CN114358486A

CN114358486A - Reliability allocation method, apparatus, computer device and storage medium

Info

Publication number: CN114358486A
Application number: CN202111442288.4A
Authority: CN
Inventors: 杨礼浩; 杨洪旗; 潘勇; 路艳春; 刁斌; 吴祥蔚
Original assignee: China Electronic Product Reliability and Environmental Testing Research Institute
Current assignee: China Electronic Product Reliability and Environmental Testing Research Institute
Priority date: 2021-11-30
Filing date: 2021-11-30
Publication date: 2022-04-15

Abstract

The application relates to a reliability allocation method, a reliability allocation device, a computer device, a storage medium and a computer program product. The method comprises the following steps: determining a target weight vector based on an interval relation matrix, wherein the interval relation matrix is a matrix obtained according to a priority relation matrix, and the priority relation matrix is a matrix determined according to a scoring result obtained by scoring constraint conditions influencing reliability distribution; acquiring a first distribution index of the reliability of equipment in an equipment system; and determining a second distribution index of the reliability of the primary system in the equipment system according to a first relation, wherein the first relation is a relation among the target weight vector, the distribution index and the second distribution index. The method can improve the accuracy of the reliability distribution result.

Description

Reliability allocation method, apparatus, computer device and storage medium

Technical Field

The present application relates to the field of reliability allocation technologies, and in particular, to a reliability allocation method, apparatus, computer device, storage medium, and computer program product.

Background

The reliability distribution is an important basic work in the equipment development process, and has important significance for determining and quantitatively analyzing the reliability index. The reliability distribution is to divide the system reliability index specified in the equipment development stage from top to bottom step by step according to a certain distribution principle and method, and reasonably distribute each system, subsystem and equipment.

In the process of developing modern equipment, the characteristics of system complexity and function diversification are more and more obvious, the factors influencing the reliability of a complex system are more, and the factors influencing the reliability comprise partial uncertain factors which can not be quantitatively described, so that the data of the related reliability is relatively lacked, and the development of distribution work is not facilitated.

At present, a scoring distribution method is mainly adopted to carry out reliability distribution on a complex system, however, the method depends heavily on the experience and self level of scoring experts, and the distribution result is strong in subjectivity, so that the distribution result is low in precision.

Disclosure of Invention

In view of the above, it is necessary to provide a reliability allocation method, an apparatus, a computer device, a computer readable storage medium, and a computer program product, which can improve the accuracy of the reliability allocation result, in view of the above technical problems.

In a first aspect, the present application provides a reliability allocation method. The method comprises the following steps:

determining a target weight vector based on an interval relation matrix, wherein the interval relation matrix is a matrix obtained according to a priority relation matrix, and the priority relation matrix is a matrix determined according to a grading result obtained by grading constraint conditions influencing reliability distribution;

acquiring a first distribution index of the reliability of equipment in an equipment system;

and determining a second distribution index of the reliability of the primary system in the equipment system according to a first relation, wherein the first relation is a relation among the target weight vector, the first distribution index and the second distribution index.

In one embodiment, the method further comprises:

acquiring the reliability relation between the primary system and each subsystem of the primary system;

determining the minimum element value in all element values in the target weight vector according to the target weight vector;

determining a third distribution index of the reliability of the subsystem corresponding to the minimum element value according to a second relation, wherein the second relation is a relation among the reliability relation, the second distribution index, a ratio of each element value in the target weight vector to the minimum element value, and the third distribution index;

determining a fourth distribution index of the reliability of each of the other subsystems according to a third relation, wherein the third relation is a relation among the third distribution index, the fourth distribution index and a ratio of the element value corresponding to the subsystem to the minimum element value, and the other subsystems include the subsystems except the subsystem corresponding to the minimum element value.

In one embodiment, determining the target weight vector based on the interval relation matrix comprises:

determining a weight vector of a fuzzy consistency matrix according to a fourth relational expression, wherein the fourth relational expression is a relational expression among a reciprocal judgment matrix, the weight vector and a historical weight vector of the fuzzy consistency matrix determined at the last time, the reciprocal judgment matrix is a matrix obtained by converting the fuzzy consistency matrix, and the fuzzy consistency matrix is a matrix determined according to a matrix obtained by randomly sampling the interval relational matrix;

if the difference between the weight vector and the historical weight vector is less than or equal to a first preset difference threshold value, the weight vector is taken as the target weight vector.

In one embodiment, the method further comprises:

if the difference between the weight vector and the historical weight vector is greater than the first preset difference threshold, the step of determining the weight vector of the fuzzy consistency matrix according to the fourth relational expression is repeatedly executed until the weight vector corresponding to the difference less than or equal to the first preset difference threshold is determined, and the determined weight vector corresponding to the difference less than or equal to the first preset difference threshold is used as the target weight vector.

In one embodiment, determining the target weight vector based on the interval relation matrix further includes:

determining M weighting weight vectors which are smaller than or equal to the weight vector corresponding to the difference of the first preset difference threshold value, wherein M is an integer larger than 1;

determining the weight vector difference between the weighting weight vector and a historical weighting weight vector, wherein the historical weighting weight vector is the weighting weight vector of the N weight vectors determined at the last time, the N weight vectors are the front weight vectors in the weight vectors corresponding to the difference of the M being less than or equal to the first preset difference threshold, and N is equal to the difference between M and 1;

if the weight vector difference is less than or equal to a second preset difference threshold, the weighted weight vector is taken as the target weight vector.

In one embodiment, the method further comprises:

if the difference of the weight vector is larger than the second preset difference threshold, determining that M +1 is smaller than or equal to the weight vector corresponding to the difference of the first preset difference threshold;

taking M +1 as M, and repeatedly executing the step of determining the weight vector difference between the weighting weight vector and the historical weighting weight vector until determining the weighting weight vector corresponding to the weight vector difference which is less than or equal to the second preset difference threshold value;

and taking the weighted weight vector corresponding to the weight vector difference which is less than or equal to the second preset difference threshold value as the target weight vector.

In one embodiment, the method further comprises:

obtaining a plurality of grading results, wherein each grading result is obtained by grading a plurality of constraint conditions of the equipment system;

determining a corresponding priority relation matrix according to each scoring result;

and determining the interval relation matrix according to the priority relation matrix corresponding to each grading result.

