstanding equipment maintenance strategies. Strategic equipment maintenance strategies are a beneficial means to increase the value of a company's assets, and therefore, consideration must be given to strategy selection.

The equipment fault not only affects the operation of the equipment, but also causes certain economic loss, and along with the increasing complexity of the equipment, the maintenance work difficulty is larger and larger, the maintenance resources are continuously increased, and the maintenance time is longer and longer, so that a more appropriate maintenance strategy is selected, and the loss is greatly reduced as much as possible.

At present, the evaluation of the equipment maintenance strategy is generally limited to a certain aspect, and an index system for comprehensive consideration is not provided. When the existing equipment maintenance strategy is evaluated, the existing equipment maintenance strategy is mainly divided into two types: qualitative and quantitative methods. The qualitative method is too subjective, the quantitative method is too dependent on data, and the result is influenced too much by the drastic change of the data.

Disclosure of Invention

The invention provides a complicated equipment maintenance strategy selection method based on an extensional theory, which can overcome some or some defects in the prior art.

The invention discloses a complicated equipment maintenance strategy selection method based on an extension theory, which comprises the following steps:

step S1, establishing an evaluation index system, where the established evaluation index system includes a primary index set a, where a is { a ═ a }_j1, 2, … … n, where a_jRepresenting the jth primary index, wherein n is a positive integer;

step S2, determining a weight matrix W of all primary indexes based on the correlation matrix method, where W is { W ═ W }_j|j＝1，2，……n}，w_jA weight representing the jth primary index;

step S3, establishing a maintenance strategy set X, X ═ X_i1, 2, … … m, where x is_iRepresenting the ith maintenance strategy, and taking a positive integer as m; each maintenance strategy x_iAll contain all the first-level indexes in the first-level index set A, and the x < th > index_iThe original data value of all the first-level indexes in each maintenance strategy is collected into an original index data set A_i，A_i＝{a_ij|i＝1，2，……m，j＝1，2，……n}，a_ijRaw data representing the jth primary index in the ith maintenance strategy;

step S4, for all the original index data sets A_iDimension removing processing is carried out, and a dimension removing index data set A 'is further obtained'_i，A′_i＝{v_ij|i＝1，2，……m，j＝1，2，……n}，v_ijDimensionless data representing a jth primary index in an ith maintenance strategy;

and step S5, selecting an optimal maintenance strategy based on an extension theory.

Compared with the existing qualitative or quantitative evaluation method, the method disclosed by the invention can combine the advantages of qualitative and quantitative analysis, can perform quantitative analysis on the influence of each index based on the correlation matrix method, and can perform qualitative analysis on the strategy based on the development set theory, so that better mutual support can be realized, and the evaluation result is more scientific and accurate.

Preferably, in step S2, the weighting matrix W of all primary indexes is determined by the a · gulin method (KLEE method). So that all primary indexes can be better quantitatively analyzed.

Preferably, step S2 specifically includes the steps of,

step S21, obtaining importance R (A) of each primary index_j)，R(A_j) Indicates the importance of the j-th index, j ═ 1, 2, … … n;

in the step, the nth primary index a is used_nIs denoted as 1, i.e. R (A)_n) 1 is ═ 1; and the ratio of the importance of the j-th index to the j + 1-th index is recorded as R (A)_j，A_j+1) Then according to the formula R (A)_j)＝R(A_j，A_j+1)×R(A_j+1) The importance R (A) of all the first-level indexes can be obtained_j)；

Step S22, importance R (A) for each primary index_j) Normalization processing is carried out, and the value after normalization processing is taken as the weight w of the corresponding first-level index_j；

In this step, according to the formula

Obtaining the weight w of all the first-level indexes_j。

Through the steps S21 and S22, the weight w of each primary index is preferably obtained_jAnd thus the weight matrix W can be preferably constructed.

Preferably, in step S4, the larger the value, the better the index, and the corresponding dimensionless data v_ijThe calculation formula of (a) is as follows,

for the index with smaller numerical value and better numerical value, the corresponding dimensionless data v_ijThe calculation formula of (a) is as follows,

through the method, the dimensionless operation can be preferably carried out on the original data, and further the unification and standardization of all the original data can be preferably realized.