In a second aspect, the present application further provides a reliability assigning apparatus. The device comprises:

the first determination module is used for determining a target weight vector based on an interval relation matrix, wherein the interval relation matrix is obtained according to a priority relation matrix, and the priority relation matrix is determined according to a scoring result obtained by scoring a constraint condition influencing reliability distribution;

the system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring a first distribution index of the reliability of equipment in the equipment system;

a second determining module, configured to determine a second distribution indicator of the reliability of the primary system in the equipment system according to a first relation, where the first relation is a relation between the target weight vector, the first distribution indicator, and the second distribution indicator.

In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the steps of any of the above methods when the processor executes the computer program.

In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of any of the methods described above.

In a fifth aspect, the present application further provides a computer program product. The computer program product comprising a computer program that when executed by a processor implements the steps of any of the methods described above.

The reliability allocation method, the apparatus, the computer device, the storage medium and the computer program product determine the target weight vector based on an interval relation matrix, wherein the interval relation matrix is obtained according to a priority relation matrix, the priority relation matrix is determined according to a scoring result obtained by scoring a constraint condition influencing reliability allocation, a first allocation index of reliability of equipment in the equipment system is obtained, and then a second allocation index of reliability of a primary system in the equipment system is determined according to a first relational expression, wherein the first relational expression is a relational expression among the target weight vector, the first allocation index and the second allocation index. In the traditional scoring distribution method, the result of scoring the constraint condition by an expert is directly used as an input parameter of weighted analysis, the weighted analysis is carried out based on a weighted analysis method, the specific weight of the object to be distributed is analyzed and calculated, and then the reliability distribution is carried out based on the specific weight. In the traditional scoring and allocating method, in the process of weighting analysis, the judgment of the constraint condition is greatly influenced by the subjectivity of a person, so that the conditions that the judgment is unreasonable and does not accord with the reality may exist, and the final allocating result is deviated. The reliability allocation method provided in this embodiment determines the priority relationship matrix according to the scoring result of the expert on the constraint condition, and obtains the interval relationship matrix according to the priority relationship matrix, thereby determining the target weight vector according to the interval relationship matrix, and performing reliability allocation based on the target weight vector. Therefore, the problem of high subjectivity of the distribution result caused by directly carrying out weighted analysis on the scoring result of the constraint condition in the traditional method is solved, the problem of low precision of the distribution result in the traditional method is further solved, and the accuracy of the reliable distribution result is improved.

Drawings

Fig. 1 is a general flowchart of a reliability allocation method according to an embodiment of the present disclosure;

fig. 2 is a schematic flowchart of a reliability allocation method provided in an embodiment of the present application;

FIG. 3 is a schematic diagram illustrating a process for determining reliability indicators of various subsystems according to an embodiment of the present disclosure;

fig. 4 is a schematic flowchart of determining a target weight vector through a single simulation provided in an embodiment of the present application;

fig. 5 is a schematic flowchart of determining a target weight vector through multiple simulations according to an embodiment of the present application;

FIG. 6 is a schematic diagram of another process for determining target weight vectors through multiple simulation determinations, which is provided in the embodiment of the present application;

fig. 7 is a schematic flowchart of determining an interval relationship matrix provided in an embodiment of the present application;

fig. 8 is a schematic structural diagram of a reliability assigning apparatus provided in an embodiment of the present application;

fig. 9 is an internal structural diagram of a computer device in the embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

In this embodiment, a reliability allocation method is provided, and this embodiment is illustrated by applying this method to a computer device, it is to be understood that this method may also be applied to a server, and may also be applied to a system including a computer device and a server, and is implemented by interaction between the computer device and the server.

In the process of modern equipment development, the characteristics of equipment system complexity and function diversification are more and more obvious, and higher requirements are also put forward on the reliability level of the equipment system. The reliability distribution is used as a key reliability work item, and has important significance for the development of reliability design and analysis work of the equipment system. However, the complex equipment has the characteristics of lack of reliability data, fuzzy information quantity and the like in the development stage, and the traditional single reliability allocation method cannot be completely suitable for the reliability allocation work of a modern complex equipment system due to the limitation of the traditional single reliability allocation method, so that the problem that the reliability index of the complex equipment system is difficult to allocate is caused, and further the deep development of the reliability design and analysis work of the equipment is influenced.

In view of the above problems, the present embodiment provides a reliability allocation method using monte carlo simulation in combination with fuzzy hierarchical analysis, which can be used in large complex equipment systems with multiple system components, complex structural relationships, and multiple task functions, such as ships, satellites, and radars. The reliability distribution method provided by the embodiment can effectively solve the problem of distribution of reliability indexes of primary systems and subsystems of large-scale complex systems such as ships, satellites and radars, and plays an important role in promoting engineering application of a simulation-based reliability distribution technology in the large-scale complex systems and promoting equipment reliability engineering construction and the reliability level of the whole equipment.

In order to more clearly describe the reliability allocation method provided by the present embodiment, the reliability allocation method is explained with reference to fig. 1. Referring to fig. 1, fig. 1 is a general flowchart of a reliability allocation method according to an embodiment of the present disclosure.

The equipment system has a hierarchical relationship, the equipment, the primary systems, the subsystems, the equipment and the like are respectively arranged from high to low, the reliability distribution is a process from high level to low level, and index distribution is firstly carried out on each primary system according to the reliability index of the equipment in the whole equipment system; and according to the distribution result of the primary system and the same technical scheme, index distribution is carried out on each subsystem under the primary system.

As shown in fig. 1, the reliability allocation method provided in this embodiment first needs to perform system definition on the equipment system, and then performs fuzzy hierarchical analysis according to the system definition, where the fuzzy hierarchical analysis is mainly used to determine the interval relationship matrix. And then, a Monte Carlo simulation method is used for carrying out simulation and solution on the basis of fuzzy hierarchical analysis. The simulation and solution comprises the following steps: and performing simulation sampling and initial weight vector solving, precision judgment under single simulation and precision judgment under multiple simulations, and further finally determining a target weight vector. So that the reliability assignment of the equipment system can be made based on the finally determined target weight vector. The reliability distribution of the equipment system comprises the reliability distribution of a primary system in the equipment system, and the reliability distribution result of the primary system, and the reliability distribution of each subsystem of the primary system in the equipment system, so that the distribution result of the reliability distribution is obtained.

The system definition refers to analyzing and defining an equipment system for reliability distribution, and comprises analyzing and defining equipment types, system compositions, functional structure relations and constraint conditions. The constraint conditions include the complexity, importance, technical level, environmental conditions, operating time, etc. of each unit, and are factors that affect the reliability allocation of the equipment system. And further, establishing a reliability model of the equipment system by determining the functional structure composition of the system and the logical relationship among the equipment system compositions.

Fig. 2 is a schematic flowchart of a reliability allocation method provided in an embodiment of the present application, where the method is applied to a computer device or a server, and in an embodiment, as shown in fig. 2, the method includes the following steps:

s201, determining a target weight vector based on an interval relation matrix, wherein the interval relation matrix is obtained according to a priority relation matrix, and the priority relation matrix is determined according to a grading result obtained by grading constraint conditions influencing reliability distribution.

In this embodiment, the priority relationship matrix is a matrix determined according to a scoring result obtained by scoring a constraint condition affecting reliability allocation by an expert, and further a section relationship matrix is determined according to the priority relationship matrix, so that a target weight vector can be determined through the section relationship matrix. The interval relation matrix is represented as P, and the target weight vector is represented as W^F，W^F＝(ω₁，ω₂，……，ω_n)^T。

Wherein,

n is the number of the next stage. When the second distribution index for calculating the reliability of the primary system based on the first distribution index of the reliability of the equipment is needed, n is the number of the secondary systems of the equipment.

The target weight vector is determined based on the interval relation matrix, and may be determined by precision determination under single simulation or multiple simulations, which is not limited in this embodiment.

S202, a first distribution index of the reliability of equipment in the equipment system is obtained.

In the present embodiment, assuming that the distribution index of the equipment in the equipment system is the reliability, the first distribution index of the reliability of the equipment in the equipment system, that is, the reliability distribution index of the equipment, is represented as R_mThe index is known. It is understood that the allocation index may be set to other contents, and the present embodiment is described with the allocation index as the reliability.

And S203, determining a second distribution index of the reliability of the primary system in the equipment system according to a first relational expression, wherein the first relational expression is a relational expression among the target weight vector, the first distribution index and the second distribution index.

In this embodiment, the second distribution index of each primary system in the equipment system, i.e. the reliability distribution index of the primary system, is assumed to be R_mi. Since the primary system is generally a series model, expression (1) representing the first relational expression holds. The first relation is a target weight vector and a first distribution index R_mAnd a second distribution index R_miThe relation between them.

Wherein, i is 1-n, and n is the number of the first-level system.

In the first relation, R_mAs is known, the target weight vector is known, i.e. each element ω in the target weight vector is known_iAre also known. Thus, according to the first relation, the second distribution index R of each primary system can be obtained_mi. For example, if n is 4, 4 primary systems are connected in series, the second distribution index of the 1 st primary system in the series model

Second allocation target for 2 nd primary system

Second allocation target for 3 rd primary system

Second allocation target for 4 th primary system

The reliability allocation method provided in this embodiment determines a target weight vector based on an interval relation matrix, where the interval relation matrix is a matrix obtained according to a priority relation matrix, the priority relation matrix is a matrix determined according to a scoring result obtained by scoring a constraint condition affecting reliability allocation, obtains a first allocation index of reliability of equipment in an equipment system, and further determines a second allocation index of reliability of a primary system in the equipment system according to a first relation, where the first relation is a relation among the target weight vector, the first allocation index, and the second allocation index. In the traditional scoring distribution method, the result of scoring the constraint condition by an expert is directly used as an input parameter of weighted analysis, the weighted analysis is carried out based on a weighted analysis method, the specific weight of the object to be distributed is analyzed and calculated, and then the reliability distribution is carried out based on the specific weight. In the traditional scoring and allocating method, in the process of weighting analysis, the judgment of the constraint condition is greatly influenced by the subjectivity of a person, so that the conditions that the judgment is unreasonable and does not accord with the reality may exist, and the final allocating result is deviated. The reliability allocation method provided in this embodiment determines the priority relationship matrix according to the scoring result of the expert on the constraint condition, and obtains the interval relationship matrix according to the priority relationship matrix, thereby determining the target weight vector according to the interval relationship matrix, and performing reliability allocation based on the target weight vector. Therefore, the problem of high subjectivity of the distribution result caused by directly carrying out weighted analysis on the scoring result of the constraint condition in the traditional method is solved, the problem of low precision of the distribution result in the traditional method is further solved, and the accuracy of the reliable distribution result is improved.

Fig. 3 is a schematic flowchart of a process for determining a reliability indicator of each subsystem provided in an embodiment of the present application, and referring to fig. 3, this embodiment relates to an implementation manner of how to determine the reliability indicator of each subsystem. On the basis of the above embodiment, the reliability allocation method further includes the following steps:

s301, acquiring the reliability relation between the primary system and each subsystem of the primary system.

In this embodiment, the reliability relationship between the primary system and each subsystem of the primary system is set as R, and R is a functional expression, which may be set according to the specific situation of the equipment system, and this embodiment is not limited.

S302, according to the target weight vector, determining the minimum element value in each element value in the target weight vector.

In the present embodiment, the target weight vector is represented as W^F＝(ω₁，ω₂，……，ω_n)^TAnd n is the next level number. In this case, it is necessary to calculate the third distribution index and the fourth distribution index of the reliability of each subsystem based on the second distribution index of the reliability of the primary system, and therefore n is the number of subsystems.

ω_iFor each element in the target weight vector. I.e. when i takes 1 to n, omega₁～ω_nIs W^FEach element of (1). Let omega_iMinimum value of (1) is ω₀I.e. omega₀Is equal to omega₁～ω_nMinimum of, thus ω₀Is the smallest element value among the element values in the target weight vector.