Preferably, step S5 specifically includes the steps of,

step S51, a policy evaluation level N is constructed, N ═ N { [ N ]_t|t＝1，2，……p}，N_tRepresenting the evaluation grade of the t-th strategy, and taking a positive integer as p; and constructing a classical domain R and a section domain R of each strategy evaluation level based on an extension theory_D，R＝{R_t|t＝1，2，……p}，R_tChannel for representing t-th policy evaluation levelA dictionary field;

step S52, obtaining each dimensionless index data set A'_iThe matrix of relevance K, K ═ K { K } in each classical domain R_ijt|i＝1，2，……m，j＝1，2，……n，t＝1，2，……p}，K_ijtThe association degree of a jth index in the ith maintenance strategy and a tth strategy evaluation level classical domain is represented;

step S53, obtaining an evaluation value matrix D of all maintenance strategies according to the formula D ═ W · K, where D ═ K_it|i＝1，2，……m，t＝1，2，……p}，D_itRepresenting the correlation degree of the ith maintenance strategy and the tth strategy evaluation level;

and step S54, acquiring the maintenance strategy closest to the required strategy evaluation grade according to the evaluation value matrix D.

Through steps S51-S54, the association degree between each maintenance strategy and all the strategy evaluation levels can be preferably calculated, and the optimal maintenance strategy can be preferably selected according to the calculated association degree.

Preferably, in step S53, the association degree K between the jth index in the ith maintenance strategy and the tth strategy evaluation level classical domain_ijtThe calculation is performed according to the following formula,

where ρ (v)_ij，u_tj) Representing dimensionless data v_ijDistance from the t-th policy evaluation level classical domain, D (v)_ij，u_tj，u_D) Representing dimensionless data v_ijDistance from pitch domain;

wherein the content of the first and second substances,

a is a value range u_tjB is a value range u_tjUpper limit value of (i.e. u)_tj∈<a，b>；

Wherein the content of the first and second substances,

thereby, the matrix K of the degree of association can be preferably acquired.

Preferably, step S5 is performed by the steps of,

step S51a, removing dimension index data sets A'_iSequentially carrying out weighting calculation with the weight matrix W of all the first-level indexes, and further acquiring the evaluation value of each maintenance strategy;

s51b, constructing strategy evaluation levels, and constructing a classical domain and a section domain of each strategy evaluation level based on an extension theory;

and S51c, evaluating the membership degree of each maintenance strategy and the strategy evaluation grade by adopting the association degree, and further selecting the optimal maintenance strategy.

By the above, the algorithm can be simplified preferably.

Drawings

FIG. 1 is a schematic diagram of an evaluation index system in example 1;

Detailed Description

For a further understanding of the invention, reference should be made to the following detailed description taken in conjunction with the accompanying drawings and examples. It is to be understood that the examples are illustrative of the invention and not limiting.

Example 1

The embodiment provides a method for selecting a complex equipment maintenance strategy based on an extensional theory, which comprises the following steps:

step S1, establishing an evaluation index system, where the established evaluation index system includes a primary index set a, where a is { a ═ a }_j1, 2, … … n, where a_jRepresenting the jth primary index, wherein n is a positive integer;

step S2, determining a weight matrix W of all primary indexes based on the correlation matrix method, where W is { W ═ W }_j|j＝1，2，……n}，w_jA weight representing the jth primary index;

step S3, establishing a maintenance strategy set X, X ═ X_i1, 2, … … m, where x is_iIndicating the ith maintenance strategyM is a positive integer; each maintenance strategy x_iAll contain all the first-level indexes in the first-level index set A, and the x < th > index_iThe original data value of all the first-level indexes in each maintenance strategy is collected into an original index data set A_i，A_i＝{a_ij|i＝1，2，……m，j＝1，2，……n}，a_ijRaw data representing the jth primary index in the ith maintenance strategy;

step S4, for all the original index data sets A_iDimension removing processing is carried out, and a dimension removing index data set A 'is further obtained'_i，A′_i＝{v_ij|i＝1，2，……m，j＝1，2，……n}，v_ijDimensionless data representing a jth primary index in an ith maintenance strategy;

and step S5, selecting an optimal maintenance strategy based on an extension theory.