And S303, determining a third distribution index of the reliability of the subsystem corresponding to the minimum element value according to a second relational expression, wherein the second relational expression is a reliability relation, the second distribution index, a ratio of each element value in the target weight vector to the minimum element value, and a relational expression between the third distribution indexes.

In the present embodiment, it is necessary to use the reliability assignment method according to the assignment finger of the primary systemAnd calculating distribution indexes of all subsystems of the primary system. Let the reliability distribution index of a certain primary system be R_s，R_sIs a known value.

The above-mentioned S203 has already calculated the second distribution index of each primary system in the equipment system, i.e. the reliability distribution index R of the primary system_miTherefore, this example considers R_s＝R_mi. Wherein the smallest element ω₀The third distribution index of the corresponding subsystem is R₀I.e. omega₀The reliability distribution value of the corresponding minimum subsystem is R₀. Therefore, the reliability assignment value of the subsystem with the smallest weight vector may be used to represent the reliability assignment values of other subsystems, and is an unknown number.

The following equation (2) holds according to the reliability model of the primary system.

R_s(R₁，R₂，……，R_n)＝R_s (2)

Therefore, the equation (3) representing the second relational expression of the reliability relation R and the second distribution index R holds_sAnd the value ω of each element in the target weight vector_iWith the minimum element value omega₀Ratio of

And a third distribution index R₀The relation between them.

In the second relation, R is known and R is_sKnown as being equal to R_miThe target weight vector is known, i.e. ω in the target weight vector_iAnd the minimum element ω therein₀Are also known. Therefore, only R in the second relation₀Unknown, the reliability assignment problem of this primary system can be translated into a pair R₀To solve the problem. According to the second relation, the third distribution index R can be calculated₀。

S304, determining a fourth distribution index of the reliability of each of the other subsystems according to a third relation, wherein the third relation is the third distribution index, the fourth distribution index and a relation between ratios of element values corresponding to the subsystems and minimum element values, and the other subsystems comprise systems except the subsystem corresponding to the minimum element value.

In this embodiment, a fourth distribution index of the reliability of each subsystem is assumed, that is, the reliability distribution value of each subsystem is represented by R_i. Meanwhile, expression (4) representing the third relational expression holds.

Therefore, the third distribution index R is calculated₀Then, according to R₀Calculating a fourth distribution index R using the reliability of each subsystem_i。

For example, n is 5, the primary system corresponds to 5 subsystems, and the 1 st subsystem has a reliability distribution value

Reliability assignment value of 2 nd subsystem

Reliability assignment value of 3 rd subsystem system

Reliability assignment value of 4 th subsystem

Reliability assignment value of 5 th subsystem

It will be appreciated that ω is the number of₀The reliability distribution value of the corresponding minimum subsystem is R₀If R is₁～R₅Minimum value ofIs R₃Then R is₀＝R₃。

In this embodiment, reliability relationships between the primary system and the subsystems of the primary system are obtained, and according to the target weight vector, a minimum element value of the element values in the target weight vector is determined, and then according to the second relational expression, a third distribution index of the reliability of the subsystem corresponding to the minimum element value is determined, and according to the third relational expression, a fourth distribution index of the reliability of each subsystem in other subsystems is determined. The third distribution index and the fourth distribution index of the reliability of each subsystem are determined based on the target weight vector determined by the interval relation moment, so that the accuracy of the reliability distribution result is improved.

Fig. 4 is a schematic flowchart of a process for determining a target weight vector through single simulation provided in an embodiment of the present application, and referring to fig. 4, the present embodiment relates to an implementation manner of how to determine a target weight vector through single simulation. On the basis of the above embodiment, the above S201 further includes the following steps:

s401, determining a weight vector of the fuzzy consistency matrix according to a fourth relational expression, wherein the fourth relational expression is a relational expression among a reciprocal judgment matrix, the weight vector and a historical weight vector of the fuzzy consistency matrix determined at the last time, the reciprocal judgment matrix is a matrix obtained by converting the fuzzy consistency matrix, and the fuzzy consistency matrix is a matrix determined according to a matrix obtained by randomly sampling the interval relational matrix.

In the embodiment, the interval relation matrix P is randomly and uniformly sampled to obtain a matrix based on sampling

Based on post-sampling matrix

Fuzzy consistency matrix can be established

Wherein r is_ijCan be determined by the following formulae (5) to (7).

Obtaining a fuzzy consistency matrix

Then, the fuzzy consistency matrix is solved by using a row and normalization method

The initial weight vector is marked as W₀The following formula (8) can be obtained.

To solve the problem of the precision and convergence of the solution under the single simulation, the present embodiment calculates the weight vector with higher precision by using the following power method. Will obscure the consistency matrix

Converting into reciprocal judgment matrix

Wherein e is_ijCan be determined by the following formula (9).

W is to be₀As the initial weight vector, an iterative operation may be performed to determine a weight vector of the fuzzy consistency matrix according to the following expression (10), i.e., a fourth relational expression. The fourth relation is reciprocal determination matrix

Weight vector W_m+1And the historical weight vector W of the fuzzy consistency matrix determined last time_mThe relation between them.

Wherein m is an integer of 0 or more, and m +1 represents the number of iterations. For example, an initial weight vector W is obtained₀Then m is 0, and the weight vector W of the fuzzy consistency matrix of the first iteration can be calculated according to the fourth relational expression₁，

S402, if the difference between the weight vector and the historical weight vector is less than or equal to a first preset difference threshold value, the weight vector is used as a target weight vector.

In this embodiment, by giving a first preset difference threshold ε₁For any epsilon₁>0. If the difference between the weight vector and the historical weight vector is less than or equal to the first preset difference threshold, that is, if the following formula (11) holds, it indicates that the precision under the single simulation has met the requirement, and the iteration may be ended.

‖W_m+1‖_max-‖W_m‖_max≤ε₁ (11)

When the iteration is finished, the weight vector obtained at the moment is marked as W_kWhen k is m +1, W_kThe formula (12) is satisfied.