Compared with the existing qualitative or quantitative evaluation method, the method in the embodiment can combine the advantages of qualitative and quantitative analysis, can perform quantitative analysis on the influence of each index based on the correlation matrix method, and can perform qualitative analysis on the strategy based on the development set theory, so that better mutual support can be realized, and the evaluation result is more scientific and accurate.

In step S1 of the present embodiment, an evaluation index system can be constructed based on experience.

As shown in FIG. 1, this embodiment is used as a specific embodiment, and the present invention is described by taking a specific evaluation index system as an example, wherein the evaluation index system comprises 5 primary indexes, each of which is quality (a)₁) Task amount (a)₂) Cost (a)₃) Safety (a)₄) And environment (a)₅) That is, in this embodiment, n is 5. Wherein, the mass (a)₁) Indicating quality of service, amount of task (a)₂) Represents the time and cost (a) required for maintenance₃) Indicating the cost of maintenance, safety (a)₄) Indicating safety during maintenance, environment (a)₅) Indicating the environmental protection of the maintenance.

In step S2 of the present embodiment, the weighting matrix W of all the primary indexes is determined by the a · gulin method (KLEE method). Therefore, the phenomenon that a certain index has a weight of 0 can be avoided better, and the importance of each primary index can be evaluated quantitatively better.

In this embodiment, step S2 specifically includes the following steps:

step S21, obtaining importance R (A) of each primary index_j)，R(A_j) Indicates the importance of the j-th index, j ═ 1, 2, … … n;

in the step, the nth primary index a is used_nIs denoted as 1, i.e. R (A)_n) 1 is ═ 1; and the ratio of the importance of the j-th index to the j + 1-th index is recorded as R (A)_j，A_j+1) Then according to the formula R (A)_j)＝R(A_j，A_j+1)×R(A_j+1) The importance R (A) of all the first-level indexes can be obtained_j)；

Step S22, importance R (A) for each primary index_j) Normalization processing is carried out, and the value after normalization processing is taken as the weight w of the corresponding first-level index_j；

In this step, according to the formula

Obtaining the weight w of all the first-level indexes_j。

Through the steps S21 and S22, the weight w of each primary index is preferably obtained_jAnd thus the weight matrix W can be preferably constructed.

In conjunction with table 1, this embodiment is given as a specific embodiment of the relationship among the importance ratio, the importance, and the weight value in this embodiment.

TABLE 1 relationship between importance ratio, importance and weight value of all primary indexes in this embodiment

Index (a)j)	Ratio of importance (R (A)j，Aj+1))	Importance (R (A)j))	Weight (w)j)
				Mass (a)1)	R(A1，A2)＝4	R(A1)＝4	w1＝0.4
Task volume (a)2)	R(A2，A3)＝0.5	R(A2)＝1	w2＝0.1
				Cost (a)3)	R(A3，A4)＝1	R(A3)＝2	w3＝0.2
Safety (a)4)	R(A4，A5)＝2	R(A4)＝2	w4＝0.2
				Environment (a)5)	//	R(A5)＝1	w5＝0.1

As can be seen from the above table, the weight matrix W in the present embodiment is [0.4, 0.1, 0.2, 0.2, 0.1 ].

In step S3 of this embodiment, by establishing the maintenance strategy set X, all the candidate maintenance strategies can be preferably listed, and then screening is performed, so that the optimal maintenance strategy can be preferably selected.

This embodiment is a specific embodiment, and the present invention is described by taking a maintenance strategy set X as an example.

This example gives 3 maintenance strategies (i.e., i ═ 3), which are raw data for each index of the 3 maintenance strategies in this example, as shown in table 2.

TABLE 2 raw data for each index of the 3 maintenance strategies in this example

	Mass (a)1)	Task volume (a)2)	Cost (a)3)	Safety (a)4)	Environment (a)5)
						Maintenance strategy x1	200％	1000 hours	1000 ten thousand	10％	50 ten thousand
Maintenance strategy x2	100％	500 hours	400 ten thousand	6％	40 ten thousand
						Maintenance strategy x3	50％	100 hours	200 ten thousand	2％	30 ten thousand

In this embodiment, the improvement rate of the performance of the apparatus before and after maintenance is selected as the quality (a)₁) Selecting the time spent on maintenance as the task amount (a)₂) Selecting the cost (a) as the cost required by maintenance₃) Selecting the risk rate of accidents in the maintenance process as the safety (a)₄) Selecting the environmental maintenance cost after the maintenance is finished as the environment (a)₅) The original data of (1).

In step S4 of the present embodiment, the larger the value is, the better the index is, the corresponding dimensionless data v_ijThe calculation formula of (a) is as follows,

for the index with smaller numerical value and better numerical value, the corresponding dimensionless data v_ijThe calculation formula of (a) is as follows,

through the method, the dimensionless operation can be preferably carried out on the original data, and further the unification and standardization of all the original data can be preferably realized.

For the present embodiment, the mass (a)₁) The larger the index, the more preferable the index, and the smaller the index, the more preferable the index.

Shown in table 3, is the dimensionless data for each index of the 3 maintenance strategies in this embodiment.

TABLE 3 Dedimensionless data for each index of 3 maintenance strategies in this example

	Mass (a)1)	Task volume (a)2)	Cost (a)3)	Safety (a)4)	Environment (a)5)
						Maintenance strategy x1	1	0.1	0.2	0.2	0.6
Maintenance strategy x2	0.5	0.2	0.5	0.3	0.75
						Maintenance strategy x3	0.25	1	1	1	1

In this embodiment, step S5 specifically includes the following steps,

step S51, a policy evaluation level N is constructed, N ═ N { [ N ]_t|t＝1，2，……p}，N_tRepresenting the evaluation grade of the t-th strategy, and taking a positive integer as p; and constructing a classical domain R and a section domain R of each strategy evaluation level based on an extension theory_D，R＝{R_t|t＝1，2，……p}，R_tA classic field representing a t-th policy evaluation level;

step S52, obtaining each dimensionless index data set A^′ _iThe matrix of relevance K, K ═ K { K } in each classical domain R_ijt|i＝1，2，……m，j＝1，2，……n，t＝1，2，……p}，K_ijtThe association degree of a jth index in the ith maintenance strategy and a tth strategy evaluation level classical domain is represented;

step S53, obtaining an evaluation value matrix D of all maintenance strategies according to the formula D ═ W · K, where D ═ K_it|i＝1，2，……m，t＝1，2，……p}，D_itRepresenting the correlation degree of the ith maintenance strategy and the tth strategy evaluation level;

and step S54, acquiring the maintenance strategy closest to the required strategy evaluation grade according to the evaluation value matrix D.

Through steps S51-S54, the association degree between each maintenance strategy and all the strategy evaluation levels can be preferably calculated, and the optimal maintenance strategy can be preferably selected according to the calculated association degree.

In step S51 of the present embodiment, R_t＝(N_t，a_j，u_tj)，u_tjRepresents a primary index a_jAt a policy evaluation level N_tThe value range of (1); r_D＝(N_D，a_j，u_D)，N_DDenotes the totality of policy evaluation levels, u_D(0, 1). By the above, a certain level index a can be better matched_jAt a certain policy evaluation level N_tThe value range is adjusted, so that the method has better flexibility and accuracy.

This embodiment is a specific example, and the present invention will be described with p ═ 3 as an example. That is, in the present embodiment, there are 3 policy evaluation levels, each of which is N₁、N₂And N₃In which N is₁Indicating bad, N₂In the representation, N₃Indicating a good representation.

As a specific example, the classical domain R and the nodal domain R of the present invention_DThe construction of (A) is as follows:

in step S53 of this embodiment, the association degree K between the jth index in the ith maintenance strategy and the tth strategy evaluation level classical domain_ijtThe calculation is performed according to the following formula,

where ρ (v)_ij，u_tj) Representing dimensionless data v_ijDistance from the t-th policy evaluation level classical domain, D (v)_ij，u_tj，u_D) Representing dimensionless data v_ijDistance from pitch domain;

wherein the content of the first and second substances,

a is a value range u_tjB is a value range u_tjUpper limit value of (i.e. u)_tj∈<a，b>；

Wherein the content of the first and second substances,

in this embodiment, the x-th₁The relevancy matrix K1 for each maintenance strategy is,

according to the formula, D is W.K, that is, the x-th₁The value matrix D1 for each repair strategy is,

D1＝(-0.46 -0.73 -0.42)。

in this embodiment, the x-th₂The relevancy matrix K2 for each maintenance strategy is,

according to the formula, D is W.K, that is, the x-th₁The value matrix D2 for each repair strategy is,

D2＝(-0.1 -0.15 -0.44)。

in this embodiment, the x-th₃The relevancy matrix K3 for each maintenance strategy is,

according to the formula, D is W.K, that is, the x-th₁The value matrix D3 for each repair strategy is,

D3＝(-0.66 -0.8 -0.28)。

in a clear view of the above, it is known that,

in step S54 of the present embodiment, when the maintenance strategy is selected, the maintenance strategy having the highest degree of membership to the required strategy evaluation level may be selected.