For example, m is 0, and the first iteration weight vector W can be calculated according to the fourth relation₁If | W₁‖_max-‖W₀‖_max≤ε₁If k is equal to m +1 is equal to 1, then W is added_kI.e. W₁As the target weight vector, the weight vector of the target,

in this embodiment, a weight vector of the fuzzy consistency matrix is determined according to a fourth relational expression, where the fourth relational expression is a relational expression between a reciprocal determination matrix, the weight vector, and a historical weight vector of the fuzzy consistency matrix determined most recently, the reciprocal determination matrix is a matrix obtained by converting the fuzzy consistency matrix, and the fuzzy consistency matrix is a matrix determined according to a matrix obtained by randomly sampling the interval relational matrix. And if the difference between the weight vector and the historical weight vector is less than or equal to a first preset difference threshold value, taking the weight vector as a target weight vector. The iteration is carried out through the fourth relational expression, so that the weight vector meeting the precision requirement under single simulation can be obtained, the weight vector is used as a target weight vector, and the reliability distribution is carried out through the target weight vector, so that the accuracy of the reliability distribution result is improved.

Optionally, the step S402 may be implemented as follows:

if the difference between the weight vector and the historical weight vector is larger than a first preset difference threshold value, the step of determining the weight vector of the fuzzy consistency matrix according to the fourth relational expression is repeatedly executed until the weight vector corresponding to the difference smaller than or equal to the first preset difference threshold value is determined, and the weight vector corresponding to the determined difference smaller than or equal to the first preset difference threshold value is used as the target weight vector.

In this embodiment, if | W_m+1‖_max-‖W_m‖_max>ε₁Then m continues to add 1 and iterate until findingTo satisfy | W_m+1‖_max-‖W_m‖_max≤ε₁And W at this time_k＝W_m+1A target weight vector.

For example, m is 0, and the first iteration weight vector W can be calculated according to the fourth relation₁If | W₁‖_max-‖W₀‖_max>ε₁Then the iteration continues. When m is 1, the weight vector W of the second iteration can be calculated according to the fourth relational expression₂If | W₂‖_max-‖W₁‖_max>ε₁If k is m +1 is 2, then W is added_kI.e. W₂As the target weight vector.

In this embodiment, if the difference between the weight vector and the historical weight vector is greater than the first preset difference threshold, the step of determining the weight vector of the fuzzy consistency matrix according to the fourth relational expression is repeatedly performed until the weight vector corresponding to the difference smaller than or equal to the first preset difference threshold is determined, and the weight vector corresponding to the determined difference smaller than or equal to the first preset difference threshold is used as the target weight vector. The weight vector under single simulation which does not meet the precision requirement is continuously iterated, so that the weight vector under single simulation which meets the precision requirement is obtained, the weight vector is used as a target weight vector, and reliability distribution is performed through the target weight vector, so that the accuracy of the reliability distribution result is improved.

Fig. 5 is a schematic flowchart of a process for determining a target weight vector through multiple simulations provided in an embodiment of the present application, and referring to fig. 5, the present embodiment relates to an implementation manner of how to determine a target weight vector through multiple simulations. On the basis of the above embodiment, the above S201 further includes the following steps:

s501, determining M weighting weight vectors which are smaller than or equal to weight vectors corresponding to differences of a first preset difference threshold, wherein M is an integer larger than 1.

In this embodiment, to perform multiple simulations, the ordering weight vector obtained by M single simulations is recorded as Q_M，Q_MM is less than or equal to a first preset difference thresholdA weighted weight vector of the weight vector corresponding to the difference of (2). Q_M＝(ω_1M，ω_2M，……，ω_nM)^TM is the number of times of single simulation meeting the precision requirement, namely M sequencing weight vectors are obtained by the M times of single simulation, and n is the lower level number. Furthermore, a weighted weight vector of the weight vectors obtained by the single simulation satisfying the accuracy requirement is calculated M times by using a weighted average method, and W is recorded^M，

Wherein each element is

Satisfies the following formula (13), j is 1 to n.

For example, if n is 4 and M is 2, a total of 2 single simulations are performed to satisfy the accuracy requirement, and if the first iteration is performed only 2 times from the random sampling step, the weight vector W satisfying the accuracy requirement is obtained₂(ii) a The second time starts from the random sampling step, and if 5 times of iteration is performed, a weight vector W meeting the precision requirement is obtained₅. Therefore, the 2 single simulation results in the ordering weight vector of Q_MAnd M is 1 and 2.

Q₁＝(ω₁₁，ω₂₁，ω₃₁，ω₄₁）^T＝W₂

Q₂＝(ω₁₂，ω₂₂，ω₃₁，ω₄₂)^T＝W₅

To Q₁And Q₂Calculating the weighted weight vector to obtain a weighted weight vector W^MI.e. W²，

Wherein, W²Each element in (1) can be determined according to the relation (13).

Wherein ω is₁₁、ω₂₁、ω₃₁And ω₄₁May be based on an ordering weight vector Q₁I.e. W₂To know, ω₁₂、ω₂₂、ω₃₂And ω₄₂May be based on an ordering weight vector Q₂I.e. W₅And (5) obtaining the result.

S502, determining the weight vector difference between the weighting weight vector and the historical weighting weight vector, wherein the historical weighting weight vector is the weighting weight vector of the N weight vectors determined at the latest time, the N weight vectors are the front weight vectors in the weight vectors corresponding to the difference that M is less than or equal to the first preset difference threshold, and N is equal to the difference between M and 1.

In the present embodiment, a weighting weight vector W is determined^MAnd a historical weighted weight vector W^M-1The difference of the weight vector is | W^M-W^M-1|. E.g. n is 4, M is 3, and the weighted weight vector is W³，W³Is by ordering the weight vector Q₁、Q₂And Q₃And (6) calculating. And the historical weighted weight vector is W²，W²Is based on the preceding weight vector Q₁And Q₂And (6) calculating. More particularly, to. According to

According to the formula (13),

wherein Q is₁＝(ω₁₁，ω₂₁，ω₃₁，ω₄₁)^T，Q₂＝(ω₁₂，ω₂₂，ω₃₂，ω₄₂)^T。

And S503, if the weight vector difference is less than or equal to a second preset difference threshold, taking the weighted weight vector as a target weight vector.