Wherein, the association degree D of the ith maintenance strategy and the tth strategy evaluation level_itIndicating the distance of the maintenance strategy from the rating of the corresponding strategy, i.e. D_itThe larger the absolute value of (a) is, the farther the distance from the corresponding policy evaluation level is, the less the policy evaluation level is.

In selecting a desired maintenance strategy as in this embodiment, it is sufficient to perform vertical screening within a desired strategy evaluation level, for example, the desired strategy evaluation level is "good" N₃When is, x₃The maintenance strategy is closest to the strategy evaluation level N₃So the x-th should be selected₃And (4) maintenance strategies. As another example, the desired policy evaluation level is "Medium" N₂Then the x-th principle should be applied₂And (4) maintenance strategies.

In addition, in step S1 in this embodiment, in the established evaluation index system, each primary index can be evaluated by at least one secondary index, each secondary index can pass through a plurality of tertiary indexes, and so on.

When there are multiple levels of indices in the evaluation index system, the method in step S2 can be used to obtain the weight of each level of indices. In addition, when the raw data of the primary index in step S3 is obtained, weighting calculation can be performed from the lowest-level index layer by layer upward, and the raw data of the final primary index can be obtained better, so that the raw index data set a can be obtained better_i。

Example 2

The embodiment also provides a complex equipment maintenance strategy selection method based on the theory of the open science, which is different from the embodiment 1 in that: step S5 is performed using the following steps,

step S51a, removing dimension index data sets A'_iSequentially carrying out weighting calculation with the weight matrix W of all the first-level indexes, and further acquiring the evaluation value of each maintenance strategy;

s51b, constructing strategy evaluation levels, and constructing a classical domain and a section domain of each strategy evaluation level based on an extension theory;

and S51c, evaluating the membership degree of each maintenance strategy and the strategy evaluation grade by adopting the association degree, and further selecting the optimal maintenance strategy.

The method of this embodiment is different from embodiment 1 in that the dimensionless index dataset a 'is first checked out in this embodiment'_iAlthough the accuracy is lower than that of the method in embodiment 1, this embodiment is a preferred embodiment, which can greatly simplify the determination process.

The present invention and its embodiments have been described above schematically, without limitation, and what is shown in the drawings is only one of the embodiments of the present invention, and the actual structure is not limited thereto. Therefore, if the person skilled in the art receives the teaching, without departing from the spirit of the invention, the person skilled in the art shall not inventively design the similar structural modes and embodiments to the technical solution, but shall fall within the scope of the invention.

Claims (7)

1. A method for selecting a complex equipment maintenance strategy based on an extension theory comprises the following steps:

step S1, establishing an evaluation index system, where the established evaluation index system includes a primary index set a, where a is { a ═ a }_j1, 2, … … n, where a_jRepresenting the jth primary index, wherein n is a positive integer;

step S2, determining a weight matrix W of all primary indexes based on the correlation matrix method, where W is { W ═ W }_j|j＝1，2，……n}，w_jA weight representing the jth primary index;

step S3, establishing a maintenance strategy set X, X ═ X_i1, 2, … … m, where x is_iRepresenting the ith maintenance strategy, and taking a positive integer as m; each maintenance strategy x_iAll contain all the first-level indexes in the first-level index set A, and the x < th > index_iThe original data value of all the first-level indexes in each maintenance strategy is collected into an original index data set A_i，A_i＝{a_ij|i＝1，2，……m，j＝1，2，……n}，a_ijRaw data representing the jth primary index in the ith maintenance strategy;

step S4, for all the original index data sets A_iDimension removing processing is carried out, and a dimension removing index data set A 'is further obtained'_i，A′_i＝{v_ij|i＝1，2，……m，j＝1，2，……n}，v_ijDimensionless data representing a jth primary index in an ith maintenance strategy;

and step S5, selecting an optimal maintenance strategy based on an extension theory.

2. The method for selecting the maintenance strategy of the complex equipment based on the theory of topology as claimed in claim 1, wherein: in step S2, a · gulin method (KLEE method) is used to determine the weight matrix W of all the primary indexes.

3. The method for selecting the complex equipment maintenance strategy based on the theory of topology as claimed in claim 2, wherein: the step S2 specifically includes the following steps,

step S21, obtaining importance R (A) of each primary index_j)，R(A_j) Indicates the importance of the j-th index, j ═ 1, 2, … … n;

in the step, the nth primary index a is used_nIs denoted as 1, i.e. R (A)_n) 1 is ═ 1; and the ratio of the importance of the j-th index to the j + 1-th index is recorded as R (A)_j，A_j+1) Then according to the formula R (A)_j)＝R(A_j，A_j+1)×R(A_j+1) The importance R (A) of all the first-level indexes can be obtained_j)；

Step S22, importance R (A) for each primary index_j) Normalization processing is carried out, and the value after normalization processing is taken as the weight w of the corresponding first-level index_j；

In this step, according to the formula

Obtaining the weight w of all the first-level indexes_j。

4. The method for selecting the maintenance strategy of the complex equipment based on the theory of topology as claimed in claim 1, wherein: in step S4, for the index with larger numerical value and better numerical value, the corresponding dimensionless data v_ijThe calculation formula of (a) is as follows,

for the index with smaller numerical value and better numerical value, the corresponding dimensionless data v_ijThe calculation formula of (a) is as follows,

5. the method for selecting the maintenance strategy of the complex equipment based on the theory of topology as claimed in claim 1, wherein: the step S5 specifically includes the following steps,

step S51, a policy evaluation level N is constructed, N ═ N { [ N ]_t|t＝1，2，……p}，N_tRepresenting the evaluation grade of the t-th strategy, and taking a positive integer as p; and constructing a classical domain R and a section domain R of each strategy evaluation level based on an extension theory_D，R＝{R_t|t＝1，2，……p}，R_tA classic field representing a t-th policy evaluation level;

step S52, obtaining each dimensionless index data set A^′ _iThe matrix of relevance K, K ═ K { K } in each classical domain R_ijt|i＝1，2，……m，j＝1，2，……n，t＝1，2，……p}，K_ijtThe association degree of a jth index in the ith maintenance strategy and a tth strategy evaluation level classical domain is represented;

step S53, obtaining an evaluation value matrix D of all maintenance strategies according to the formula D ═ W · K, where D ═ K_it|i＝1，2，……m，t＝1，2，……p}，D_itRepresenting the correlation degree of the ith maintenance strategy and the tth strategy evaluation level;

and step S54, acquiring the maintenance strategy closest to the required strategy evaluation grade according to the evaluation value matrix D.

6. The method for selecting the complex equipment maintenance strategy based on the theory of topology as claimed in claim 5, wherein: in step S53, the association degree K of the jth index in the ith maintenance strategy and the tth strategy evaluation level classical domain_ijtThe calculation is performed according to the following formula,

where ρ (v)_ij，u_tj) Representing dimensionless data v_ijDistance from the t-th policy evaluation level classical domain, D (v)_ij，u_tj，u_D) Representing dimensionless data v_ijDistance from pitch domain;

wherein the content of the first and second substances,

a is a value range u_tjB is a value range u_tjUpper limit value of (i.e. u)_tj∈<a，b>；

Wherein the content of the first and second substances,

7. the method for selecting the complex equipment maintenance strategy based on the theory of topology as claimed in claim 5, wherein: step S5 is performed using the following steps,

step S51a, removing dimension index data sets A'_iSequentially carrying out weighting calculation with the weight matrix W of all the first-level indexes, and further acquiring the evaluation value of each maintenance strategy;

s51b, constructing strategy evaluation levels, and constructing a classical domain and a section domain of each strategy evaluation level based on an extension theory;

and S51c, evaluating the membership degree of each maintenance strategy and the strategy evaluation grade by adopting the association degree, and further selecting the optimal maintenance strategy.

Images