In this embodiment, by giving a second preset difference threshold ε₂For any epsilon₂>0. Determining the weight vector difference between the weighted weight vector and the historical weighted weight vector, and ending the iteration if the weight vector difference is less than or equal to a second preset difference threshold value, i.e. if the following formula (14) holds.

‖W^M-W^M-1‖≤ε₂ (14)

When the iteration is finished, the weighting weight vector is obtained as the target weight vector.

For example, M takes 2 and calculates | W²-W¹‖≤ε₂Then weight the weight vector W²As the target weight vector.

In this embodiment, a weighted weight vector of a weight vector corresponding to a difference between M and a first preset difference threshold is determined, where M is an integer greater than 1, and then a difference between the weighted weight vector and a historical weighted weight vector is determined, where the historical weighted weight vector is a weighted weight vector of N weight vectors determined most recently, the N weight vectors are previous weight vectors of the weight vectors corresponding to a difference between M and a first preset difference threshold, and N is equal to a difference between M and 1, and if the difference between the weight vectors is less than or equal to a second preset difference threshold, the weighted weight vector is taken as a target weight vector. Due to the fact that the second preset difference threshold value is used for judging, the weighted weight vector meeting the precision requirement under multiple times of simulation can be obtained based on the weight vector obtained through single simulation meeting the essential requirement, the weighted weight vector is used as the target weight vector, reliability distribution is conducted through the target weight vector, and therefore accuracy of the reliability distribution result is improved.

Fig. 6 is a schematic flowchart of another process for determining a target weight vector through multiple simulations, which is provided in an embodiment of the present application, and referring to fig. 6, the present embodiment relates to an implementation manner of how to determine the target weight vector. On the basis of the above embodiment, the reliability allocation method further includes the following steps:

s601, if the difference of the weight vectors is greater than the second preset difference threshold, determining that M +1 is less than or equal to the weighted weight vector of the weight vector corresponding to the difference of the first preset difference threshold.

In the present embodiment, if the weight vector difference is greater than the second predetermined difference threshold, i.e., | W^M-W^M-1‖>ε₂Then, M +1 is used to determine that M +1 is less than or equal to the weighted weight vector of the weight vector corresponding to the difference of the first preset difference threshold. For example, when M takes 2, Q is calculated₁And Q₂Corresponding weighted weight vector W²，‖W²-W¹‖>ε₂If so, starting the step of random sampling with the M +1 again, and performing iteration to obtain a weight vector W meeting the requirement of single simulation precision_k＝Q₃Then according to Q₁、Q₂And Q₃Obtain the corresponding weighted weight vector W³。

S602, taking M +1 as M, and repeatedly performing the step of determining the weight vector difference between the weighting weight vector and the historical weighting weight vector until determining the weighting weight vector corresponding to the weight vector difference smaller than or equal to the second preset difference threshold.

In the present embodiment, according to the step of S601, for example, a weighting weight vector W is obtained³. In this case, M +1 is defined as M, and M is defined as3. If | W³-W²‖≤ε₂The calculation may be stopped. But if | W³-W²‖>ε₂If M continues to add 1, calculate Q₁、Q₂、Q₃And Q₄Corresponding weighted weight vector W⁴And making a judgment if | W⁴-W³‖>ε₂The calculation may be stopped.

And S603, taking the weighted weight vector corresponding to the weight vector difference smaller than or equal to the second preset difference threshold value as a target weight vector.

In the present embodiment, it will be finally determined that the formula (14) is satisfied, that is, the weight vector difference W is less than or equal to the second preset difference threshold^MThe corresponding weighted weight vector is used as the target weight vector. For example, W calculated from S602⁴As the target weight vector.

In this embodiment, if the difference between the weight vectors is greater than the second preset difference threshold, a weight vector corresponding to a difference between M +1 and the first preset difference threshold is determined, and M +1 is taken as M, and the step of determining the difference between the weight vector and the historical weight vector is repeatedly performed until a weight vector corresponding to a difference between the weight vectors less than or equal to the second preset difference threshold is determined, and then a weight vector corresponding to a difference between the determined weight vectors less than or equal to the second preset difference threshold is taken as a target weight vector. The weighted weight vector under multiple times of simulation which does not meet the precision requirement is continuously calculated, so that the weighted weight vector under multiple times of simulation which meets the precision requirement is obtained based on the weight vector obtained by single time of simulation which meets the essential requirement, the weighted weight vector is used as the target weight vector, and then the reliability distribution is carried out through the target weight vector, thereby improving the accuracy of the reliability distribution result.

Fig. 7 is a schematic flowchart of a process for determining an interval relationship matrix provided in an embodiment of the present application, and referring to fig. 7, the embodiment relates to an implementation manner of how to determine the interval relationship matrix. On the basis of the above embodiment, the reliability allocation method further includes the following steps:

s701, obtaining a plurality of grading results, wherein each grading result is obtained by grading a plurality of constraint conditions of the equipment system.

In this embodiment, the construction of the priority relationship matrix depends on the evaluation of the expert on each constraint condition such as complexity, technical maturity, etc. Therefore, in order to reduce or eliminate the influence of human subjective factors, the present embodiment collects the scores of multiple experts on multiple constraint conditions of the equipment system at the same time, and obtains multiple scoring results.

And S702, determining a corresponding priority relation matrix according to each scoring result.

In this embodiment, after obtaining each scoring result, after comprehensively considering the scoring results of the experts on the multiple constraint conditions, the corresponding priority relationship matrix is determined.

More specifically, after 1 expert scores a plurality of constraint conditions, and comprehensively considers the scoring result, 1 priority relation matrix is obtained and recorded as

a_ijThe score comparison value of the subsystem i relative to the constraint condition of the subsystem j adopts the scale of 0.1-0.9, and n is the number of the subsystems. Wherein 0.5 represents that the scoring weights of the two subsystems are the same; 0.5-0.9 indicate that subsystem i has sequentially increased weights relative to subsystem j, and 0.1-0.5 indicate that subsystem i has sequentially decreased weights relative to subsystem j. The priority relationship matrix properties generally satisfy the following expressions (15) to (17).

a_ij＝0.5 (15)

a_ij+a_ji＝1 (16)

0<a_ij<1 (17)

After 1 expert scores a plurality of constraint conditions, 1 priority relation matrix can be determined

Therefore, after the plurality of experts score the plurality of constraint conditions, the corresponding plurality of priority relationship matrixes can be determined.

And S703, determining an interval relation matrix according to the priority relation matrix corresponding to each grading result.

In this embodiment, after scoring the plurality of constraint conditions, a plurality of experts may determine a plurality of corresponding priority relationship matrices, and each priority relationship matrix has a score comparison value of the subsystem i relative to the constraint condition of the subsystem j, which is equivalent to establishing a score interval. Finding a in each priority relation matrix_ijThe minimum value and the maximum value of the interval relation matrix can be determined. The interval relationship matrix is represented by the following formula (18) as P.

Wherein,

the minimum value of the interval is represented,

represents the maximum value of the interval, and the following equations (19) to (20) hold.

According to the formula (15), when i is n, a is a_nn＝a_iiSince 0.5, the interval relation matrix P is further expressed by equation (21).

In this embodiment, a plurality of scoring results are obtained, and each scoring result is a result obtained by scoring a plurality of constraint conditions of the equipment system, and a corresponding priority relationship matrix is determined according to each scoring result. And determining an interval relation matrix according to the priority relation matrix corresponding to each grading result. And determining an interval relation matrix according to the priority relation matrix corresponding to each grading result. Because a plurality of experts are adopted to score a plurality of constraint conditions, an interval relation matrix is determined based on the scoring interval, and the influence of artificial subjective factors is weakened or eliminated. And further the accuracy of the distribution result of the reliability distribution is improved.

It should be understood that, although the steps in the flowcharts related to the embodiments as described above are sequentially displayed as indicated by arrows, the steps are not necessarily performed sequentially as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be rotated or alternated with other steps or at least a part of the steps or stages in other steps.

Based on the same inventive concept, the embodiment of the present application further provides a reliability allocation apparatus for implementing the reliability allocation method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so specific limitations in one or more embodiments of the reliability allocation device provided below can be referred to the limitations of the reliability allocation method in the foregoing, and details are not described here.

Referring to fig. 8, fig. 8 is a schematic structural diagram of a reliability allocation apparatus provided in an embodiment of the present application, where the apparatus 800 includes: a first determining module 801, a first obtaining module 802, and a second determining module 803, wherein:

the first determining module 801 is configured to determine a target weight vector based on an interval relation matrix, where the interval relation matrix is a matrix obtained according to a priority relation matrix, and the priority relation matrix is a matrix determined according to a scoring result obtained by scoring a constraint condition affecting reliability allocation.

A first obtaining module 802, configured to obtain a first distribution indicator of reliability of equipment in the equipment system.

The second determining module 803 is configured to determine a second distribution index of the reliability of the primary system in the equipment system according to a first relation, where the first relation is a relation among the target weight vector, the first distribution index, and the second distribution index.

The reliability allocation apparatus provided in this embodiment determines the target weight vector based on an interval relation matrix, where the interval relation matrix is a matrix obtained according to a priority relation matrix, the priority relation matrix is a matrix determined according to a scoring result obtained by scoring a constraint condition affecting reliability allocation, obtains a first allocation index of reliability of equipment in the equipment system, and further determines a second allocation index of reliability of a primary system in the equipment system according to a first relation, where the first relation is a relation among the target weight vector, the first allocation index, and the second allocation index. In the traditional scoring distribution method, the result of scoring the constraint condition by an expert is directly used as an input parameter of weighted analysis, the weighted analysis is carried out based on a weighted analysis method, the specific weight of the object to be distributed is analyzed and calculated, and then the reliability distribution is carried out based on the specific weight. In the traditional scoring and allocating method, in the process of weighting analysis, the judgment of the constraint condition is greatly influenced by the subjectivity of a person, so that the conditions that the judgment is unreasonable and does not accord with the reality may exist, and the final allocating result is deviated. The reliability allocation method provided in this embodiment determines the priority relationship matrix according to the scoring result of the expert on the constraint condition, and obtains the interval relationship matrix according to the priority relationship matrix, thereby determining the target weight vector according to the interval relationship matrix, and performing reliability allocation based on the target weight vector. Therefore, the problem of high subjectivity of the distribution result caused by directly carrying out weighted analysis on the scoring result of the constraint condition in the traditional method is solved, the problem of low precision of the distribution result in the traditional method is further solved, and the accuracy of the reliable distribution result is improved.

Optionally, the apparatus 800 further includes:

and the second acquisition module is used for acquiring the reliability relation between the primary system and each subsystem of the primary system.

And the third determining module is used for determining the minimum element value in all the element values in the target weight vector according to the target weight vector.

And the fourth determining module is used for determining a third distribution index of the reliability of the subsystem corresponding to the minimum element value according to a second relational expression, wherein the second relational expression is the reliability relation, the second distribution index, the ratio of each element value in the target weight vector to the minimum element value, and the relational expression between the third distribution indexes.

Optionally, the first determining module 801 includes:

the first determining unit is used for determining weight vectors of the fuzzy consistency matrix according to a fourth relational expression, wherein the fourth relational expression is a relational expression among a reciprocal judgment matrix, the weight vectors and historical weight vectors of the fuzzy consistency matrix determined at the last time, the reciprocal judgment matrix is a matrix obtained by converting the fuzzy consistency matrix, and the fuzzy consistency matrix is a matrix determined according to a matrix obtained by randomly sampling the interval relational matrix.

And the second determining unit is used for taking the weight vector as a target weight vector if the difference between the weight vector and the historical weight vector is less than or equal to a first preset difference threshold value.

Optionally, the second determining unit is further configured to, if the difference between the weight vector and the historical weight vector is greater than a first preset difference threshold, repeat the step of determining the weight vector of the fuzzy consistency matrix according to the fourth relational expression until the weight vector corresponding to the difference smaller than or equal to the first preset difference threshold is determined, and use the determined weight vector corresponding to the difference smaller than or equal to the first preset difference threshold as the target weight vector.

Optionally, the first determining module 801 further includes:

and a third determining unit, configured to determine M weighted weight vectors that are less than or equal to a weight vector corresponding to a difference of the first preset difference threshold, where M is an integer greater than 1.

And the fourth determining unit is used for determining the weight vector difference between the weighting weight vector and the historical weighting weight vector, wherein the historical weighting weight vector is the weighting weight vector of the N weight vectors determined at the last time, the N weight vectors are the front weight vectors in the weight vectors corresponding to the difference of M smaller than or equal to the first preset difference threshold value, and N is equal to the difference of M and 1.

And the fifth determining unit is used for taking the weighted weight vector as the target weight vector if the weight vector difference is less than or equal to a second preset difference threshold value.

Optionally, the fifth determining unit further includes:

the first determining subunit is configured to determine, if the difference between the weight vectors is greater than a second preset difference threshold, a weighted weight vector of the weight vector corresponding to a difference between M +1 and the first preset difference threshold.

And the second determining subunit is used for taking M +1 as M, and repeatedly executing the step of determining the weight vector difference between the weighting weight vector and the historical weighting weight vector until determining the weighting weight vector corresponding to the weight vector difference smaller than or equal to a second preset difference threshold value.

And the third determining subunit is used for taking the weighted weight vector corresponding to the determined weight vector difference which is less than or equal to the second preset difference threshold value as the target weight vector.

Optionally, the apparatus 800 further includes:

and the third acquisition module is used for acquiring a plurality of grading results, and each grading result is obtained by grading a plurality of constraint conditions of the equipment system.

And the second determining module is used for determining the corresponding priority relation matrix according to each grading result.

And the third determining module is used for determining an interval relation matrix according to the priority relation matrix corresponding to each grading result.

The various modules in the reliability distribution apparatus described above may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

Fig. 9 is an internal structural diagram of a computer device in the embodiment of the present application, and in the embodiment, a computer device is provided, and an internal structural diagram of the computer device may be as shown in fig. 9. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a reliability allocation method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.

Those skilled in the art will appreciate that the architecture shown in fig. 9 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

In one embodiment, a computer device is provided, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the reliability allocation method provided by the above-mentioned embodiments when executing the computer program. The implementation principle and technical effect are similar to those of the above method embodiments, and are not described herein again.

In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the reliability allocation method provided by the above-mentioned embodiments. The implementation principle and technical effect are similar to those of the above method embodiments, and are not described herein again.

In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, performs the steps of the reliability allocation method provided by the above-described embodiments. The implementation principle and technical effect are similar to those of the above method embodiments, and are not described herein again.

It should be noted that, the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), Magnetic Random Access Memory (MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases referred to in various embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.

The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A reliability allocation method, characterized in that the method comprises:

determining a target weight vector based on an interval relation matrix, wherein the interval relation matrix is obtained according to a priority relation matrix, and the priority relation matrix is determined according to a grading result obtained by grading constraint conditions influencing reliability distribution;

2. The method of claim 1, further comprising:

determining a fourth distribution index of the reliability of each of the other subsystems according to a third relation, wherein the third relation is the third distribution index, the fourth distribution index and a relation between ratios of element values corresponding to the subsystems and the minimum element value, and the other subsystems include the subsystems except the subsystem corresponding to the minimum element value.

3. The method of claim 1, wherein determining the target weight vector based on the interval relationship matrix comprises:

determining a weight vector of a fuzzy consistency matrix according to a fourth relational expression, wherein the fourth relational expression is a relational expression among a reciprocal judgment matrix, the weight vector and a historical weight vector of the fuzzy consistency matrix determined at the last time, the reciprocal judgment matrix is a matrix obtained by converting the fuzzy consistency matrix, and the fuzzy consistency matrix is a matrix determined according to a matrix obtained by randomly sampling the interval relation matrix;

and if the difference between the weight vector and the historical weight vector is less than or equal to a first preset difference threshold value, taking the weight vector as the target weight vector.

4. The method of claim 3, further comprising:

if the difference between the weight vector and the historical weight vector is larger than the first preset difference threshold, the step of determining the weight vector of the fuzzy consistency matrix according to the fourth relational expression is repeatedly executed until the weight vector corresponding to the difference smaller than or equal to the first preset difference threshold is determined, and the determined weight vector corresponding to the difference smaller than or equal to the first preset difference threshold is used as the target weight vector.

5. The method of claim 3, wherein determining the target weight vector based on the interval relationship matrix comprises:

determining M weighting weight vectors which are smaller than or equal to the weight vectors corresponding to the differences of the first preset difference threshold, wherein M is an integer larger than 1;

determining a weight vector difference between the weighting weight vector and a historical weighting weight vector, wherein the historical weighting weight vector is a weighting weight vector of N weight vectors determined at the last time, the N weight vectors are the front weight vectors in the weight vectors corresponding to the difference of M being less than or equal to the first preset difference threshold, and N is equal to the difference between M and 1;

and if the weight vector difference is less than or equal to a second preset difference threshold value, taking the weighted weight vector as the target weight vector.

6. The method of claim 5, further comprising:

if the difference of the weight vectors is larger than the second preset difference threshold, determining that M +1 is smaller than or equal to the weight vector corresponding to the difference of the first preset difference threshold;

taking M +1 as M, and repeatedly executing the step of determining the weight vector difference between the weighting weight vector and the historical weighting weight vector until determining the weighting weight vector corresponding to the weight vector difference smaller than or equal to the second preset difference threshold value;

and taking the weighted weight vector corresponding to the weight vector difference smaller than or equal to the second preset difference threshold value as the target weight vector.

7. The method of claim 1, further comprising:

obtaining a plurality of scoring results, wherein each scoring result is obtained by scoring a plurality of constraint conditions of the equipment system;

determining a corresponding priority relation matrix according to each evaluation result;

and determining the interval relation matrix according to the priority relation matrix corresponding to each evaluation result.

8. A reliability assigning apparatus, characterized in that the apparatus comprises:

a second determining module, configured to determine a second distribution indicator of reliability of a primary system in the equipment system according to a first relation, where the first relation is a relation among the target weight vector, the first distribution indicator, and the second distribution indicator.

9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 7.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.

11. A computer program product comprising a computer program, characterized in that the computer program realizes the steps of the method of any one of claims 1 to 7 when executed by a processor.